04013nas a2200685 4500008004100000022001400041245016200055210006900217260001600286300001200302490000800314520192200322100002102244700001702265700002302282700001502305700002202320700002002342700001702362700001602379700001702395700001802412700001202430700002302442700002202465700002302487700002502510700001702535700001802552700001902570700002602589700002002615700002402635700002202659700002102681700002002702700002202722700002102744700001902765700002402784700002002808700002302828700002502851700002502876700002502901700002702926700001902953700002402972700002102996700002203017700002103039700002203060700001903082700002003101710003403121710003603155710003603191710006403227856003603291 2019 eng d a1537-660500aImpact of Rare and Common Genetic Variants on Diabetes Diagnosis by Hemoglobin A1c in Multi-Ancestry Cohorts: The Trans-Omics for Precision Medicine Program.0 aImpact of Rare and Common Genetic Variants on Diabetes Diagnosis c2019 Oct 03 a706-7180 v1053 a
Hemoglobin A1c (HbA1c) is widely used to diagnose diabetes and assess glycemic control in individuals with diabetes. However, nonglycemic determinants, including genetic variation, may influence how accurately HbA1c reflects underlying glycemia. Analyzing the NHLBI Trans-Omics for Precision Medicine (TOPMed) sequence data in 10,338 individuals from five studies and four ancestries (6,158 Europeans, 3,123 African-Americans, 650 Hispanics, and 407 East Asians), we confirmed five regions associated with HbA1c (GCK in Europeans and African-Americans, HK1 in Europeans and Hispanics, FN3K and/or FN3KRP in Europeans, and G6PD in African-Americans and Hispanics) and we identified an African-ancestry-specific low-frequency variant (rs1039215 in HBG2 and HBE1, minor allele frequency (MAF) = 0.03). The most associated G6PD variant (rs1050828-T, p.Val98Met, MAF = 12% in African-Americans, MAF = 2% in Hispanics) lowered HbA1c (-0.88% in hemizygous males, -0.34% in heterozygous females) and explained 23% of HbA1c variance in African-Americans and 4% in Hispanics. Additionally, we identified a rare distinct G6PD coding variant (rs76723693, p.Leu353Pro, MAF = 0.5%; -0.98% in hemizygous males, -0.46% in heterozygous females) and detected significant association with HbA1c when aggregating rare missense variants in G6PD. We observed similar magnitude and direction of effects for rs1039215 (HBG2) and rs76723693 (G6PD) in the two largest TOPMed African American cohorts, and we replicated the rs76723693 association in the UK Biobank African-ancestry participants. These variants in G6PD and HBG2 were monomorphic in the European and Asian samples. African or Hispanic ancestry individuals carrying G6PD variants may be underdiagnosed for diabetes when screened with HbA1c. Thus, assessment of these variants should be considered for incorporation into precision medicine approaches for diabetes diagnosis.
1 aSarnowski, Chloe1 aLeong, Aaron1 aRaffield, Laura, M1 aWu, Peitao1 ade Vries, Paul, S1 aDiCorpo, Daniel1 aGuo, Xiuqing1 aXu, Huichun1 aLiu, Yongmei1 aZheng, Xiuwen1 aHu, Yao1 aBrody, Jennifer, A1 aGoodarzi, Mark, O1 aHidalgo, Bertha, A1 aHighland, Heather, M1 aJain, Deepti1 aLiu, Ching-Ti1 aNaik, Rakhi, P1 aO'Connell, Jeffrey, R1 aPerry, James, A1 aPorneala, Bianca, C1 aSelvin, Elizabeth1 aWessel, Jennifer1 aPsaty, Bruce, M1 aCurran, Joanne, E1 aPeralta, Juan, M1 aBlangero, John1 aKooperberg, Charles1 aMathias, Rasika1 aJohnson, Andrew, D1 aReiner, Alexander, P1 aMitchell, Braxton, D1 aCupples, Adrienne, L1 aVasan, Ramachandran, S1 aCorrea, Adolfo1 aMorrison, Alanna, C1 aBoerwinkle, Eric1 aRotter, Jerome, I1 aRich, Stephen, S1 aManning, Alisa, K1 aDupuis, Josée1 aMeigs, James, B1 aTOPMed Diabetes Working Group1 aTOPMed Hematology Working Group1 aTOPMed Hemostasis Working Group1 aNational Heart, Lung, and Blood Institute TOPMed Consortium uhttps://chs-nhlbi.org/node/820505153nas a2201369 4500008004100000022001400041245011700055210006900172260001500241300000900256490000700265520124400272100002301516700001901539700002101558700002701579700002001606700002201626700001901648700002601667700002601693700002001719700002001739700002301759700003001782700001901812700002301831700002901854700002801883700002401911700001901935700001901954700001201973700002401985700002102009700001702030700002502047700001902072700001802091700001502109700002402124700002002148700002002168700002202188700002002210700001702230700002602247700002602273700002102299700002402320700002302344700001802367700001902385700002102404700001902425700002102444700002202465700002102487700002102508700001902529700002102548700002202569700002002591700002102611700002002632700001902652700002202671700001802693700002202711700002402733700002002757700002302777700002002800700001802820700002202838700001402860700002002874700002002894700002202914700002402936700001802960700001702978700002402995700002103019700001803040700002403058700002003082700002203102700002303124700003003147700002503177700002503202700002603227700001903253700001903272700002103291700002003312700001403332700001903346700002503365700001703390700001903407700002103426700002203447700002703469700002203496700002503518700002203543700002403565700002103589700002003610700002003630700002003650710006503670710001203735856003603747 2021 eng d a2041-172300aChromosome Xq23 is associated with lower atherogenic lipid concentrations and favorable cardiometabolic indices.0 aChromosome Xq23 is associated with lower atherogenic lipid conce c2021 04 12 a21820 v123 aAutosomal genetic analyses of blood lipids have yielded key insights for coronary heart disease (CHD). However, X chromosome genetic variation is understudied for blood lipids in large sample sizes. We now analyze genetic and blood lipid data in a high-coverage whole X chromosome sequencing study of 65,322 multi-ancestry participants and perform replication among 456,893 European participants. Common alleles on chromosome Xq23 are strongly associated with reduced total cholesterol, LDL cholesterol, and triglycerides (min P = 8.5 × 10), with similar effects for males and females. Chromosome Xq23 lipid-lowering alleles are associated with reduced odds for CHD among 42,545 cases and 591,247 controls (P = 1.7 × 10), and reduced odds for diabetes mellitus type 2 among 54,095 cases and 573,885 controls (P = 1.4 × 10). Although we observe an association with increased BMI, waist-to-hip ratio adjusted for BMI is reduced, bioimpedance analyses indicate increased gluteofemoral fat, and abdominal MRI analyses indicate reduced visceral adiposity. Co-localization analyses strongly correlate increased CHRDL1 gene expression, particularly in adipose tissue, with reduced concentrations of blood lipids.
1 aNatarajan, Pradeep1 aPampana, Akhil1 aGraham, Sarah, E1 aRuotsalainen, Sanni, E1 aPerry, James, A1 ade Vries, Paul, S1 aBroome, Jai, G1 aPirruccello, James, P1 aHonigberg, Michael, C1 aAragam, Krishna1 aWolford, Brooke1 aBrody, Jennifer, A1 aAntonacci-Fulton, Lucinda1 aArden, Moscati1 aAslibekyan, Stella1 aAssimes, Themistocles, L1 aBallantyne, Christie, M1 aBielak, Lawrence, F1 aBis, Joshua, C1 aCade, Brian, E1 aDo, Ron1 aDoddapaneni, Harsha1 aEmery, Leslie, S1 aHung, Yi-Jen1 aIrvin, Marguerite, R1 aKhan, Alyna, T1 aLange, Leslie1 aLee, Jiwon1 aLemaitre, Rozenn, N1 aMartin, Lisa, W1 aMetcalf, Ginger1 aMontasser, May, E1 aMoon, Jee-Young1 aMuzny, Donna1 aO'Connell, Jeffrey, R1 aPalmer, Nicholette, D1 aPeralta, Juan, M1 aPeyser, Patricia, A1 aStilp, Adrienne, M1 aTsai, Michael1 aWang, Fei, Fei1 aWeeks, Daniel, E1 aYanek, Lisa, R1 aWilson, James, G1 aAbecasis, Goncalo1 aArnett, Donna, K1 aBecker, Lewis, C1 aBlangero, John1 aBoerwinkle, Eric1 aBowden, Donald, W1 aChang, Yi-Cheng1 aChen, Yii-der, I1 aChoi, Won, Jung1 aCorrea, Adolfo1 aCurran, Joanne, E1 aDaly, Mark, J1 aDutcher, Susan, K1 aEllinor, Patrick, T1 aFornage, Myriam1 aFreedman, Barry, I1 aGabriel, Stacey1 aGermer, Soren1 aGibbs, Richard, A1 aHe, Jiang1 aHveem, Kristian1 aJarvik, Gail, P1 aKaplan, Robert, C1 aKardia, Sharon, L R1 aKenny, Eimear1 aKim, Ryan, W1 aKooperberg, Charles1 aLaurie, Cathy, C1 aLee, Seonwook1 aLloyd-Jones, Don, M1 aLoos, Ruth, J F1 aLubitz, Steven, A1 aMathias, Rasika, A1 aMartinez, Karine, A Viaud1 aMcGarvey, Stephen, T1 aMitchell, Braxton, D1 aNickerson, Deborah, A1 aNorth, Kari, E1 aPalotie, Aarno1 aPark, Cheol, Joo1 aPsaty, Bruce, M1 aRao, D, C1 aRedline, Susan1 aReiner, Alexander, P1 aSeo, Daekwan1 aSeo, Jeong-Sun1 aSmith, Albert, V1 aTracy, Russell, P1 aVasan, Ramachandran, S1 aKathiresan, Sekar1 aCupples, Adrienne, L1 aRotter, Jerome, I1 aMorrison, Alanna, C1 aRich, Stephen, S1 aRipatti, Samuli1 aWiller, Cristen1 aPeloso, Gina, M1 aNHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium1 aFinnGen uhttps://chs-nhlbi.org/node/871104364nas 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/866405763nas 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/891405141nas 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/915805493nas 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/9484