Defining the role of common variation in the genomic and biological architecture of adult human height.
- Authors
- Wood, Andrew R; Esko, Tonu; Yang, Jian; Vedantam, Sailaja; Pers, Tune H; Gustafsson, Stefan; Chu, Audrey Y; Estrada, Karol; Luan, Jian'an; Kutalik, ZoltΓ‘n; Amin, Najaf; Buchkovich, Martin L; Croteau-Chonka, Damien C; Day, Felix R; Duan, Yanan; Fall, Tove; Fehrmann, Rudolf; Ferreira, Teresa; Jackson, Anne U; Karjalainen, Juha; Lo, Ken Sin; Locke, Adam E; MΓ€gi, Reedik; Mihailov, Evelin; Porcu, Eleonora; Randall, Joshua C; Scherag, AndrΓ©; Vinkhuyzen, Anna A E; Westra, Harm-Jan; Winkler, Thomas W; Workalemahu, Tsegaselassie; Zhao, Jing Hua; Absher, Devin; Albrecht, Eva; Anderson, Denise; Baron, Jeffrey; Beekman, Marian; Demirkan, Ayse; Ehret, Georg B; Feenstra, Bjarke; Feitosa, Mary F; Fischer, Krista; Fraser, Ross M; Goel, Anuj; Gong, Jian; Justice, Anne E; Kanoni, Stavroula; Kleber, Marcus E; Kristiansson, Kati; Lim, Unhee; Lotay, Vaneet; Lui, Julian C; Mangino, Massimo; Mateo Leach, Irene; Medina-Gomez, Carolina; Nalls, Michael A; Nyholt, Dale R; Palmer, Cameron D; Pasko, Dorota; Pechlivanis, Sonali; Prokopenko, Inga; Ried, Janina S; Ripke, Stephan; Shungin, Dmitry; StancΓ‘kovΓ‘, Alena; Strawbridge, Rona J; Sung, Yun Ju; Tanaka, Toshiko; Teumer, Alexander; Trompet, Stella; van der Laan, Sander W; van Setten, Jessica; Van Vliet-Ostaptchouk, Jana V; Wang, Zhaoming; Yengo, LoΓ―c; Zhang, Weihua; Afzal, Uzma; ArnlΓΆv, Johan; Arscott, Gillian M; Bandinelli, Stefania; Barrett, Amy; Bellis, Claire; Bennett, Amanda J; Berne, Christian; BlΓΌher, Matthias; Bolton, Jennifer L; BΓΆttcher, Yvonne; Boyd, Heather A; Bruinenberg, Marcel; Buckley, Brendan M; Buyske, Steven; Caspersen, Ida H; Chines, Peter S; Clarke, Robert; Claudi-Boehm, Simone; Cooper, Matthew; Daw, E Warwick; De Jong, Pim A; Deelen, Joris; Delgado, Graciela; Denny, Josh C; Dhonukshe-Rutten, Rosalie; Dimitriou, Maria; Doney, Alex S F; DΓΆrr, Marcus; Eklund, Niina; Eury, Elodie; Folkersen, Lasse; Garcia, Melissa E; Geller, Frank; Giedraitis, Vilmantas; Go, Alan S; Grallert, Harald; Grammer, Tanja B; GrΓ€Γler, JΓΌrgen; GrΓΆnberg, Henrik; de Groot, Lisette C P G M; Groves, Christopher J; Haessler, Jeffrey; Hall, Per; Haller, Toomas; Hallmans, Goran; Hannemann, Anke; Hartman, Catharina A; Hassinen, Maija; Hayward, Caroline; Heard-Costa, Nancy L; Helmer, Quinta; Hemani, Gibran; Henders, Anjali K; Hillege, Hans L; Hlatky, Mark A; Hoffmann, Wolfgang; Hoffmann, Per; Holmen, Oddgeir; Houwing-Duistermaat, Jeanine J; Illig, Thomas; Isaacs, Aaron; James, Alan L; Jeff, Janina; Johansen, Berit; Johansson, Γ sa; Jolley, Jennifer; Juliusdottir, Thorhildur; Junttila, Juhani; Kho, Abel N; Kinnunen, Leena; Klopp, Norman; Kocher, Thomas; Kratzer, Wolfgang; Lichtner, Peter; Lind, Lars; LindstrΓΆm, Jaana; Lobbens, StΓ©phane; Lorentzon, Mattias; Lu, Yingchang; Lyssenko, Valeriya; Magnusson, Patrik K E; Mahajan, Anubha; Maillard, Marc; McArdle, Wendy L; McKenzie, Colin A; McLachlan, Stela; McLaren, Paul J; Menni, Cristina; Merger, Sigrun; Milani, Lili; Moayyeri, Alireza; Monda, Keri L; Morken, Mario A; MΓΌller, Gabriele; MΓΌller-Nurasyid, Martina; Musk, Arthur W; Narisu, Narisu; Nauck, Matthias; Nolte, Ilja M; NΓΆthen, Markus M; Oozageer, Laticia; Pilz, Stefan; Rayner, Nigel W; Renstrom, Frida; Robertson, Neil R; Rose, Lynda M; Roussel, Ronan; Sanna, Serena; Scharnagl, Hubert; Scholtens, Salome; Schumacher, Fredrick R; Schunkert, Heribert; Scott, Robert A; Sehmi, Joban; Seufferlein, Thomas; Shi, Jianxin; Silventoinen, Karri; Smit, Johannes H; Smith, Albert Vernon; Smolonska, Joanna; Stanton, Alice V; Stirrups, Kathleen; Stott, David J; Stringham, Heather M; SundstrΓΆm, Johan; Swertz, Morris A; SyvΓ€nen, Ann-Christine; Tayo, Bamidele O; Thorleifsson, Gudmar; Tyrer, Jonathan P; van Dijk, Suzanne; van Schoor, Natasja M; van der Velde, Nathalie; van Heemst, Diana; van Oort, Floor V A; Vermeulen, Sita H; Verweij, Niek; Vonk, Judith M; Waite, Lindsay L; Waldenberger, Melanie; Wennauer, Roman; Wilkens, Lynne R; Willenborg, Christina; Wilsgaard, Tom; Wojczynski, Mary K; Wong, Andrew; Wright, Alan F; Zhang, Qunyuan; Arveiler, Dominique; Bakker, Stephan J L; Beilby, John; Bergman, Richard N; Bergmann, Sven; Biffar, Reiner; Blangero, John; Boomsma, Dorret I; Bornstein, Stefan R; Bovet, Pascal; Brambilla, Paolo; Brown, Morris J; Campbell, Harry; Caulfield, Mark J; Chakravarti, Aravinda; Collins, Rory; Collins, Francis S; Crawford, Dana C; Cupples, L Adrienne; Danesh, John; de Faire, Ulf; den Ruijter, Hester M; Erbel, Raimund; Erdmann, Jeanette; Eriksson, Johan G; Farrall, Martin; Ferrannini, Ele; FerriΓ¨res, Jean; Ford, Ian; Forouhi, Nita G; Forrester, Terrence; Gansevoort, Ron T; Gejman, Pablo V; Gieger, Christian; Golay, Alain; Gottesman, Omri; Gudnason, Vilmundur; Gyllensten, Ulf; Haas, David W; Hall, Alistair S; Harris, Tamara B; Hattersley, Andrew T; Heath, Andrew C; Hengstenberg, Christian; Hicks, Andrew A; Hindorff, Lucia A; Hingorani, Aroon D; Hofman, Albert; Hovingh, G Kees; Humphries, Steve E; Hunt, Steven C; Hypponen, Elina; Jacobs, Kevin B; Jarvelin, Marjo-Riitta; Jousilahti, Pekka; Jula, Antti M; Kaprio, Jaakko; Kastelein, John J P; Kayser, Manfred; Kee, Frank; Keinanen-Kiukaanniemi, Sirkka M; Kiemeney, Lambertus A; Kooner, Jaspal S; Kooperberg, Charles; Koskinen, Seppo; Kovacs, Peter; Kraja, Aldi T; Kumari, Meena; Kuusisto, Johanna; Lakka, Timo A; Langenberg, Claudia; Le Marchand, Loic; LehtimΓ€ki, Terho; Lupoli, Sara; Madden, Pamela A F; MΓ€nnistΓΆ, Satu; Manunta, Paolo; Marette, AndrΓ©; Matise, Tara C; McKnight, Barbara; Meitinger, Thomas; Moll, Frans L; Montgomery, Grant W; Morris, Andrew D; Morris, Andrew P; Murray, Jeffrey C; Nelis, Mari; Ohlsson, Claes; Oldehinkel, Albertine J; Ong, Ken K; Ouwehand, Willem H; Pasterkamp, Gerard; Peters, Annette; Pramstaller, Peter P; Price, Jackie F; Qi, Lu; Raitakari, Olli T; Rankinen, Tuomo; Rao, D C; Rice, Treva K; Ritchie, Marylyn; Rudan, Igor; Salomaa, Veikko; Samani, Nilesh J; Saramies, Jouko; Sarzynski, Mark A; Schwarz, Peter E H; Sebert, Sylvain; Sever, Peter; Shuldiner, Alan R; Sinisalo, Juha; Steinthorsdottir, Valgerdur; Stolk, Ronald P; Tardif, Jean-Claude; TΓΆnjes, Anke; Tremblay, Angelo; Tremoli, Elena; Virtamo, Jarmo; Vohl, Marie-Claude; Electronic Medical Records and Genomics (eMEMERGEGE) Consortium; MIGen Consortium; PAGEGE Consortium; LifeLines Cohort Study; Amouyel, Philippe; Asselbergs, Folkert W; Assimes, Themistocles L; Bochud, Murielle; Boehm, Bernhard O; Boerwinkle, Eric; Bottinger, Erwin P; Bouchard, Claude; Cauchi, StΓ©phane; Chambers, John C; Chanock, Stephen J; Cooper, Richard S; de Bakker, Paul I W; Dedoussis, George; Ferrucci, Luigi; Franks, Paul W; Froguel, Philippe; Groop, Leif C; Haiman, Christopher A; Hamsten, Anders; Hayes, M Geoffrey; Hui, Jennie; Hunter, David J; Hveem, Kristian; Jukema, J Wouter; Kaplan, Robert C; Kivimaki, Mika; Kuh, Diana; Laakso, Markku; Liu, Yongmei; Martin, Nicholas G; MΓ€rz, Winfried; Melbye, Mads; Moebus, Susanne; Munroe, Patricia B; NjΓΈlstad, Inger; Oostra, Ben A; Palmer, Colin N A; Pedersen, Nancy L; Perola, Markus; PΓ©russe, Louis; Peters, Ulrike; Powell, Joseph E; Power, Chris; Quertermous, Thomas; Rauramaa, Rainer; Reinmaa, Eva; Ridker, Paul M; Rivadeneira, Fernando; Rotter, Jerome I; Saaristo, Timo E; Saleheen, Danish; Schlessinger, David; Slagboom, P Eline; Snieder, Harold; Spector, Tim D; Strauch, Konstantin; Stumvoll, Michael; Tuomilehto, Jaakko; Uusitupa, Matti; van der Harst, Pim; VΓΆlzke, Henry; Walker, Mark; Wareham, Nicholas J; Watkins, Hugh; Wichmann, H-Erich; Wilson, James F; Zanen, Pieter; Deloukas, Panos; Heid, Iris M; Lindgren, Cecilia M; Mohlke, Karen L; Speliotes, Elizabeth K; Thorsteinsdottir, Unnur; Barroso, InΓͺs; Fox, Caroline S; North, Kari E; Strachan, David P; Beckmann, Jacques S; Berndt, Sonja I; Boehnke, Michael; Borecki, Ingrid B; McCarthy, Mark I; Metspalu, Andres; Stefansson, Kari; Uitterlinden, AndrΓ© G; van Duijn, Cornelia M; Franke, Lude; Willer, Cristen J; Price, Alkes L; Lettre, Guillaume; Loos, Ruth J F; Weedon, Michael N; Ingelsson, Erik; O'Connell, Jeffrey R; Abecasis, Goncalo R; Chasman, Daniel I; Goddard, Michael E; Visscher, Peter M; Hirschhorn, Joel N; Frayling, Timothy M
- Year
- 2014
- Journal
- Nature genetics
- PMID
- 25282103
- DOI
- 10.1038/ng.3097
- PMCID
- PMC4250049
Using genome-wide data from 253,288 individuals, we identified 697 variants at genome-wide significance that together explained one-fifth of the heritability for adult height. By testing different numbers of variants in independent studies, we show that the most strongly associated βΌ2,000, βΌ3,700 and βΌ9,500 SNPs explained βΌ21%, βΌ24% and βΌ29% of phenotypic variance. Furthermore, all common variants together captured 60% of heritability. The 697 variants clustered in 423 loci were enriched for genes, pathways and tissue types known to be involved in growth and together implicated genes and pathways not highlighted in earlier efforts, such as signaling by fibroblast growth factors, WNT/Ξ²-catenin and chondroitin sulfate-related genes. We identified several genes and pathways not previously connected with human skeletal growth, including mTOR, osteoglycin and binding of hyaluronic acid. Our results indicate a genetic architecture for human height that is characterized by a very large but finite number (thousands) of causal variants.
Regional association plots for loci with multiple association signalsPanels a to d highlight examples of multiple signals after approximate conditional joint multiple-SNP analysis GCTA-COJO analysis. SNPs are shaded and shaped based on the index SNP with which they are in strongest LD (r2>0.4). Panels aβc show the majority of signals clustering in and around a single gene (ACAN, ADAMTS17, PTCH1, respectively) whereas panel d shows the multiple signals clustering through proximity.
Quantifying the variance explained by height associated SNPs at different levels of significanceThe SNPs were selected from the approximate conditional and joint multiple SNPs association analysis using GCTA-COJO analysis with the target cohort being excluded from the meta-analysis. a, b, c, Partitioning the variance in the SNP-derived genetic predictor using a within-family analysis. The SNP-based predictor was adjusted by the first 20 PCs. The four variance-covariance components Vg, Ve, Cg and Ce are defined in Online Methods. d, Accuracy of predicting phenotype by the genetic predictor in unrelated individuals. The prediction R2 shown on the y-axis is the squared correlation between phenotype and predictor. The SNP-based predictor was adjusted by the first 20 PCs. The solid line is the average prediction R2 weighted by sample size over the five cohorts. The dashed line is the prediction accuracy inferred from the within-family prediction analysis (Equation 19 in Online Methods). e, The variance explained by the SNPs was estimated by the whole-genome estimation method in GCTA. The phenotype was adjusted by the first 20 PCs. Each error bar represents the standard error of the estimate. The estimates from all the five cohorts (B-PROOF, FRAM, QIMR, TwinGene and WTCCC-T2D) were averaged by the inverse-variance approach. The dashed line is the variance explained inferred from the within-family prediction analysis. In panels d and e, the number shown in each column is the number of SNPs used in the analysis.
Tissue enrichment combined with pruned gene set networkGenes within genome-wide significant height associated loci enriched for several relevant tissue annotations as well as gene sets. a, Genes in associated loci tended to be highly expressed in tissues related to chondrocytes and osteoblasts (cartilage, joints, and spine), and other musculoskeletal, cardiovascular and endocrine tissue-types. The analysis was conducted based on the DEPICT method and 37,427 human microarray samples. Tissue annotations are sorted by physiological system and significance. Significantly enriched (FDR<0.05) tissues are color-coded in black. b, Significantly enriched reconstituted gene sets (P-value<1Γ10β11, FDR<1Γ10β5) identified by DEPICT. Nodes represent reconstituted gene sets and are color-coded by statistical significance. Edge thickness between nodes is proportional to degree of gene overlap as measured by the Jaccard index. Nodes with gene overlap greater than 25% were collapsed into single meta nodes and marked by blue borders. c, reconstituted gene sets comprised by the Chordate Embryonic Development meta node, which represented several gene sets relevant to human height (e.g. ossification, embryonic skeletal system development, and limb development).
| # | Section | Preview |
|---|---|---|
| 40 | ONLINE METHODS β Theory and method to partition the variance in a genetic predictor | From this model, the additive genetic variance is |
| 41 | ONLINE METHODS β Theory and method to partition the variance in a genetic predictor | [2]var(g)=βivar(xi)bi2+βiβj(iβ j)cov(xi,xj)bibj with the first component being the expectedβ¦ |
| 42 | ONLINE METHODS β Theory and method to partition the variance in a genetic predictor | Considering a pair of full siblings in a family, the additive genetic covariance between the sibs is |
| 43 | ONLINE METHODS β Theory and method to partition the variance in a genetic predictor | For full sibs, |
| 44 | ONLINE METHODS β Theory and method to partition the variance in a genetic predictor | cov(x1i, x2i ) = Β½var(xi ), |
| 45 | ONLINE METHODS β Theory and method to partition the variance in a genetic predictor | cov(x1i, x2j) = Β½cov(xi, xj) for SNPs that are in LD, andβ¦ |
| 46 | ONLINE METHODS β Theory and method to partition the variance in a genetic predictor | Let Vg=βivar(xi)bi2+βiβj(iβ j)cov(xi,xj)bibj | (SNPs are in LD), andβ¦ |
| 47 | ONLINE METHODS β Theory and method to partition the variance in a genetic predictor | Therefore, the genetic variance is |
| 48 | ONLINE METHODS β Theory and method to partition the variance in a genetic predictor | The genetic covariance between a pair of full-sibs is |
| 49 | ONLINE METHODS β Theory and method to partition the variance in a genetic predictor | If we take a set of SNPs with their effects estimated from GCTA-COJO analysis (Online Methods), andβ¦ |
| 50 | ONLINE METHODS β Theory and method to partition the variance in a genetic predictor | [6]g^=βixib^i where bΜ is the estimate of b with bΜ =b + e with e being the error in estimatingβ¦ |
| 51 | ONLINE METHODS β Theory and method to partition the variance in a genetic predictor | If we assume b and e are independent and denote Ve=βivar(xi)ei2 andβ¦ |
| 52 | ONLINE METHODS β Theory and method to partition the variance in a genetic predictor | The covariance between the predictors of a pair of full-sibs is |
| 53 | ONLINE METHODS β Theory and method to partition the variance in a genetic predictor | The covariance between the true phenotype and the predictor of a same individual is |
| 54 | ONLINE METHODS β Theory and method to partition the variance in a genetic predictor | The covariance between the true phenotype of one sib and the predictor of the other sib is |
| 55 | ONLINE METHODS β Theory and method to partition the variance in a genetic predictor | If we define ΞΔ = Δ1 β Δ2 and Ξy = y1 β y2, |
| 56 | ONLINE METHODS β Theory and method to partition the variance in a genetic predictor | We therefore can calculate these four parameters as |
| 57 | ONLINE METHODS β Theory and method to partition the variance in a genetic predictor | [13]Vg=cov(Ξy,Ξg^) [14]Ve=var(Ξg^)-Vg [15]Cg=cov(y,g^)-Vg [16]Ce=2cov(g^1,g^2)-var(g^)-Cg whereβ¦ |
| 58 | ONLINE METHODS β Theory and method to partition the variance in a genetic predictor | To assess the prediction accuracy, we usually perform a regression analysis of the real phenotypeβ¦ |
| 59 | ONLINE METHODS β Theory and method to partition the variance in a genetic predictor | [17]y=Ξ²0+g^Ξ²1+Ξ΅ so that the regression slope is actually |
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| Comparison of methods that use whole genome data to estimate the heritability and genetic architecture of complex traits. | Evans LM et al. | β | 2018 | β |
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| Dissection of genetic variation and evidence for pleiotropy in male pattern baldness. | Yap CX et al. | β | 2018 | β |
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| Estimation of Genetic Correlation via Linkage Disequilibrium Score Regression and Genomic Restricted Maximum Likelihood. | Ni G et al. | β | 2018 | β |
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| Exploring Coronary Artery Disease GWAs Targets With Functional Links to Immunometabolism. | Hughes MF et al. | β | 2018 | β |
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| Exploring the Genetic Correlation Between Growth and Immunity Based on Summary Statistics of Genome-Wide Association Studies. | Zhang Z et al. | β | 2018 | β |
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| Genetic Contributions to The Association Between Adult Height and Head and Neck Cancer: A Mendelian Randomization Analysis. | Pastorino R et al. | β | 2018 | β |
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| Genome-Wide Association Study and Selection Signatures Detect Genomic Regions Associated with Seed Yield and Oil Quality in Flax. | You FM et al. | β | 2018 | β |
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| Method to estimate the approximate samples size that yield a certain number of significant GWAS signals in polygenic traits. | Bacanu SA et al. | β | 2018 | β |
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| Osterix regulates corticalization for longitudinal bone growth via integrin Ξ²3 expression. | Moon YJ et al. | β | 2018 | β |
| PhenoSpD: an integrated toolkit for phenotypic correlation estimation and multiple testing correction using GWAS summary statistics. | Zheng J et al. | β | 2018 | β |
| Phenotype-Specific Enrichment of Mendelian Disorder Genes near GWAS Regions across 62 Complex Traits. | Freund MK et al. | β | 2018 | β |
| Polygenic adaptation and convergent evolution on growth and cardiac genetic pathways in African and Asian rainforest hunter-gatherers. | Bergey CM et al. | β | 2018 | β |
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| Post-GWAS in Psychiatric Genetics: A Developmental Perspective on the "Other" Next Steps. | Dick DM et al. | β | 2018 | β |
| Power Analysis for Genetic Association Test (PAGEANT) provides insights to challenges for rare variant association studies. | Derkach A et al. | β | 2018 | β |
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| Relationship of genetic determinants of height with cardiometabolic and pulmonary traits in the Hispanic Community Health Study/Study of Latinos. | Sofer T et al. | β | 2018 | β |
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| Trans-Ethnic Polygenic Analysis Supports Genetic Overlaps of Lumbar Disc Degeneration With Height, Body Mass Index, and Bone Mineral Density. | Zhou X et al. | β | 2018 | β |
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| GWAS for male-pattern baldness identifies 71 susceptibility loci explaining 38% of the risk. | Pirastu N et al. | β | 2017 | β |
| Height associated variants demonstrate assortative mating in human populations. | Li X et al. | β | 2017 | β |
| Heritability Estimation using a Regularized Regression Approach (HERRA): Applicable to continuous, dichotomous or age-at-onset outcome. | Gorfine M et al. | β | 2017 | β |
| Human longevity: 25 genetic loci associated in 389,166 UK biobank participants. | Pilling LC et al. | β | 2017 | β |
| Identification of 153 new loci associated with heel bone mineral density and functional involvement of GPC6 in osteoporosis. | Kemp JP et al. | β | 2017 | β |
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| Mendelian randomisation analysis provides no evidence for a relationship between adult height and testicular cancer risk. | Levy M et al. | β | 2017 | β |
| Mendelian randomization incorporating uncertainty about pleiotropy. | Thompson JR et al. | β | 2017 | β |
| Modeling prior information of common genetic variants improves gene discovery for neuroticism. | Lo MT et al. | β | 2017 | β |
| Monoallelic BMP2 Variants Predicted to Result in Haploinsufficiency Cause Craniofacial, Skeletal, and Cardiac Features Overlapping Those of 20p12 Deletions. | Tan TY et al. | β | 2017 | β |
| Mortality selection in a genetic sample and implications for association studies. | Domingue BW et al. | β | 2017 | β |
| New Blood Pressure-Associated Loci Identified in Meta-Analyses of 475β000 Individuals. | Kraja AT et al. | β | 2017 | β |
| Novel Common Variants Associated with Obesity and Type 2 Diabetes Detected Using a cFDR Method. | Zhang Q et al. | β | 2017 | β |
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| Penetrance of Polygenic Obesity Susceptibility Loci across the Body Mass Index Distribution. | Abadi A et al. | β | 2017 | β |
| Polygenicity and Epistasis Underlie Fitness-Proximal Traits in the <i>Caenorhabditis elegans</i> Multiparental Experimental Evolution (CeMEE) Panel. | Noble LM et al. | β | 2017 | β |
| Predicting causal variants affecting expression by using whole-genome sequencing and RNA-seq from multiple human tissues. | Brown AA et al. | β | 2017 | β |
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| Pubertal growth and adult height in relation to breast cancer risk in African American women. | Bertrand KA et al. | β | 2017 | β |
| Quantifying the regulatory effect size of <i>cis</i>-acting genetic variation using allelic fold change. | Mohammadi P et al. | β | 2017 | β |
| Secular trends in stature of late 20th century white South Africans and two European populations. | Myburgh J et al. | β | 2017 | β |
| Short stature homeobox-containing gene duplications in 3.7% of girls withΒ tall stature and normal karyotypes. | Upners EN et al. | β | 2017 | β |
| SQC: secure quality control for meta-analysis of genome-wide association studies. | Huang Z et al. | β | 2017 | β |
| Systematic tissue-specific functional annotation of the human genome highlights immune-related DNA elements for late-onset Alzheimer's disease. | Lu Q et al. | β | 2017 | β |
| The genetic underpinnings of body fat distribution. | Pulit SL et al. | β | 2017 | β |
| The population genomics of archaeological transition in west Iberia: Investigation of ancient substructure using imputation and haplotype-based methods. | Martiniano R et al. | β | 2017 | β |
| The relative contribution of DNA methylation and genetic variants on protein biomarkers for human diseases. | Ahsan M et al. | β | 2017 | β |
| The Role of Obesity, Type 2 Diabetes, and Metabolic Factors in Pancreatic Cancer: A Mendelian Randomization Study. | Carreras-Torres R et al. | β | 2017 | β |
| Transcriptome-wide association study revealed two novel genes associated with nonobstructive azoospermia in a Chinese population. | Jiang T et al. | β | 2017 | β |
| Will Big Data Close the Missing Heritability Gap? | Kim H et al. | β | 2017 | β |