04364nas 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 a
BACKGROUND: 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/866405976nas a2201393 4500008004100000022001400041245013400055210006900189260001600258300003400274520189200308100002102200700001402221700001702235700002202252700003002274700002502304700002502329700001502354700002302369700002302392700001702415700002002432700002202452700001302474700001902487700002402506700001902530700001802549700002302567700001902590700002602609700001602635700002302651700002402674700002102698700002902719700002202748700001602770700001902786700001802805700002102823700001802844700002602862700002202888700002102910700001802931700001602949700002002965700001902985700001803004700002403022700002503046700002203071700002103093700002203114700001703136700001703153700002003170700002103190700003303211700002403244700001703268700002203285700003403307700002503341700002203366700002103388700002103409700001903430700002203449700002003471700001903491700001403510700002503524700002103549700002503570700001703595700001403612700002303626700001803649700001703667700002203684700002103706700002203727700002403749700002103773700001903794700002203813700002103835700001903856700002603875700002103901700002003922700001903942700001903961700002503980700002004005700002304025700002404048700002204072700002304094700001404117700002004131700002004151700002004171700001904191700002104210700002204231700002404253700002104277700002504298700001804323700001704341700002104358700002404379710014304403856003604546 2022 eng d a1524-456300aInsights From a Large-Scale Whole-Genome Sequencing Study of Systolic Blood Pressure, Diastolic Blood Pressure, and Hypertension.0 aInsights From a LargeScale WholeGenome Sequencing Study of Systo c2022 Jun 02 a101161HYPERTENSIONAHA122193243 aBACKGROUND: The availability of whole-genome sequencing data in large studies has enabled the assessment of coding and noncoding variants across the allele frequency spectrum for their associations with blood pressure.
METHODS: We conducted a multiancestry whole-genome sequencing analysis of blood pressure among 51 456 Trans-Omics for Precision Medicine and Centers for Common Disease Genomics program participants (stage-1). Stage-2 analyses leveraged array data from UK Biobank (N=383 145), Million Veteran Program (N=318 891), and Reasons for Geographic and Racial Differences in Stroke (N=10 643) participants, along with whole-exome sequencing data from UK Biobank (N=199 631) participants.
RESULTS: Two blood pressure signals achieved genome-wide significance in meta-analyses of stage-1 and stage-2 single variant findings (<5×10). Among them, a rare intergenic variant at novel locus, , was associated with lower systolic blood pressure in stage-1 (beta [SE]=-32.6 [6.0]; =4.99×10) but not stage-2 analysis (=0.11). Furthermore, a novel common variant at the known locus was suggestively associated with diastolic blood pressure in stage-1 (beta [SE]=-0.36 [0.07]; =4.18×10) and attained genome-wide significance in stage-2 (beta [SE]=-0.29 [0.03]; =7.28×10). Nineteen additional signals suggestively associated with blood pressure in meta-analysis of single and aggregate rare variant findings (<1×10 and <1×10, respectively).
DISCUSSION: We report one promising but unconfirmed rare variant for blood pressure and, more importantly, contribute insights for future blood pressure sequencing studies. Our findings suggest promise of aggregate analyses to complement single variant analysis strategies and the need for larger, diverse samples, and family studies to enable robust rare variant identification.
1 aKelly, Tanika, N1 aSun, Xiao1 aHe, Karen, Y1 aBrown, Michael, R1 aTaliun, Sarah, A Gagliano1 aHellwege, Jacklyn, N1 aIrvin, Marguerite, R1 aMi, Xuenan1 aBrody, Jennifer, A1 aFranceschini, Nora1 aGuo, Xiuqing1 aHwang, Shih-Jen1 ade Vries, Paul, S1 aGao, Yan1 aMoscati, Arden1 aNadkarni, Girish, N1 aYanek, Lisa, R1 aElfassy, Tali1 aSmith, Jennifer, A1 aChung, Ren-Hua1 aBeitelshees, Amber, L1 aPatki, Amit1 aAslibekyan, Stella1 aBlobner, Brandon, M1 aPeralta, Juan, M1 aAssimes, Themistocles, L1 aPalmas, Walter, R1 aLiu, Chunyu1 aBress, Adam, P1 aHuang, Zhijie1 aBecker, Lewis, C1 aHwa, Chii-Min1 aO'Connell, Jeffrey, R1 aCarlson, Jenna, C1 aWarren, Helen, R1 aDas, Sayantan1 aGiri, Ayush1 aMartin, Lisa, W1 aJohnson, Craig1 aFox, Ervin, R1 aBottinger, Erwin, P1 aRazavi, Alexander, C1 aVaidya, Dhananjay1 aChuang, Lee-Ming1 aChang, Yen-Pei, C1 aNaseri, Take1 aJain, Deepti1 aKang, Hyun, Min1 aHung, Adriana, M1 aSrinivasasainagendra, Vinodh1 aSnively, Beverly, M1 aGu, Dongfeng1 aMontasser, May, E1 aReupena, Muagututi'a, Sefuiva1 aHeavner, Benjamin, D1 aLeFaive, Jonathon1 aHixson, James, E1 aRice, Kenneth, M1 aWang, Fei, Fei1 aNielsen, Jonas, B1 aHuang, Jianfeng1 aKhan, Alyna, T1 aZhou, Wei1 aNierenberg, Jovia, L1 aLaurie, Cathy, C1 aArmstrong, Nicole, D1 aShi, Mengyao1 aPan, Yang1 aStilp, Adrienne, M1 aEmery, Leslie1 aWong, Quenna1 aHawley, Nicola, L1 aMinster, Ryan, L1 aCurran, Joanne, E1 aMunroe, Patricia, B1 aWeeks, Daniel, E1 aNorth, Kari, E1 aTracy, Russell, P1 aKenny, Eimear, E1 aShimbo, Daichi1 aChakravarti, Aravinda1 aRich, Stephen, S1 aReiner, Alex, P1 aBlangero, John1 aRedline, Susan1 aMitchell, Braxton, D1 aRao, Dabeeru, C1 aChen, Yii-Der, Ida1 aKardia, Sharon, L R1 aKaplan, Robert, C1 aMathias, Rasika, A1 aHe, Jiang1 aPsaty, Bruce, M1 aFornage, Myriam1 aLoos, Ruth, J F1 aCorrea, Adolfo1 aBoerwinkle, Eric1 aRotter, Jerome, I1 aKooperberg, Charles1 aEdwards, Todd, L1 aAbecasis, Goncalo, R1 aZhu, Xiaofeng1 aLevy, Daniel1 aArnett, Donna, K1 aMorrison, Alanna, C1 aNHLBI Trans-Omics for Precision Medicine TOPMed) Consortium, The Samoan Obesity, Lifestyle, and Genetic Adaptations Study (OLaGA) Group† uhttps://chs-nhlbi.org/node/909908881nas a2202605 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2022 eng d a1537-660500aRare coding variants in 35 genes associate with circulating lipid levels-A multi-ancestry analysis of 170,000 exomes.0 aRare coding variants in 35 genes associate with circulating lipi c2022 01 06 a81-960 v1093 aLarge-scale gene sequencing studies for complex traits have the potential to identify causal genes with therapeutic implications. We performed gene-based association testing of blood lipid levels with rare (minor allele frequency < 1%) predicted damaging coding variation by using sequence data from >170,000 individuals from multiple ancestries: 97,493 European, 30,025 South Asian, 16,507 African, 16,440 Hispanic/Latino, 10,420 East Asian, and 1,182 Samoan. We identified 35 genes associated with circulating lipid levels; some of these genes have not been previously associated with lipid levels when using rare coding variation from population-based samples. We prioritize 32 genes in array-based genome-wide association study (GWAS) loci based on aggregations of rare coding variants; three (EVI5, SH2B3, and PLIN1) had no prior association of rare coding variants with lipid levels. Most of our associated genes showed evidence of association among multiple ancestries. Finally, we observed an enrichment of gene-based associations for low-density lipoprotein cholesterol drug target genes and for genes closest to GWAS index single-nucleotide polymorphisms (SNPs). Our results demonstrate that gene-based associations can be beneficial for drug target development and provide evidence that the gene closest to the array-based GWAS index SNP is often the functional gene for blood lipid levels.
10aAlleles10aBlood Glucose10aCase-Control Studies10aComputational Biology10aDatabases, Genetic10aDiabetes Mellitus, Type 210aExome10aGenetic Predisposition to Disease10aGenetic Variation10aGenetics, Population10aGenome-Wide Association Study10aHumans10aLipid Metabolism10aLipids10aLiver10aMolecular Sequence Annotation10aMultifactorial Inheritance10aOpen Reading Frames10aPhenotype10aPolymorphism, Single Nucleotide1 aHindy, George1 aDornbos, Peter1 aChaffin, Mark, D1 aLiu, Dajiang, J1 aWang, Minxian1 aSelvaraj, Margaret, Sunitha1 aZhang, David1 aPark, Joseph1 aAguilar-Salinas, Carlos, A1 aAntonacci-Fulton, Lucinda1 aArdissino, Diego1 aArnett, Donna, K1 aAslibekyan, Stella1 aAtzmon, Gil1 aBallantyne, Christie, M1 aBarajas-Olmos, Francisco1 aBarzilai, Nir1 aBecker, Lewis, C1 aBielak, Lawrence, F1 aBis, Joshua, C1 aBlangero, John1 aBoerwinkle, Eric1 aBonnycastle, Lori, L1 aBottinger, Erwin1 aBowden, Donald, W1 aBown, Matthew, J1 aBrody, Jennifer, A1 aBroome, Jai, G1 aBurtt, Noel, P1 aCade, Brian, E1 aCenteno-Cruz, Federico1 aChan, Edmund1 aChang, Yi-Cheng1 aChen, Yii-der, I1 aCheng, Ching-Yu1 aChoi, Won, Jung1 aChowdhury, Raj1 aContreras-Cubas, Cecilia1 aCórdova, Emilio, J1 aCorrea, Adolfo1 aCupples, Adrienne, L1 aCurran, Joanne, E1 aDanesh, John1 ade Vries, Paul, S1 aDeFronzo, Ralph, A1 aDoddapaneni, Harsha1 aDuggirala, Ravindranath1 aDutcher, Susan, K1 aEllinor, Patrick, T1 aEmery, Leslie, S1 aFlorez, Jose, C1 aFornage, Myriam1 aFreedman, Barry, I1 aFuster, Valentin1 aGaray-Sevilla, Ma, Eugenia1 aGarcía-Ortiz, Humberto1 aGermer, Soren1 aGibbs, Richard, A1 aGieger, Christian1 aGlaser, Benjamin1 aGonzalez, Clicerio1 aGonzalez-Villalpando, Maria, Elena1 aGraff, Mariaelisa1 aGraham, Sarah, E1 aGrarup, Niels1 aGroop, Leif, C1 aGuo, Xiuqing1 aGupta, Namrata1 aHan, Sohee1 aHanis, Craig, L1 aHansen, Torben1 aHe, Jiang1 aHeard-Costa, Nancy, L1 aHung, Yi-Jen1 aHwang, Mi, Yeong1 aIrvin, Marguerite, R1 aIslas-Andrade, Sergio1 aJarvik, Gail, P1 aKang, Hyun, Min1 aKardia, Sharon, L R1 aKelly, Tanika1 aKenny, Eimear, E1 aKhan, Alyna, T1 aKim, Bong-Jo1 aKim, Ryan, W1 aKim, Young, Jin1 aKoistinen, Heikki, A1 aKooperberg, Charles1 aKuusisto, Johanna1 aKwak, Soo, Heon1 aLaakso, Markku1 aLange, Leslie, A1 aLee, Jiwon1 aLee, Juyoung1 aLee, Seonwook1 aLehman, Donna, M1 aLemaitre, Rozenn, N1 aLinneberg, Allan1 aLiu, Jianjun1 aLoos, Ruth, J F1 aLubitz, Steven, A1 aLyssenko, Valeriya1 aMa, Ronald, C W1 aMartin, Lisa, Warsinger1 aMartínez-Hernández, Angélica1 aMathias, Rasika, A1 aMcGarvey, Stephen, T1 aMcPherson, Ruth1 aMeigs, James, B1 aMeitinger, Thomas1 aMelander, Olle1 aMendoza-Caamal, Elvia1 aMetcalf, Ginger, A1 aMi, Xuenan1 aMohlke, Karen, L1 aMontasser, May, E1 aMoon, Jee-Young1 aMoreno-Macias, Hortensia1 aMorrison, Alanna, C1 aMuzny, Donna, M1 aNelson, Sarah, C1 aNilsson, Peter, M1 aO'Connell, Jeffrey, R1 aOrho-Melander, Marju1 aOrozco, Lorena1 aPalmer, Colin, N A1 aPalmer, Nicholette, D1 aPark, Cheol, Joo1 aPark, Kyong, Soo1 aPedersen, Oluf1 aPeralta, Juan, M1 aPeyser, Patricia, A1 aPost, Wendy, S1 aPreuss, Michael1 aPsaty, Bruce, M1 aQi, Qibin1 aRao, D, C1 aRedline, Susan1 aReiner, Alexander, P1 aRevilla-Monsalve, Cristina1 aRich, Stephen, S1 aSamani, Nilesh1 aSchunkert, Heribert1 aSchurmann, Claudia1 aSeo, Daekwan1 aSeo, Jeong-Sun1 aSim, Xueling1 aSladek, Rob1 aSmall, Kerrin, S1 aSo, Wing, Yee1 aStilp, Adrienne, M1 aTai, Shyong, E1 aTam, Claudia, H T1 aTaylor, Kent, D1 aTeo, Yik, Ying1 aThameem, Farook1 aTomlinson, Brian1 aTsai, Michael, Y1 aTuomi, Tiinamaija1 aTuomilehto, Jaakko1 aTusié-Luna, Teresa1 aUdler, Miriam, S1 avan Dam, Rob, M1 aVasan, Ramachandran, S1 aMartinez, Karine, A Viaud1 aWang, Fei, Fei1 aWang, Xuzhi1 aWatkins, Hugh1 aWeeks, Daniel, E1 aWilson, James, G1 aWitte, Daniel, R1 aWong, Tien-Yin1 aYanek, Lisa, R1 aKathiresan, Sekar1 aRader, Daniel, J1 aRotter, Jerome, I1 aBoehnke, Michael1 aMcCarthy, Mark, I1 aWiller, Cristen, J1 aNatarajan, Pradeep1 aFlannick, Jason, A1 aKhera, Amit, V1 aPeloso, Gina, M1 aAMP-T2D-GENES, Myocardial Infarction Genetics Consortium1 aNHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium1 aNHLBI TOPMed Lipids Working Group uhttps://chs-nhlbi.org/node/897504169nas a2200757 4500008004100000022001400041245010600055210006900161260001600230520193200246100001402178700001402192700001502206700001802221700001702239700002102256700001702277700002002294700002302314700001802337700001902355700002102374700002302395700002202418700002402440700002602464700001802490700002102508700001702529700002002546700002502566700002302591700001902614700002202633700002202655700002002677700002702697700001602724700002302740700002502763700002402788700002402812700002102836700001902857700002302876700002702899700002102926700001702947700002202964700001902986700002203005700002403027700002503051700002003076700002203096700002203118700002403140700002003164700002103184700001403205700002103219710006503240710004103305710002903346856003603375 2022 eng d a1460-208300aWhole-Exome Sequencing Study Identifies Four Novel Gene Loci Associated with Diabetic Kidney Disease.0 aWholeExome Sequencing Study Identifies Four Novel Gene Loci Asso c2022 Nov 293 aDiabetic kidney disease (DKD) is recognized as an important public health challenge. However, its genomic mechanisms are poorly understood. To identify rare variants for DKD, we conducted a whole-exome sequencing (WES) study leveraging large cohorts well-phenotyped for chronic kidney disease (CKD) and diabetes. Our two-stage whole-exome sequencing study included 4372 European and African ancestry participants from the Chronic Renal Insufficiency Cohort (CRIC) and Atherosclerosis Risk in Communities (ARIC) studies (stage-1) and 11 487 multi-ancestry Trans-Omics for Precision Medicine (TOPMed) participants (stage-2). Generalized linear mixed models, which accounted for genetic relatedness and adjusted for age, sex, and ancestry, were used to test associations between single variants and DKD. Gene-based aggregate rare variant analyses were conducted using an optimized sequence kernel association test (SKAT-O) implemented within our mixed model framework. We identified four novel exome-wide significant DKD-related loci through initiating diabetes. In single variant analyses, participants carrying a rare, in-frame insertion in the DIS3L2 gene (rs141560952) exhibited a 193-fold increased odds (95% confidence interval: 33.6, 1105) of DKD compared with non-carriers (P = 3.59 × 10-9). Likewise, each copy of a low-frequency KRT6B splice-site variant (rs425827) conferred a 5.31-fold higher odds (95% confidence interval: 3.06, 9.21) of DKD (P = 2.72 × 10-9). Aggregate gene-based analyses further identified ERAP2 (P = 4.03 × 10-8) and NPEPPS (P = 1.51 × 10-7), which are both expressed in the kidney and implicated in renin-angiotensin-aldosterone system modulated immune response. In the largest WES study of DKD, we identified novel rare variant loci attaining exome-wide significance. These findings provide new insights into the molecular mechanisms underlying DKD.
1 aPan, Yang1 aSun, Xiao1 aMi, Xuenan1 aHuang, Zhijie1 aHsu, Yenchih1 aHixson, James, E1 aMunzy, Donna1 aMetcalf, Ginger1 aFranceschini, Nora1 aTin, Adrienne1 aKöttgen, Anna1 aFrancis, Michael1 aBrody, Jennifer, A1 aKestenbaum, Bryan1 aSitlani, Colleen, M1 aMychaleckyj, Josyf, C1 aKramer, Holly1 aLange, Leslie, A1 aGuo, Xiuqing1 aHwang, Shih-Jen1 aIrvin, Marguerite, R1 aSmith, Jennifer, A1 aYanek, Lisa, R1 aVaidya, Dhananjay1 aChen, Yii-Der Ida1 aFornage, Myriam1 aLloyd-Jones, Donald, M1 aHou, Lifang1 aMathias, Rasika, A1 aMitchell, Braxton, D1 aPeyser, Patricia, A1 aKardia, Sharon, L R1 aArnett, Donna, K1 aCorrea, Adolfo1 aRaffield, Laura, M1 aVasan, Ramachandran, S1 aCupple, Adrienne1 aLevy, Daniel1 aKaplan, Robert, C1 aNorth, Kari, E1 aRotter, Jerome, I1 aKooperberg, Charles1 aReiner, Alexander, P1 aPsaty, Bruce, M1 aTracy, Russell, P1 aGibbs, Richard, A1 aMorrison, Alanna, C1 aFeldman, Harold1 aBoerwinkle, Eric1 aHe, Jiang1 aKelly, Tanika, N1 aNHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium1 aTOPMed Kidney Function Working Group1 aCRIC Study Investigators uhttps://chs-nhlbi.org/node/9258