@article {1179, title = {A meta-analysis of four genome-wide association studies of survival to age 90 years or older: the Cohorts for Heart and Aging Research in Genomic Epidemiology Consortium.}, journal = {J Gerontol A Biol Sci Med Sci}, volume = {65}, year = {2010}, month = {2010 May}, pages = {478-87}, abstract = {

BACKGROUND: Genome-wide association studies (GWAS) may yield insights into longevity.

METHODS: We performed a meta-analysis of GWAS in Caucasians from four prospective cohort studies: the Age, Gene/Environment Susceptibility-Reykjavik Study, the Cardiovascular Health Study, the Framingham Heart Study, and the Rotterdam Study participating in the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium. Longevity was defined as survival to age 90 years or older (n = 1,836); the comparison group comprised cohort members who died between the ages of 55 and 80 years (n = 1,955). In a second discovery stage, additional genotyping was conducted in the Leiden Longevity Study cohort and the Danish 1905 cohort.

RESULTS: There were 273 single-nucleotide polymorphism (SNP) associations with p < .0001, but none reached the prespecified significance level of 5 x 10(-8). Of the most significant SNPs, 24 were independent signals, and 16 of these SNPs were successfully genotyped in the second discovery stage, with one association for rs9664222, reaching 6.77 x 10(-7) for the combined meta-analysis of CHARGE and the stage 2 cohorts. The SNP lies in a region near MINPP1 (chromosome 10), a well-conserved gene involved in regulation of cellular proliferation. The minor allele was associated with lower odds of survival past age 90 (odds ratio = 0.82). Associations of interest in a homologue of the longevity assurance gene (LASS3) and PAPPA2 were not strengthened in the second stage.

CONCLUSION: Survival studies of larger size or more extreme or specific phenotypes may support or refine these initial findings.

}, keywords = {Adult, Age Factors, Aged, Aged, 80 and over, Alleles, Cohort Studies, Confidence Intervals, Female, Genome-Wide Association Study, Genotype, Humans, Longevity, Male, Middle Aged, Odds Ratio, Polymorphism, Single Nucleotide}, issn = {1758-535X}, doi = {10.1093/gerona/glq028}, author = {Newman, Anne B and Walter, Stefan and Lunetta, Kathryn L and Garcia, Melissa E and Slagboom, P Eline and Christensen, Kaare and Arnold, Alice M and Aspelund, Thor and Aulchenko, Yurii S and Benjamin, Emelia J and Christiansen, Lene and D{\textquoteright}Agostino, Ralph B and Fitzpatrick, Annette L and Franceschini, Nora and Glazer, Nicole L and Gudnason, Vilmundur and Hofman, Albert and Kaplan, Robert and Karasik, David and Kelly-Hayes, Margaret and Kiel, Douglas P and Launer, Lenore J and Marciante, Kristin D and Massaro, Joseph M and Miljkovic, Iva and Nalls, Michael A and Hernandez, Dena and Psaty, Bruce M and Rivadeneira, Fernando and Rotter, Jerome and Seshadri, Sudha and Smith, Albert V and Taylor, Kent D and Tiemeier, Henning and Uh, Hae-Won and Uitterlinden, Andr{\'e} G and Vaupel, James W and Walston, Jeremy and Westendorp, Rudi G J and Harris, Tamara B and Lumley, Thomas and van Duijn, Cornelia M and Murabito, Joanne M} } @article {5845, title = {Insulin resistance and risk of incident heart failure: Cardiovascular Health Study.}, journal = {Circ Heart Fail}, volume = {6}, year = {2013}, month = {2013 May}, pages = {364-70}, abstract = {

BACKGROUND: Patients with heart failure (HF) have higher fasting insulin levels and a higher prevalence of insulin resistance as compared with matched controls. Insulin resistance leads to structural abnormalities in the heart, such as increased left atrial size, left ventricular mass, and alterations in transmitral velocity that can precede the diagnosis of HF. It is not known whether insulin resistance precedes the development of HF or whether the relationship between insulin resistance and HF is present among adults with HF caused by nonischemic heart disease.

METHODS AND RESULTS: We examined 4425 participants (60\% women) from the Cardiovascular Health Study after excluding those with HF, myocardial infarction, or treated diabetes mellitus at baseline. We used Cox proportional hazards models to estimate the relative risk of incident HF associated with fasting insulin measured at study entry. There were 1216 cases of incident HF (1103 without antecedent myocardial infarction) during a median follow-up of 12 years (maximum, 19 years). Fasting insulin levels were positively associated with the risk of incident HF (hazard ratio, 1.10; 95\% confidence interval, 1.05-1.15, per SD change) when adjusted for age, sex, race, field center, physical activity, smoking, alcohol intake, high-density lipoprotein-cholesterol, total cholesterol, systolic blood pressure, and waist circumference. The association between fasting insulin levels and incident HF was similar for HF without antecedent myocardial infarction (hazard ratio, 1.10; 95\% confidence interval, 1.05-1.15). Measures of left atrial size, left ventricular mass, and peak A velocity at baseline were associated both with fasting insulin levels and with HF; however, additional statistical adjustment for these parameters did not completely attenuate the insulin-HF estimate (hazard ratio, 1.08; 95\% confidence interval, 1.03-1.14 per 1-SD increase in fasting insulin).

CONCLUSIONS: Fasting insulin was positively associated with adverse echocardiographic features and risk of subsequent HF in Cardiovascular Health Study participants, including those without an antecedent myocardial infarction.

CLINICAL TRIAL REGISTRATION: URL: http://www.clinicaltrials.gov. Unique identifier: NCT00005133.

}, keywords = {Aged, Female, Heart Atria, Heart Failure, Heart Ventricles, Humans, Incidence, Insulin, Insulin Resistance, Male, Middle Aged, Myocardial Infarction, Organ Size, Proportional Hazards Models, Prospective Studies}, issn = {1941-3297}, doi = {10.1161/CIRCHEARTFAILURE.112.000022}, author = {Banerjee, Dipanjan and Biggs, Mary L and Mercer, Laina and Mukamal, Kenneth and Kaplan, Robert and Barzilay, Joshua and Kuller, Lewis and Kizer, Jorge R and Djouss{\'e}, Luc and Tracy, Russell and Zieman, Susan and Lloyd-Jones, Donald and Siscovick, David and Carnethon, Mercedes} } @article {6028, title = {Sex-stratified genome-wide association studies including 270,000 individuals show sexual dimorphism in genetic loci for anthropometric traits.}, journal = {PLoS Genet}, volume = {9}, year = {2013}, month = {2013 Jun}, pages = {e1003500}, abstract = {

Given the anthropometric differences between men and women and previous evidence of sex-difference in genetic effects, we conducted a genome-wide search for sexually dimorphic associations with height, weight, body mass index, waist circumference, hip circumference, and waist-to-hip-ratio (133,723 individuals) and took forward 348 SNPs into follow-up (additional 137,052 individuals) in a total of 94 studies. Seven loci displayed significant sex-difference (FDR<5\%), including four previously established (near GRB14/COBLL1, LYPLAL1/SLC30A10, VEGFA, ADAMTS9) and three novel anthropometric trait loci (near MAP3K1, HSD17B4, PPARG), all of which were genome-wide significant in women (P<5{\texttimes}10(-8)), but not in men. Sex-differences were apparent only for waist phenotypes, not for height, weight, BMI, or hip circumference. Moreover, we found no evidence for genetic effects with opposite directions in men versus women. The PPARG locus is of specific interest due to its role in diabetes genetics and therapy. Our results demonstrate the value of sex-specific GWAS to unravel the sexually dimorphic genetic underpinning of complex traits.

}, keywords = {Anthropometry, Body Height, Body Mass Index, Body Weight, Body Weights and Measures, Female, Genetic Loci, Genome, Human, Genome-Wide Association Study, Humans, Male, Polymorphism, Single Nucleotide, Sex Characteristics, Waist Circumference, Waist-Hip Ratio}, issn = {1553-7404}, doi = {10.1371/journal.pgen.1003500}, author = {Randall, Joshua C and Winkler, Thomas W and Kutalik, Zolt{\'a}n and Berndt, Sonja I and Jackson, Anne U and Monda, Keri L and Kilpel{\"a}inen, Tuomas O and Esko, T{\~o}nu and M{\"a}gi, Reedik and Li, Shengxu and Workalemahu, Tsegaselassie and Feitosa, Mary F and Croteau-Chonka, Damien C and Day, Felix R and Fall, Tove and Ferreira, Teresa and Gustafsson, Stefan and Locke, Adam E and Mathieson, Iain and Scherag, Andre and Vedantam, Sailaja and Wood, Andrew R and Liang, Liming and Steinthorsdottir, Valgerdur and Thorleifsson, Gudmar and Dermitzakis, Emmanouil T and Dimas, Antigone S and Karpe, Fredrik and Min, Josine L and Nicholson, George and Clegg, Deborah J and Person, Thomas and Krohn, Jon P and Bauer, Sabrina and Buechler, Christa and Eisinger, Kristina and Bonnefond, Am{\'e}lie and Froguel, Philippe and Hottenga, Jouke-Jan and Prokopenko, Inga and Waite, Lindsay L and Harris, Tamara B and Smith, Albert Vernon and Shuldiner, Alan R and McArdle, Wendy L and Caulfield, Mark J and Munroe, Patricia B and Gr{\"o}nberg, Henrik and Chen, Yii-Der Ida and Li, Guo and Beckmann, Jacques S and Johnson, Toby and Thorsteinsdottir, Unnur and Teder-Laving, Maris and Khaw, Kay-Tee and Wareham, Nicholas J and Zhao, Jing Hua and Amin, Najaf and Oostra, Ben A and Kraja, Aldi T and Province, Michael A and Cupples, L Adrienne and Heard-Costa, Nancy L and Kaprio, Jaakko and Ripatti, Samuli and Surakka, Ida and Collins, Francis S and Saramies, Jouko and Tuomilehto, Jaakko and Jula, Antti and Salomaa, Veikko and Erdmann, Jeanette and Hengstenberg, Christian and Loley, Christina and Schunkert, Heribert and Lamina, Claudia and Wichmann, H Erich and Albrecht, Eva and Gieger, Christian and Hicks, Andrew A and Johansson, Asa and Pramstaller, Peter P and Kathiresan, Sekar and Speliotes, Elizabeth K and Penninx, Brenda and Hartikainen, Anna-Liisa and Jarvelin, Marjo-Riitta and Gyllensten, Ulf and Boomsma, Dorret I and Campbell, Harry and Wilson, James F and Chanock, Stephen J and Farrall, Martin and Goel, Anuj and Medina-G{\'o}mez, Carolina and Rivadeneira, Fernando and Estrada, Karol and Uitterlinden, Andr{\'e} G and Hofman, Albert and Zillikens, M Carola and den Heijer, Martin and Kiemeney, Lambertus A and Maschio, Andrea and Hall, Per and Tyrer, Jonathan and Teumer, Alexander and V{\"o}lzke, Henry and Kovacs, Peter and T{\"o}njes, Anke and Mangino, Massimo and Spector, Tim D and Hayward, Caroline and Rudan, Igor and Hall, Alistair S and Samani, Nilesh J and Attwood, Antony Paul and Sambrook, Jennifer G and Hung, Joseph and Palmer, Lyle J and Lokki, Marja-Liisa and Sinisalo, Juha and Boucher, Gabrielle and Huikuri, Heikki and Lorentzon, Mattias and Ohlsson, Claes and Eklund, Niina and Eriksson, Johan G and Barlassina, Cristina and Rivolta, Carlo and Nolte, Ilja M and Snieder, Harold and van der Klauw, Melanie M and van Vliet-Ostaptchouk, Jana V and Gejman, Pablo V and Shi, Jianxin and Jacobs, Kevin B and Wang, Zhaoming and Bakker, Stephan J L and Mateo Leach, Irene and Navis, Gerjan and van der Harst, Pim and Martin, Nicholas G and Medland, Sarah E and Montgomery, Grant W and Yang, Jian and Chasman, Daniel I and Ridker, Paul M and Rose, Lynda M and Lehtim{\"a}ki, Terho and Raitakari, Olli and Absher, Devin and Iribarren, Carlos and Basart, Hanneke and Hovingh, Kees G and Hypp{\"o}nen, Elina and Power, Chris and Anderson, Denise and Beilby, John P and Hui, Jennie and Jolley, Jennifer and Sager, Hendrik and Bornstein, Stefan R and Schwarz, Peter E H and Kristiansson, Kati and Perola, Markus and Lindstr{\"o}m, Jaana and Swift, Amy J and Uusitupa, Matti and Atalay, Mustafa and Lakka, Timo A and Rauramaa, Rainer and Bolton, Jennifer L and Fowkes, Gerry and Fraser, Ross M and Price, Jackie F and Fischer, Krista and Krjut{\r a} Kov, Kaarel and Metspalu, Andres and Mihailov, Evelin and Langenberg, Claudia and Luan, Jian{\textquoteright}an and Ong, Ken K and Chines, Peter S and Keinanen-Kiukaanniemi, Sirkka M and Saaristo, Timo E and Edkins, Sarah and Franks, Paul W and Hallmans, G{\"o}ran and Shungin, Dmitry and Morris, Andrew David and Palmer, Colin N A and Erbel, Raimund and Moebus, Susanne and N{\"o}then, Markus M and Pechlivanis, Sonali and Hveem, Kristian and Narisu, Narisu and Hamsten, Anders and Humphries, Steve E and Strawbridge, Rona J and Tremoli, Elena and Grallert, Harald and Thorand, Barbara and Illig, Thomas and Koenig, Wolfgang and M{\"u}ller-Nurasyid, Martina and Peters, Annette and Boehm, Bernhard O and Kleber, Marcus E and M{\"a}rz, Winfried and Winkelmann, Bernhard R and Kuusisto, Johanna and Laakso, Markku and Arveiler, Dominique and Cesana, Giancarlo and Kuulasmaa, Kari and Virtamo, Jarmo and Yarnell, John W G and Kuh, Diana and Wong, Andrew and Lind, Lars and de Faire, Ulf and Gigante, Bruna and Magnusson, Patrik K E and Pedersen, Nancy L and Dedoussis, George and Dimitriou, Maria and Kolovou, Genovefa and Kanoni, Stavroula and Stirrups, Kathleen and Bonnycastle, Lori L and Nj{\o}lstad, Inger and Wilsgaard, Tom and Ganna, Andrea and Rehnberg, Emil and Hingorani, Aroon and Kivimaki, Mika and Kumari, Meena and Assimes, Themistocles L and Barroso, In{\^e}s and Boehnke, Michael and Borecki, Ingrid B and Deloukas, Panos and Fox, Caroline S and Frayling, Timothy and Groop, Leif C and Haritunians, Talin and Hunter, David and Ingelsson, Erik and Kaplan, Robert and Mohlke, Karen L and O{\textquoteright}Connell, Jeffrey R and Schlessinger, David and Strachan, David P and Stefansson, Kari and van Duijn, Cornelia M and Abecasis, Goncalo R and McCarthy, Mark I and Hirschhorn, Joel N and Qi, Lu and Loos, Ruth J F and Lindgren, Cecilia M and North, Kari E and Heid, Iris M} } @article {6546, title = {Dietary protein intake and change in estimated GFR in the Cardiovascular Health Study.}, journal = {Nutrition}, volume = {30}, year = {2014}, month = {2014 Jul-Aug}, pages = {794-9}, abstract = {

OBJECTIVE: With aging, kidney function declines, as evidenced by reduced glomerular filtration rate. It is controversial whether or not high protein intake accelerates this decline. The aim of this study was to determine whether high protein intake was associated with declines in kidney function among older patients.

METHODS: We examined whether dietary protein is associated with change in kidney function (mean follow-up 6.4 y [SD = 1.4, range = 2.5-7.9] in the Cardiovascular Health Study (N = 3623). We estimated protein intake using a food frequency questionnaire and estimated glomerular filtration rate from cystatin C. Associations between protein intake and kidney function were determined by linear and logistic regression models.

RESULTS: Average protein intake was 19\% of energy intake (SD = 5\%). Twenty-seven percent (n = 963) of study participants had rapid decline in kidney function, as defined by (ΔeGFRcysC > 3 mL{\textbullet}min{\textbullet}1.73 m(2)). Protein intake (characterized as g/d and \% energy/d), was not associated with change in estimated glomerular filtration rate (P > 0.05 for all comparisons). There were also no significant associations when protein intake was separated by source (animal and vegetable).

CONCLUSION: These data suggest that higher protein intake does not have a major effect on kidney function decline among elderly men and women.

}, keywords = {Aged, Aging, Diet, Dietary Proteins, Feeding Behavior, Female, Glomerular Filtration Rate, Health, Humans, Kidney, Male, Regression Analysis, Surveys and Questionnaires}, issn = {1873-1244}, doi = {10.1016/j.nut.2013.12.006}, author = {Beasley, Jeannette M and Katz, Ronit and Shlipak, Michael and Rifkin, Dena E and Siscovick, David and Kaplan, Robert} } @article {6246, title = {Gender and telomere length: systematic review and meta-analysis.}, journal = {Exp Gerontol}, volume = {51}, year = {2014}, month = {2014 Mar}, pages = {15-27}, abstract = {

BACKGROUND: It is widely believed that females have longer telomeres than males, although results from studies have been contradictory.

METHODS: We carried out a systematic review and meta-analyses to test the hypothesis that in humans, females have longer telomeres than males and that this association becomes stronger with increasing age. Searches were conducted in EMBASE and MEDLINE (by November 2009) and additional datasets were obtained from study investigators. Eligible observational studies measured telomeres for both females and males of any age, had a minimum sample size of 100 and included participants not part of a diseased group. We calculated summary estimates using random-effects meta-analyses. Heterogeneity between studies was investigated using sub-group analysis and meta-regression.

RESULTS: Meta-analyses from 36 cohorts (36,230 participants) showed that on average females had longer telomeres than males (standardised difference in telomere length between females and males 0.090, 95\% CI 0.015, 0.166; age-adjusted). There was little evidence that these associations varied by age group (p=1.00) or cell type (p=0.29). However, the size of this difference did vary by measurement methods, with only Southern blot but neither real-time PCR nor Flow-FISH showing a significant difference. This difference was not associated with random measurement error.

CONCLUSIONS: Telomere length is longer in females than males, although this difference was not universally found in studies that did not use Southern blot methods. Further research on explanations for the methodological differences is required.

}, keywords = {Adult, Aged, Aged, 80 and over, Aging, Female, Humans, Male, Middle Aged, Sex Factors, Telomere}, issn = {1873-6815}, doi = {10.1016/j.exger.2013.12.004}, author = {Gardner, Michael and Bann, David and Wiley, Laura and Cooper, Rachel and Hardy, Rebecca and Nitsch, Dorothea and Martin-Ruiz, Carmen and Shiels, Paul and Sayer, Avan Aihie and Barbieri, Michelangela and Bekaert, Sofie and Bischoff, Claus and Brooks-Wilson, Angela and Chen, Wei and Cooper, Cyrus and Christensen, Kaare and De Meyer, Tim and Deary, Ian and Der, Geoff and Diez Roux, Ana and Fitzpatrick, Annette and Hajat, Anjum and Halaschek-Wiener, Julius and Harris, Sarah and Hunt, Steven C and Jagger, Carol and Jeon, Hyo-Sung and Kaplan, Robert and Kimura, Masayuki and Lansdorp, Peter and Li, Changyong and Maeda, Toyoki and Mangino, Massimo and Nawrot, Tim S and Nilsson, Peter and Nordfjall, Katarina and Paolisso, Giuseppe and Ren, Fu and Riabowol, Karl and Robertson, Tony and Roos, Goran and Staessen, Jan A and Spector, Tim and Tang, Nelson and Unryn, Brad and van der Harst, Pim and Woo, Jean and Xing, Chao and Yadegarfar, Mohammad E and Park, Jae Yong and Young, Neal and Kuh, Diana and von Zglinicki, Thomas and Ben-Shlomo, Yoav} } @article {7552, title = {The GH receptor exon 3 deletion is a marker of male-specific exceptional longevity associated with increased GH sensitivity and taller stature.}, journal = {Sci Adv}, volume = {3}, year = {2017}, month = {2017 Jun}, pages = {e1602025}, abstract = {

Although both growth hormone (GH) and insulin-like growth factor 1 (IGF-1) signaling were shown to regulate life span in lower organisms, the role of GH signaling in human longevity remains unclear. Because a GH receptor exon 3 deletion (d3-GHR) appears to modulate GH sensitivity in humans, we hypothesized that this polymorphism could play a role in human longevity. We report a linear increased prevalence of d3-GHR homozygosity with age in four independent cohorts of long-lived individuals: 841 participants [567 of the Longevity Genes Project (LGP) (8\% increase; P = 0.01), 152 of the Old Order Amish (16\% increase; P = 0.02), 61 of the Cardiovascular Health Study (14.2\% increase; P = 0.14), and 61 of the French Long-Lived Study (23.5\% increase; P = 0.02)]. In addition, mega analysis of males in all cohorts resulted in a significant positive trend with age (26\% increase; P = 0.007), suggesting sexual dimorphism for GH action in longevity. Further, on average, LGP d3/d3 homozygotes were 1 inch taller than the wild-type (WT) allele carriers (P = 0.05) and also showed lower serum IGF-1 levels (P = 0.003). Multivariate regression analysis indicated that the presence of d3/d3 genotype adds approximately 10 years to life span. The LGP d3/d3-GHR transformed lymphocytes exhibited superior growth and extracellular signal-regulated kinase activation, to GH treatment relative to WT GHR lymphocytes (P < 0.01), indicating a GH dose response. The d3-GHR variant is a common genetic polymorphism that modulates GH responsiveness throughout the life span and positively affects male longevity.

}, issn = {2375-2548}, doi = {10.1126/sciadv.1602025}, author = {Ben-Avraham, Danny and Govindaraju, Diddahally R and Budagov, Temuri and Fradin, Delphine and Durda, Peter and Liu, Bing and Ott, Sandy and Gutman, Danielle and Sharvit, Lital and Kaplan, Robert and Bougn{\`e}res, Pierre and Reiner, Alex and Shuldiner, Alan R and Cohen, Pinchas and Barzilai, Nir and Atzmon, Gil} } @article {8042, title = {Harmonization of Respiratory Data From 9 US Population-Based Cohorts: The NHLBI Pooled Cohorts Study.}, journal = {Am J Epidemiol}, volume = {187}, year = {2018}, month = {2018 Nov 01}, pages = {2265-2278}, abstract = {

Chronic lower respiratory diseases (CLRDs) are the fourth leading cause of death in the United States. To support investigations into CLRD risk determinants and new approaches to primary prevention, we aimed to harmonize and pool respiratory data from US general population-based cohorts. Data were obtained from prospective cohorts that performed prebronchodilator spirometry and were harmonized following 2005 ATS/ERS standards. In cohorts conducting follow-up for noncardiovascular events, CLRD events were defined as hospitalizations/deaths adjudicated as CLRD-related or assigned relevant administrative codes. Coding and variable names were applied uniformly. The pooled sample included 65,251 adults in 9 cohorts followed-up for CLRD-related mortality over 653,380 person-years during 1983-2016. Average baseline age was 52 years; 56\% were female; 49\% were never-smokers; and racial/ethnic composition was 44\% white, 22\% black, 28\% Hispanic/Latino, and 5\% American Indian. Over 96\% had complete data on smoking, clinical CLRD diagnoses, and dyspnea. After excluding invalid spirometry examinations (13\%), there were 105,696 valid examinations (median, 2 per participant). Of 29,351 participants followed for CLRD hospitalizations, median follow-up was 14 years; only 5\% were lost to follow-up at 10 years. The NHLBI Pooled Cohorts Study provides a harmonization standard applied to a large, US population-based sample that may be used to advance epidemiologic research on CLRD.

}, issn = {1476-6256}, doi = {10.1093/aje/kwy139}, author = {Oelsner, Elizabeth C and Balte, Pallavi P and Cassano, Patricia A and Couper, David and Enright, Paul L and Folsom, Aaron R and Hankinson, John and Jacobs, David R and Kalhan, Ravi and Kaplan, Robert and Kronmal, Richard and Lange, Leslie and Loehr, Laura R and London, Stephanie J and Navas Acien, Ana and Newman, Anne B and O{\textquoteright}Connor, George T and Schwartz, Joseph E and Smith, Lewis J and Yeh, Fawn and Zhang, Yiyi and Moran, Andrew E and Mwasongwe, Stanford and White, Wendy B and Yende, Sachin and Barr, R Graham} } @article {8666, title = {Sequencing of 53,831 diverse genomes from the NHLBI TOPMed Program.}, journal = {Nature}, volume = {590}, year = {2021}, month = {2021 02}, pages = {290-299}, abstract = {

The Trans-Omics for Precision Medicine (TOPMed) programme seeks to elucidate the genetic architecture and biology of heart, lung, blood and sleep disorders, with the ultimate goal of improving diagnosis, treatment and prevention of these diseases. The initial phases of the programme focused on whole-genome sequencing of individuals with rich phenotypic data and diverse backgrounds. Here we describe the TOPMed goals and design as well as the available resources and early insights obtained from the sequence data. The resources include a variant browser, a genotype imputation server, and genomic and phenotypic data that are available through dbGaP (Database of Genotypes and Phenotypes). In the first 53,831 TOPMed samples, we detected more than 400~million single-nucleotide and insertion or deletion variants after alignment with the reference genome. Additional previously undescribed variants were detected through assembly of unmapped reads and customized analysis in highly variable loci. Among the more than 400~million detected variants, 97\% have frequencies of less than 1\% and 46\% are singletons that are present in only one individual (53\% among unrelated individuals). These rare variants provide insights into mutational processes and recent human evolutionary history. The extensive catalogue of genetic variation in TOPMed studies provides unique opportunities for exploring the contributions of rare and noncoding sequence variants to phenotypic variation. Furthermore, combining TOPMed haplotypes with modern imputation methods improves the power and reach of genome-wide association studies to include variants down to a frequency of approximately 0.01\%.

}, issn = {1476-4687}, doi = {10.1038/s41586-021-03205-y}, author = {Taliun, Daniel and Harris, Daniel N and Kessler, Michael D and Carlson, Jedidiah and Szpiech, Zachary A and Torres, Raul and Taliun, Sarah A Gagliano and Corvelo, Andr{\'e} and Gogarten, Stephanie M and Kang, Hyun Min and Pitsillides, Achilleas N and LeFaive, Jonathon and Lee, Seung-Been and Tian, Xiaowen and Browning, Brian L and Das, Sayantan and Emde, Anne-Katrin and Clarke, Wayne E and Loesch, Douglas P and Shetty, Amol C and Blackwell, Thomas W and Smith, Albert V and Wong, Quenna and Liu, Xiaoming and Conomos, Matthew P and Bobo, Dean M and Aguet, Francois and Albert, Christine and Alonso, Alvaro and Ardlie, Kristin G and Arking, Dan E and Aslibekyan, Stella and Auer, Paul L and Barnard, John and Barr, R Graham and Barwick, Lucas and Becker, Lewis C and Beer, Rebecca L and Benjamin, Emelia J and Bielak, Lawrence F and Blangero, John and Boehnke, Michael and Bowden, Donald W and Brody, Jennifer A and Burchard, Esteban G and Cade, Brian E and Casella, James F and Chalazan, Brandon and Chasman, Daniel I and Chen, Yii-Der Ida and Cho, Michael H and Choi, Seung Hoan and Chung, Mina K and Clish, Clary B and Correa, Adolfo and Curran, Joanne E and Custer, Brian and Darbar, Dawood and Daya, Michelle and de Andrade, Mariza and DeMeo, Dawn L and Dutcher, Susan K and Ellinor, Patrick T and Emery, Leslie S and Eng, Celeste and Fatkin, Diane and Fingerlin, Tasha and Forer, Lukas and Fornage, Myriam and Franceschini, Nora and Fuchsberger, Christian and Fullerton, Stephanie M and Germer, Soren and Gladwin, Mark T and Gottlieb, Daniel J and Guo, Xiuqing and Hall, Michael E and He, Jiang and Heard-Costa, Nancy L and Heckbert, Susan R and Irvin, Marguerite R and Johnsen, Jill M and Johnson, Andrew D and Kaplan, Robert and Kardia, Sharon L R and Kelly, Tanika and Kelly, Shannon and Kenny, Eimear E and Kiel, Douglas P and Klemmer, Robert and Konkle, Barbara A and Kooperberg, Charles and K{\"o}ttgen, Anna and Lange, Leslie A and Lasky-Su, Jessica and Levy, Daniel and Lin, Xihong and Lin, Keng-Han and Liu, Chunyu and Loos, Ruth J F and Garman, Lori and Gerszten, Robert and Lubitz, Steven A and Lunetta, Kathryn L and Mak, Angel C Y and Manichaikul, Ani and Manning, Alisa K and Mathias, Rasika A and McManus, David D and McGarvey, Stephen T and Meigs, James B and Meyers, Deborah A and Mikulla, Julie L and Minear, Mollie A and Mitchell, Braxton D and Mohanty, Sanghamitra and Montasser, May E and Montgomery, Courtney and Morrison, Alanna C and Murabito, Joanne M and Natale, Andrea and Natarajan, Pradeep and Nelson, Sarah C and North, Kari E and O{\textquoteright}Connell, Jeffrey R and Palmer, Nicholette D and Pankratz, Nathan and Peloso, Gina M and Peyser, Patricia A and Pleiness, Jacob and Post, Wendy S and Psaty, Bruce M and Rao, D C and Redline, Susan and Reiner, Alexander P and Roden, Dan and Rotter, Jerome I and Ruczinski, Ingo and Sarnowski, Chloe and Schoenherr, Sebastian and Schwartz, David A and Seo, Jeong-Sun and Seshadri, Sudha and Sheehan, Vivien A and Sheu, Wayne H and Shoemaker, M Benjamin and Smith, Nicholas L and Smith, Jennifer A and Sotoodehnia, Nona and Stilp, Adrienne M and Tang, Weihong and Taylor, Kent D and Telen, Marilyn and Thornton, Timothy A and Tracy, Russell P and Van Den Berg, David J and Vasan, Ramachandran S and Viaud-Martinez, Karine A and Vrieze, Scott and Weeks, Daniel E and Weir, Bruce S and Weiss, Scott T and Weng, Lu-Chen and Willer, Cristen J and Zhang, Yingze and Zhao, Xutong and Arnett, Donna K and Ashley-Koch, Allison E and Barnes, Kathleen C and Boerwinkle, Eric and Gabriel, Stacey and Gibbs, Richard and Rice, Kenneth M and Rich, Stephen S and Silverman, Edwin K and Qasba, Pankaj and Gan, Weiniu and Papanicolaou, George J and Nickerson, Deborah A and Browning, Sharon R and Zody, Michael C and Z{\"o}llner, Sebastian and Wilson, James G and Cupples, L Adrienne and Laurie, Cathy C and Jaquish, Cashell E and Hernandez, Ryan D and O{\textquoteright}Connor, Timothy D and Abecasis, Goncalo R} } @article {8779, title = {Whole-genome sequencing association analysis of quantitative red blood cell phenotypes: The NHLBI TOPMed program.}, journal = {Am J Hum Genet}, volume = {108}, year = {2021}, month = {2021 05 06}, pages = {874-893}, abstract = {

Whole-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.

}, keywords = {Adult, Aged, Chromosomes, Human, Pair 16, Datasets as Topic, Erythrocytes, Female, Gene Editing, Genetic Variation, Genome-Wide Association Study, HEK293 Cells, Humans, Male, Middle Aged, National Heart, Lung, and Blood Institute (U.S.), Phenotype, Quality Control, Reproducibility of Results, United States}, issn = {1537-6605}, doi = {10.1016/j.ajhg.2021.04.003}, author = {Hu, Yao and Stilp, Adrienne M and McHugh, Caitlin P and Rao, Shuquan and Jain, Deepti and Zheng, Xiuwen and Lane, John and M{\'e}ric de Bellefon, S{\'e}bastian and Raffield, Laura M and Chen, Ming-Huei and Yanek, Lisa R and Wheeler, Marsha and Yao, Yao and Ren, Chunyan and Broome, Jai and Moon, Jee-Young and de Vries, Paul S and Hobbs, Brian D and Sun, Quan and Surendran, Praveen and Brody, Jennifer A and Blackwell, Thomas W and Choquet, Helene and Ryan, Kathleen and Duggirala, Ravindranath and Heard-Costa, Nancy and Wang, Zhe and Chami, Nathalie and Preuss, Michael H and Min, Nancy and Ekunwe, Lynette and Lange, Leslie A and Cushman, Mary and Faraday, Nauder and Curran, Joanne E and Almasy, Laura and Kundu, Kousik and Smith, Albert V and Gabriel, Stacey and Rotter, Jerome I and Fornage, Myriam and Lloyd-Jones, Donald M and Vasan, Ramachandran S and Smith, Nicholas L and North, Kari E and Boerwinkle, Eric and Becker, Lewis C and Lewis, Joshua P and Abecasis, Goncalo R and Hou, Lifang and O{\textquoteright}Connell, Jeffrey R and Morrison, Alanna C and Beaty, Terri H and Kaplan, Robert and Correa, Adolfo and Blangero, John and Jorgenson, Eric and Psaty, Bruce M and Kooperberg, Charles and Walton, Russell T and Kleinstiver, Benjamin P and Tang, Hua and Loos, Ruth J F and Soranzo, Nicole and Butterworth, Adam S and Nickerson, Debbie and Rich, Stephen S and Mitchell, Braxton D and Johnson, Andrew D and Auer, Paul L and Li, Yun and Mathias, Rasika A and Lettre, Guillaume and Pankratz, Nathan and Laurie, Cathy C and Laurie, Cecelia A and Bauer, Daniel E and Conomos, Matthew P and Reiner, Alexander P} } @article {8914, title = {Whole-genome sequencing in diverse subjects identifies genetic correlates of leukocyte traits: The NHLBI TOPMed program.}, journal = {Am J Hum Genet}, volume = {108}, year = {2021}, month = {2021 10 07}, pages = {1836-1851}, abstract = {

Many 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.

}, keywords = {Asthma, Biomarkers, Dermatitis, Atopic, Genetic Predisposition to Disease, Genome, Human, Genome-Wide Association Study, Humans, Leukocytes, National Heart, Lung, and Blood Institute (U.S.), Phenotype, Polymorphism, Single Nucleotide, Prognosis, Proteome, Pulmonary Disease, Chronic Obstructive, Quantitative Trait Loci, United Kingdom, United States, Whole Genome Sequencing}, issn = {1537-6605}, doi = {10.1016/j.ajhg.2021.08.007}, author = {Mikhaylova, Anna V and McHugh, Caitlin P and Polfus, Linda M and Raffield, Laura M and Boorgula, Meher Preethi and Blackwell, Thomas W and Brody, Jennifer A and Broome, Jai and Chami, Nathalie and Chen, Ming-Huei and Conomos, Matthew P and Cox, Corey and Curran, Joanne E and Daya, Michelle and Ekunwe, Lynette and Glahn, David C and Heard-Costa, Nancy and Highland, Heather M and Hobbs, Brian D and Ilboudo, Yann and Jain, Deepti and Lange, Leslie A and Miller-Fleming, Tyne W and Min, Nancy and Moon, Jee-Young and Preuss, Michael H and Rosen, Jonathon and Ryan, Kathleen and Smith, Albert V and Sun, Quan and Surendran, Praveen and de Vries, Paul S and Walter, Klaudia and Wang, Zhe and Wheeler, Marsha and Yanek, Lisa R and Zhong, Xue and Abecasis, Goncalo R and Almasy, Laura and Barnes, Kathleen C and Beaty, Terri H and Becker, Lewis C and Blangero, John and Boerwinkle, Eric and Butterworth, Adam S and Chavan, Sameer and Cho, Michael H and Choquet, Helene and Correa, Adolfo and Cox, Nancy and DeMeo, Dawn L and Faraday, Nauder and Fornage, Myriam and Gerszten, Robert E and Hou, Lifang and Johnson, Andrew D and Jorgenson, Eric and Kaplan, Robert and Kooperberg, Charles and Kundu, Kousik and Laurie, Cecelia A and Lettre, Guillaume and Lewis, Joshua P and Li, Bingshan and Li, Yun and Lloyd-Jones, Donald M and Loos, Ruth J F and Manichaikul, Ani and Meyers, Deborah A and Mitchell, Braxton D and Morrison, Alanna C and Ngo, Debby and Nickerson, Deborah A and Nongmaithem, Suraj and North, Kari E and O{\textquoteright}Connell, Jeffrey R and Ortega, Victor E and Pankratz, Nathan and Perry, James A and Psaty, Bruce M and Rich, Stephen S and Soranzo, Nicole and Rotter, Jerome I and Silverman, Edwin K and Smith, Nicholas L and Tang, Hua and Tracy, Russell P and Thornton, Timothy A and Vasan, Ramachandran S and Zein, Joe and Mathias, Rasika A and Reiner, Alexander P and Auer, Paul L} } @article {9156, title = {Pooled Cohort Probability Score for Subclinical Airflow Obstruction.}, journal = {Ann Am Thorac Soc}, volume = {19}, year = {2022}, month = {2022 08}, pages = {1294-1304}, abstract = {

Early detection of chronic obstructive pulmonary disease (COPD) is a public health priority. Airflow obstruction is the single most important risk factor for adverse COPD outcomes, but spirometry is not routinely recommended for screening. To describe the burden of subclinical airflow obstruction (SAO) and to develop a probability score for SAO to inform potential detection and prevention programs. Lung function and clinical data were harmonized and pooled across nine U.S. general population cohorts. Adults with respiratory symptoms, inhaler use, or prior diagnosis of COPD or asthma were excluded. A probability score for prevalent SAO (forced expiratory volume in 1 second/forced vital capacity < 0.70) was developed via hierarchical group-lasso regularization from clinical variables in strata of sex and smoking status, and its discriminative accuracy for SAO was assessed in the pooled cohort as well as in an external validation cohort (NHANES [National Health and Nutrition Examination Survey] 2011-2012). Incident hospitalizations and deaths due to COPD (respiratory events) were defined by adjudication or administrative criteria in four of nine cohorts. Of 33,546 participants (mean age 52 yr, 54\% female, 44\% non-Hispanic White), 4,424 (13.2\%) had prevalent SAO. The incidence of respiratory events ( = 14,024) was threefold higher in participants with SAO versus those without (152 vs. 39 events/10,000 person-years). The probability score, which was based on six commonly available variables (age, sex, race and/or ethnicity, body mass index, smoking status, and smoking pack-years) was well calibrated and showed excellent discrimination in both the testing sample (C-statistic, 0.81; 95\% confidence interval [CI], 0.80-0.82) and in NHANES (C-statistic, 0.83; 95\% CI, 0.80-0.86). Among participants with predicted probabilities ⩾ 15\%, 3.2 would need to undergo spirometry to detect one case of SAO. Adults with SAO demonstrate excess respiratory hospitalization and mortality. A probability score for SAO using commonly available clinical risk factors may be suitable for targeting screening and primary prevention strategies.

}, keywords = {Adult, Female, Forced Expiratory Volume, Humans, Lung, Male, Middle Aged, Nutrition Surveys, Pulmonary Disease, Chronic Obstructive, Risk Factors, Spirometry, Vital Capacity}, issn = {2325-6621}, doi = {10.1513/AnnalsATS.202109-1020OC}, author = {Bhatt, Surya P and Balte, Pallavi P and Schwartz, Joseph E and Jaeger, Byron C and Cassano, Patricia A and Chaves, Paulo H and Couper, David and Jacobs, David R and Kalhan, Ravi and Kaplan, Robert and Lloyd-Jones, Donald and Newman, Anne B and O{\textquoteright}Connor, George and Sanders, Jason L and Smith, Benjamin M and Sun, Yifei and Umans, Jason G and White, Wendy B and Yende, Sachin and Oelsner, Elizabeth C} } @article {9109, title = {The Value of Rare Genetic Variation in the Prediction of Common Obesity in European Ancestry Populations.}, journal = {Front Endocrinol (Lausanne)}, volume = {13}, year = {2022}, month = {2022}, pages = {863893}, abstract = {

Polygenic risk scores (PRSs) aggregate the effects of genetic variants across the genome and are used to predict risk of complex diseases, such as obesity. Current PRSs only include common variants (minor allele frequency (MAF) >=1\%), whereas the contribution of rare variants in PRSs to predict disease remains unknown. Here, we examine whether augmenting the standard common variant PRS (PRS) with a rare variant PRS (PRS) improves prediction of obesity. We used genome-wide genotyped and imputed data on 451,145 European-ancestry participants of the UK Biobank, as well as whole exome sequencing (WES) data on 184,385 participants. We performed single variant analyses (for both common and rare variants) and gene-based analyses (for rare variants) for association with BMI (kg/m), obesity (BMI >= 30 kg/m), and extreme obesity (BMI >= 40 kg/m). We built PRSs and PRSs using a range of methods (Clumping+Thresholding [C+T], PRS-CS, lassosum, gene-burden test). We selected the best-performing PRSs and assessed their performance in 36,757 European-ancestry unrelated participants with whole genome sequencing (WGS) data from the Trans-Omics for Precision Medicine (TOPMed) program. The best-performing PRS explained 10.1\% of variation in BMI, and 18.3\% and 22.5\% of the susceptibility to obesity and extreme obesity, respectively, whereas the best-performing PRS explained 1.49\%, and 2.97\% and 3.68\%, respectively. The PRS was associated with an increased risk of obesity and extreme obesity (OR = 1.37 per SD, = 1.7x10; OR = 1.55 per SD, = 3.8x10), which was attenuated, after adjusting for PRS (OR = 1.08 per SD, = 9.8x10; OR= 1.09 per SD, = 0.02). When PRS and PRS are combined, the increase in explained variance attributed to PRS was small (incremental Nagelkerke R = 0.24\% for obesity and 0.51\% for extreme obesity). Consistently, combining PRS to PRS provided little improvement to the prediction of obesity (PRS AUC = 0.591; PRS AUC = 0.708; PRS AUC = 0.710). In summary, while rare variants show convincing association with BMI, obesity and extreme obesity, the PRS provides limited improvement over PRS in the prediction of obesity risk, based on these large populations.

}, keywords = {Gene Frequency, Genetic Variation, Genome-Wide Association Study, Humans, Obesity, Whole Genome Sequencing}, issn = {1664-2392}, doi = {10.3389/fendo.2022.863893}, author = {Wang, Zhe and Choi, Shing Wan and Chami, Nathalie and Boerwinkle, Eric and Fornage, Myriam and Redline, Susan and Bis, Joshua C and Brody, Jennifer A and Psaty, Bruce M and Kim, Wonji and McDonald, Merry-Lynn N and Regan, Elizabeth A and Silverman, Edwin K and Liu, Ching-Ti and Vasan, Ramachandran S and Kalyani, Rita R and Mathias, Rasika A and Yanek, Lisa R and Arnett, Donna K and Justice, Anne E and North, Kari E and Kaplan, Robert and Heckbert, Susan R and de Andrade, Mariza and Guo, Xiuqing and Lange, Leslie A and Rich, Stephen S and Rotter, Jerome I and Ellinor, Patrick T and Lubitz, Steven A and Blangero, John and Shoemaker, M Benjamin and Darbar, Dawood and Gladwin, Mark T and Albert, Christine M and Chasman, Daniel I and Jackson, Rebecca D and Kooperberg, Charles and Reiner, Alexander P and O{\textquoteright}Reilly, Paul F and Loos, Ruth J F} } @article {9387, title = {Aberrant activation of TCL1A promotes stem cell expansion in clonal haematopoiesis.}, journal = {Nature}, volume = {616}, year = {2023}, month = {2023 Apr}, pages = {755-763}, abstract = {

Mutations in a diverse set of driver genes increase the fitness of haematopoietic stem cells (HSCs), leading to clonal haematopoiesis. These lesions are precursors for blood cancers, but the basis of their fitness advantage remains largely unknown, partly owing to a paucity of large cohorts in which the clonal expansion rate has been assessed by longitudinal sampling. Here, to circumvent this limitation, we developed a method to infer the expansion rate from data from a single time point. We applied this method to 5,071 people with clonal haematopoiesis. A genome-wide association study revealed that a common inherited polymorphism in the TCL1A promoter was associated with a slower expansion rate in clonal haematopoiesis overall, but the effect varied by driver gene. Those carrying this protective allele exhibited markedly reduced growth rates or prevalence of clones with driver mutations in TET2, ASXL1, SF3B1 and SRSF2, but~this effect was not seen in~clones with~driver mutations in DNMT3A. TCL1A was not expressed in normal or DNMT3A-mutated HSCs, but the introduction of mutations in TET2 or ASXL1 led to the expression of TCL1A protein and the expansion of HSCs in vitro. The protective allele restricted TCL1A expression and expansion of mutant HSCs, as did experimental~knockdown of TCL1A expression. Forced expression of TCL1A promoted the expansion of human HSCs in vitro and mouse HSCs in vivo. Our results indicate that the fitness advantage of several commonly mutated driver genes in clonal haematopoiesis may be mediated by TCL1A activation.

}, keywords = {Alleles, Animals, Clonal Hematopoiesis, Genome-Wide Association Study, Hematopoiesis, Hematopoietic Stem Cells, Humans, Mice, Mutation, Promoter Regions, Genetic}, issn = {1476-4687}, doi = {10.1038/s41586-023-05806-1}, author = {Weinstock, Joshua S and Gopakumar, Jayakrishnan and Burugula, Bala Bharathi and Uddin, Md Mesbah and Jahn, Nikolaus and Belk, Julia A and Bouzid, Hind and Daniel, Bence and Miao, Zhuang and Ly, Nghi and Mack, Taralynn M and Luna, Sofia E and Prothro, Katherine P and Mitchell, Shaneice R and Laurie, Cecelia A and Broome, Jai G and Taylor, Kent D and Guo, Xiuqing and Sinner, Moritz F and von Falkenhausen, Aenne S and K{\"a}{\"a}b, Stefan and Shuldiner, Alan R and O{\textquoteright}Connell, Jeffrey R and Lewis, Joshua P and Boerwinkle, Eric and Barnes, Kathleen C and Chami, Nathalie and Kenny, Eimear E and Loos, Ruth J F and Fornage, Myriam and Hou, Lifang and Lloyd-Jones, Donald M and Redline, Susan and Cade, Brian E and Psaty, Bruce M and Bis, Joshua C and Brody, Jennifer A and Silverman, Edwin K and Yun, Jeong H and Qiao, Dandi and Palmer, Nicholette D and Freedman, Barry I and Bowden, Donald W and Cho, Michael H and DeMeo, Dawn L and Vasan, Ramachandran S and Yanek, Lisa R and Becker, Lewis C and Kardia, Sharon L R and Peyser, Patricia A and He, Jiang and Rienstra, Michiel and van der Harst, Pim and Kaplan, Robert and Heckbert, Susan R and Smith, Nicholas L and Wiggins, Kerri L and Arnett, Donna K and Irvin, Marguerite R and Tiwari, Hemant and Cutler, Michael J and Knight, Stacey and Muhlestein, J Brent and Correa, Adolfo and Raffield, Laura M and Gao, Yan and de Andrade, Mariza and Rotter, Jerome I and Rich, Stephen S and Tracy, Russell P and Konkle, Barbara A and Johnsen, Jill M and Wheeler, Marsha M and Smith, J Gustav and Melander, Olle and Nilsson, Peter M and Custer, Brian S and Duggirala, Ravindranath and Curran, Joanne E and Blangero, John and McGarvey, Stephen and Williams, L Keoki and Xiao, Shujie and Yang, Mao and Gu, C Charles and Chen, Yii-Der Ida and Lee, Wen-Jane and Marcus, Gregory M and Kane, John P and Pullinger, Clive R and Shoemaker, M Benjamin and Darbar, Dawood and Roden, Dan M and Albert, Christine and Kooperberg, Charles and Zhou, Ying and Manson, JoAnn E and Desai, Pinkal and Johnson, Andrew D and Mathias, Rasika A and Blackwell, Thomas W and Abecasis, Goncalo R and Smith, Albert V and Kang, Hyun M and Satpathy, Ansuman T and Natarajan, Pradeep and Kitzman, Jacob O and Whitsel, Eric A and Reiner, Alexander P and Bick, Alexander G and Jaiswal, Siddhartha} } @article {9286, title = {Genetic control of mRNA splicing as a potential mechanism for incomplete penetrance of rare coding variants.}, journal = {bioRxiv}, year = {2023}, month = {2023 Jan 31}, abstract = {

Exonic variants present some of the strongest links between genotype and phenotype. However, these variants can have significant inter-individual pathogenicity differences, known as variable penetrance. In this study, we propose a model where genetically controlled mRNA splicing modulates the pathogenicity of exonic variants. By first cataloging exonic inclusion from RNA-seq data in GTEx v8, we find that pathogenic alleles are depleted on highly included exons. Using a large-scale phased WGS data from the TOPMed consortium, we observe that this effect may be driven by common splice-regulatory genetic variants, and that natural selection acts on haplotype configurations that reduce the transcript inclusion of putatively pathogenic variants, especially when limiting to haploinsufficient genes. Finally, we test if this effect may be relevant for autism risk using families from the Simons Simplex Collection, but find that splicing of pathogenic alleles has a penetrance reducing effect here as well. Overall, our results indicate that common splice-regulatory variants may play a role in reducing the damaging effects of rare exonic variants.

}, doi = {10.1101/2023.01.31.526505}, author = {Einson, Jonah and Glinos, Dafni and Boerwinkle, Eric and Castaldi, Peter and Darbar, Dawood and de Andrade, Mariza and Ellinor, Patrick and Fornage, Myriam and Gabriel, Stacey and Germer, Soren and Gibbs, Richard and Hersh, Craig P and Johnsen, Jill and Kaplan, Robert and Konkle, Barbara A and Kooperberg, Charles and Nassir, Rami and Loos, Ruth J F and Meyers, Deborah A and Mitchell, Braxton D and Psaty, Bruce and Vasan, Ramachandran S and Rich, Stephen S and Rienstra, Michael and Rotter, Jerome I and Saferali, Aabida and Shoemaker, M Benjamin and Silverman, Edwin and Smith, Albert Vernon and Mohammadi, Pejman and Castel, Stephane E and Iossifov, Ivan and Lappalainen, Tuuli} } @article {9419, title = {The genetic determinants of recurrent somatic mutations in 43,693 blood genomes.}, journal = {Sci Adv}, volume = {9}, year = {2023}, month = {2023 Apr 28}, pages = {eabm4945}, abstract = {

Nononcogenic somatic mutations are thought to be uncommon and inconsequential. To test this, we analyzed 43,693 National Heart, Lung and Blood Institute Trans-Omics for Precision Medicine blood whole genomes from 37 cohorts and identified 7131 non-missense somatic mutations that are recurrently mutated in at least 50 individuals. These recurrent non-missense somatic mutations (RNMSMs) are not clearly explained by other clonal phenomena such as clonal hematopoiesis. RNMSM prevalence increased with age, with an average 50-year-old having 27 RNMSMs. Inherited germline variation associated with RNMSM acquisition. These variants were found in genes involved in adaptive immune function, proinflammatory cytokine production, and lymphoid lineage commitment. In addition, the presence of eight specific RNMSMs associated with blood cell traits at effect sizes comparable to Mendelian genetic mutations. Overall, we found that somatic mutations in blood are an unexpectedly common phenomenon with ancestry-specific determinants and human health consequences.

}, keywords = {Germ-Line Mutation, Hematopoiesis, Humans, Middle Aged, Mutation, Mutation, Missense, Phenotype}, issn = {2375-2548}, doi = {10.1126/sciadv.abm4945}, author = {Weinstock, Joshua S and Laurie, Cecelia A and Broome, Jai G and Taylor, Kent D and Guo, Xiuqing and Shuldiner, Alan R and O{\textquoteright}Connell, Jeffrey R and Lewis, Joshua P and Boerwinkle, Eric and Barnes, Kathleen C and Chami, Nathalie and Kenny, Eimear E and Loos, Ruth J F and Fornage, Myriam and Redline, Susan and Cade, Brian E and Gilliland, Frank D and Chen, Zhanghua and Gauderman, W James and Kumar, Rajesh and Grammer, Leslie and Schleimer, Robert P and Psaty, Bruce M and Bis, Joshua C and Brody, Jennifer A and Silverman, Edwin K and Yun, Jeong H and Qiao, Dandi and Weiss, Scott T and Lasky-Su, Jessica and DeMeo, Dawn L and Palmer, Nicholette D and Freedman, Barry I and Bowden, Donald W and Cho, Michael H and Vasan, Ramachandran S and Johnson, Andrew D and Yanek, Lisa R and Becker, Lewis C and Kardia, Sharon and He, Jiang and Kaplan, Robert and Heckbert, Susan R and Smith, Nicholas L and Wiggins, Kerri L and Arnett, Donna K and Irvin, Marguerite R and Tiwari, Hemant and Correa, Adolfo and Raffield, Laura M and Gao, Yan and de Andrade, Mariza and Rotter, Jerome I and Rich, Stephen S and Manichaikul, Ani W and Konkle, Barbara A and Johnsen, Jill M and Wheeler, Marsha M and Custer, Brian S and Duggirala, Ravindranath and Curran, Joanne E and Blangero, John and Gui, Hongsheng and Xiao, Shujie and Williams, L Keoki and Meyers, Deborah A and Li, Xingnan and Ortega, Victor and McGarvey, Stephen and Gu, C Charles and Chen, Yii-Der Ida and Lee, Wen-Jane and Shoemaker, M Benjamin and Darbar, Dawood and Roden, Dan and Albert, Christine and Kooperberg, Charles and Desai, Pinkal and Blackwell, Thomas W and Abecasis, Goncalo R and Smith, Albert V and Kang, Hyun M and Mathias, Rasika and Natarajan, Pradeep and Jaiswal, Siddhartha and Reiner, Alexander P and Bick, Alexander G} } @article {9586, title = {Machine learning models for blood pressure phenotypes combining multiple polygenic risk scores.}, journal = {medRxiv}, year = {2023}, month = {2023 Dec 14}, abstract = {

We construct non-linear machine learning (ML) prediction models for systolic and diastolic blood pressure (SBP, DBP) using demographic and clinical variables and polygenic risk scores (PRSs). We developed a two-model ensemble, consisting of a baseline model, where prediction is based on demographic and clinical variables only, and a genetic model, where we also include PRSs. We evaluate the use of a linear versus a non-linear model at both the baseline and the genetic model levels and assess the improvement in performance when incorporating multiple PRSs. We report the ensemble model{\textquoteright}s performance as percentage variance explained (PVE) on a held-out test dataset. A non-linear baseline model improved the PVEs from 28.1\% to 30.1\% (SBP) and 14.3\% to 17.4\% (DBP) compared with a linear baseline model. Including seven PRSs in the genetic model computed based on the largest available GWAS of SBP/DBP improved the genetic model PVE from 4.8\% to 5.1\% (SBP) and 4.7\% to 5\% (DBP) compared to using a single PRS. Adding additional 14 PRSs computed based on two independent GWASs further increased the genetic model PVE to 6.3\% (SBP) and 5.7\% (DBP). PVE differed across self-reported race/ethnicity groups, with primarily all non-White groups benefitting from the inclusion of additional PRSs.

}, doi = {10.1101/2023.12.13.23299909}, author = {Hrytsenko, Yana and Shea, Benjamin and Elgart, Michael and Kurniansyah, Nuzulul and Lyons, Genevieve and Morrison, Alanna C and Carson, April P and Haring, Bernhard and Mitchel, Braxton D and Psaty, Bruce M and Jaeger, Byron C and Gu, C Charles and Kooperberg, Charles and Levy, Daniel and Lloyd-Jones, Donald and Choi, Eunhee and Brody, Jennifer A and Smith, Jennifer A and Rotter, Jerome I and Moll, Matthew and Fornage, Myriam and Simon, Noah and Castaldi, Peter and Casanova, Ramon and Chung, Ren-Hua and Kaplan, Robert and Loos, Ruth J F and Kardia, Sharon L R and Rich, Stephen S and Redline, Susan and Kelly, Tanika and O{\textquoteright}Connor, Timothy and Zhao, Wei and Kim, Wonji and Guo, Xiuqing and Der Ida Chen, Yii and Sofer, Tamar} } @article {9412, title = {Multi-ancestry transcriptome-wide association analyses yield insights into tobacco use biology and drug repurposing.}, journal = {Nat Genet}, volume = {55}, year = {2023}, month = {2023 Feb}, pages = {291-300}, abstract = {

Most transcriptome-wide association studies (TWASs) so far focus on European ancestry and lack diversity. To overcome this limitation, we aggregated genome-wide association study (GWAS) summary statistics, whole-genome sequences and expression quantitative trait locus (eQTL) data from diverse ancestries. We developed a new approach, TESLA (multi-ancestry integrative study using an optimal linear combination of association statistics), to integrate an eQTL dataset with a multi-ancestry GWAS. By exploiting shared phenotypic effects between ancestries and accommodating potential effect heterogeneities, TESLA improves power over other TWAS methods. When applied to tobacco use phenotypes, TESLA identified 273 new genes, up to 55\% more compared with alternative TWAS methods. These hits and subsequent fine mapping using TESLA point to target genes with biological relevance. In silico drug-repurposing analyses highlight several drugs with known efficacy, including dextromethorphan and galantamine, and new drugs such as muscle relaxants that may be repurposed for treating nicotine addiction.

}, keywords = {Biology, Drug Repositioning, Genetic Predisposition to Disease, Genome-Wide Association Study, Humans, Polymorphism, Single Nucleotide, Tobacco Use, Transcriptome}, issn = {1546-1718}, doi = {10.1038/s41588-022-01282-x}, author = {Chen, Fang and Wang, Xingyan and Jang, Seon-Kyeong and Quach, Bryan C and Weissenkampen, J Dylan and Khunsriraksakul, Chachrit and Yang, Lina and Sauteraud, Renan and Albert, Christine M and Allred, Nicholette D D and Arnett, Donna K and Ashley-Koch, Allison E and Barnes, Kathleen C and Barr, R Graham and Becker, Diane M and Bielak, Lawrence F and Bis, Joshua C and Blangero, John and Boorgula, Meher Preethi and Chasman, Daniel I and Chavan, Sameer and Chen, Yii-der I and Chuang, Lee-Ming and Correa, Adolfo and Curran, Joanne E and David, Sean P and Fuentes, Lisa de Las and Deka, Ranjan and Duggirala, Ravindranath and Faul, Jessica D and Garrett, Melanie E and Gharib, Sina A and Guo, Xiuqing and Hall, Michael E and Hawley, Nicola L and He, Jiang and Hobbs, Brian D and Hokanson, John E and Hsiung, Chao A and Hwang, Shih-Jen and Hyde, Thomas M and Irvin, Marguerite R and Jaffe, Andrew E and Johnson, Eric O and Kaplan, Robert and Kardia, Sharon L R and Kaufman, Joel D and Kelly, Tanika N and Kleinman, Joel E and Kooperberg, Charles and Lee, I-Te and Levy, Daniel and Lutz, Sharon M and Manichaikul, Ani W and Martin, Lisa W and Marx, Olivia and McGarvey, Stephen T and Minster, Ryan L and Moll, Matthew and Moussa, Karine A and Naseri, Take and North, Kari E and Oelsner, Elizabeth C and Peralta, Juan M and Peyser, Patricia A and Psaty, Bruce M and Rafaels, Nicholas and Raffield, Laura M and Reupena, Muagututi{\textquoteright}a Sefuiva and Rich, Stephen S and Rotter, Jerome I and Schwartz, David A and Shadyab, Aladdin H and Sheu, Wayne H-H and Sims, Mario and Smith, Jennifer A and Sun, Xiao and Taylor, Kent D and Telen, Marilyn J and Watson, Harold and Weeks, Daniel E and Weir, David R and Yanek, Lisa R and Young, Kendra A and Young, Kristin L and Zhao, Wei and Hancock, Dana B and Jiang, Bibo and Vrieze, Scott and Liu, Dajiang J} } @article {9851, title = {The expected polygenic risk score (ePRS) framework: an equitable metric for quantifying polygenetic risk via modeling of ancestral makeup.}, journal = {medRxiv}, year = {2024}, month = {2024 Dec 20}, abstract = {

Polygenic risk scores (PRSs) depend on genetic ancestry due to differences in allele frequencies between ancestral populations. This leads to implementation challenges in diverse populations. We propose a framework to calibrate PRS based on ancestral makeup. We define a metric called "expected PRS" (ePRS), the expected value of a PRS based on one{\textquoteright}s global or local admixture patterns. We further define the "residual PRS" (rPRS), measuring the deviation of the PRS from the ePRS. Simulation studies confirm that it suffices to adjust for ePRS to obtain nearly unbiased estimates of the PRS-outcome association without further adjusting for PCs. Using the TOPMed dataset, the estimated effect size of the rPRS adjusting for the ePRS is similar to the estimated effect of the PRS adjusting for genetic PCs. Similarly, we applied the ePRS framework to six cardiovascular-related traits in the All of Us dataset, and the results are consistent with those from the TOPMed analysis. The ePRS framework can protect from population stratification in association analysis and provide an equitable strategy to quantify genetic risk across diverse populations.

}, doi = {10.1101/2024.03.05.24303738}, author = {Huang, Yu-Jyun and Kurniansyah, Nuzulul and Goodman, Matthew O and Spitzer, Brian W and Wang, Jiongming and Stilp, Adrienne and Laurie, Cecelia and de Vries, Paul S and Chen, Han and Min, Yuan-I and Sims, Mario and Peloso, Gina M and Guo, Xiuqing and Bis, Joshua C and Brody, Jennifer A and Raffield, Laura M and Smith, Jennifer A and Zhao, Wei and Rotter, Jerome I and Rich, Stephen S and Redline, Susan and Fornage, Myriam and Kaplan, Robert and Franceschini, Nora and Levy, Daniel and Morrison, Alanna C and Boerwinkle, Eric and Smith, Nicholas L and Kooperberg, Charles and Psaty, Bruce M and Z{\"o}llner, Sebastian and Sofer, Tamar} } @article {9702, title = {Machine learning models for predicting blood pressure phenotypes by combining multiple polygenic risk scores.}, journal = {Sci Rep}, volume = {14}, year = {2024}, month = {2024 May 30}, pages = {12436}, abstract = {

We construct non-linear machine learning (ML) prediction models for systolic and diastolic blood pressure (SBP, DBP) using demographic and clinical variables and polygenic risk scores (PRSs). We developed a two-model ensemble, consisting of a baseline model, where prediction is based on demographic and clinical variables only, and a genetic model, where we also include PRSs. We evaluate the use of a linear versus a non-linear model at both the baseline and the genetic model levels and assess the improvement in performance when incorporating multiple PRSs. We report the ensemble model{\textquoteright}s performance as percentage variance explained (PVE) on a held-out test dataset. A non-linear baseline model improved the PVEs from 28.1 to 30.1\% (SBP) and 14.3\% to 17.4\% (DBP) compared with a linear baseline model. Including seven PRSs in the genetic model computed based on the largest available GWAS of SBP/DBP improved the genetic model PVE from 4.8 to 5.1\% (SBP) and 4.7 to 5\% (DBP) compared to using a single PRS. Adding additional 14 PRSs computed based on two independent GWASs further increased the genetic model PVE to 6.3\% (SBP) and 5.7\% (DBP). PVE differed across self-reported race/ethnicity groups, with primarily all non-White groups benefitting from the inclusion of additional PRSs. In summary, non-linear ML models improves BP prediction in models incorporating diverse populations.

}, keywords = {Blood Pressure, Female, Genetic Predisposition to Disease, Genetic Risk Score, Genome-Wide Association Study, Humans, Hypertension, Machine Learning, Male, Middle Aged, Models, Genetic, Multifactorial Inheritance, Phenotype, Risk Factors}, issn = {2045-2322}, doi = {10.1038/s41598-024-62945-9}, author = {Hrytsenko, Yana and Shea, Benjamin and Elgart, Michael and Kurniansyah, Nuzulul and Lyons, Genevieve and Morrison, Alanna C and Carson, April P and Haring, Bernhard and Mitchell, Braxton D and Psaty, Bruce M and Jaeger, Byron C and Gu, C Charles and Kooperberg, Charles and Levy, Daniel and Lloyd-Jones, Donald and Choi, Eunhee and Brody, Jennifer A and Smith, Jennifer A and Rotter, Jerome I and Moll, Matthew and Fornage, Myriam and Simon, Noah and Castaldi, Peter and Casanova, Ramon and Chung, Ren-Hua and Kaplan, Robert and Loos, Ruth J F and Kardia, Sharon L R and Rich, Stephen S and Redline, Susan and Kelly, Tanika and O{\textquoteright}Connor, Timothy and Zhao, Wei and Kim, Wonji and Guo, Xiuqing and Ida Chen, Yii-Der and Sofer, Tamar} }