02390nas a2200397 4500008004100000022001400041245016800055210006900223260001600292300001200308490000800320520125500328653002101583653000901604653002201613653003001635653001101665653001101676653002001687653001701707653002501724653000901749653002601758653001501784653002101799653001701820653001801837100001701855700001501872700001301887700001601900700001401916700001201930700001401942856003601956 1996 eng d a0002-926200aWhite blood cell counts in persons aged 65 years or more from the Cardiovascular Health Study. Correlations with baseline clinical and demographic characteristics.0 aWhite blood cell counts in persons aged 65 years or more from th c1996 Jun 01 a1107-150 v1433 a
A higher white blood cell (WBC) count has been shown to be a risk factor for myocardial infarction and stroke in middle-aged populations. This study evaluated the relation between baseline WBC count and other risk factors, as well as subclinical and prevalent disease, in the Cardiovascular Health Study, an epidemiologic study of coronary heart disease and stroke in 5,201 persons aged 65 years or older. Baseline data were collected over a 12-month period in 1989-1990. WBC counts were statistically significantly higher in people with prevalent and subclinical atherosclerotic cardiovascular disease than in those who were free of disease. WBC counts correlated (p < 0.01) positively with coagulation factors, measures of glucose metabolism, creatinine, smoking, and triglycerides. In contrast, WBC counts correlated negatively with high density lipoprotein cholesterol, forced expiratory volume, forced vital capacity, and height. The correlations between WBC counts and risk factors were similar in both the entire cohort and the subgroup of persons who had never smoked. The authors conclude that WBC counts in the elderly are associated with prevalent and subclinical atherosclerotic cardiovascular disease, as well as its risk factors.
10aAge Distribution10aAged10aAged, 80 and over10aCerebrovascular Disorders10aFemale10aHumans10aLeukocyte Count10aLeukocytosis10aLongitudinal Studies10aMale10aMyocardial Infarction10aPrevalence10aReference Values10aRisk Factors10aUnited States1 aBovill, E, G1 aBild, D, E1 aHeiss, G1 aKuller, L H1 aLee, M, H1 aRock, R1 aWahl, P W uhttps://chs-nhlbi.org/node/145602974nas a2200397 4500008004100000022001400041245006500055210006400120260001300184300001200197490000700209520192800216653000902144653002502153653001602178653002802194653001102222653001802233653001102251653002502262653000902287653001402296653003202310653001702342653001802359653001602377653001602393100001702409700001302426700001402439700001502453700001802468700001502486710004002501856003502541 2001 eng d a0002-861400aWeight change in old age and its association with mortality.0 aWeight change in old age and its association with mortality c2001 Oct a1309-180 v493 aOBJECTIVES: Previous studies of weight change and mortality in older adults have relied on self-reported weight loss, have not evaluated weight gain, or have had limited information on health status. Our objective was to determine whether 5% weight gain or loss in 3 years was predictive of mortality in a large sample of older adults.
DESIGN: Longitudinal observational cohort study.
SETTING: Four U.S. communities.
PARTICIPANTS: Four thousand seven hundred fourteen community-dwelling older adults, age 65 and older.
MEASUREMENTS: Weight gain or loss of 5% in a 3-year period was examined in relationship to baseline health status and interim health events. Risk for subsequent mortality was estimated in those with weight loss or weight gain compared with the group whose weight was stable.
RESULTS: Weight changes occurred in 34.6% of women and 27.3% of men, with weight loss being more frequent than gain. Weight loss was associated with older age, black race, higher weight, lower waist circumference, current smoking, stroke, any hospitalization, death of a spouse, activities of daily living disability, lower grip strength, and slower gait speed. Weight loss but not weight gain of 5% or more was associated with an increased risk of mortality that persisted after multivariate adjustment (Hazard ratio (HR) = 1.67, 95% CI = 1.29-2.15) and was similar in those with no serious illness in the period of weight change. Those with weight loss and low baseline weight had the highest crude mortality rate, although the HR for weight loss was similar for all tertiles of baseline weight and for those with or without a special diet, compared with those whose weight was stable.
CONCLUSIONS: This study confirms that even modest decline in body weight is an important and independent marker of risk of mortality in older adults.
10aAged10aAnalysis of Variance10aBody Weight10aChi-Square Distribution10aFemale10aHealth Status10aHumans10aLongitudinal Studies10aMale10aMortality10aProportional Hazards Models10aRisk Factors10aUnited States10aWeight Gain10aWeight Loss1 aNewman, A, B1 aYanez, D1 aHarris, T1 aDuxbury, A1 aEnright, P, L1 aFried, L P1 aCardiovascular Study Research Group uhttps://chs-nhlbi.org/node/68302322nas a2200193 4500008004100000022001400041245008400055210006900139260001600208300000600224490000600230520176400236100001702000700002002017700002302037700001802060700001502078856003502093 2002 eng d a1468-670800aWeight-modification trials in older adults: what should the outcome measure be?0 aWeightmodification trials in older adults what should the outcom c2002 Jan 07 a10 v33 aBACKGROUND: Overweight older adults are often counseled to lose weight, even though there is little evidence of excess mortality in that age group. Overweight and underweight may be more associated with health status than with mortality, but few clinical trials of any kind have been based on maximizing years of healthy life (YHL), as opposed to years of life (YOL). OBJECTIVE: This paper examines the relationship of body mass index (BMI) to both YHL and YOL. Results were used to determine whether clinical trials of weight-modification based on improving YHL would be more powerful than studies based on survival. DESIGN: We used data from a cohort of 4,878 non-smoking men and women aged 65-100 at baseline (mean age 73) and followed 7 years. We estimated mean YHL and YOL in four categories of BMI: underweight, normal, overweight, and obese. RESULTS: Subjects averaged 6.3 YOL and 4.6 YHL of a possible 7 years. Both measures were higher for women and whites. For men, none of the BMI groups was significantly different from the normal group on either YOL or YHL. For women, the obese had significantly lower YHL (but not YOL) than the normals, and the underweight had significantly lower YOL and YHL. The overweight group was not significantly different from the normal group on either measure. CONCLUSIONS: Clinical trials of weight loss interventions for obese older women would require fewer participants if YHL rather than YOL was the outcome measure. Interventions for obese men or for the merely overweight are not likely to achieve differences in either YOL or YHL. Evaluations of interventions for the underweight (which would presumably address the causes of their low weight) may be conducted efficiently using either outcome measure.
1 aDiehr, Paula1 aNewman, Anne, B1 aJackson, Sharon, A1 aKuller, Lewis1 aPowe, Neil uhttps://chs-nhlbi.org/node/68702407nas a2200337 4500008004100000022001400041245009400055210006900149260001300218300001100231490000700242520145700249653000901706653001001715653001101725653001101736653001801747653003101765653000901796653001601805653001701821653001101838100002101849700002001870700002101890700002101911700001601932700002501948710006101973856003502034 2004 eng d a1524-462800aWhite matter hyperintensity on cranial magnetic resonance imaging: a predictor of stroke.0 aWhite matter hyperintensity on cranial magnetic resonance imagin c2004 Aug a1821-50 v353 aBACKGROUND AND PURPOSE: We have previously reported that several "silent" infarcts found on magnetic resonance imaging (MRI) were a risk factor for stroke. Several recent reports have shown that high white matter grade (WMG) and increasing WMG over time were risk factors for stroke. We tested the hypothesis that high WMG > or =2 was a predictor of risk for stroke, independent of other risk factors.
METHODS: We examined the extent of white matter hyperintensity on cranial MRI of 3293 participants from the Cardiovascular Health Study (CHS). The degree of white matter hyperintensity was graded from least severe (grade=0) to most severe (grade=9). Participants were followed-up for an average of 7 years for the occurrence of a stroke. Clinical stroke diagnoses were based on hospital records reviewed by an adjudication committee expert in stroke diagnosis. During this period, 278 strokes occurred. Results The relative risk of stroke increased significantly as the WMG increased. The risk of stroke was 2.8% per year for participants with high WMG (grades > or =5), compared with only 0.6% for participants with grades 0 to 1.Conclusions The risk of stroke with high WMG is independent of traditional stroke risk factors and persists when controlling for MRI infarcts, another subclinical imaging marker of cerebrovascular disease. Assessment of white matter disease may be valuable in assessing future risk of stroke.
10aAged10aBrain10aFemale10aHumans10aLeukoaraiosis10aMagnetic Resonance Imaging10aMale10aRadiography10aRisk Factors10aStroke1 aKuller, Lewis, H1 aLongstreth, W T1 aArnold, Alice, M1 aBernick, Charles1 aBryan, Nick1 aBeauchamp, Norman, J1 aCardiovascular Health Study Collaborative Research Group uhttps://chs-nhlbi.org/node/78802874nas a2200409 4500008004100000022001400041245014200055210006900197260001300266300001400279490000700293520165500300653000901955653002201964653004501986653001102031653001802042653001802060653001102078653001702089653000902106653002602115653003602141653002402177653002402201653001802225653001602243100002602259700002302285700002302308700001902331700001902350700002102369700001902390700002002409856003502429 2005 eng d a0002-861400aWeight loss, muscle strength, and angiotensin-converting enzyme inhibitors in older adults with congestive heart failure or hypertension.0 aWeight loss muscle strength and angiotensinconverting enzyme inh c2005 Nov a1996-20000 v533 aOBJECTIVES: To determine whether angiotensin-converting enzyme (ACE) inhibitor use may be associated with weight maintenance and sustained muscle strength (measured by grip strength) in older adults.
DESIGN: Data from the Cardiovascular Health Study (CHS), a community-based prospective cohort study of 5,888 older adults, were used.
SETTING: Subjects were recruited from four U.S. sites beginning in 1989; this analysis included data through 2001.
PARTICIPANTS: CHS participants with congestive heart failure (CHF) or treated hypertension.
MEASUREMENTS: The exposure, current ACE inhibitor use, was ascertained by medication inventory at annual clinic visits; the outcomes were weight change and grip-strength change during the following year. Multivariate linear regression was used, accounting for correlations between observations on the same participant over time.
RESULTS: The average annual weight change was -0.38 kg in 2,834 participants (14,443 person-years) with treated hypertension and -0.62 kg in 342 participants (980 person-years) with CHF. ACE inhibitor use was associated with less annual weight loss after adjustment for potential confounders: a difference of 0.17 kg (95% confidence interval (CI)=0.05-0.29) in those with treated hypertension and 0.29 kg (95% CI=-0.25-0.83) in those with CHF. There was no evidence of association between ACE inhibitor use and grip-strength change.
CONCLUSION: ACE inhibitor use may be associated with weight maintenance, but not maintenance of muscle strength, in older adults with treated hypertension.
10aAged10aAged, 80 and over10aAngiotensin-Converting Enzyme Inhibitors10aFemale10aHand Strength10aHeart Failure10aHumans10aHypertension10aMale10aMultivariate Analysis10aOutcome Assessment, Health Care10aProspective Studies10aStatistics as Topic10aUnited States10aWeight Loss1 aSchellenbaum, Gina, D1 aSmith, Nicholas, L1 aHeckbert, Susan, R1 aLumley, Thomas1 aRea, Thomas, D1 aFurberg, Curt, D1 aLyles, Mary, F1 aPsaty, Bruce, M uhttps://chs-nhlbi.org/node/86802245nas a2200445 4500008004100000022001400041245011900055210006900174260001300243300001200256490000700268520094500275653003901220653000901259653002201268653001501290653001001305653002801315653002401343653004001367653001101407653002501418653001801443653001101461653002501472653003101497653000901528653002601537653001701563653001601580100002101596700002101617700002001638700002101658700002301679700002201702700002001724700002001744856003501764 2007 eng d a1558-149700aWhite matter grade and ventricular volume on brain MRI as markers of longevity in the cardiovascular health study.0 aWhite matter grade and ventricular volume on brain MRI as marker c2007 Sep a1307-150 v283 aHigh white matter grade (WMG) on magnetic resonance imaging (MRI) is a risk factor for dementia, stroke and disability. Higher ventricular size is a marker of brain "atrophy." In the Cardiovascular Health Study (CHS) (n=3245) mean age 75 years, 50% black and 40% men, we evaluated WM and ventricular grade (VG), total, cardiovascular and noncardiovascular mortality and longevity before and after adjusting for numerous determinants of longevity over an approximate 10-12 years of follow-up. A low WMG and VG was a marker for low total, cardiovascular and noncardiovascular mortality and for increased longevity over 10+ years of follow-up. We estimated that a 75-year-old with WMG below median would have about a 5-6 years greater longevity and for VG about 3 years, than above the median even after adjustment for numerous risk factors. Low WMG and VG on MRI is a powerful determinant of long-term survival among older individuals.
10aAfrican Continental Ancestry Group10aAged10aAged, 80 and over10aBiomarkers10aBrain10aCardiovascular Diseases10aCerebral Ventricles10aEuropean Continental Ancestry Group10aFemale10aGeriatric Assessment10aHealth Status10aHumans10aLongitudinal Studies10aMagnetic Resonance Imaging10aMale10aRetrospective Studies10aRisk Factors10aSex Factors1 aKuller, Lewis, H1 aArnold, Alice, M1 aLongstreth, W T1 aManolio, Teri, A1 aO'Leary, Daniel, H1 aBurke, Gregory, L1 aFried, Linda, P1 aNewman, Anne, B uhttps://chs-nhlbi.org/node/90702836nas a2200397 4500008004100000022001400041245009000055210006900145260001300214300001000227490000700237520176100244653003102005653000902036653002202045653002002067653001602087653001102103653002902114653001102143653002002154653001502174653000902189653001402198653001702212653001602229653001802245653001802263100001702281700002202298700002502320700002002345700001802365700001902383856003602402 2008 eng d a1532-541500aWeight, mortality, years of healthy life, and active life expectancy in older adults.0 aWeight mortality years of healthy life and active life expectanc c2008 Jan a76-830 v563 aOBJECTIVES: To determine whether weight categories predict subsequent mortality and morbidity in older adults.
DESIGN: Multistate life tables, using data from the Cardiovascular Health Study, a longitudinal population-based cohort of older adults.
SETTING: Data were provided by community-dwelling seniors in four U.S. counties: Forsyth County, North Carolina; Sacramento County, California; Washington County, Maryland; and Allegheny County, Pennsylvania.
PARTICIPANTS: Five thousand eight hundred eighty-eight adults aged 65 and older at baseline.
MEASUREMENTS: The age- and sex-specific probabilities of transition from one health state to another and from one weight category to another were estimated. From these probabilities, future life expectancy, years of healthy life, active life expectancy, and the number of years spent in each weight and health category after age 65 were estimated.
RESULTS: Women who are healthy and of normal weight at age 65 have a life expectancy of 22.1 years. Of that, they spend, on average, 9.6 years as overweight or obese and 5.3 years in fair or poor health. For both men and women, being underweight at age 65 was associated with worse outcomes than being normal weight, whereas being overweight or obese was rarely associated with worse outcomes than being normal weight and was sometimes associated with significantly better outcomes.
CONCLUSION: Similar to middle-aged populations, older adults are likely to be or to become overweight or obese, but higher weight is not associated with worse health in this age group. Thus, the number of older adults at a "healthy" weight may be much higher than currently believed.
10aActivities of Daily Living10aAged10aAged, 80 and over10aBody Mass Index10aBody Weight10aFemale10aHealth Status Indicators10aHumans10aLife Expectancy10aLife Style10aMale10aPrognosis10aRisk Factors10aSex Factors10aSurvival Rate10aUnited States1 aDiehr, Paula1 aO'Meara, Ellen, S1 aFitzpatrick, Annette1 aNewman, Anne, B1 aKuller, Lewis1 aBurke, Gregory uhttps://chs-nhlbi.org/node/100002532nas a2200469 4500008004100000022001400041245008400055210006900139260001300208300001400221490000700235520120900242653000901451653002201460653001001482653002201492653001001514653002401524653001101548653001101559653003101570653001801601653002501619653003101644653000901675653002101684653002701705653002901732653002401761653002601785100001901811700002001830700002101850700002401871700002701895700001801922700001901940700002401959700002201983700002102005856003602026 2012 eng d a1558-149700aWhite matter lesions and brain gray matter volume in cognitively normal elders.0 aWhite matter lesions and brain gray matter volume in cognitively c2012 Apr a834.e7-160 v333 aCerebral white matter lesions (WMLs) reflect small vessel disease, are common in elderly individuals, and are associated with cognitive impairment. We sought to determine the relationships between WMLs, age, gray matter (GM) volume, and cognition in the Cardiovascular Health Study (CHS). From the Cardiovascular Health Study we selected 740 cognitively normal controls with a 1.5 T magnetic resonance imaging (MRI) scan of the brain and a detailed diagnostic evaluation. WML severity was determined using a standardized visual rating system. GM volumes were analyzed using voxel-based morphometry implemented in the Statistical Parametric Mapping software. WMLs were inversely correlated with GM volume, with the greatest volume loss in the frontal cortex. Age-related atrophy was observed in the hippocampus and posterior cingulate cortex. Regression analyses revealed links among age, APOE*4 allele, hypertension, WMLs, GM volume, and digit symbol substitution test scores. Both advancing age and hypertension predict higher WML load, which is itself associated with GM atrophy. Longitudinal data are needed to confirm the temporal sequence of events leading to a decline in cognitive function.
10aAged10aAged, 80 and over10aAging10aApolipoprotein E410aBrain10aCognition Disorders10aFemale10aHumans10aImaging, Three-Dimensional10aLeukoaraiosis10aLongitudinal Studies10aMagnetic Resonance Imaging10aMale10aMemory Disorders10aMental Status Schedule10aNeuropsychological Tests10aRegression Analysis10aRetrospective Studies1 aRaji, Cyrus, A1 aLopez, Oscar, L1 aKuller, Lewis, H1 aCarmichael, Owen, T1 aLongstreth, William, T1 aGach, Michael1 aBoardman, John1 aBernick, Charles, B1 aThompson, Paul, M1 aBecker, James, T uhttps://chs-nhlbi.org/node/133302102nas a2200469 4500008004100000022001400041245008200055210006900137260001300206300001200219490000700231520073300238653002100971653002600992653002301018653002201041653001801063653003401081653001301115653001701128653001101145653002401156100002401180700001901204700002301223700001801246700001201264700001801276700001701294700001301311700001801324700001901342700001601361700001901377700003001396700002001426700002501446700001901471700002101490710008501511856003601596 2013 eng d a1546-171800aWhole-genome sequence-based analysis of high-density lipoprotein cholesterol.0 aWholegenome sequencebased analysis of highdensity lipoprotein ch c2013 Aug a899-9010 v453 aWe describe initial steps for interrogating whole-genome sequence data to characterize the genetic architecture of a complex trait, levels of high-density lipoprotein cholesterol (HDL-C). We report whole-genome sequencing and analysis of 962 individuals from the Cohorts for Heart and Aging Research in Genetic Epidemiology (CHARGE) studies. From this analysis, we estimate that common variation contributes more to heritability of HDL-C levels than rare variation, and screening for mendelian variants for dyslipidemia identified individuals with extreme HDL-C levels. Whole-genome sequencing analyses highlight the value of regulatory and non-protein-coding regions of the genome in addition to protein-coding regions.
10aCholesterol, HDL10aComputational Biology10aDatabases, Genetic10aGenetic Variation10aGenome, Human10aGenome-Wide Association Study10aGenomics10aHeterozygote10aHumans10aOpen Reading Frames1 aMorrison, Alanna, C1 aVoorman, Arend1 aJohnson, Andrew, D1 aLiu, Xiaoming1 aYu, Jin1 aLi, Alexander1 aMuzny, Donna1 aYu, Fuli1 aRice, Kenneth1 aZhu, Chengsong1 aBis, Joshua1 aHeiss, Gerardo1 aO'Donnell, Christopher, J1 aPsaty, Bruce, M1 aCupples, Adrienne, L1 aGibbs, Richard1 aBoerwinkle, Eric1 aCohorts for Heart and Aging Research in Genetic Epidemiology (CHARGE) Consortium uhttps://chs-nhlbi.org/node/628303118nas a2200373 4500008004100000022001400041245016800055210006900223260001300292300001600305490000700321520198600328653000902314653002802323653003502351653002102386653001602407653001102423653001102434653001402445653002502459653000902484653003102493653001702524653003202541653001102573653001802584100002202602700002002624700002102644700002302665700002002688856003602708 2014 eng d a1097-679500aWhat do carotid intima-media thickness and plaque add to the prediction of stroke and cardiovascular disease risk in older adults? The cardiovascular health study.0 aWhat do carotid intimamedia thickness and plaque add to the pred c2014 Sep a998-1005.e20 v273 aBACKGROUND: The aim of this study was to evaluate whether the addition of ultrasound carotid intima-media thickness (CIMT) measurements and risk categories of plaque help predict incident stroke and cardiovascular disease (CVD) in older adults.
METHODS: Carotid ultrasound studies were recorded in the multicenter Cardiovascular Health Study. CVD was defined as coronary heart disease plus heart failure plus stroke. Ten-year risk prediction Cox proportional-hazards models for stroke and CVD were calculated using Cardiovascular Health Study-specific coefficients for Framingham risk score factors. Categories of CIMT and CIMT plus plaque were added to Framingham risk score prediction models, and categorical net reclassification improvement (NRI) and Harrell's c-statistic were calculated.
RESULTS: In 4,384 Cardiovascular Health Study participants (61% women, 14% black; mean baseline age, 72 ± 5 years) without CVD at baseline, higher CIMT category and the presence of plaque were both associated with higher incidence rates for stroke and CVD. The addition of CIMT improved the ability of Framingham risk score-type risk models to discriminate cases from noncases of incident stroke and CVD (NRI = 0.062, P = .015, and NRI = 0.027, P < .001, respectively), with no further improvement by adding plaque. For both outcomes, NRI was driven by down-classifying those without incident disease. Although the addition of plaque to CIMT did not result in a significant NRI for either outcome, it was significant among those without incident disease.
CONCLUSIONS: In older adults, the addition of CIMT modestly improves 10-year risk prediction for stroke and CVD beyond a traditional risk factor model, mainly by down-classifying risk in those without stroke or CVD; the addition of plaque to CIMT adds no statistical benefit in the overall cohort, although there is evidence of down-classification in those without events.
10aAged10aCardiovascular Diseases10aCarotid Intima-Media Thickness10aCarotid Stenosis10aComorbidity10aFemale10aHumans10aIncidence10aLongitudinal Studies10aMale10aReproducibility of Results10aRisk Factors10aSensitivity and Specificity10aStroke10aSurvival Rate1 aGardin, Julius, M1 aBartz, Traci, M1 aPolak, Joseph, F1 aO'Leary, Daniel, H1 aWong, Nathan, D uhttps://chs-nhlbi.org/node/656405955nas a2201657 4500008004100000022001400041245011000055210006900165260001600234300001100250490000700261520138800268653001001656653000901666653002201675653002101697653001901718653001801737653001001755653001101765653002201776653001901798653001701817653003401834653001301868653001101881653001101892653000901903653001601912653001401928653003601942653002801978653002702006653001902033653002702052653002602079100002102105700001402126700001402140700001602154700002202170700002202192700001702214700002002231700002102251700002102272700001302293700002002306700001702326700001502343700001702358700002002375700001402395700002702409700001802436700002202454700001602476700001902492700001702511700002102528700002302549700002002572700001802592700002202610700001802632700001502650700001202665700001702677700001502694700001802709700001902727700001902746700001902765700002502784700002602809700002102835700002502856700002102881700001902902700002502921700001702946700002502963700001202988700002303000700002003023700002603043700002903069700002303098700002803121700001803149700002003167700002303187700002103210700001903231700001803250700002803268700001903296700002403315700002303339700002803362700001703390700002203407700002203429700002403451700002403475700002003499700002303519700002203542700001603564700001703580700002403597700002003621700001803641700002003659700002103679700001603700700001903716700002203735700002803757700002303785700003003808700001903838700001903857700002503876700002103901700002003922700002103942700002203963700001603985700002004001700002504021700002404046700002104070700002604091700002504117700002104142700002204163700002304185710005304208856003604261 2014 eng d a1537-660500aWhole-exome sequencing identifies rare and low-frequency coding variants associated with LDL cholesterol.0 aWholeexome sequencing identifies rare and lowfrequency coding va c2014 Feb 06 a233-450 v943 aElevated low-density lipoprotein cholesterol (LDL-C) is a treatable, heritable risk factor for cardiovascular disease. Genome-wide association studies (GWASs) have identified 157 variants associated with lipid levels but are not well suited to assess the impact of rare and low-frequency variants. To determine whether rare or low-frequency coding variants are associated with LDL-C, we exome sequenced 2,005 individuals, including 554 individuals selected for extreme LDL-C (>98(th) or <2(nd) percentile). Follow-up analyses included sequencing of 1,302 additional individuals and genotype-based analysis of 52,221 individuals. We observed significant evidence of association between LDL-C and the burden of rare or low-frequency variants in PNPLA5, encoding a phospholipase-domain-containing protein, and both known and previously unidentified variants in PCSK9, LDLR and APOB, three known lipid-related genes. The effect sizes for the burden of rare variants for each associated gene were substantially higher than those observed for individual SNPs identified from GWASs. We replicated the PNPLA5 signal in an independent large-scale sequencing study of 2,084 individuals. In conclusion, this large whole-exome-sequencing study for LDL-C identified a gene not known to be implicated in LDL-C and provides unique insight into the design and analysis of similar experiments.
10aAdult10aAged10aApolipoproteins E10aCholesterol, LDL10aCohort Studies10aDyslipidemias10aExome10aFemale10aFollow-Up Studies10aGene Frequency10aGenetic Code10aGenome-Wide Association Study10aGenotype10aHumans10aLipase10aMale10aMiddle Aged10aPhenotype10aPolymorphism, Single Nucleotide10aProprotein Convertase 910aProprotein Convertases10aReceptors, LDL10aSequence Analysis, DNA10aSerine Endopeptidases1 aLange, Leslie, A1 aHu, Youna1 aZhang, He1 aXue, Chenyi1 aSchmidt, Ellen, M1 aTang, Zheng-Zheng1 aBizon, Chris1 aLange, Ethan, M1 aSmith, Joshua, D1 aTurner, Emily, H1 aJun, Goo1 aKang, Hyun, Min1 aPeloso, Gina1 aAuer, Paul1 aLi, Kuo-Ping1 aFlannick, Jason1 aZhang, Ji1 aFuchsberger, Christian1 aGaulton, Kyle1 aLindgren, Cecilia1 aLocke, Adam1 aManning, Alisa1 aSim, Xueling1 aRivas, Manuel, A1 aHolmen, Oddgeir, L1 aGottesman, Omri1 aLu, Yingchang1 aRuderfer, Douglas1 aStahl, Eli, A1 aDuan, Qing1 aLi, Yun1 aDurda, Peter1 aJiao, Shuo1 aIsaacs, Aaron1 aHofman, Albert1 aBis, Joshua, C1 aCorrea, Adolfo1 aGriswold, Michael, E1 aJakobsdottir, Johanna1 aSmith, Albert, V1 aSchreiner, Pamela, J1 aFeitosa, Mary, F1 aZhang, Qunyuan1 aHuffman, Jennifer, E1 aCrosby, Jacy1 aWassel, Christina, L1 aDo, Ron1 aFranceschini, Nora1 aMartin, Lisa, W1 aRobinson, Jennifer, G1 aAssimes, Themistocles, L1 aCrosslin, David, R1 aRosenthal, Elisabeth, A1 aTsai, Michael1 aRieder, Mark, J1 aFarlow, Deborah, N1 aFolsom, Aaron, R1 aLumley, Thomas1 aFox, Ervin, R1 aCarlson, Christopher, S1 aPeters, Ulrike1 aJackson, Rebecca, D1 aDuijn, Cornelia, M1 aUitterlinden, André, G1 aLevy, Daniel1 aRotter, Jerome, I1 aTaylor, Herman, A1 aGudnason, Vilmundur1 aSiscovick, David, S1 aFornage, Myriam1 aBorecki, Ingrid, B1 aHayward, Caroline1 aRudan, Igor1 aChen, Eugene1 aBottinger, Erwin, P1 aLoos, Ruth, J F1 aSætrom, Pål1 aHveem, Kristian1 aBoehnke, Michael1 aGroop, Leif1 aMcCarthy, Mark1 aMeitinger, Thomas1 aBallantyne, Christie, M1 aGabriel, Stacey, B1 aO'Donnell, Christopher, J1 aPost, Wendy, S1 aNorth, Kari, E1 aReiner, Alexander, P1 aBoerwinkle, Eric1 aPsaty, Bruce, M1 aAltshuler, David1 aKathiresan, Sekar1 aLin, Dan-Yu1 aJarvik, Gail, P1 aCupples, Adrienne, L1 aKooperberg, Charles1 aWilson, James, G1 aNickerson, Deborah, A1 aAbecasis, Goncalo, R1 aRich, Stephen, S1 aTracy, Russell, P1 aWiller, Cristen, J1 aNHLBI Grand Opportunity Exome Sequencing Project uhttps://chs-nhlbi.org/node/657704741nas a2201021 4500008004100000022001400041245008000055210006900135260001300204300001200217490000700229520186200236653001002098653000902108653001902117653002402136653001102160653003802171653003402209653001102243653002602254653000902280653001602289653002402305653001702329100001702346700002602363700001902389700002102408700002002429700002502449700002302474700002002497700002902517700002102546700001602567700002202583700002202605700001802627700002402645700002302669700002002692700002502712700002002737700002302757700001402780700002402794700002602818700002402844700001902868700001902887700001902906700002102925700002102946700001702967700002102984700001603005700002003021700002603041700001903067700002303086700002103109700002003130700001803150700002003168700002003188700002103208700002403229700002403253700002103277700001503298700002603313700001803339700002303357700002003380700001903400700002303419700002003442700001903462700002203481700002003503700002203523700002003545700002003565700002203585710007603607856003603683 2015 eng d a1524-462800aWhite Matter Lesion Progression: Genome-Wide Search for Genetic Influences.0 aWhite Matter Lesion Progression GenomeWide Search for Genetic In c2015 Nov a3048-570 v463 aBACKGROUND AND PURPOSE: White matter lesion (WML) progression on magnetic resonance imaging is related to cognitive decline and stroke, but its determinants besides baseline WML burden are largely unknown. Here, we estimated heritability of WML progression, and sought common genetic variants associated with WML progression in elderly participants from the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium.
METHODS: Heritability of WML progression was calculated in the Framingham Heart Study. The genome-wide association study included 7773 elderly participants from 10 cohorts. To assess the relative contribution of genetic factors to progression of WML, we compared in 7 cohorts risk models including demographics, vascular risk factors plus single-nucleotide polymorphisms that have been shown to be associated cross-sectionally with WML in the current and previous association studies.
RESULTS: A total of 1085 subjects showed WML progression. The heritability estimate for WML progression was low at 6.5%, and no single-nucleotide polymorphisms achieved genome-wide significance (P<5×10(-8)). Four loci were suggestive (P<1×10(-5)) of an association with WML progression: 10q24.32 (rs10883817, P=1.46×10(-6)); 12q13.13 (rs4761974, P=8.71×10(-7)); 20p12.1 (rs6135309, P=3.69×10(-6)); and 4p15.31 (rs7664442, P=2.26×10(-6)). Variants that have been previously related to WML explained only 0.8% to 11.7% more of the variance in WML progression than age, vascular risk factors, and baseline WML burden.
CONCLUSIONS: Common genetic factors contribute little to the progression of age-related WML in middle-aged and older adults. Future research on determinants of WML progression should focus more on environmental, lifestyle, or host-related biological factors.
10aAdult10aAged10aCohort Studies10aDisease Progression10aFemale10aGenetic Predisposition to Disease10aGenome-Wide Association Study10aHumans10aLeukoencephalopathies10aMale10aMiddle Aged10aProspective Studies10aWhite Matter1 aHofer, Edith1 aCavalieri, Margherita1 aBis, Joshua, C1 aDeCarli, Charles1 aFornage, Myriam1 aSigurdsson, Sigurdur1 aSrikanth, Velandai1 aTrompet, Stella1 aVerhaaren, Benjamin, F J1 aWolf, Christiane1 aYang, Qiong1 aAdams, Hieab, H H1 aAmouyel, Philippe1 aBeiser, Alexa1 aBuckley, Brendan, M1 aCallisaya, Michele1 aChauhan, Ganesh1 ade Craen, Anton, J M1 aDufouil, Carole1 aDuijn, Cornelia, M1 aFord, Ian1 aFreudenberger, Paul1 aGottesman, Rebecca, F1 aGudnason, Vilmundur1 aHeiss, Gerardo1 aHofman, Albert1 aLumley, Thomas1 aMartinez, Oliver1 aMazoyer, Bernard1 aMoran, Chris1 aNiessen, Wiro, J1 aPhan, Thanh1 aPsaty, Bruce, M1 aSatizabal, Claudia, L1 aSattar, Naveed1 aSchilling, Sabrina1 aShibata, Dean, K1 aSlagboom, Eline1 aSmith, Albert1 aStott, David, J1 aTaylor, Kent, D1 aThomson, Russell1 aTöglhofer, Anna, M1 aTzourio, Christophe1 avan Buchem, Mark1 aWang, Jing1 aWestendorp, Rudi, G J1 aWindham, Gwen1 aVernooij, Meike, W1 aZijdenbos, Alex1 aBeare, Richard1 aDebette, Stephanie1 aIkram, Arfan, M1 aJukema, Wouter1 aLauner, Lenore, J1 aLongstreth, W T1 aMosley, Thomas, H1 aSeshadri, Sudha1 aSchmidt, Helena1 aSchmidt, Reinhold1 aCohorts for Heart and Aging Research in Genomic Epidemiology Consortium uhttps://chs-nhlbi.org/node/686103189nas a2200433 4500008004100000022001400041245005100055210005000106260001300156300001300169490000700182520196300189100002202152700002302174700002502197700002202222700001802244700003002262700001902292700002102311700002202332700001902354700002502373700002102398700002602419700001702445700002002462700002002482700002402502700002202526700002402548700002402572700002302596700001902619700002402638700001902662710003802681856003602719 2016 eng d a1553-740400aWhole Exome Sequencing in Atrial Fibrillation.0 aWhole Exome Sequencing in Atrial Fibrillation c2016 Sep ae10062840 v123 aAtrial fibrillation (AF) is a morbid and heritable arrhythmia. Over 35 genes have been reported to underlie AF, most of which were described in small candidate gene association studies. Replication remains lacking for most, and therefore the contribution of coding variation to AF susceptibility remains poorly understood. We examined whole exome sequencing data in a large community-based sample of 1,734 individuals with and 9,423 without AF from the Framingham Heart Study, Cardiovascular Health Study, Atherosclerosis Risk in Communities Study, and NHLBI-GO Exome Sequencing Project and meta-analyzed the results. We also examined whether genetic variation was enriched in suspected AF genes (N = 37) in AF cases versus controls. The mean age ranged from 59 to 73 years; 8,656 (78%) were of European ancestry. None of the 99,404 common variants evaluated was significantly associated after adjusting for multiple testing. Among the most significantly associated variants was a common (allele frequency = 86%) missense variant in SYNPO2L (rs3812629, p.Pro707Leu, [odds ratio 1.27, 95% confidence interval 1.13-1.43, P = 6.6x10-5]) which lies at a known AF susceptibility locus and is in linkage disequilibrium with a top marker from prior analyses at the locus. We did not observe significant associations between rare variants and AF in gene-based tests. Individuals with AF did not display any statistically significant enrichment for common or rare coding variation in previously implicated AF genes. In conclusion, we did not observe associations between coding genetic variants and AF, suggesting that large-effect coding variation is not the predominant mechanism underlying AF. A coding variant in SYNPO2L requires further evaluation to determine whether it is causally related to AF. Efforts to identify biologically meaningful coding variation underlying AF may require large sample sizes or populations enriched for large genetic effects.
1 aLubitz, Steven, A1 aBrody, Jennifer, A1 aBihlmeyer, Nathan, A1 aRoselli, Carolina1 aWeng, Lu-Chen1 aChristophersen, Ingrid, E1 aAlonso, Alvaro1 aBoerwinkle, Eric1 aGibbs, Richard, A1 aBis, Joshua, C1 aCupples, Adrienne, L1 aMohler, Peter, J1 aNickerson, Deborah, A1 aMuzny, Donna1 aPerez, Marco, V1 aPsaty, Bruce, M1 aSoliman, Elsayed, Z1 aSotoodehnia, Nona1 aLunetta, Kathryn, L1 aBenjamin, Emelia, J1 aHeckbert, Susan, R1 aArking, Dan, E1 aEllinor, Patrick, T1 aLin, Honghuang1 aNHLBI GO Exome Sequencing Project uhttps://chs-nhlbi.org/node/725002324nas a2200793 4500008004100000022001400041245015800055210006900213260001600282300000800298490000700306100002100313700002300334700002200357700002100379700001700400700002300417700002000440700001800460700002000478700001500498700001800513700002600531700002200557700002000579700001700599700002900616700002000645700001300665700001600678700002100694700002000715700002100735700001500756700002100771700001600792700001500808700002000823700002300843700002000866700002100886700002000907700002800927700002000955700002200975700002300997700002001020700002901040700002201069700002301091700002101114700002101135700002601156700001901182700002801201700002101229700002001250700002001270700001901290700002101309700002001330700002301350700003001373700002101403700002501424700002201449700002301471856003601494 2016 eng d a1537-660500aWhole-Exome Sequencing Identifies Loci Associated with Blood Cell Traits and Reveals a Role for Alternative GFI1B Splice Variants in Human Hematopoiesis.0 aWholeExome Sequencing Identifies Loci Associated with Blood Cell c2016 Sep 01 a7850 v991 aPolfus, Linda, M1 aKhajuria, Rajiv, K1 aSchick, Ursula, M1 aPankratz, Nathan1 aPazoki, Raha1 aBrody, Jennifer, A1 aChen, Ming-Huei1 aAuer, Paul, L1 aFloyd, James, S1 aHuang, Jie1 aLange, Leslie1 avan Rooij, Frank, J A1 aGibbs, Richard, A1 aMetcalf, Ginger1 aMuzny, Donna1 aVeeraraghavan, Narayanan1 aWalter, Klaudia1 aChen, Lu1 aYanek, Lisa1 aBecker, Lewis, C1 aPeloso, Gina, M1 aWakabayashi, Aoi1 aKals, Mart1 aMetspalu, Andres1 aEsko, Tõnu1 aFox, Keolu1 aWallace, Robert1 aFranceschini, Nora1 aMatijevic, Nena1 aRice, Kenneth, M1 aBartz, Traci, M1 aLyytikäinen, Leo-Pekka1 aKähönen, Mika1 aLehtimäki, Terho1 aRaitakari, Olli, T1 aLi-Gao, Ruifang1 aMook-Kanamori, Dennis, O1 aLettre, Guillaume1 aDuijn, Cornelia, M1 aFranco, Oscar, H1 aRich, Stephen, S1 aRivadeneira, Fernando1 aHofman, Albert1 aUitterlinden, André, G1 aWilson, James, G1 aPsaty, Bruce, M1 aSoranzo, Nicole1 aDehghan, Abbas1 aBoerwinkle, Eric1 aZhang, Xiaoling1 aJohnson, Andrew, D1 aO'Donnell, Christopher, J1 aJohnsen, Jill, M1 aReiner, Alexander, P1 aGanesh, Santhi, K1 aSankaran, Vijay, G uhttps://chs-nhlbi.org/node/726305481nas a2201285 4500008004100000022001400041245015800055210006900213260001600282520179200298100001902090700001702109700002102126700001702147700002902164700002102193700002402214700002002238700001302258700002002271700002102291700002102312700001302333700001502346700001902361700001602380700002202396700002102418700002302439700002302462700002502485700001902510700001902529700002102548700002502569700003002594700002302624700002702647700002002674700002302694700002202717700002202739700001802761700002802779700001902807700002402826700002202850700002102872700002002893700002402913700002002937700001902957700001602976700001802992700002003010700001803030700002203048700001903070700001803089700002003107700002003127700001903147700002003166700001803186700001903204700002003223700003103243700001903274700002003293700002003313700001903333700001903352700002203371700001903393700002003412700002103432700002003453700002103473700002203494700002103516700002503537700002003562700002403582700002503606700002303631700002203654700001703676700001903693700002303712700002003735700001903755700002003774700002403794700002603818700002003844700001903864700001503883700002103898700002403919700002403943700001903967700002003986700002804006700002004034700001704054700002004071700002304091710004504114856003604159 2018 eng d a1476-557800aWhole exome sequencing study identifies novel rare and common Alzheimer's-Associated variants involved in immune response and transcriptional regulation.0 aWhole exome sequencing study identifies novel rare and common Al c2018 Aug 143 aThe Alzheimer's Disease Sequencing Project (ADSP) undertook whole exome sequencing in 5,740 late-onset Alzheimer disease (AD) cases and 5,096 cognitively normal controls primarily of European ancestry (EA), among whom 218 cases and 177 controls were Caribbean Hispanic (CH). An age-, sex- and APOE based risk score and family history were used to select cases most likely to harbor novel AD risk variants and controls least likely to develop AD by age 85 years. We tested ~1.5 million single nucleotide variants (SNVs) and 50,000 insertion-deletion polymorphisms (indels) for association to AD, using multiple models considering individual variants as well as gene-based tests aggregating rare, predicted functional, and loss of function variants. Sixteen single variants and 19 genes that met criteria for significant or suggestive associations after multiple-testing correction were evaluated for replication in four independent samples; three with whole exome sequencing (2,778 cases, 7,262 controls) and one with genome-wide genotyping imputed to the Haplotype Reference Consortium panel (9,343 cases, 11,527 controls). The top findings in the discovery sample were also followed-up in the ADSP whole-genome sequenced family-based dataset (197 members of 42 EA families and 501 members of 157 CH families). We identified novel and predicted functional genetic variants in genes previously associated with AD. We also detected associations in three novel genes: IGHG3 (p = 9.8 × 10), an immunoglobulin gene whose antibodies interact with β-amyloid, a long non-coding RNA AC099552.4 (p = 1.2 × 10), and a zinc-finger protein ZNF655 (gene-based p = 5.0 × 10). The latter two suggest an important role for transcriptional regulation in AD pathogenesis.
1 aBis, Joshua, C1 aJian, Xueqiu1 aKunkle, Brian, W1 aChen, Yuning1 aHamilton-Nelson, Kara, L1 aBush, William, S1 aSalerno, William, J1 aLancour, Daniel1 aMa, Yiyi1 aRenton, Alan, E1 aMarcora, Edoardo1 aFarrell, John, J1 aZhao, Yi1 aQu, Liming1 aAhmad, Shahzad1 aAmin, Najaf1 aAmouyel, Philippe1 aBeecham, Gary, W1 aBelow, Jennifer, E1 aCampion, Dominique1 aCharbonnier, Camille1 aChung, Jaeyoon1 aCrane, Paul, K1 aCruchaga, Carlos1 aCupples, Adrienne, L1 aDartigues, Jean-François1 aDebette, Stephanie1 aDeleuze, Jean-Francois1 aFulton, Lucinda1 aGabriel, Stacey, B1 aGenin, Emmanuelle1 aGibbs, Richard, A1 aGoate, Alison1 aGrenier-Boley, Benjamin1 aGupta, Namrata1 aHaines, Jonathan, L1 aHavulinna, Aki, S1 aHelisalmi, Seppo1 aHiltunen, Mikko1 aHowrigan, Daniel, P1 aIkram, Arfan, M1 aKaprio, Jaakko1 aKonrad, Jan1 aKuzma, Amanda1 aLander, Eric, S1 aLathrop, Mark1 aLehtimäki, Terho1 aLin, Honghuang1 aMattila, Kari1 aMayeux, Richard1 aMuzny, Donna, M1 aNasser, Waleed1 aNeale, Benjamin1 aNho, Kwangsik1 aNicolas, Gaël1 aPatel, Devanshi1 aPericak-Vance, Margaret, A1 aPerola, Markus1 aPsaty, Bruce, M1 aQuenez, Olivier1 aRajabli, Farid1 aRedon, Richard1 aReitz, Christiane1 aRemes, Anne, M1 aSalomaa, Veikko1 aSarnowski, Chloe1 aSchmidt, Helena1 aSchmidt, Michael1 aSchmidt, Reinhold1 aSoininen, Hilkka1 aThornton, Timothy, A1 aTosto, Giuseppe1 aTzourio, Christophe1 avan der Lee, Sven, J1 aDuijn, Cornelia, M1 aVardarajan, Badri1 aWang, Weixin1 aWijsman, Ellen1 aWilson, Richard, K1 aWitten, Daniela1 aWorley, Kim, C1 aZhang, Xiaoling1 aBellenguez, Céline1 aLambert, Jean-Charles1 aKurki, Mitja, I1 aPalotie, Aarno1 aDaly, Mark1 aBoerwinkle, Eric1 aLunetta, Kathryn, L1 aDeStefano, Anita, L1 aDupuis, Josée1 aMartin, Eden, R1 aSchellenberg, Gerard, D1 aSeshadri, Sudha1 aNaj, Adam, C1 aFornage, Myriam1 aFarrer, Lindsay, A1 aAlzheimer’s Disease Sequencing Project uhttps://chs-nhlbi.org/node/778502912nas a2200457 4500008004100000022001400041245009600055210006900151260001300220300001200233490000600245520159000251100002501841700001901866700002001885700002101905700002001926700002001946700002501966700002001991700002102011700001402032700002202046700002102068700001702089700001802106700001602124700001802140700002002158700001902178700002002197700002402217700002102241700002502262700001902287700003102306700002002337700001802357710004302375856003602418 2018 eng d a2328-950300aWhole genome sequencing of Caribbean Hispanic families with late-onset Alzheimer's disease.0 aWhole genome sequencing of Caribbean Hispanic families with late c2018 Apr a406-4170 v53 aObjective: To identify rare causal variants underlying known loci that segregate with late-onset Alzheimer's disease (LOAD) in multiplex families.
Methods: We analyzed whole genome sequences (WGS) from 351 members of 67 Caribbean Hispanic (CH) families from Dominican Republic and New York multiply affected by LOAD. Members of 67 CH and additional 47 Caucasian families underwent WGS as a part of the Alzheimer's Disease Sequencing Project (ADSP). All members of 67 CH families, an additional 48 CH families and an independent CH case-control cohort were subsequently genotyped for validation. Patients met criteria for LOAD, and controls were determined to be dementia free. We investigated rare variants segregating within families and gene-based associations with disease within LOAD GWAS loci.
Results: A variant in p.R434W, segregated significantly with LOAD in two large families (OR = 5.77, 95% CI: 1.07-30.9, = 0.041). In addition, missense mutations in and under previously reported linkage peaks at 7q14.3 and 11q12.3 segregated completely in one family and in follow-up genotyping both were nominally significant ( < 0.05). We also identified rare variants in a number of genes associated with LOAD in prior genome wide association studies, including ( = 0.049), ( = 0.0098) and ( = 0.040).
Conclusions and Relevance: Rare variants in multiple genes influence the risk of LOAD disease in multiplex families. These results suggest that rare variants may underlie loci identified in genome wide association studies.
1 aVardarajan, Badri, N1 aBarral, Sandra1 aJaworski, James1 aBeecham, Gary, W1 aBlue, Elizabeth1 aTosto, Giuseppe1 aReyes-Dumeyer, Dolly1 aMedrano, Martin1 aLantigua, Rafael1 aNaj, Adam1 aThornton, Timothy1 aDeStefano, Anita1 aMartin, Eden1 aSan Wang, Li-1 aBrown, Lisa1 aBush, William1 aDuijn, Cornelia1 aGoate, Allison1 aFarrer, Lindsay1 aHaines, Jonathan, L1 aBoerwinkle, Eric1 aSchellenberg, Gerard1 aWijsman, Ellen1 aPericak-Vance, Margaret, A1 aMayeux, Richard1 aSan Wang, Li-1 aAlzheimer's Disease Sequencing Project uhttps://chs-nhlbi.org/node/766102737nas a2200301 4500008004100000022001400041245008700055210006900142260001300211300001200224490000600236520182600242100002202068700002202090700002002112700002302132700001902155700002102174700002302195700002802218700002102246700002002267700002502287700002402312700002002336710004302356856003602399 2018 eng d a2328-950300aWhole-exome sequencing in 20,197 persons for rare variants in Alzheimer's disease.0 aWholeexome sequencing in 20197 persons for rare variants in Alzh c2018 Jul a832-8420 v53 aObjective: The genetic bases of Alzheimer's disease remain uncertain. An international effort to fully articulate genetic risks and protective factors is underway with the hope of identifying potential therapeutic targets and preventive strategies. The goal here was to identify and characterize the frequency and impact of rare and ultra-rare variants in Alzheimer's disease, using whole-exome sequencing in 20,197 individuals.
Methods: We used a gene-based collapsing analysis of loss-of-function ultra-rare variants in a case-control study design with data from the Washington Heights-Inwood Columbia Aging Project, the Alzheimer's Disease Sequencing Project and unrelated individuals from the Institute of Genomic Medicine at Columbia University.
Results: We identified 19 cases carrying extremely rare loss-of-function variants among a collection of 6,965 cases and a single loss-of-function variant among 13,252 controls ( = 2.17 × 10; OR: 36.2 [95% CI: 5.8-1493.0]). Age-at-onset was 7 years earlier for patients with qualifying variant compared with noncarriers. No other gene attained a study-wide level of statistical significance, but multiple top-ranked genes, including , and were among candidates for follow-up studies.
Interpretation: This study implicates ultra-rare, loss-of-function variants in as a significant genetic risk factor for Alzheimer's disease and provides a comprehensive dataset comparing the burden of rare variation in nearly all human genes in Alzheimer's disease cases and controls. This is the first investigation to establish a genome-wide statistically significant association between multiple extremely rare loss-of-function variants in and Alzheimer's disease in a large whole-exome study of unrelated cases and controls.
1 aRaghavan, Neha, S1 aBrickman, Adam, M1 aAndrews, Howard1 aManly, Jennifer, J1 aSchupf, Nicole1 aLantigua, Rafael1 aWolock, Charles, J1 aKamalakaran, Sitharthan1 aPetrovski, Slave1 aTosto, Giuseppe1 aVardarajan, Badri, N1 aGoldstein, David, B1 aMayeux, Richard1 aAlzheimer's Disease Sequencing Project uhttps://chs-nhlbi.org/node/781004340nas a2200733 4500008004100000022001400041245013200055210006900187260001300256300001200269490000700281520227500288100001602563700002202579700002002601700002502621700002302646700001302669700002702682700001902709700001602728700002602744700001902770700001702789700002202806700002002828700001702848700002802865700002102893700002402914700001702938700002402955700001802979700002602997700002003023700002003043700002103063700002403084700002003108700002203128700002303150700002403173700002003197700001603217700001803233700001803251700001503269700002003284700002103304700002103325700001503346700001803361700001603379700002303395700001703418700001603435700001803451700002703469700001703496700002003513700002003533700001703553856003603570 2020 eng d a2574-830000aWhole Blood DNA Methylation Signatures of Diet Are Associated With Cardiovascular Disease Risk Factors and All-Cause Mortality.0 aWhole Blood DNA Methylation Signatures of Diet Are Associated Wi c2020 Aug ae0027660 v133 aBACKGROUND: DNA methylation patterns associated with habitual diet have not been well studied.
METHODS: Diet quality was characterized using a Mediterranean-style diet score and the Alternative Healthy Eating Index score. We conducted ethnicity-specific and trans-ethnic epigenome-wide association analyses for diet quality and leukocyte-derived DNA methylation at over 400 000 CpGs (cytosine-guanine dinucleotides) in 5 population-based cohorts including 6662 European ancestry, 2702 African ancestry, and 360 Hispanic ancestry participants. For diet-associated CpGs identified in epigenome-wide analyses, we conducted Mendelian randomization (MR) analysis to examine their relations to cardiovascular disease risk factors and examined their longitudinal associations with all-cause mortality.
RESULTS: We identified 30 CpGs associated with either Mediterranean-style diet score or Alternative Healthy Eating Index, or both, in European ancestry participants. Among these CpGs, 12 CpGs were significantly associated with all-cause mortality (Bonferroni corrected <1.6×10). Hypermethylation of cg18181703 () was associated with higher scores of both Mediterranean-style diet score and Alternative Healthy Eating Index and lower risk for all-cause mortality (=5.7×10). Ten additional diet-associated CpGs were nominally associated with all-cause mortality (<0.05). MR analysis revealed 8 putatively causal associations for 6 CpGs with 4 cardiovascular disease risk factors (body mass index, triglycerides, high-density lipoprotein cholesterol concentrations, and type 2 diabetes mellitus; Bonferroni corrected MR <4.5×10). For example, hypermethylation of cg11250194 () was associated with lower triglyceride concentrations (MR, =1.5×10).and hypermethylation of cg02079413 (; ) was associated with body mass index (corrected MR, =1×10).
CONCLUSIONS: Habitual diet quality was associated with differential peripheral leukocyte DNA methylation levels of 30 CpGs, most of which were also associated with multiple health outcomes, in European ancestry individuals. These findings demonstrate that integrative genomic analysis of dietary information may reveal molecular targets for disease prevention and treatment.
1 aMa, Jiantao1 aRebholz, Casey, M1 aBraun, Kim, V E1 aReynolds, Lindsay, M1 aAslibekyan, Stella1 aXia, Rui1 aBiligowda, Niranjan, G1 aHuan, Tianxiao1 aLiu, Chunyu1 aMendelson, Michael, M1 aJoehanes, Roby1 aHu, Emily, A1 aVitolins, Mara, Z1 aWood, Alexis, C1 aLohman, Kurt1 aOchoa-Rosales, Carolina1 avan Meurs, Joyce1 aUitterlinden, Andre1 aLiu, Yongmei1 aElhadad, Mohamed, A1 aHeier, Margit1 aWaldenberger, Melanie1 aPeters, Annette1 aColicino, Elena1 aWhitsel, Eric, A1 aBaldassari, Antoine1 aGharib, Sina, A1 aSotoodehnia, Nona1 aBrody, Jennifer, A1 aSitlani, Colleen, M1 aTanaka, Toshiko1 aHill, David1 aCorley, Janie1 aDeary, Ian, J1 aZhang, Yan1 aSchöttker, Ben1 aBrenner, Hermann1 aWalker, Maura, E1 aYe, Shumao1 aNguyen, Steve1 aPankow, Jim1 aDemerath, Ellen, W1 aZheng, Yinan1 aHou, Lifang1 aLiang, Liming1 aLichtenstein, Alice, H1 aHu, Frank, B1 aFornage, Myriam1 aVoortman, Trudy1 aLevy, Daniel uhttps://chs-nhlbi.org/node/844604776nas a2201261 4500008004100000022001400041245010300055210006900158260001500227300000900242490000700251520104900258653001001307653002201317653000901339653002201348653005001370653002901420653002401449653001101473653002201484653001701506653003801523653003401561653001101595653005001606653000901656653000901665653001601674653003601690653004101726653004301767653004001810653004601850653002801896100001701924700001601941700001801957700001801975700001501993700001502008700001702023700001902040700001702059700003002076700002902106700002202135700002302157700001302180700002102193700001902214700001902233700001502252700002002267700001902287700002302306700002002329700002502349700002002374700002002394700002402414700002002438700002702458700002102485700002002506700001702526700001902543700002002562700001702582700001702599700002202616700002302638700002002661700002502681700002802706700002202734700002402756700001702780700002602797700002002823700002302843700003102866700002502897700001902922700002002941700002102961700002402982700001903006700002003025700002103045700002403066700002503090700002403115700002203139700002003161700002103181700002503202700002303227700002603250700002503276700002403301700001703325700002003342700002103362710006503383710003003448856003603478 2020 eng d a2041-172300aWhole genome sequence analysis of pulmonary function and COPD in 19,996 multi-ethnic participants.0 aWhole genome sequence analysis of pulmonary function and COPD in c2020 10 14 a51820 v113 aChronic obstructive pulmonary disease (COPD), diagnosed by reduced lung function, is a leading cause of morbidity and mortality. We performed whole genome sequence (WGS) analysis of lung function and COPD in a multi-ethnic sample of 11,497 participants from population- and family-based studies, and 8499 individuals from COPD-enriched studies in the NHLBI Trans-Omics for Precision Medicine (TOPMed) Program. We identify at genome-wide significance 10 known GWAS loci and 22 distinct, previously unreported loci, including two common variant signals from stratified analysis of African Americans. Four novel common variants within the regions of PIAS1, RGN (two variants) and FTO show evidence of replication in the UK Biobank (European ancestry n ~ 320,000), while colocalization analyses leveraging multi-omic data from GTEx and TOPMed identify potential molecular mechanisms underlying four of the 22 novel loci. Our study demonstrates the value of performing WGS analyses and multi-omic follow-up in cohorts of diverse ancestry.
10aAdult10aAfrican Americans10aAged10aAged, 80 and over10aAlpha-Ketoglutarate-Dependent Dioxygenase FTO10aCalcium-Binding Proteins10aFeasibility Studies10aFemale10aFollow-Up Studies10aGenetic Loci10aGenetic Predisposition to Disease10aGenome-Wide Association Study10aHumans10aIntracellular Signaling Peptides and Proteins10aLung10aMale10aMiddle Aged10aPolymorphism, Single Nucleotide10aProtein Inhibitors of Activated STAT10aPulmonary Disease, Chronic Obstructive10aRespiratory Physiological Phenomena10aSmall Ubiquitin-Related Modifier Proteins10aWhole Genome Sequencing1 aZhao, Xutong1 aQiao, Dandi1 aYang, Chaojie1 aKasela, Silva1 aKim, Wonji1 aMa, Yanlin1 aShrine, Nick1 aBatini, Chiara1 aSofer, Tamar1 aTaliun, Sarah, A Gagliano1 aSakornsakolpat, Phuwanat1 aBalte, Pallavi, P1 aProkopenko, Dmitry1 aYu, Bing1 aLange, Leslie, A1 aDupuis, Josée1 aCade, Brian, E1 aLee, Jiwon1 aGharib, Sina, A1 aDaya, Michelle1 aLaurie, Cecelia, A1 aRuczinski, Ingo1 aCupples, Adrienne, L1 aLoehr, Laura, R1 aBartz, Traci, M1 aMorrison, Alanna, C1 aPsaty, Bruce, M1 aVasan, Ramachandran, S1 aWilson, James, G1 aTaylor, Kent, D1 aDurda, Peter1 aJohnson, Craig1 aCornell, Elaine1 aGuo, Xiuqing1 aLiu, Yongmei1 aTracy, Russell, P1 aArdlie, Kristin, G1 aAguet, Francois1 aVanDenBerg, David, J1 aPapanicolaou, George, J1 aRotter, Jerome, I1 aBarnes, Kathleen, C1 aJain, Deepti1 aNickerson, Deborah, A1 aMuzny, Donna, M1 aMetcalf, Ginger, A1 aDoddapaneni, Harshavardhan1 aDugan-Perez, Shannon1 aGupta, Namrata1 aGabriel, Stacey1 aRich, Stephen, S1 aO'Connor, George, T1 aRedline, Susan1 aReed, Robert, M1 aLaurie, Cathy, C1 aDaviglus, Martha, L1 aPreudhomme, Liana, K1 aBurkart, Kristin, M1 aKaplan, Robert, C1 aWain, Louise, V1 aTobin, Martin, D1 aLondon, Stephanie, J1 aLappalainen, Tuuli1 aOelsner, Elizabeth, C1 aAbecasis, Goncalo, R1 aSilverman, Edwin, K1 aBarr, Graham1 aCho, Michael, H1 aManichaikul, Ani1 aNHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium1 aTOPMed Lung Working Group uhttps://chs-nhlbi.org/node/863903425nas a2200577 4500008004100000022001400041245020400055210006900259260001500328300001400343490000700357520172900364653000902093653001102102653000902113653001102122653002302133653000902156653002402165653001502189653001202204653001802216100002202234700002102256700002802277700002002305700002102325700002102346700002402367700001602391700002402407700002002431700002202451700001802473700001902491700002002510700002402530700002002554700001902574700002102593700001702614700002302631700002202654700002202676700002302698700001902721700002902740700002202769700002002791856003602811 2021 eng d a1758-535X00aWhat Cut-Point in Gait Speed Best Discriminates Community-Dwelling Older Adults With Mobility Complaints From Those Without? A Pooled Analysis From the Sarcopenia Definitions and Outcomes Consortium.0 aWhat CutPoint in Gait Speed Best Discriminates CommunityDwelling c2021 09 13 ae321-e3270 v763 aBACKGROUND: Cut-points to define slow walking speed have largely been derived from expert opinion.
METHODS: Study participants (13 589 men and 5043 women aged ≥65years) had walking speed (m/s) measured over 4-6 m (mean ± SD: 1.20 ± 0.27 m/s in men and 0.94 ± 0.24 m/s in women.) Mobility limitation was defined as any self-reported difficulty with walking approximately 1/4 mile (prevalence: 12.6% men, 26.4% women). Sex-stratified classification and regression tree (CART) models with 10-fold cross-validation identified walking speed cut-points that optimally discriminated those who reported mobility limitation from those who did not.
RESULTS: Among 5043 women, CART analysis identified 2 cut-points, classifying 4144 (82.2%) with walking speed ≥0.75 m/s, which we labeled as "fast"; 478 (9.5%) as "intermediate" (walking speed ≥0.62 m/s but <0.75 m/s); and 421 (8.3%) as "slow" (walking speed <0.62 m/s). Among 13 589 men, CART analysis identified 3 cut-points, classifying 10 001 (73.6%) with walking speed ≥1.00 m/s ("very fast"); 2901 (21.3%) as "fast" (walking speed ≥0.74 m/s but <1.00 m/s); 497 (3.7%) as "intermediate" (walking speed ≥0.57 m/s but <0.74 m/s); and 190 (1.4%) as "slow" (walking speed <0.57 m/s). Prevalence of self-reported mobility limitation was lowest in the "fast" or "very fast" (11% for men and 19% for women) and highest in the "slow" (60.5% in men and 71.0% in women). Rounding the 2 slower cut-points to 0.60 m/s and 0.75 m/s reclassified very few participants.
CONCLUSIONS: Cut-points in walking speed of approximately 0.60 m/s and 0.75 m/s discriminate those with self-reported mobility limitation from those without.
10aAged10aFemale10aGait10aHumans10aIndependent Living10aMale10aMobility Limitation10aSarcopenia10aWalking10aWalking Speed1 aCawthon, Peggy, M1 aPatel, Sheena, M1 aKritchevsky, Stephen, B1 aNewman, Anne, B1 aSantanasto, Adam1 aKiel, Douglas, P1 aTravison, Thomas, G1 aLane, Nancy1 aCummings, Steven, R1 aOrwoll, Eric, S1 aDuchowny, Kate, A1 aKwok, Timothy1 aHirani, Vasant1 aSchousboe, John1 aKarlsson, Magnus, K1 aMellström, Dan1 aOhlsson, Claes1 aLjunggren, Osten1 aXue, Qian-Li1 aShardell, Michelle1 aJordan, Joanne, M1 aPencina, Karol, M1 aFielding, Roger, A1 aMagaziner, Jay1 aCorrea-de-Araujo, Rosaly1 aBhasin, Shalender1 aManini, Todd, M uhttps://chs-nhlbi.org/node/898904364nas a2200997 4500008004100000022001400041245016200055210006900217260001300286300001100299490000700310520151100317100002001828700002201848700002301870700002301893700002301916700002601939700002501965700002001990700002102010700002602031700001702057700002502074700002202099700001702121700001702138700002002155700002002175700002302195700001702218700002102235700001802256700001802274700001902292700001202311700001802323700001502341700002302356700002802379700001802407700002002425700002002445700002102465700002302486700002002509700002102529700002302550700001802573700002202591700002302613700002202636700001802658700002002676700002302696700002302719700001902742700002002761700001402781700002002795700002202815700002002837700002102857700002102878700001702899700002402916700001902940700002502959700001402984700002302998700002403021700002303045700002503068700002603093700002103119700002703140700002203167700001703189700002103206700001903227700002103246700001603267700002403283700002303307856003603330 2021 eng d a2352-396400aWhole genome sequence analyses of eGFR in 23,732 people representing multiple ancestries in the NHLBI trans-omics for precision medicine (TOPMed) consortium.0 aWhole genome sequence analyses of eGFR in 23732 people represent c2021 Jan a1031570 v633 aBACKGROUND: Genetic factors that influence kidney traits have been understudied for low frequency and ancestry-specific variants.
METHODS: We combined whole genome sequencing (WGS) data from 23,732 participants from 10 NHLBI Trans-Omics for Precision Medicine (TOPMed) Program multi-ethnic studies to identify novel loci for estimated glomerular filtration rate (eGFR). Participants included European, African, East Asian, and Hispanic ancestries. We applied linear mixed models using a genetic relationship matrix estimated from the WGS data and adjusted for age, sex, study, and ethnicity.
FINDINGS: When testing single variants, we identified three novel loci driven by low frequency variants more commonly observed in non-European ancestry (PRKAA2, rs180996919, minor allele frequency [MAF] 0.04%, P = 6.1 × 10; METTL8, rs116951054, MAF 0.09%, P = 4.5 × 10; and MATK, rs539182790, MAF 0.05%, P = 3.4 × 10). We also replicated two known loci for common variants (rs2461702, MAF=0.49, P = 1.2 × 10, nearest gene GATM, and rs71147340, MAF=0.34, P = 3.3 × 10, CDK12). Testing aggregated variants within a gene identified the MAF gene. A statistical approach based on local ancestry helped to identify replication samples for ancestry-specific variants.
INTERPRETATION: This study highlights challenges in studying variants influencing kidney traits that are low frequency in populations and more common in non-European ancestry.
1 aLin, Bridget, M1 aGrinde, Kelsey, E1 aBrody, Jennifer, A1 aBreeze, Charles, E1 aRaffield, Laura, M1 aMychaleckyj, Josyf, C1 aThornton, Timothy, A1 aPerry, James, A1 aBaier, Leslie, J1 aFuentes, Lisa, de Las1 aGuo, Xiuqing1 aHeavner, Benjamin, D1 aHanson, Robert, L1 aHung, Yi-Jen1 aQian, Huijun1 aHsiung, Chao, A1 aHwang, Shih-Jen1 aIrvin, Margaret, R1 aJain, Deepti1 aKelly, Tanika, N1 aKobes, Sayuko1 aLange, Leslie1 aLash, James, P1 aLi, Yun1 aLiu, Xiaoming1 aMi, Xuenan1 aMusani, Solomon, K1 aPapanicolaou, George, J1 aParsa, Afshin1 aReiner, Alex, P1 aSalimi, Shabnam1 aSheu, Wayne, H-H1 aShuldiner, Alan, R1 aTaylor, Kent, D1 aSmith, Albert, V1 aSmith, Jennifer, A1 aTin, Adrienne1 aVaidya, Dhananjay1 aWallace, Robert, B1 aYamamoto, Kenichi1 aSakaue, Saori1 aMatsuda, Koichi1 aKamatani, Yoichiro1 aMomozawa, Yukihide1 aYanek, Lisa, R1 aYoung, Betsi, A1 aZhao, Wei1 aOkada, Yukinori1 aAbecasis, Gonzalo1 aPsaty, Bruce, M1 aArnett, Donna, K1 aBoerwinkle, Eric1 aCai, Jianwen1 aDer Chen, Ida, Yii-1 aCorrea, Adolfo1 aCupples, Adrienne, L1 aHe, Jiang1 aKardia, Sharon, Lr1 aKooperberg, Charles1 aMathias, Rasika, A1 aMitchell, Braxton, D1 aNickerson, Deborah, A1 aTurner, Steve, T1 aVasan, Ramachandran, S1 aRotter, Jerome, I1 aLevy, Daniel1 aKramer, Holly, J1 aKöttgen, Anna1 aRich, Stephen, S1 aLin, Dan-Yu1 aBrowning, Sharon, R1 aFranceschini, Nora uhttps://chs-nhlbi.org/node/866404419nas a2200925 4500008004100000022001400041245011400055210006900169260001600238520175700254100002002011700001202031700001402043700001702057700001602074700002002090700002102110700002002131700002302151700002502174700002302199700001902222700002002241700001902261700002002280700002302300700002002323700002102343700002002364700002302384700001902407700001402426700002002440700001902460700002502479700001802504700002002522700002102542700001902563700002102582700002502603700002002628700001902648700002202667700002002689700002002709700002002729700001602749700002002765700002402785700002102809700002702830700002002857700001502877700002502892700002402917700002202941700001902963700002602982700002103008700002003029700002703049700002103076700002203097700002103119700002303140700001403163700002203177700002403199700002303223700002303246700001203269700001803281700002203299700002503321700002303346700002303369710006503392856003603457 2021 eng d a1460-208300aWhole genome sequence analysis of platelet traits in the NHLBI trans-omics for precision medicine initiative.0 aWhole genome sequence analysis of platelet traits in the NHLBI t c2021 Sep 063 aPlatelets play a key role in thrombosis and hemostasis. Platelet count (PLT) and mean platelet volume (MPV) are highly heritable quantitative traits, with hundreds of genetic signals previously identified, mostly in European ancestry populations. We here utilize whole genome sequencing from NHLBI's Trans-Omics for Precision Medicine Initiative (TOPMed) in a large multi-ethnic sample to further explore common and rare variation contributing to PLT (n = 61 200) and MPV (n = 23 485). We identified and replicated secondary signals at MPL (rs532784633) and PECAM1 (rs73345162), both more common in African ancestry populations. We also observed rare variation in Mendelian platelet related disorder genes influencing variation in platelet traits in TOPMed cohorts (not enriched for blood disorders). For example, association of GP9 with lower PLT and higher MPV was partly driven by a pathogenic Bernard-Soulier syndrome variant (rs5030764, p.Asn61Ser), and the signals at TUBB1 and CD36 were partly driven by loss of function variants not annotated as pathogenic in ClinVar (rs199948010 and rs571975065). However, residual signal remained for these gene-based signals after adjusting for lead variants, suggesting that additional variants in Mendelian genes with impacts in general population cohorts remain to be identified. Gene-based signals were also identified at several GWAS identified loci for genes not annotated for Mendelian platelet disorders (PTPRH, TET2, CHEK2), with somatic variation driving the result at TET2. These results highlight the value of whole genome sequencing in populations of diverse genetic ancestry to identify novel regulatory and coding signals, even for well-studied traits like platelet traits.
1 aLittle, Amarise1 aHu, Yao1 aSun, Quan1 aJain, Deepti1 aBroome, Jai1 aChen, Ming-Huei1 aThibord, Florian1 aMcHugh, Caitlin1 aSurendran, Praveen1 aBlackwell, Thomas, W1 aBrody, Jennifer, A1 aBhan, Arunoday1 aChami, Nathalie1 aVries, Paul, S1 aEkunwe, Lynette1 aHeard-Costa, Nancy1 aHobbs, Brian, D1 aManichaikul, Ani1 aMoon, Jee-Young1 aPreuss, Michael, H1 aRyan, Kathleen1 aWang, Zhe1 aWheeler, Marsha1 aYanek, Lisa, R1 aAbecasis, Goncalo, R1 aAlmasy, Laura1 aBeaty, Terri, H1 aBecker, Lewis, C1 aBlangero, John1 aBoerwinkle, Eric1 aButterworth, Adam, S1 aChoquet, Helene1 aCorrea, Adolfo1 aCurran, Joanne, E1 aFaraday, Nauder1 aFornage, Myriam1 aGlahn, David, C1 aHou, Lifang1 aJorgenson, Eric1 aKooperberg, Charles1 aLewis, Joshua, P1 aLloyd-Jones, Donald, M1 aLoos, Ruth, J F1 aMin, Nancy1 aMitchell, Braxton, D1 aMorrison, Alanna, C1 aNickerson, Debbie1 aNorth, Kari, E1 aO'Connell, Jeffrey, R1 aPankratz, Nathan1 aPsaty, Bruce, M1 aVasan, Ramachandran, S1 aRich, Stephen, S1 aRotter, Jerome, I1 aSmith, Albert, V1 aSmith, Nicholas, L1 aTang, Hua1 aTracy, Russell, P1 aConomos, Matthew, P1 aLaurie, Cecelia, A1 aMathias, Rasika, A1 aLi, Yun1 aAuer, Paul, L1 aThornton, Timothy1 aReiner, Alexander, P1 aJohnson, Andrew, D1 aRaffield, Laura, M1 aNHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium uhttps://chs-nhlbi.org/node/891303828nas a2200625 4500008004100000022001400041245010800055210006900163260001500232300000800247490000700255520201900262100001902281700001502300700001702315700001702332700001502349700001402364700002002378700002402398700001702422700002402439700002002463700001602483700001302499700002102512700002202533700001802555700001902573700002102592700002002613700002202633700002202655700002002677700002002697700002302717700002502740700002402765700001902789700002502808700002202833700002602855700001902881700002002900700002202920700002102942700002202963700002702985700002103012700001803033700001903051710006503070710003103135856003603166 2021 eng d a1756-994X00aWhole-genome association analyses of sleep-disordered breathing phenotypes in the NHLBI TOPMed program.0 aWholegenome association analyses of sleepdisordered breathing ph c2021 08 26 a1360 v133 aBACKGROUND: Sleep-disordered breathing is a common disorder associated with significant morbidity. The genetic architecture of sleep-disordered breathing remains poorly understood. Through the NHLBI Trans-Omics for Precision Medicine (TOPMed) program, we performed the first whole-genome sequence analysis of sleep-disordered breathing.
METHODS: The study sample was comprised of 7988 individuals of diverse ancestry. Common-variant and pathway analyses included an additional 13,257 individuals. We examined five complementary traits describing different aspects of sleep-disordered breathing: the apnea-hypopnea index, average oxyhemoglobin desaturation per event, average and minimum oxyhemoglobin saturation across the sleep episode, and the percentage of sleep with oxyhemoglobin saturation < 90%. We adjusted for age, sex, BMI, study, and family structure using MMSKAT and EMMAX mixed linear model approaches. Additional bioinformatics analyses were performed with MetaXcan, GIGSEA, and ReMap.
RESULTS: We identified a multi-ethnic set-based rare-variant association (p = 3.48 × 10) on chromosome X with ARMCX3. Additional rare-variant associations include ARMCX3-AS1, MRPS33, and C16orf90. Novel common-variant loci were identified in the NRG1 and SLC45A2 regions, and previously associated loci in the IL18RAP and ATP2B4 regions were associated with novel phenotypes. Transcription factor binding site enrichment identified associations with genes implicated with respiratory and craniofacial traits. Additional analyses identified significantly associated pathways.
CONCLUSIONS: We have identified the first gene-based rare-variant associations with objectively measured sleep-disordered breathing traits. Our results increase the understanding of the genetic architecture of sleep-disordered breathing and highlight associations in genes that modulate lung development, inflammation, respiratory rhythmogenesis, and HIF1A-mediated hypoxic response.
1 aCade, Brian, E1 aLee, Jiwon1 aSofer, Tamar1 aWang, Heming1 aZhang, Man1 aChen, Han1 aGharib, Sina, A1 aGottlieb, Daniel, J1 aGuo, Xiuqing1 aLane, Jacqueline, M1 aLiang, Jingjing1 aLin, Xihong1 aMei, Hao1 aPatel, Sanjay, R1 aPurcell, Shaun, M1 aSaxena, Richa1 aShah, Neomi, A1 aEvans, Daniel, S1 aHanis, Craig, L1 aHillman, David, R1 aMukherjee, Sutapa1 aPalmer, Lyle, J1 aStone, Katie, L1 aTranah, Gregory, J1 aAbecasis, Goncalo, R1 aBoerwinkle, Eric, A1 aCorrea, Adolfo1 aCupples, Adrienne, L1 aKaplan, Robert, C1 aNickerson, Deborah, A1 aNorth, Kari, E1 aPsaty, Bruce, M1 aRotter, Jerome, I1 aRich, Stephen, S1 aTracy, Russell, P1 aVasan, Ramachandran, S1 aWilson, James, G1 aZhu, Xiaofeng1 aRedline, Susan1 aNHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium1 aTOPMed Sleep Working Group uhttps://chs-nhlbi.org/node/892005743nas a2201309 4500008004100000022001400041245011800055210006900173260001500242300001200257490000800269520205100277653001002328653000902338653003202347653002202379653001702401653001102418653001702429653002202446653003402468653001702502653001102519653000902530653001602539653005302555653001402608653002002622653003102642653001802673100001202691700002302703700002302726700001702749700001702766700001802783700001502801700003602816700002302852700002002875700001902895700002002914700001302934700001702947700001602964700002002980700002203000700002003022700001403042700002303056700002303079700002503102700002003127700001903147700002803166700002303194700001403217700002003231700002303251700001503274700002003289700002103309700001803330700002003348700002203368700001803390700001803408700002103426700002003447700002203467700002003489700002703509700002703536700002303563700001903586700002103605700002103626700002103647700002503668700001603693700002603709700002403735700002003759700001903779700001903798700001903817700002003836700002003856700002403876700002303900700002903923700001403952700002003966700002003986700002504006700002204031700002104053700002504074700002304099700001804122700001204140700002304152700002204175700002104197700002104218700002304239700002104262700002404283700002504307710006504332856003604397 2021 eng d a1537-660500aWhole-genome sequencing association analysis of quantitative red blood cell phenotypes: The NHLBI TOPMed program.0 aWholegenome sequencing association analysis of quantitative red c2021 05 06 a874-8930 v1083 aWhole-genome sequencing (WGS), a powerful tool for detecting novel coding and non-coding disease-causing variants, has largely been applied to clinical diagnosis of inherited disorders. Here we leveraged WGS data in up to 62,653 ethnically diverse participants from the NHLBI Trans-Omics for Precision Medicine (TOPMed) program and assessed statistical association of variants with seven red blood cell (RBC) quantitative traits. We discovered 14 single variant-RBC trait associations at 12 genomic loci, which have not been reported previously. Several of the RBC trait-variant associations (RPN1, ELL2, MIDN, HBB, HBA1, PIEZO1, and G6PD) were replicated in independent GWAS datasets imputed to the TOPMed reference panel. Most of these discovered variants are rare/low frequency, and several are observed disproportionately among non-European Ancestry (African, Hispanic/Latino, or East Asian) populations. We identified a 3 bp indel p.Lys2169del (g.88717175_88717177TCT[4]) (common only in the Ashkenazi Jewish population) of PIEZO1, a gene responsible for the Mendelian red cell disorder hereditary xerocytosis (MIM: 194380), associated with higher mean corpuscular hemoglobin concentration (MCHC). In stepwise conditional analysis and in gene-based rare variant aggregated association analysis, we identified several of the variants in HBB, HBA1, TMPRSS6, and G6PD that represent the carrier state for known coding, promoter, or splice site loss-of-function variants that cause inherited RBC disorders. Finally, we applied base and nuclease editing to demonstrate that the sentinel variant rs112097551 (nearest gene RPN1) acts through a cis-regulatory element that exerts long-range control of the gene RUVBL1 which is essential for hematopoiesis. Together, these results demonstrate the utility of WGS in ethnically diverse population-based samples and gene editing for expanding knowledge of the genetic architecture of quantitative hematologic traits and suggest a continuum between complex trait and Mendelian red cell disorders.
10aAdult10aAged10aChromosomes, Human, Pair 1610aDatasets as Topic10aErythrocytes10aFemale10aGene Editing10aGenetic Variation10aGenome-Wide Association Study10aHEK293 Cells10aHumans10aMale10aMiddle Aged10aNational Heart, Lung, and Blood Institute (U.S.)10aPhenotype10aQuality Control10aReproducibility of Results10aUnited States1 aHu, Yao1 aStilp, Adrienne, M1 aMcHugh, Caitlin, P1 aRao, Shuquan1 aJain, Deepti1 aZheng, Xiuwen1 aLane, John1 ade Bellefon, Sébastian, Méric1 aRaffield, Laura, M1 aChen, Ming-Huei1 aYanek, Lisa, R1 aWheeler, Marsha1 aYao, Yao1 aRen, Chunyan1 aBroome, Jai1 aMoon, Jee-Young1 ade Vries, Paul, S1 aHobbs, Brian, D1 aSun, Quan1 aSurendran, Praveen1 aBrody, Jennifer, A1 aBlackwell, Thomas, W1 aChoquet, Helene1 aRyan, Kathleen1 aDuggirala, Ravindranath1 aHeard-Costa, Nancy1 aWang, Zhe1 aChami, Nathalie1 aPreuss, Michael, H1 aMin, Nancy1 aEkunwe, Lynette1 aLange, Leslie, A1 aCushman, Mary1 aFaraday, Nauder1 aCurran, Joanne, E1 aAlmasy, Laura1 aKundu, Kousik1 aSmith, Albert, V1 aGabriel, Stacey1 aRotter, Jerome, I1 aFornage, Myriam1 aLloyd-Jones, Donald, M1 aVasan, Ramachandran, S1 aSmith, Nicholas, L1 aNorth, Kari, E1 aBoerwinkle, Eric1 aBecker, Lewis, C1 aLewis, Joshua, P1 aAbecasis, Goncalo, R1 aHou, Lifang1 aO'Connell, Jeffrey, R1 aMorrison, Alanna, C1 aBeaty, Terri, H1 aKaplan, Robert1 aCorrea, Adolfo1 aBlangero, John1 aJorgenson, Eric1 aPsaty, Bruce, M1 aKooperberg, Charles1 aWalton, Russell, T1 aKleinstiver, Benjamin, P1 aTang, Hua1 aLoos, Ruth, J F1 aSoranzo, Nicole1 aButterworth, Adam, S1 aNickerson, Debbie1 aRich, Stephen, S1 aMitchell, Braxton, D1 aJohnson, Andrew, D1 aAuer, Paul, L1 aLi, Yun1 aMathias, Rasika, A1 aLettre, Guillaume1 aPankratz, Nathan1 aLaurie, Cathy, C1 aLaurie, Cecelia, A1 aBauer, Daniel, E1 aConomos, Matthew, P1 aReiner, Alexander, P1 aNHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium uhttps://chs-nhlbi.org/node/877905763nas a2201477 4500008004100000022001400041245012500055210006900180260001500249300001400264490000800278520153200286653001101818653001501829653002301844653003801867653001801905653003401923653001101957653001501968653005301983653001402036653003602050653001402086653001302100653004302113653002802156653001902184653001802203653002802221100002402249700002302273700002102296700002302317700002902340700002502369700002302394700001602417700002002433700002002453700002402473700001502497700002202512700001902534700002002553700002002573700002302593700002502616700002002641700001802661700001702679700002102696700002802717700001502745700002002760700002302780700002002803700001902823700002102842700001402863700002302877700002202900700002002922700001402942700002002956700001902976700001502995700002503010700001803035700002403053700002003077700002103097700001903118700002103137700002503158700001903183700002003202700002003222700001903242700001503261700001903276700002003295700002003315700002403335700001603359700002303375700002003398700001903418700002403437700001803461700002303479700002203502700002103524700001703545700001203562700002703574700002003601700002103621700002303642700002503665700002403690700001503714700002603729700002303755700001903778700002603797700002203823700002103845700002003866700002003886700002103906700002003927700002203947700002403969700002303993700001404016700002204030700002504052700002704077700001404104700002304118700002504141700001804166710006504184856003604249 2021 eng d a1537-660500aWhole-genome sequencing in diverse subjects identifies genetic correlates of leukocyte traits: The NHLBI TOPMed program.0 aWholegenome sequencing in diverse subjects identifies genetic co c2021 10 07 a1836-18510 v1083 aMany common and rare variants associated with hematologic traits have been discovered through imputation on large-scale reference panels. However, the majority of genome-wide association studies (GWASs) have been conducted in Europeans, and determining causal variants has proved challenging. We performed a GWAS of total leukocyte, neutrophil, lymphocyte, monocyte, eosinophil, and basophil counts generated from 109,563,748 variants in the autosomes and the X chromosome in the Trans-Omics for Precision Medicine (TOPMed) program, which included data from 61,802 individuals of diverse ancestry. We discovered and replicated 7 leukocyte trait associations, including (1) the association between a chromosome X, pseudo-autosomal region (PAR), noncoding variant located between cytokine receptor genes (CSF2RA and CLRF2) and lower eosinophil count; and (2) associations between single variants found predominantly among African Americans at the S1PR3 (9q22.1) and HBB (11p15.4) loci and monocyte and lymphocyte counts, respectively. We further provide evidence indicating that the newly discovered eosinophil-lowering chromosome X PAR variant might be associated with reduced susceptibility to common allergic diseases such as atopic dermatitis and asthma. Additionally, we found a burden of very rare FLT3 (13q12.2) variants associated with monocyte counts. Together, these results emphasize the utility of whole-genome sequencing in diverse samples in identifying associations missed by European-ancestry-driven GWASs.
10aAsthma10aBiomarkers10aDermatitis, Atopic10aGenetic Predisposition to Disease10aGenome, Human10aGenome-Wide Association Study10aHumans10aLeukocytes10aNational Heart, Lung, and Blood Institute (U.S.)10aPhenotype10aPolymorphism, Single Nucleotide10aPrognosis10aProteome10aPulmonary Disease, Chronic Obstructive10aQuantitative Trait Loci10aUnited Kingdom10aUnited States10aWhole Genome Sequencing1 aMikhaylova, Anna, V1 aMcHugh, Caitlin, P1 aPolfus, Linda, M1 aRaffield, Laura, M1 aBoorgula, Meher, Preethi1 aBlackwell, Thomas, W1 aBrody, Jennifer, A1 aBroome, Jai1 aChami, Nathalie1 aChen, Ming-Huei1 aConomos, Matthew, P1 aCox, Corey1 aCurran, Joanne, E1 aDaya, Michelle1 aEkunwe, Lynette1 aGlahn, David, C1 aHeard-Costa, Nancy1 aHighland, Heather, M1 aHobbs, Brian, D1 aIlboudo, Yann1 aJain, Deepti1 aLange, Leslie, A1 aMiller-Fleming, Tyne, W1 aMin, Nancy1 aMoon, Jee-Young1 aPreuss, Michael, H1 aRosen, Jonathon1 aRyan, Kathleen1 aSmith, Albert, V1 aSun, Quan1 aSurendran, Praveen1 ade Vries, Paul, S1 aWalter, Klaudia1 aWang, Zhe1 aWheeler, Marsha1 aYanek, Lisa, R1 aZhong, Xue1 aAbecasis, Goncalo, R1 aAlmasy, Laura1 aBarnes, Kathleen, C1 aBeaty, Terri, H1 aBecker, Lewis, C1 aBlangero, John1 aBoerwinkle, Eric1 aButterworth, Adam, S1 aChavan, Sameer1 aCho, Michael, H1 aChoquet, Helene1 aCorrea, Adolfo1 aCox, Nancy1 aDeMeo, Dawn, L1 aFaraday, Nauder1 aFornage, Myriam1 aGerszten, Robert, E1 aHou, Lifang1 aJohnson, Andrew, D1 aJorgenson, Eric1 aKaplan, Robert1 aKooperberg, Charles1 aKundu, Kousik1 aLaurie, Cecelia, A1 aLettre, Guillaume1 aLewis, Joshua, P1 aLi, Bingshan1 aLi, Yun1 aLloyd-Jones, Donald, M1 aLoos, Ruth, J F1 aManichaikul, Ani1 aMeyers, Deborah, A1 aMitchell, Braxton, D1 aMorrison, Alanna, C1 aNgo, Debby1 aNickerson, Deborah, A1 aNongmaithem, Suraj1 aNorth, Kari, E1 aO'Connell, Jeffrey, R1 aOrtega, Victor, E1 aPankratz, Nathan1 aPerry, James, A1 aPsaty, Bruce, M1 aRich, Stephen, S1 aSoranzo, Nicole1 aRotter, Jerome, I1 aSilverman, Edwin, K1 aSmith, Nicholas, L1 aTang, Hua1 aTracy, Russell, P1 aThornton, Timothy, A1 aVasan, Ramachandran, S1 aZein, Joe1 aMathias, Rasika, A1 aReiner, Alexander, P1 aAuer, Paul, L1 aNHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium uhttps://chs-nhlbi.org/node/891403215nas a2200481 4500008004100000022001400041245015300055210006900208260001600277520174000293100002102033700001402054700002302068700002002091700001902111700002502130700002602155700001802181700002102199700001802220700002202238700002102260700002102281700002202302700002402324700001902348700002102367700002302388700001902411700002202430700002302452700001702475700002002492700001802512700002802530700002302558700001902581700003002600700002002630700002402650700002302674856003602697 2022 eng d a1460-208300aWhole exome sequencing of 14 389 individuals from the ESP and CHARGE consortia identifies novel rare variation associated with hemostatic factors.0 aWhole exome sequencing of 14 389 individuals from the ESP and CH c2022 May 123 aPlasma levels of fibrinogen, coagulation factors VII and VIII, and von Willebrand factor (vWF) are four intermediate phenotypes that are heritable and have been associated with the risk of clinical thrombotic events. To identify rare and low-frequency variants associated with these hemostatic factors, we conducted whole exome sequencing in 10 860 individuals of European ancestry (EA) and 3529 African Americans (AAs) from the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium and the National Heart, Lung, and Blood Institute's Exome Sequencing Project (ESP). Gene-based tests demonstrated significant associations with rare variation (minor allele frequency < 5%) in FGG (with fibrinogen, p = 9.1x10-13), F7 (with factor VII, p = 1.3x10-72; seven novel variants), and VWF (with factor VIII and vWF; p = 3.2x10-14; one novel variant). These eight novel rare variant associations were independent of the known common variants at these loci and tended to have much larger effect sizes. In addition, one of the rare novel variants in F7 was significantly associated with an increased risk of venous thromboembolism in AAs (Ile200Ser; rs141219108; p = 4.2x10-5). After restricting gene-based analyses to only loss-of-function variants, a novel significant association was detected and replicated between factor VIII levels and a stop-gain mutation exclusive to African Americans (rs3211938) in CD36. This variant has previously been linked to dyslipidemia but not with levels of a hemostatic factor. These efforts represent the largest integration of whole exome sequence data from two national projects to identify genetic variation associated with plasma hemostatic factors.
1 aPankratz, Nathan1 aWei, Peng1 aBrody, Jennifer, A1 aChen, Ming-Huei1 aVries, Paul, S1 aHuffman, Jennifer, E1 aStimson, Mary, Rachel1 aAuer, Paul, L1 aBoerwinkle, Eric1 aCushman, Mary1 aMaat, Moniek, P M1 aFolsom, Aaron, R1 aFranco, Oscar, H1 aGibbs, Richard, A1 aHaagenson, Kelly, K1 aHofman, Albert1 aJohnsen, Jill, M1 aKovar, Christie, L1 aKraaij, Robert1 aMcKnight, Barbara1 aMetcalf, Ginger, A1 aMuzny, Donna1 aPsaty, Bruce, M1 aTang, Weihong1 aUitterlinden, André, G1 aRooij, Jeroen, G J1 aDehghan, Abbas1 aO'Donnell, Christopher, J1 aReiner, Alex, P1 aMorrison, Alanna, C1 aSmith, Nicholas, L uhttps://chs-nhlbi.org/node/910705141nas a2201381 4500008004100000022001400041245014300055210006900198260001500267300000800282490000600290520117000296653003001466653001201496653001201508653001101520653001201531653005301543653002601596653003601622653002301658653002701681653001801708100002001726700002201746700002201768700002601790700002301816700002501839700001501864700002101879700002501900700001801925700002401943700002201967700002301989700002002012700001702032700002002049700002602069700001902095700002202114700001902136700001702155700001702172700003302189700002102222700001902243700001802262700002602280700002002306700002102326700002302347700002602370700002202396700002402418700002302442700002202465700002002487700002202507700002002529700002502549700002102574700002102595700001802616700001802634700002002652700002102672700002102693700001702714700002102731700003102752700002502783700003402808700002202842700002302864700002102887700001602908700001302924700001402937700002002951700001902971700002102990700001903011700002103030700001903051700002503070700002203095700002803117700001403145700002303159700002403182700001703206700002403223700001503247700002303262700002503285700002503310700002403335700002403359700002003383700001903403700002303422700002003445700002703465700003003492700002003522700002103542700001903563700002103582700002203603700001603625700001903641700002003660700002103680700002203701856003603723 2022 eng d a2399-364200aWhole genome sequence association analysis of fasting glucose and fasting insulin levels in diverse cohorts from the NHLBI TOPMed program.0 aWhole genome sequence association analysis of fasting glucose an c2022 07 28 a7560 v53 aThe genetic determinants of fasting glucose (FG) and fasting insulin (FI) have been studied mostly through genome arrays, resulting in over 100 associated variants. We extended this work with high-coverage whole genome sequencing analyses from fifteen cohorts in NHLBI's Trans-Omics for Precision Medicine (TOPMed) program. Over 23,000 non-diabetic individuals from five race-ethnicities/populations (African, Asian, European, Hispanic and Samoan) were included. Eight variants were significantly associated with FG or FI across previously identified regions MTNR1B, G6PC2, GCK, GCKR and FOXA2. We additionally characterize suggestive associations with FG or FI near previously identified SLC30A8, TCF7L2, and ADCY5 regions as well as APOB, PTPRT, and ROBO1. Functional annotation resources including the Diabetes Epigenome Atlas were compiled for each signal (chromatin states, annotation principal components, and others) to elucidate variant-to-function hypotheses. We provide a catalog of nucleotide-resolution genomic variation spanning intergenic and intronic regions creating a foundation for future sequencing-based investigations of glycemic traits.
10aDiabetes Mellitus, Type 210aFasting10aGlucose10aHumans10aInsulin10aNational Heart, Lung, and Blood Institute (U.S.)10aNerve Tissue Proteins10aPolymorphism, Single Nucleotide10aPrecision Medicine10aReceptors, Immunologic10aUnited States1 aDiCorpo, Daniel1 aGaynor, Sheila, M1 aRussell, Emily, M1 aWesterman, Kenneth, E1 aRaffield, Laura, M1 aMajarian, Timothy, D1 aWu, Peitao1 aSarnowski, Chloe1 aHighland, Heather, M1 aJackson, Anne1 aHasbani, Natalie, R1 ade Vries, Paul, S1 aBrody, Jennifer, A1 aHidalgo, Bertha1 aGuo, Xiuqing1 aPerry, James, A1 aO'Connell, Jeffrey, R1 aLent, Samantha1 aMontasser, May, E1 aCade, Brian, E1 aJain, Deepti1 aWang, Heming1 aAlbanus, Ricardo, D'Oliveira1 aVarshney, Arushi1 aYanek, Lisa, R1 aLange, Leslie1 aPalmer, Nicholette, D1 aAlmeida, Marcio1 aPeralta, Juan, M1 aAslibekyan, Stella1 aBaldridge, Abigail, S1 aBertoni, Alain, G1 aBielak, Lawrence, F1 aChen, Chung-Shiuan1 aChen, Yii-Der Ida1 aChoi, Won, Jung1 aGoodarzi, Mark, O1 aFloyd, James, S1 aIrvin, Marguerite, R1 aKalyani, Rita, R1 aKelly, Tanika, N1 aLee, Seonwook1 aLiu, Ching-Ti1 aLoesch, Douglas1 aManson, JoAnn, E1 aMinster, Ryan, L1 aNaseri, Take1 aPankow, James, S1 aRasmussen-Torvik, Laura, J1 aReiner, Alexander, P1 aReupena, Muagututi'a, Sefuiva1 aSelvin, Elizabeth1 aSmith, Jennifer, A1 aWeeks, Daniel, E1 aXu, Huichun1 aYao, Jie1 aZhao, Wei1 aParker, Stephen1 aAlonso, Alvaro1 aArnett, Donna, K1 aBlangero, John1 aBoerwinkle, Eric1 aCorrea, Adolfo1 aCupples, Adrienne, L1 aCurran, Joanne, E1 aDuggirala, Ravindranath1 aHe, Jiang1 aHeckbert, Susan, R1 aKardia, Sharon, L R1 aKim, Ryan, W1 aKooperberg, Charles1 aLiu, Simin1 aMathias, Rasika, A1 aMcGarvey, Stephen, T1 aMitchell, Braxton, D1 aMorrison, Alanna, C1 aPeyser, Patricia, A1 aPsaty, Bruce, M1 aRedline, Susan1 aShuldiner, Alan, R1 aTaylor, Kent, D1 aVasan, Ramachandran, S1 aViaud-Martinez, Karine, A1 aFlorez, Jose, C1 aWilson, James, G1 aSladek, Robert1 aRich, Stephen, S1 aRotter, Jerome, I1 aLin, Xihong1 aDupuis, Josée1 aMeigs, James, B1 aWessel, Jennifer1 aManning, Alisa, K uhttps://chs-nhlbi.org/node/915803611nas a2200889 4500008004100000022001400041245012300055210006900178260001600247300000900263490000700272520111000279653001601389653003401405653001101439653002801450100002301478700002301501700001701524700002701541700001801568700001301586700002401599700002301623700001501646700001901661700002301680700001301703700002401716700002001740700001301760700001901773700002001792700002201812700001801834700002001852700001301872700001701885700001501902700002001917700001901937700001901956700001801975700002101993700001902014700002002033700002202053700002002075700002202095700002102117700002002138700002502158700002402183700002002207700002002227700002102247700002202268700001402290700002202304700002102326700002502347700002502372700002102397700002302418700002302441700002602464700002402490700001202514700002802526700002702554700002102581700002402602700002102626700001802647700002002665856003602685 2022 eng d a2041-172300aWhole genome sequencing identifies structural variants contributing to hematologic traits in the NHLBI TOPMed program.0 aWhole genome sequencing identifies structural variants contribut c2022 Dec 08 a75920 v133 aGenome-wide association studies have identified thousands of single nucleotide variants and small indels that contribute to variation in hematologic traits. While structural variants are known to cause rare blood or hematopoietic disorders, the genome-wide contribution of structural variants to quantitative blood cell trait variation is unknown. Here we utilized whole genome sequencing data in ancestrally diverse participants of the NHLBI Trans Omics for Precision Medicine program (N = 50,675) to detect structural variants associated with hematologic traits. Using single variant tests, we assessed the association of common and rare structural variants with red cell-, white cell-, and platelet-related quantitative traits and observed 21 independent signals (12 common and 9 rare) reaching genome-wide significance. The majority of these associations (N = 18) replicated in independent datasets. In genome-editing experiments, we provide evidence that a deletion associated with lower monocyte counts leads to disruption of an S1PR3 monocyte enhancer and decreased S1PR3 expression.
10aBlood Cells10aGenome-Wide Association Study10aHumans10aWhole Genome Sequencing1 aWheeler, Marsha, M1 aStilp, Adrienne, M1 aRao, Shuquan1 aHalldorsson, Bjarni, V1 aBeyter, Doruk1 aWen, Jia1 aMihkaylova, Anna, V1 aMcHugh, Caitlin, P1 aLane, John1 aJiang, Min-Zhi1 aRaffield, Laura, M1 aJun, Goo1 aSedlazeck, Fritz, J1 aMetcalf, Ginger1 aYao, Yao1 aBis, Joshua, B1 aChami, Nathalie1 ade Vries, Paul, S1 aDesai, Pinkal1 aFloyd, James, S1 aGao, Yan1 aKammers, Kai1 aKim, Wonji1 aMoon, Jee-Young1 aRatan, Aakrosh1 aYanek, Lisa, R1 aAlmasy, Laura1 aBecker, Lewis, C1 aBlangero, John1 aCho, Michael, H1 aCurran, Joanne, E1 aFornage, Myriam1 aKaplan, Robert, C1 aLewis, Joshua, P1 aLoos, Ruth, J F1 aMitchell, Braxton, D1 aMorrison, Alanna, C1 aPreuss, Michael1 aPsaty, Bruce, M1 aRich, Stephen, S1 aRotter, Jerome, I1 aTang, Hua1 aTracy, Russell, P1 aBoerwinkle, Eric1 aAbecasis, Goncalo, R1 aBlackwell, Thomas, W1 aSmith, Albert, V1 aJohnson, Andrew, D1 aMathias, Rasika, A1 aNickerson, Deborah, A1 aConomos, Matthew, P1 aLi, Yun1 aÞorsteinsdottir, Unnur1 aMagnússon, Magnús, K1 aStefansson, Kari1 aPankratz, Nathan, D1 aBauer, Daniel, E1 aAuer, Paul, L1 aReiner, Alex, P uhttps://chs-nhlbi.org/node/926104169nas a2200757 4500008004100000022001400041245010600055210006900161260001600230520193200246100001402178700001402192700001502206700001802221700001702239700002102256700001702277700002002294700002302314700001802337700001902355700002102374700002302395700002202418700002402440700002602464700001802490700002102508700001702529700002002546700002502566700002302591700001902614700002202633700002202655700002002677700002702697700001602724700002302740700002502763700002402788700002402812700002102836700001902857700002302876700002702899700002102926700001702947700002202964700001902986700002203005700002403027700002503051700002003076700002203096700002203118700002403140700002003164700002103184700001403205700002103219710006503240710004103305710002903346856003603375 2022 eng d a1460-208300aWhole-Exome Sequencing Study Identifies Four Novel Gene Loci Associated with Diabetic Kidney Disease.0 aWholeExome Sequencing Study Identifies Four Novel Gene Loci Asso c2022 Nov 293 aDiabetic kidney disease (DKD) is recognized as an important public health challenge. However, its genomic mechanisms are poorly understood. To identify rare variants for DKD, we conducted a whole-exome sequencing (WES) study leveraging large cohorts well-phenotyped for chronic kidney disease (CKD) and diabetes. Our two-stage whole-exome sequencing study included 4372 European and African ancestry participants from the Chronic Renal Insufficiency Cohort (CRIC) and Atherosclerosis Risk in Communities (ARIC) studies (stage-1) and 11 487 multi-ancestry Trans-Omics for Precision Medicine (TOPMed) participants (stage-2). Generalized linear mixed models, which accounted for genetic relatedness and adjusted for age, sex, and ancestry, were used to test associations between single variants and DKD. Gene-based aggregate rare variant analyses were conducted using an optimized sequence kernel association test (SKAT-O) implemented within our mixed model framework. We identified four novel exome-wide significant DKD-related loci through initiating diabetes. In single variant analyses, participants carrying a rare, in-frame insertion in the DIS3L2 gene (rs141560952) exhibited a 193-fold increased odds (95% confidence interval: 33.6, 1105) of DKD compared with non-carriers (P = 3.59 × 10-9). Likewise, each copy of a low-frequency KRT6B splice-site variant (rs425827) conferred a 5.31-fold higher odds (95% confidence interval: 3.06, 9.21) of DKD (P = 2.72 × 10-9). Aggregate gene-based analyses further identified ERAP2 (P = 4.03 × 10-8) and NPEPPS (P = 1.51 × 10-7), which are both expressed in the kidney and implicated in renin-angiotensin-aldosterone system modulated immune response. In the largest WES study of DKD, we identified novel rare variant loci attaining exome-wide significance. These findings provide new insights into the molecular mechanisms underlying DKD.
1 aPan, Yang1 aSun, Xiao1 aMi, Xuenan1 aHuang, Zhijie1 aHsu, Yenchih1 aHixson, James, E1 aMunzy, Donna1 aMetcalf, Ginger1 aFranceschini, Nora1 aTin, Adrienne1 aKöttgen, Anna1 aFrancis, Michael1 aBrody, Jennifer, A1 aKestenbaum, Bryan1 aSitlani, Colleen, M1 aMychaleckyj, Josyf, C1 aKramer, Holly1 aLange, Leslie, A1 aGuo, Xiuqing1 aHwang, Shih-Jen1 aIrvin, Marguerite, R1 aSmith, Jennifer, A1 aYanek, Lisa, R1 aVaidya, Dhananjay1 aChen, Yii-Der Ida1 aFornage, Myriam1 aLloyd-Jones, Donald, M1 aHou, Lifang1 aMathias, Rasika, A1 aMitchell, Braxton, D1 aPeyser, Patricia, A1 aKardia, Sharon, L R1 aArnett, Donna, K1 aCorrea, Adolfo1 aRaffield, Laura, M1 aVasan, Ramachandran, S1 aCupple, Adrienne1 aLevy, Daniel1 aKaplan, Robert, C1 aNorth, Kari, E1 aRotter, Jerome, I1 aKooperberg, Charles1 aReiner, Alexander, P1 aPsaty, Bruce, M1 aTracy, Russell, P1 aGibbs, Richard, A1 aMorrison, Alanna, C1 aFeldman, Harold1 aBoerwinkle, Eric1 aHe, Jiang1 aKelly, Tanika, N1 aNHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium1 aTOPMed Kidney Function Working Group1 aCRIC Study Investigators uhttps://chs-nhlbi.org/node/925803215nas a2200589 4500008004100000245016000041210006900201260000800270300001400278490000700292520160900299100001101908700002101919700001801940700002001958700001601978700001501994700002402009700001802033700002102051700001402072700001502086700002102101700001502122700001102137700001202148700001802160700001302178700001802191700001902209700001502228700001802243700001702261700001702278700001102295700002002306700002302326700001602349700001802365700002302383700002002406700001502426700001202441700001602453700002802469700002102497700001602518700001702534700001902551700001902570856003602589 2022 eng d00a{Whole-Genome Sequencing Association Analyses of Stroke and Its Subtypes in Ancestrally Diverse Populations From Trans-Omics for Precision Medicine Project0 aWholeGenome Sequencing Association Analyses of Stroke and Its Su cMar a875–8850 v533 aStroke is the leading cause of death and long-term disability worldwide. Previous genome-wide association studies identified 51 loci associated with stroke (mostly ischemic) and its subtypes among predominantly European populations. Using whole-genome sequencing in ancestrally diverse populations from the Trans-Omics for Precision Medicine (TOPMed) Program, we aimed to identify novel variants, especially low-frequency or ancestry-specific variants, associated with all stroke, ischemic stroke and its subtypes (large artery, cardioembolic, and small vessel), and hemorrhagic stroke and its subtypes (intracerebral and subarachnoid).\ Whole-genome sequencing data were available for 6833 stroke cases and 27 116 controls, including 22 315 European, 7877 Black, 2616 Hispanic/Latino, 850 Asian, 54 Native American, and 237 other ancestry participants. In TOPMed, we performed single variant association analysis examining 40 million common variants and aggregated association analysis focusing on rare variants. We also combined TOPMed European populations with over 28 000 additional European participants from the UK BioBank genome-wide array data through meta-analysis.\ .\ We represent the first association analysis for stroke and its subtypes using whole-genome sequencing data from ancestrally diverse populations. While our findings suggest the potential benefits of combining whole-genome sequencing data with populations of diverse genetic backgrounds to identify possible low-frequency or ancestry-specific variants, they also highlight the need to increase genome coverage and sample sizes.1 aHu, Y.1 aHaessler, J., W.1 aManansala, R.1 aWiggins, K., L.1 aMoscati, A.1 aBeiser, A.1 aHeard-Costa, N., L.1 aSarnowski, C.1 aRaffield, L., M.1 aChung, J.1 aMarini, S.1 aAnderson, C., D.1 aRosand, J.1 aXu, H.1 aSun, X.1 aKelly, T., N.1 aWong, Q.1 aLange, L., A.1 aRotter, J., I.1 aCorrea, A.1 aVasan, R., S.1 aSeshadri, S.1 aRich, S., S.1 aDo, R.1 aLoos, R., J. F.1 aLongstreth, W., T.1 aBis, J., C.1 aPsaty, B., M.1 aTirschwell, D., L.1 aAssimes, T., L.1 aSilver, B.1 aLiu, S.1 aJackson, R.1 aWassertheil-Smoller, S.1 aMitchell, B., D.1 aFornage, M.1 aAuer, P., L.1 aReiner, A., P.1 aKooperberg, C. uhttps://chs-nhlbi.org/node/899607212nas a2201753 4500008004100000245012700041210006900168260001600237520220800253100002502461700001902486700001602505700001902521700002302540700001902563700002302582700002102605700001802626700002002644700002402664700002302688700002902711700002302740700002002763700002102783700002102804700002102825700002402846700002202870700001902892700001502911700002102926700002002947700001802967700001902985700002103004700002503025700002603050700002103076700001903097700001903116700002803135700002203163700002203185700001903207700002003226700001803246700001703264700001503281700002003296700002803316700001703344700001703361700002703378700002503405700002003430700002003450700002003470700002403490700001203514700001603526700002003542700002803562700001603590700002103606700001903627700002103646700002703667700002103694700002403715700001603739700002203755700002403777700002503801700001703826700001403843700002103857700002003878700002203898700001903920700002003939700001503959700002003974700002203994700002404016700002004040700001804060700002004078700002704098700001804125700001704143700002104160700001604181700001804197700002404215700002004239700002004259700002604279700001904305700002004324700002304344700002304367700002504390700002004415700003004435700001804465700002304483700001804506700002104524700001904545700002004564700001804584700001904602700002304621700002204644700002004666700001904686700002204705700002004727700001904747700002304766700002104789700002004810700002304830700002504853700002904878700002904907700001904936700002604955700001904981700002205000700002005022700002405042700002005066700001705086700001805103700002105121700001305142700003205155700002305187700002205210700002205232700002505254700002405279700002305303710003105326710006505357856003605422 2023 eng d00aWhole genome analysis of plasma fibrinogen reveals population-differentiated genetic regulators with putative liver roles.0 aWhole genome analysis of plasma fibrinogen reveals populationdif c2023 Jun 123 aUNLABELLED: Genetic studies have identified numerous regions associated with plasma fibrinogen levels in Europeans, yet missing heritability and limited inclusion of non-Europeans necessitates further studies with improved power and sensitivity. Compared with array-based genotyping, whole genome sequencing (WGS) data provides better coverage of the genome and better representation of non-European variants. To better understand the genetic landscape regulating plasma fibrinogen levels, we meta-analyzed WGS data from the NHLBI's Trans-Omics for Precision Medicine (TOPMed) program (n=32,572), with array-based genotype data from the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium (n=131,340) imputed to the TOPMed or Haplotype Reference Consortium panel. We identified 18 loci that have not been identified in prior genetic studies of fibrinogen. Of these, four are driven by common variants of small effect with reported MAF at least 10% higher in African populations. Three ( , and signals contain predicted deleterious missense variants. Two loci, and , each harbor two conditionally distinct, non-coding variants. The gene region encoding the protein chain subunits ( ), contains 7 distinct signals, including one novel signal driven by rs28577061, a variant common (MAF=0.180) in African reference panels but extremely rare (MAF=0.008) in Europeans. Through phenome-wide association studies in the VA Million Veteran Program, we found associations between fibrinogen polygenic risk scores and thrombotic and inflammatory disease phenotypes, including an association with gout. Our findings demonstrate the utility of WGS to augment genetic discovery in diverse populations and offer new insights for putative mechanisms of fibrinogen regulation.
KEY POINTS: Largest and most diverse genetic study of plasma fibrinogen identifies 54 regions (18 novel), housing 69 conditionally distinct variants (20 novel).Sufficient power achieved to identify signal driven by African population variant.Links to (1) liver enzyme, blood cell and lipid genetic signals, (2) liver regulatory elements, and (3) thrombotic and inflammatory disease.
1 aHuffman, Jennifer, E1 aNicolas, Jayna1 aHahn, Julie1 aHeath, Adam, S1 aRaffield, Laura, M1 aYanek, Lisa, R1 aBrody, Jennifer, A1 aThibord, Florian1 aAlmasy, Laura1 aBartz, Traci, M1 aBielak, Lawrence, F1 aBowler, Russell, P1 aCarrasquilla, Germán, D1 aChasman, Daniel, I1 aChen, Ming-Huei1 aEmmert, David, B1 aGhanbari, Mohsen1 aHaessle, Jeffery1 aHottenga, Jouke-Jan1 aKleber, Marcus, E1 aLe, Ngoc-Quynh1 aLee, Jiwon1 aLewis, Joshua, P1 aLi-Gao, Ruifang1 aLuan, Jian'an1 aMalmberg, Anni1 aMangino, Massimo1 aMarioni, Riccardo, E1 aMartinez-Perez, Angel1 aPankratz, Nathan1 aPolasek, Ozren1 aRichmond, Anne1 aRodriguez, Benjamin, At1 aRotter, Jerome, I1 aSteri, Maristella1 aSuchon, Pierre1 aTrompet, Stella1 aWeiss, Stefan1 aZare, Marjan1 aAuer, Paul1 aCho, Michael, H1 aChristofidou, Paraskevi1 aDavies, Gail1 ade Geus, Eco1 aDeleuze, Jean-Francois1 aDelgado, Graciela, E1 aEkunwe, Lynette1 aFaraday, Nauder1 aGögele, Martin1 aGreinacher, Andreas1 aHe, Gao1 aHoward, Tom1 aJoshi, Peter, K1 aKilpeläinen, Tuomas, O1 aLahti, Jari1 aLinneberg, Allan1 aNaitza, Silvia1 aNoordam, Raymond1 aPaüls-Vergés, Ferran1 aRich, Stephen, S1 aRosendaal, Frits, R1 aRudan, Igor1 aRyan, Kathleen, A1 aSouto, Juan, Carlos1 avan Rooij, Frank, Ja1 aWang, Heming1 aZhao, Wei1 aBecker, Lewis, C1 aBeswick, Andrew1 aBrown, Michael, R1 aCade, Brian, E1 aCampbell, Harry1 aCho, Kelly1 aCrapo, James, D1 aCurran, Joanne, E1 ade Maat, Moniek, Pm1 aDoyle, Margaret1 aElliott, Paul1 aFloyd, James, S1 aFuchsberger, Christian1 aGrarup, Niels1 aGuo, Xiuqing1 aHarris, Sarah, E1 aHou, Lifang1 aKolcic, Ivana1 aKooperberg, Charles1 aMenni, Cristina1 aNauck, Matthias1 aO'Connell, Jeffrey, R1 aOrrù, Valeria1 aPsaty, Bruce, M1 aRäikkönen, Katri1 aSmith, Jennifer, A1 aSoria, José, Manuel1 aStott, David, J1 aVlieg, Astrid, van Hylcka1 aWatkins, Hugh1 aWillemsen, Gonneke1 aWilson, Peter1 aBen-Shlomo, Yoav1 aBlangero, John1 aBoomsma, Dorret1 aCox, Simon, R1 aDehghan, Abbas1 aEriksson, Johan, G1 aFiorillo, Edoardo1 aFornage, Myriam1 aHansen, Torben1 aHayward, Caroline1 aIkram, Arfan, M1 aJukema, Wouter1 aKardia, Sharon, Lr1 aLange, Leslie, A1 aMärz, Winfried1 aMathias, Rasika, A1 aMitchell, Braxton, D1 aMook-Kanamori, Dennis, O1 aMorange, Pierre-Emmanuel1 aPedersen, Oluf1 aPramstaller, Peter, P1 aRedline, Susan1 aReiner, Alexander1 aRidker, Paul, M1 aSilverman, Edwin, K1 aSpector, Tim, D1 aVölker, Uwe1 aWareham, Nick1 aWilson, James, F1 aYao, Jie1 aTrégouët, David-Alexandre1 aJohnson, Andrew, D1 aWolberg, Alisa, S1 ade Vries, Paul, S1 aSabater-Lleal, Maria1 aMorrison, Alanna, C1 aSmith, Nicholas, L1 aVA Million Veteran Program1 aNHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium uhttps://chs-nhlbi.org/node/944903549nas a2200541 4500008004100000022001400041245010100055210006900156260001600225300001200241520193400253100002302187700002302210700002402233700001702257700002402274700002002298700002002318700002702338700002402365700001902389700002302408700002402431700002302455700001402478700002102492700002202513700002302535700002402558700002502582700001702607700001802624700002202642700002102664700002302685700002302708700002202731700002102753700002002774700002702794700001402821700002402835700002102859700002302880700002102903710004702924856003602971 2023 eng d a2574-830000aWhole Genome Analysis of Venous Thromboembolism: the Trans-Omics for Precision Medicine Program.0 aWhole Genome Analysis of Venous Thromboembolism the TransOmics f c2023 Mar 24 ae0035323 aBackground Risk for venous thromboembolism has a strong genetic component. Whole genome sequencingfrom the Trans-Omics for Precision Medicine program allowed us to look for new associations, particularly rare variants missed by standard genome-wide association studies. Methods The 3793 cases and 7834 controls (11.6% of cases were Black, Hispanic/Latino, or Asian American) were analyzed using a single variant approach and an aggregate gene-based approach using our primary filter (included only loss-of-function and missense variants predicted to be deleterious) and our secondary filter (included all missense variants). Results Single variant analyses identified associations at 5 known loci. Aggregate gene-based analyses identified only (odds ratio, 6.2 for carriers of rare variants; =7.4×10) when using our primary filter. Employing our secondary variant filter led to a smaller effect size at (odds ratio, 3.8; =1.6×10), while excluding variants found only in rare isoforms led to a larger one (odds ratio, 7.5). Different filtering strategies improved the signal for 2 other known genes: became significant (minimum =1.8×10 with the secondary filter), while did not (minimum =4.4×10 with minor allele frequency <0.0005). Results were largely the same when restricting the analyses to include only unprovoked cases; however, one novel gene, , became significant (=4.4×10 using all missense variants with minor allele frequency <0.0005). Conclusions Here, we have demonstrated the importance of using multiple variant filtering strategies, as we detected additional genes when filtering variants based on their predicted deleteriousness, frequency, and presence on the most expressed isoforms. Our primary analyses did not identify new candidate loci; thus larger follow-up studies are needed to replicate the novel locus and to identify additional rare variation associated with venous thromboembolism.
1 aSeyerle, Amanda, A1 aLaurie, Cecelia, A1 aCoombes, Brandon, J1 aJain, Deepti1 aConomos, Matthew, P1 aBrody, Jennifer1 aChen, Ming-Huei1 aGogarten, Stephanie, M1 aBeutel, Kathleen, M1 aGupta, Namrata1 aHeckbert, Susan, R1 aJackson, Rebecca, D1 aJohnson, Andrew, D1 aKo, Darae1 aManson, JoAnn, E1 aMcKnight, Barbara1 aMetcalf, Ginger, A1 aMorrison, Alanna, C1 aReiner, Alexander, P1 aSofer, Tamar1 aTang, Weihong1 aWiggins, Kerri, L1 aBoerwinkle, Eric1 ade Andrade, Mariza1 aGabriel, Stacey, B1 aGibbs, Richard, A1 aLaurie, Cathy, C1 aPsaty, Bruce, M1 aVasan, Ramachandran, S1 aRice, Ken1 aKooperberg, Charles1 aPankow, James, S1 aSmith, Nicholas, L1 aPankratz, Nathan1 aTrans-Omics for Precision Medicine Program uhttps://chs-nhlbi.org/node/932103713nas a2200661 4500008004100000022001400041245015600055210006900211260000900280300001200289490000700301520176300308100002502071700003302096700001802129700002902147700002602176700002002202700001902222700002302241700001402264700002202278700002002300700002302320700001702343700002502360700001902385700001802404700002402422700002002446700002102466700002602487700002202513700002602535700002002561700001402581700002102595700001402616700001402630700002102644700002102665700002102686700002102707700002402728700002002752700002402772700002702796700002002823700002002843700001902863700002102882700002202903700002302925700002102948700002502969700002102994856003603015 2023 eng d a1664-802100aWhole genome sequence analysis of apparent treatment resistant hypertension status in participants from the Trans-Omics for Precision Medicine program.0 aWhole genome sequence analysis of apparent treatment resistant h c2023 a12782150 v143 aApparent treatment-resistant hypertension (aTRH) is characterized by the use of four or more antihypertensive (AHT) classes to achieve blood pressure (BP) control. In the current study, we conducted single-variant and gene-based analyses of aTRH among individuals from 12 Trans-Omics for Precision Medicine cohorts with whole-genome sequencing data. Cases were defined as individuals treated for hypertension (HTN) taking three different AHT classes, with average systolic BP ≥ 140 or diastolic BP ≥ 90 mmHg, or four or more medications regardless of BP ( = 1,705). A normotensive control group was defined as individuals with BP < 140/90 mmHg ( = 22,079), not on AHT medication. A second control group comprised individuals who were treatment responsive on one AHT medication with BP < 140/ 90 mmHg ( = 5,424). Logistic regression with kinship adjustment using the Scalable and Accurate Implementation of Generalized mixed models (SAIGE) was performed, adjusting for age, sex, and genetic ancestry. We assessed variants using SKAT-O in rare-variant analyses. Single-variant and gene-based tests were conducted in a pooled multi-ethnicity stratum, as well as self-reported ethnic/racial strata (European and African American). One variant in the known HTN locus, , was a top finding in the multi-ethnic analysis ( = 8.23E-07) for the normotensive control group [rs12476527, odds ratio (95% confidence interval) = 0.80 (0.74-0.88)]. This variant was replicated in the Vanderbilt University Medical Center's DNA repository data. Aggregate gene-based signals included the genes and . Additional work validating these loci in larger, more diverse populations, is warranted to determine whether these regions influence the pathobiology of aTRH.
1 aArmstrong, Nicole, D1 aSrinivasasainagendra, Vinodh1 aAmmous, Farah1 aAssimes, Themistocles, L1 aBeitelshees, Amber, L1 aBrody, Jennifer1 aCade, Brian, E1 aChen, Yii-Der, Ida1 aChen, Han1 ade Vries, Paul, S1 aFloyd, James, S1 aFranceschini, Nora1 aGuo, Xiuqing1 aHellwege, Jacklyn, N1 aHouse, John, S1 aHwu, Chii-Min1 aKardia, Sharon, L R1 aLange, Ethan, M1 aLange, Leslie, A1 aMcDonough, Caitrin, W1 aMontasser, May, E1 aO'Connell, Jeffrey, R1 aShuey, Megan, M1 aSun, Xiao1 aTanner, Rikki, M1 aWang, Zhe1 aZhao, Wei1 aCarson, April, P1 aEdwards, Todd, L1 aKelly, Tanika, N1 aKenny, Eimear, E1 aKooperberg, Charles1 aLoos, Ruth, J F1 aMorrison, Alanna, C1 aMotsinger-Reif, Alison1 aPsaty, Bruce, M1 aRao, Dabeeru, C1 aRedline, Susan1 aRich, Stephen, S1 aRotter, Jerome, I1 aSmith, Jennifer, A1 aSmith, Albert, V1 aIrvin, Marguerite, R1 aArnett, Donna, K uhttps://chs-nhlbi.org/node/958105493nas a2201573 4500008004100000245011200041210006900153260001600222520100600238100001801244700002301262700002201285700002501307700002001332700001501352700001401367700002001381700002101401700002201422700001401444700001401458700002401472700002101496700002401517700001901541700002901560700002201589700001901611700001801630700002801648700002001676700001901696700001901715700002101734700002401755700001901779700001701798700002801815700001701843700002201860700002301882700002401905700001701929700001801946700002501964700002001989700002102009700001602030700002002046700001602066700001302082700001802095700002402113700001702137700002102154700002402175700002102199700002002220700002002240700001702260700002202277700002802299700002302327700002402350700001702374700002302391700003002414700002602444700002102470700002002491700001902511700002302530700001402553700002402567700002402591700001802615700002802633700002402661700002302685700002202708700002702730700001702757700002102774700002102795700002202816700002202838700002302860700001902883700002302902700001402925700002402939700002102963700002802984700002403012700001903036700002103055700002503076700002103101700002303122700002203145700002203167700002003189700002303209700001403232700002303246700001603269700002503285700002403310700002103334700002503355700001903380700002003399700002303419700002503442700002103467700002203488700002403510700002303534700002003557700002203577700002003599700001803619700002503637700001603662700001603678700002503694700002103719700001803740700002003758700001903778700002103797710006503818856003603883 2023 eng d00aWHOLE GENOME SEQUENCING ANALYSIS OF BODY MASS INDEX IDENTIFIES NOVEL AFRICAN ANCESTRY-SPECIFIC RISK ALLELE.0 aWHOLE GENOME SEQUENCING ANALYSIS OF BODY MASS INDEX IDENTIFIES N c2023 Aug 223 aObesity is a major public health crisis associated with high mortality rates. Previous genome-wide association studies (GWAS) investigating body mass index (BMI) have largely relied on imputed data from European individuals. This study leveraged whole-genome sequencing (WGS) data from 88,873 participants from the Trans-Omics for Precision Medicine (TOPMed) Program, of which 51% were of non-European population groups. We discovered 18 BMI-associated signals ( < 5 × 10 ). Notably, we identified and replicated a novel low frequency single nucleotide polymorphism (SNP) in that was common in individuals of African descent. Using a diverse study population, we further identified two novel secondary signals in known BMI loci and pinpointed two likely causal variants in the and loci. Our work demonstrates the benefits of combining WGS and diverse cohorts in expanding current catalog of variants and genes confer risk for obesity, bringing us one step closer to personalized medicine.
1 aZhang, Xinruo1 aBrody, Jennifer, A1 aGraff, Mariaelisa1 aHighland, Heather, M1 aChami, Nathalie1 aXu, Hanfei1 aWang, Zhe1 aFerrier, Kendra1 aChittoor, Geetha1 aJosyula, Navya, S1 aLi, Xihao1 aLi, Zilin1 aAllison, Matthew, A1 aBecker, Diane, M1 aBielak, Lawrence, F1 aBis, Joshua, C1 aBoorgula, Meher, Preethi1 aBowden, Donald, W1 aBroome, Jai, G1 aButh, Erin, J1 aCarlson, Christopher, S1 aChang, Kyong-Mi1 aChavan, Sameer1 aChiu, Yen-Feng1 aChuang, Lee-Ming1 aConomos, Matthew, P1 aDeMeo, Dawn, L1 aDu, Margaret1 aDuggirala, Ravindranath1 aEng, Celeste1 aFohner, Alison, E1 aFreedman, Barry, I1 aGarrett, Melanie, E1 aGuo, Xiuqing1 aHaiman, Chris1 aHeavner, Benjamin, D1 aHidalgo, Bertha1 aHixson, James, E1 aHo, Yuk-Lam1 aHobbs, Brian, D1 aHu, Donglei1 aHui, Qin1 aHwu, Chii-Min1 aJackson, Rebecca, D1 aJain, Deepti1 aKalyani, Rita, R1 aKardia, Sharon, L R1 aKelly, Tanika, N1 aLange, Ethan, M1 aLeNoir, Michael1 aLi, Changwei1 aLe Marchand, Loic1 aMcDonald, Merry-Lynn, N1 aMcHugh, Caitlin, P1 aMorrison, Alanna, C1 aNaseri, Take1 aO'Connell, Jeffrey1 aO'Donnell, Christopher, J1 aPalmer, Nicholette, D1 aPankow, James, S1 aPerry, James, A1 aPeters, Ulrike1 aPreuss, Michael, H1 aRao, D, C1 aRegan, Elizabeth, A1 aReupena, Sefuiva, M1 aRoden, Dan, M1 aRodriguez-Santana, Jose1 aSitlani, Colleen, M1 aSmith, Jennifer, A1 aTiwari, Hemant, K1 aVasan, Ramachandran, S1 aWang, Zeyuan1 aWeeks, Daniel, E1 aWessel, Jennifer1 aWiggins, Kerri, L1 aWilkens, Lynne, R1 aWilson, Peter, W F1 aYanek, Lisa, R1 aYoneda, Zachary, T1 aZhao, Wei1 aZöllner, Sebastian1 aArnett, Donna, K1 aAshley-Koch, Allison, E1 aBarnes, Kathleen, C1 aBlangero, John1 aBoerwinkle, Eric1 aBurchard, Esteban, G1 aCarson, April, P1 aChasman, Daniel, I1 aChen, Yii-Der Ida1 aCurran, Joanne, E1 aFornage, Myriam1 aGordeuk, Victor, R1 aHe, Jiang1 aHeckbert, Susan, R1 aHou, Lifang1 aIrvin, Marguerite, R1 aKooperberg, Charles1 aMinster, Ryan, L1 aMitchell, Braxton, D1 aNouraie, Mehdi1 aPsaty, Bruce, M1 aRaffield, Laura, M1 aReiner, Alexander, P1 aRich, Stephen, S1 aRotter, Jerome, I1 aShoemaker, Benjamin1 aSmith, Nicholas, L1 aTaylor, Kent, D1 aTelen, Marilyn, J1 aWeiss, Scott, T1 aZhang, Yingze1 aCosta, Nancy, Heard-1 aSun, Yan, V1 aLin, Xihong1 aCupples, Adrienne, L1 aLange, Leslie, A1 aLiu, Ching-Ti1 aLoos, Ruth, J F1 aNorth, Kari, E1 aJustice, Anne, E1 aNHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium uhttps://chs-nhlbi.org/node/948403779nas a2200673 4500008004100000245013300041210006900174260001600243520177100259100001902030700002202049700001402071700002002085700002002105700001502125700001902140700002202159700002702181700001402208700002102222700002002243700001902263700002102282700001902303700001702322700002202339700002102361700002202382700002502404700002102429700002002450700002502470700002402495700002202519700001902541700001602560700002302576700002002599700001902619700001502638700002102653700002302674700002202697700002402719700002002743700001902763700001902782700001602801700002002817700002402837700002502861700002302886700001602909700001802925700002302943710006502966710003803031856003603069 2023 eng d00aWhole Genome Sequencing Based Analysis of Inflammation Biomarkers in the Trans-Omics for Precision Medicine (TOPMed) Consortium.0 aWhole Genome Sequencing Based Analysis of Inflammation Biomarker c2023 Sep 123 aInflammation biomarkers can provide valuable insight into the role of inflammatory processes in many diseases and conditions. Sequencing based analyses of such biomarkers can also serve as an exemplar of the genetic architecture of quantitative traits. To evaluate the biological insight, which can be provided by a multi-ancestry, whole-genome based association study, we performed a comprehensive analysis of 21 inflammation biomarkers from up to 38,465 individuals with whole-genome sequencing from the Trans-Omics for Precision Medicine (TOPMed) program. We identified 22 distinct single-variant associations across 6 traits - E-selectin, intercellular adhesion molecule 1, interleukin-6, lipoprotein-associated phospholipase A2 activity and mass, and P-selectin - that remained significant after conditioning on previously identified associations for these inflammatory biomarkers. We further expanded upon known biomarker associations by pairing the single-variant analysis with a rare variant set-based analysis that further identified 19 significant rare variant set-based associations with 5 traits. These signals were distinct from both significant single variant association signals within TOPMed and genetic signals observed in prior studies, demonstrating the complementary value of performing both single and rare variant analyses when analyzing quantitative traits. We also confirm several previously reported signals from semi-quantitative proteomics platforms. Many of these signals demonstrate the extensive allelic heterogeneity and ancestry-differentiated variant-trait associations common for inflammation biomarkers, a characteristic we hypothesize will be increasingly observed with well-powered, large-scale analyses of complex traits.
1 aJiang, Min-Zhi1 aGaynor, Sheila, M1 aLi, Xihao1 aVan Buren, Eric1 aStilp, Adrienne1 aButh, Erin1 aWang, Fei, Fei1 aManansala, Regina1 aGogarten, Stephanie, M1 aLi, Zilin1 aPolfus, Linda, M1 aSalimi, Shabnam1 aBis, Joshua, C1 aPankratz, Nathan1 aYanek, Lisa, R1 aDurda, Peter1 aTracy, Russell, P1 aRich, Stephen, S1 aRotter, Jerome, I1 aMitchell, Braxton, D1 aLewis, Joshua, P1 aPsaty, Bruce, M1 aPratte, Katherine, A1 aSilverman, Edwin, K1 aKaplan, Robert, C1 aAvery, Christy1 aNorth, Kari1 aMathias, Rasika, A1 aFaraday, Nauder1 aLin, Honghuang1 aWang, Biqi1 aCarson, April, P1 aNorwood, Arnita, F1 aGibbs, Richard, A1 aKooperberg, Charles1 aLundin, Jessica1 aPeters, Ulrike1 aDupuis, Josée1 aHou, Lifang1 aFornage, Myriam1 aBenjamin, Emelia, J1 aReiner, Alexander, P1 aBowler, Russell, P1 aLin, Xihong1 aAuer, Paul, L1 aRaffield, Laura, M1 aNHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium1 aTOPMed Inflammation Working Group uhttps://chs-nhlbi.org/node/950002589nas a2200457 4500008004100000022001400041245008600055210006900141260001600210300000900226490000700235520126900242653001401511653003401525653001101559653001501570653003601585653002801621100002401649700002201673700001701695700002201712700001401734700002001748700001401768700002101782700002101803700001401824700001401838700002201852700002401874700002401898700002101922700001801943700002501961700002001986700002202006700001302028710005402041856003602095 2023 eng d a2041-172300aWhole-Genome Sequencing Analysis of Human Metabolome in Multi-Ethnic Populations.0 aWholeGenome Sequencing Analysis of Human Metabolome in MultiEthn c2023 May 30 a31110 v143 aCirculating metabolite levels may reflect the state of the human organism in health and disease, however, the genetic architecture of metabolites is not fully understood. We have performed a whole-genome sequencing association analysis of both common and rare variants in up to 11,840 multi-ethnic participants from five studies with up to 1666 circulating metabolites. We have discovered 1985 novel variant-metabolite associations, and validated 761 locus-metabolite associations reported previously. Seventy-nine novel variant-metabolite associations have been replicated, including three genetic loci located on the X chromosome that have demonstrated its involvement in metabolic regulation. Gene-based analysis have provided further support for seven metabolite-replicated loci pairs and their biologically plausible genes. Among those novel replicated variant-metabolite pairs, follow-up analyses have revealed that 26 metabolites have colocalized with 21 tissues, seven metabolite-disease outcome associations have been putatively causal, and 7 metabolites might be regulated by plasma protein levels. Our results have depicted the genetic contribution to circulating metabolite levels, providing additional insights into understanding human disease.
10aEthnicity10aGenome-Wide Association Study10aHumans10aMetabolome10aPolymorphism, Single Nucleotide10aQuantitative Trait Loci1 aFeofanova, Elena, V1 aBrown, Michael, R1 aAlkis, Taryn1 aManuel, Astrid, M1 aLi, Xihao1 aTahir, Usman, A1 aLi, Zilin1 aMendez, Kevin, M1 aKelly, Rachel, S1 aQi, Qibin1 aChen, Han1 aLarson, Martin, G1 aLemaitre, Rozenn, N1 aMorrison, Alanna, C1 aGrieser, Charles1 aWong, Kari, E1 aGersztern, Robert, E1 aZhao, Zhongming1 aLasky-Su, Jessica1 aYu, Bing1 aNHLBI Trans-Omics for Precision Medicine (TOPMed) uhttps://chs-nhlbi.org/node/9376