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1.  Deep targeted sequencing of 12 breast cancer susceptibility regions in 4611 women across four different ethnicities 
Background
Although genome-wide association studies (GWASs) have identified thousands of disease susceptibility regions, the underlying causal mechanism in these regions is not fully known. It is likely that the GWAS signal originates from one or many as yet unidentified causal variants.
Methods
Using next-generation sequencing, we characterized 12 breast cancer susceptibility regions identified by GWASs in 2288 breast cancer cases and 2323 controls across four populations of African American, European, Japanese, and Hispanic ancestry.
Results
After genotype calling and quality control, we identified 137,530 single-nucleotide variants (SNVs); of those, 87.2 % had a minor allele frequency (MAF) <0.005. For SNVs with MAF >0.005, we calculated the smallest number of SNVs needed to obtain a posterior probability set (PPS) such that there is 90 % probability that the causal SNV is included. We found that the PPS for two regions, 2q35 and 11q13, contained less than 5 % of the original SNVs, dramatically decreasing the number of potentially causal SNVs. However, we did not find strong evidence supporting a causal role for any individual SNV. In addition, there were no significant gene-based rare SNV associations after correcting for multiple testing.
Conclusions
This study illustrates some of the challenges faced in fine-mapping studies in the post-GWAS era, most importantly the large sample sizes needed to identify rare-variant associations or to distinguish the effects of strongly correlated common SNVs.
Electronic supplementary material
The online version of this article (doi:10.1186/s13058-016-0772-7) contains supplementary material, which is available to authorized users.
doi:10.1186/s13058-016-0772-7
PMCID: PMC5097387  PMID: 27814745
Breast cancer; Fine-mapping; Next-generation sequencing; Multiethnic analysis; GWAS
2.  Explicit modeling of ancestry improves polygenic risk scores and BLUP prediction 
Genetic epidemiology  2015;39(6):427-438.
Polygenic prediction using genome-wide SNPs can provide high prediction accuracy for complex traits. Here, we investigate the question of how to account for genetic ancestry when conducting polygenic prediction. We show that the accuracy of polygenic prediction in structured populations may be partly due to genetic ancestry. However, we hypothesized that explicitly modeling ancestry could improve polygenic prediction accuracy. We analyzed three GWAS of hair color, tanning ability and basal cell carcinoma (BCC) in European Americans (sample size from 7,440 to 9,822) and considered two widely used polygenic prediction approaches: polygenic risk scores (PRS) and Best Linear Unbiased Prediction (BLUP). We compared polygenic prediction without correction for ancestry to polygenic prediction with ancestry as a separate component in the model. In 10-fold cross-validation using the PRS approach, the R2 for hair color increased by 66% (0.0456 to 0.0755; p<10−16), the R2 for tanning ability increased by 123% (0.0154 to 0.0344; p<10−16) and the liability-scale R2 for BCC increased by 68% (0.0138 to 0.0232; p<10−16) when explicitly modeling ancestry, which prevents ancestry effects from entering into each SNP effect and being over-weighted. Surprisingly, explicitly modeling ancestry produces a similar improvement when using the BLUP approach, which fits all SNPs simultaneously in a single variance component and causes ancestry to be underweighted. We validate our findings via simulations, which show that the differences in prediction accuracy will increase in magnitude as sample sizes increase. In summary, our results show that explicitly modeling ancestry can be important in both PRS and BLUP prediction.
doi:10.1002/gepi.21906
PMCID: PMC4734143  PMID: 25995153
Genome-wide association study; Polygenic prediction; Principal component analysis; Pigmentation; Basal cell carcinoma
3.  Fast and accurate long-range phasing in a UK Biobank cohort 
Nature genetics  2016;48(7):811-816.
Recent work has leveraged the extensive genotyping of the Icelandic population to perform long-range phasing (LRP), enabling accurate imputation and association analysis of rare variants in target samples typed on genotyping arrays. Here, we develop a fast and accurate LRP method, Eagle, that extends this paradigm to populations with much smaller proportions of genotyped samples by harnessing long (>4cM) identical-by-descent (IBD) tracts shared among distantly related individuals. We applied Eagle to N≈150,000 samples (0.2% of the British population) from the UK Biobank, and we determined that it is 1–2 orders of magnitude faster than existing methods while achieving similar or better phasing accuracy (switch error rate ≈0.3%, corresponding to perfect phase in a majority of 10Mb segments). We also observed that when used within an imputation pipeline, Eagle pre-phasing improved downstream imputation accuracy compared to pre-phasing in batches using existing methods (as necessary to achieve comparable computational cost).
doi:10.1038/ng.3571
PMCID: PMC4925291  PMID: 27270109
4.  New data and an old puzzle: the negative association between schizophrenia and rheumatoid arthritis 
Lee, S Hong | Byrne, Enda M | Hultman, Christina M | Kähler, Anna | Vinkhuyzen, Anna AE | Ripke, Stephan | Andreassen, Ole A | Frisell, Thomas | Gusev, Alexander | Hu, Xinli | Karlsson, Robert | Mantzioris, Vasilis X | McGrath, John J | Mehta, Divya | Stahl, Eli A | Zhao, Qiongyi | Kendler, Kenneth S | Sullivan, Patrick F | Price, Alkes L | O’Donovan, Michael | Okada, Yukinori | Mowry, Bryan J | Raychaudhuri, Soumya | Wray, Naomi R | Byerley, William | Cahn, Wiepke | Cantor, Rita M | Cichon, Sven | Cormican, Paul | Curtis, David | Djurovic, Srdjan | Escott-Price, Valentina | Gejman, Pablo V | Georgieva, Lyudmila | Giegling, Ina | Hansen, Thomas F | Ingason, Andrés | Kim, Yunjung | Konte, Bettina | Lee, Phil H | McIntosh, Andrew | McQuillin, Andrew | Morris, Derek W | Nöthen, Markus M | O’Dushlaine, Colm | Olincy, Ann | Olsen, Line | Pato, Carlos N | Pato, Michele T | Pickard, Benjamin S | Posthuma, Danielle | Rasmussen, Henrik B | Rietschel, Marcella | Rujescu, Dan | Schulze, Thomas G | Silverman, Jeremy M | Thirumalai, Srinivasa | Werge, Thomas | Agartz, Ingrid | Amin, Farooq | Azevedo, Maria H | Bass, Nicholas | Black, Donald W | Blackwood, Douglas H R | Bruggeman, Richard | Buccola, Nancy G | Choudhury, Khalid | Cloninger, Robert C | Corvin, Aiden | Craddock, Nicholas | Daly, Mark J | Datta, Susmita | Donohoe, Gary J | Duan, Jubao | Dudbridge, Frank | Fanous, Ayman | Freedman, Robert | Freimer, Nelson B | Friedl, Marion | Gill, Michael | Gurling, Hugh | De Haan, Lieuwe | Hamshere, Marian L | Hartmann, Annette M | Holmans, Peter A | Kahn, René S | Keller, Matthew C | Kenny, Elaine | Kirov, George K | Krabbendam, Lydia | Krasucki, Robert | Lawrence, Jacob | Lencz, Todd | Levinson, Douglas F | Lieberman, Jeffrey A | Lin, Dan-Yu | Linszen, Don H | Magnusson, Patrik KE | Maier, Wolfgang | Malhotra, Anil K | Mattheisen, Manuel | Mattingsdal, Morten | McCarroll, Steven A | Medeiros, Helena | Melle, Ingrid | Milanova, Vihra | Myin-Germeys, Inez | Neale, Benjamin M | Ophoff, Roel A | Owen, Michael J | Pimm, Jonathan | Purcell, Shaun M | Puri, Vinay | Quested, Digby J | Rossin, Lizzy | Ruderfer, Douglas | Sanders, Alan R | Shi, Jianxin | Sklar, Pamela | St. Clair, David | Stroup, T Scott | Van Os, Jim | Visscher, Peter M | Wiersma, Durk | Zammit, Stanley | Bridges, S Louis | Choi, Hyon K | Coenen, Marieke JH | de Vries, Niek | Dieud, Philippe | Greenberg, Jeffrey D | Huizinga, Tom WJ | Padyukov, Leonid | Siminovitch, Katherine A | Tak, Paul P | Worthington, Jane | De Jager, Philip L | Denny, Joshua C | Gregersen, Peter K | Klareskog, Lars | Mariette, Xavier | Plenge, Robert M | van Laar, Mart | van Riel, Piet
Background: A long-standing epidemiological puzzle is the reduced rate of rheumatoid arthritis (RA) in those with schizophrenia (SZ) and vice versa. Traditional epidemiological approaches to determine if this negative association is underpinned by genetic factors would test for reduced rates of one disorder in relatives of the other, but sufficiently powered data sets are difficult to achieve. The genomics era presents an alternative paradigm for investigating the genetic relationship between two uncommon disorders.
Methods: We use genome-wide common single nucleotide polymorphism (SNP) data from independently collected SZ and RA case-control cohorts to estimate the SNP correlation between the disorders. We test a genotype X environment (GxE) hypothesis for SZ with environment defined as winter- vs summer-born.
Results: We estimate a small but significant negative SNP-genetic correlation between SZ and RA (−0.046, s.e. 0.026, P = 0.036). The negative correlation was stronger for the SNP set attributed to coding or regulatory regions (−0.174, s.e. 0.071, P = 0.0075). Our analyses led us to hypothesize a gene-environment interaction for SZ in the form of immune challenge. We used month of birth as a proxy for environmental immune challenge and estimated the genetic correlation between winter-born and non-winter born SZ to be significantly less than 1 for coding/regulatory region SNPs (0.56, s.e. 0.14, P  = 0.00090).
Conclusions: Our results are consistent with epidemiological observations of a negative relationship between SZ and RA reflecting, at least in part, genetic factors. Results of the month of birth analysis are consistent with pleiotropic effects of genetic variants dependent on environmental context.
doi:10.1093/ije/dyv136
PMCID: PMC4881824  PMID: 26286434
Schizophrenia; rheumatoid arthritis; genetic relationship; pleiotropy
5.  Abundant contribution of short tandem repeats to gene expression variation in humans 
Nature genetics  2015;48(1):22-29.
The contribution of repetitive elements to quantitative human traits is largely unknown. Here, we report a genome-wide survey of the contribution of Short Tandem Repeats (STRs), one of the most polymorphic and abundant repeat classes, to gene expression in humans. Our survey identified 2,060 significant expression STRs (eSTRs). These eSTRs were replicable in orthogonal populations and expression assays. We used variance partitioning to disentangle the contribution of eSTRs from linked SNPs and indels and found that eSTRs contribute 10%–15% of the cis-heritability mediated by all common variants. Further functional genomic analyses showed that eSTRs are enriched in conserved regions, co-localize with regulatory elements, and can modulate certain histone modifications. By analyzing known GWAS hits and searching for new associations in 1,685 deeply-phenotyped whole-genomes, we found that eSTRs are enriched in various clinically-relevant conditions. These results highlight the contribution of short tandem repeats to the genetic architecture of quantitative human traits.
doi:10.1038/ng.3461
PMCID: PMC4909355  PMID: 26642241
6.  Contrasting genetic architectures of schizophrenia and other complex diseases using fast variance components analysis 
Nature genetics  2015;47(12):1385-1392.
Heritability analyses of GWAS cohorts have yielded important insights into complex disease architecture, and increasing sample sizes hold the promise of further discoveries. Here, we analyze the genetic architecture of schizophrenia in 49,806 samples from the PGC, and nine complex diseases in 54,734 samples from the GERA cohort. For schizophrenia, we infer an overwhelmingly polygenic disease architecture in which ≥71% of 1Mb genomic regions harbor ≥1 variant influencing schizophrenia risk. We also observe significant enrichment of heritability in GC-rich regions and in higher-frequency SNPs for both schizophrenia and GERA diseases. In bivariate analyses, we observe significant genetic correlations (ranging from 0.18 to 0.85) among several pairs of GERA diseases; genetic correlations were on average 1.3x stronger than correlations of overall disease liabilities. To accomplish these analyses, we developed a fast algorithm for multi-component, multi-trait variance components analysis that overcomes prior computational barriers that made such analyses intractable at this scale.
doi:10.1038/ng.3431
PMCID: PMC4666835  PMID: 26523775
7.  Partitioning heritability by functional annotation using genome-wide association summary statistics 
Nature genetics  2015;47(11):1228-1235.
Recent work has demonstrated that some functional categories of the genome contribute disproportionately to the heritability of complex diseases. Here, we analyze a broad set of functional elements, including cell-type-specific elements, to estimate their polygenic contributions to heritability in genome-wide association studies (GWAS) of 17 complex diseases and traits with an average sample size of 73,599. To enable this analysis, we introduce a new method, stratified LD score regression, for partitioning heritability from GWAS summary statistics while accounting for linked markers. This new method is computationally tractable at very large sample sizes, and leverages genome-wide information. Our results include a large enrichment of heritability in conserved regions across many traits; a very large immunological disease-specific enrichment of heritability in FANTOM5 enhancers; and many cell-type-specific enrichments including significant enrichment of central nervous system cell types in body mass index, age at menarche, educational attainment, and smoking behavior.
doi:10.1038/ng.3404
PMCID: PMC4626285  PMID: 26414678
8.  An Atlas of Genetic Correlations across Human Diseases and Traits 
Nature genetics  2015;47(11):1236-1241.
Identifying genetic correlations between complex traits and diseases can provide useful etiological insights and help prioritize likely causal relationships. The major challenges preventing estimation of genetic correlation from genome-wide association study (GWAS) data with current methods are the lack of availability of individual genotype data and widespread sample overlap among meta-analyses. We circumvent these difficulties by introducing a technique – cross-trait LD Score regression – for estimating genetic correlation that requires only GWAS summary statistics and is not biased by sample overlap. We use this method to estimate 276 genetic correlations among 24 traits. The results include genetic correlations between anorexia nervosa and schizophrenia, anorexia and obesity and associations between educational attainment and several diseases. These results highlight the power of genome-wide analyses, since there currently are no significantly associated SNPs for anorexia nervosa and only three for educational attainment.
doi:10.1038/ng.3406
PMCID: PMC4797329  PMID: 26414676
9.  Large-scale genomic analyses link reproductive ageing to hypothalamic signaling, breast cancer susceptibility and BRCA1-mediated DNA repair 
Day, Felix R. | Ruth, Katherine S. | Thompson, Deborah J. | Lunetta, Kathryn L. | Pervjakova, Natalia | Chasman, Daniel I. | Stolk, Lisette | Finucane, Hilary K. | Sulem, Patrick | Bulik-Sullivan, Brendan | Esko, Tõnu | Johnson, Andrew D. | Elks, Cathy E. | Franceschini, Nora | He, Chunyan | Altmaier, Elisabeth | Brody, Jennifer A. | Franke, Lude L. | Huffman, Jennifer E. | Keller, Margaux F. | McArdle, Patrick F. | Nutile, Teresa | Porcu, Eleonora | Robino, Antonietta | Rose, Lynda M. | Schick, Ursula M. | Smith, Jennifer A. | Teumer, Alexander | Traglia, Michela | Vuckovic, Dragana | Yao, Jie | Zhao, Wei | Albrecht, Eva | Amin, Najaf | Corre, Tanguy | Hottenga, Jouke-Jan | Mangino, Massimo | Smith, Albert V. | Tanaka, Toshiko | Abecasis, Goncalo | Andrulis, Irene L. | Anton-Culver, Hoda | Antoniou, Antonis C. | Arndt, Volker | Arnold, Alice M. | Barbieri, Caterina | Beckmann, Matthias W. | Beeghly-Fadiel, Alicia | Benitez, Javier | Bernstein, Leslie | Bielinski, Suzette J. | Blomqvist, Carl | Boerwinkle, Eric | Bogdanova, Natalia V. | Bojesen, Stig E. | Bolla, Manjeet K. | Borresen-Dale, Anne-Lise | Boutin, Thibaud S | Brauch, Hiltrud | Brenner, Hermann | Brüning, Thomas | Burwinkel, Barbara | Campbell, Archie | Campbell, Harry | Chanock, Stephen J. | Chapman, J. Ross | Chen, Yii-Der Ida | Chenevix-Trench, Georgia | Couch, Fergus J. | Coviello, Andrea D. | Cox, Angela | Czene, Kamila | Darabi, Hatef | De Vivo, Immaculata | Demerath, Ellen W. | Dennis, Joe | Devilee, Peter | Dörk, Thilo | dos-Santos-Silva, Isabel | Dunning, Alison M. | Eicher, John D. | Fasching, Peter A. | Faul, Jessica D. | Figueroa, Jonine | Flesch-Janys, Dieter | Gandin, Ilaria | Garcia, Melissa E. | García-Closas, Montserrat | Giles, Graham G. | Girotto, Giorgia G. | Goldberg, Mark S. | González-Neira, Anna | Goodarzi, Mark O. | Grove, Megan L. | Gudbjartsson, Daniel F. | Guénel, Pascal | Guo, Xiuqing | Haiman, Christopher A. | Hall, Per | Hamann, Ute | Henderson, Brian E. | Hocking, Lynne J. | Hofman, Albert | Homuth, Georg | Hooning, Maartje J. | Hopper, John L. | Hu, Frank B. | Huang, Jinyan | Humphreys, Keith | Hunter, David J. | Jakubowska, Anna | Jones, Samuel E. | Kabisch, Maria | Karasik, David | Knight, Julia A. | Kolcic, Ivana | Kooperberg, Charles | Kosma, Veli-Matti | Kriebel, Jennifer | Kristensen, Vessela | Lambrechts, Diether | Langenberg, Claudia | Li, Jingmei | Li, Xin | Lindström, Sara | Liu, Yongmei | Luan, Jian’an | Lubinski, Jan | Mägi, Reedik | Mannermaa, Arto | Manz, Judith | Margolin, Sara | Marten, Jonathan | Martin, Nicholas G. | Masciullo, Corrado | Meindl, Alfons | Michailidou, Kyriaki | Mihailov, Evelin | Milani, Lili | Milne, Roger L. | Müller-Nurasyid, Martina | Nalls, Michael | Neale, Ben M. | Nevanlinna, Heli | Neven, Patrick | Newman, Anne B. | Nordestgaard, Børge G. | Olson, Janet E. | Padmanabhan, Sandosh | Peterlongo, Paolo | Peters, Ulrike | Petersmann, Astrid | Peto, Julian | Pharoah, Paul D.P. | Pirastu, Nicola N. | Pirie, Ailith | Pistis, Giorgio | Polasek, Ozren | Porteous, David | Psaty, Bruce M. | Pylkäs, Katri | Radice, Paolo | Raffel, Leslie J. | Rivadeneira, Fernando | Rudan, Igor | Rudolph, Anja | Ruggiero, Daniela | Sala, Cinzia F. | Sanna, Serena | Sawyer, Elinor J. | Schlessinger, David | Schmidt, Marjanka K. | Schmidt, Frank | Schmutzler, Rita K. | Schoemaker, Minouk J. | Scott, Robert A. | Seynaeve, Caroline M. | Simard, Jacques | Sorice, Rossella | Southey, Melissa C. | Stöckl, Doris | Strauch, Konstantin | Swerdlow, Anthony | Taylor, Kent D. | Thorsteinsdottir, Unnur | Toland, Amanda E. | Tomlinson, Ian | Truong, Thérèse | Tryggvadottir, Laufey | Turner, Stephen T. | Vozzi, Diego | Wang, Qin | Wellons, Melissa | Willemsen, Gonneke | Wilson, James F. | Winqvist, Robert | Wolffenbuttel, Bruce B.H.R. | Wright, Alan F. | Yannoukakos, Drakoulis | Zemunik, Tatijana | Zheng, Wei | Zygmunt, Marek | Bergmann, Sven | Boomsma, Dorret I. | Buring, Julie E. | Ferrucci, Luigi | Montgomery, Grant W. | Gudnason, Vilmundur | Spector, Tim D. | van Duijn, Cornelia M | Alizadeh, Behrooz Z. | Ciullo, Marina | Crisponi, Laura | Easton, Douglas F. | Gasparini, Paolo P. | Gieger, Christian | Harris, Tamara B. | Hayward, Caroline | Kardia, Sharon L.R. | Kraft, Peter | McKnight, Barbara | Metspalu, Andres | Morrison, Alanna C. | Reiner, Alex P. | Ridker, Paul M. | Rotter, Jerome I. | Toniolo, Daniela | Uitterlinden, André G. | Ulivi, Sheila | Völzke, Henry | Wareham, Nicholas J. | Weir, David R. | Yerges-Armstrong, Laura M. | Price, Alkes L. | Stefansson, Kari | Visser, Jenny A. | Ong, Ken K. | Chang-Claude, Jenny | Murabito, Joanne M. | Perry, John R.B. | Murray, Anna
Nature genetics  2015;47(11):1294-1303.
Menopause timing has a substantial impact on infertility and risk of disease, including breast cancer, but the underlying mechanisms are poorly understood. We report a dual strategy in ~70,000 women to identify common and low-frequency protein-coding variation associated with age at natural menopause (ANM). We identified 44 regions with common variants, including two harbouring additional rare missense alleles of large effect. We found enrichment of signals in/near genes involved in delayed puberty, highlighting the first molecular links between the onset and end of reproductive lifespan. Pathway analyses revealed a major association with DNA damage-response (DDR) genes, including the first common coding variant in BRCA1 associated with any complex trait. Mendelian randomisation analyses supported a causal effect of later ANM on breast cancer risk (~6% risk increase per-year, P=3×10−14), likely mediated by prolonged sex hormone exposure, rather than DDR mechanisms.
doi:10.1038/ng.3412
PMCID: PMC4661791  PMID: 26414677
10.  Genetic and environmental components of family history in type 2 diabetes 
Human genetics  2014;134(2):259-267.
Family history of diabetes is a major risk factor for type 2 diabetes (T2D), but whether this association derives from shared genetic or environmental factors is unclear. To address this question, we developed a statistical framework that models four components of variance, including known and unknown genetic and environmental factors, using a liability threshold model. Focusing on parental history, we simulated case-control studies with two first-degree relatives for each individual, assuming 50% genetic similarity and a range of values of environmental similarity. By comparing the association of parental history with T2D in our simulations to case-control studies of T2D nested in the Nurses’ Health Study and Health Professionals Follow-up Study, we estimate that first-degree relatives have a correlation of 23% (95%CI: 15-27%) in their environmental contribution to T2D liability and that this shared environment is responsible for 32% (95%CI: 24-36%) of the association between parental history and T2D, with the remainder due to shared genetics. Estimates are robust to varying model parameter values and our framework can be extended to different definitions of family history. In conclusion, we find that the association between parental history and T2D derives from predominately genetic but also environmental effects.
doi:10.1007/s00439-014-1519-0
PMCID: PMC4293229  PMID: 25543539
family history; genetic; environment; type 2 diabetes; liability threshold
11.  Progress and promise in understanding the genetic basis of common diseases 
Susceptibility to common human diseases is influenced by both genetic and environmental factors. The explosive growth of genetic data, and the knowledge that it is generating, are transforming our biological understanding of these diseases. In this review, we describe the technological and analytical advances that have enabled genome-wide association studies to be successful in identifying a large number of genetic variants robustly associated with common disease. We examine the biological insights that these genetic associations are beginning to produce, from functional mechanisms involving individual genes to biological pathways linking associated genes, and the identification of functional annotations, some of which are cell-type-specific, enriched in disease associations. Although most efforts have focused on identifying and interpreting genetic variants that are irrefutably associated with disease, it is increasingly clear that—even at large sample sizes—these represent only the tip of the iceberg of genetic signal, motivating polygenic analyses that consider the effects of genetic variants throughout the genome, including modest effects that are not individually statistically significant. As data from an increasingly large number of diseases and traits are analysed, pleiotropic effects (defined as genetic loci affecting multiple phenotypes) can help integrate our biological understanding. Looking forward, the next generation of population-scale data resources, linking genomic information with health outcomes, will lead to another step-change in our ability to understand, and treat, common diseases.
doi:10.1098/rspb.2015.1684
PMCID: PMC4707742  PMID: 26702037
common diseases; genome-wide association studies; human genetics
12.  Fast and accurate imputation of summary statistics enhances evidence of functional enrichment 
Bioinformatics  2014;30(20):2906-2914.
Motivation: Imputation using external reference panels (e.g. 1000 Genomes) is a widely used approach for increasing power in genome-wide association studies and meta-analysis. Existing hidden Markov models (HMM)-based imputation approaches require individual-level genotypes. Here, we develop a new method for Gaussian imputation from summary association statistics, a type of data that is becoming widely available.
Results: In simulations using 1000 Genomes (1000G) data, this method recovers 84% (54%) of the effective sample size for common (>5%) and low-frequency (1–5%) variants [increasing to 87% (60%) when summary linkage disequilibrium information is available from target samples] versus the gold standard of 89% (67%) for HMM-based imputation, which cannot be applied to summary statistics. Our approach accounts for the limited sample size of the reference panel, a crucial step to eliminate false-positive associations, and it is computationally very fast. As an empirical demonstration, we apply our method to seven case–control phenotypes from the Wellcome Trust Case Control Consortium (WTCCC) data and a study of height in the British 1958 birth cohort (1958BC). Gaussian imputation from summary statistics recovers 95% (105%) of the effective sample size (as quantified by the ratio of χ2 association statistics) compared with HMM-based imputation from individual-level genotypes at the 227 (176) published single nucleotide polymorphisms (SNPs) in the WTCCC (1958BC height) data. In addition, for publicly available summary statistics from large meta-analyses of four lipid traits, we publicly release imputed summary statistics at 1000G SNPs, which could not have been obtained using previously published methods, and demonstrate their accuracy by masking subsets of the data. We show that 1000G imputation using our approach increases the magnitude and statistical evidence of enrichment at genic versus non-genic loci for these traits, as compared with an analysis without 1000G imputation. Thus, imputation of summary statistics will be a valuable tool in future functional enrichment analyses.
Availability and implementation: Publicly available software package available at http://bogdan.bioinformatics.ucla.edu/software/.
Contact: bpasaniuc@mednet.ucla.edu or aprice@hsph.harvard.edu
Supplementary information: Supplementary materials are available at Bioinformatics online.
doi:10.1093/bioinformatics/btu416
PMCID: PMC4184260  PMID: 24990607
13.  New approaches to disease mapping in admixed populations 
Nature reviews. Genetics  2011;12(8):523-528.
Admixed populations such as African Americans and Hispanic Americans are often medically underserved and bear a disproportionately high burden of disease. Owing to the diversity of their genomes, these populations have both advantages and disadvantages for genetic studies of complex phenotypes. Advances in statistical methodologies that can infer genetic contributions from ancestral populations may yield new insights into the aetiology of disease and may contribute to the applicability of genomic medicine to these admixed population groups.
doi:10.1038/nrg3002
PMCID: PMC3142784  PMID: 21709689
15.  LD Score Regression Distinguishes Confounding from Polygenicity in Genome-Wide Association Studies 
Nature genetics  2015;47(3):291-295.
Both polygenicity (i.e., many small genetic effects) and confounding biases, such as cryptic relatedness and population stratification, can yield an inflated distribution of test statistics in genome-wide association studies (GWAS). However, current methods cannot distinguish between inflation from true polygenic signal and bias. We have developed an approach, LD Score regression, that quantifies the contribution of each by examining the relationship between test statistics and linkage disequilibrium (LD). The LD Score regression intercept can be used to estimate a more powerful and accurate correction factor than genomic control. We find strong evidence that polygenicity accounts for the majority of test statistic inflation in many GWAS of large sample size.
doi:10.1038/ng.3211
PMCID: PMC4495769  PMID: 25642630
16.  Efficient Bayesian mixed model analysis increases association power in large cohorts 
Nature genetics  2015;47(3):284-290.
Linear mixed models are a powerful statistical tool for identifying genetic associations and avoiding confounding. However, existing methods are computationally intractable in large cohorts, and may not optimize power. All existing methods require time cost O(MN2) (where N = #samples and M = #SNPs) and implicitly assume an infinitesimal genetic architecture in which effect sizes are normally distributed, which can limit power. Here, we present a far more efficient mixed model association method, BOLT-LMM, which requires only a small number of O(MN)-time iterations and increases power by modeling more realistic, non-infinitesimal genetic architectures via a Bayesian mixture prior on marker effect sizes. We applied BOLT-LMM to nine quantitative traits in 23,294 samples from the Women’s Genome Health Study (WGHS) and observed significant increases in power, consistent with simulations. Theory and simulations show that the boost in power increases with cohort size, making BOLT-LMM appealing for GWAS in large cohorts.
doi:10.1038/ng.3190
PMCID: PMC4342297  PMID: 25642633
17.  Leveraging population admixture to explain missing heritability of complex traits 
Nature genetics  2014;46(12):1356-1362.
Despite recent progress on estimating the heritability explained by genotyped SNPs (hg2), a large gap between hg2 and estimates of total narrow-sense heritability (h2) remains. Explanations for this gap include rare variants, or upward bias in family-based estimates of h2 due to shared environment or epistasis. We estimate h2 from unrelated individuals in admixed populations by first estimating the heritability explained by local ancestry (hγ2). We show that hγ2 = 2FSTCθ(1−θ)h2, where FSTC measures frequency differences between populations at causal loci and θ is the genome-wide ancestry proportion. Our approach is not susceptible to biases caused by epistasis or shared environment. We examined 21,497 African Americans from three cohorts, analyzing 13 phenotypes. For height and BMI, we obtained h2 estimates of 0.55 ± 0.09 and 0.23 ± 0.06, respectively, which are larger than estimates of hg2 in these and other data, but smaller than family-based estimates of h2.
doi:10.1038/ng.3139
PMCID: PMC4244251  PMID: 25383972
18.  Defining the role of common variation in the genomic and biological architecture of adult human height 
Wood, Andrew R | Esko, Tonu | Yang, Jian | Vedantam, Sailaja | Pers, Tune H | Gustafsson, Stefan | Chu, Audrey Y | Estrada, Karol | Luan, Jian’an | Kutalik, Zoltán | 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 | Mägi, Reedik | Mihailov, Evelin | Porcu, Eleonora | Randall, Joshua C | Scherag, André | Vinkhuyzen, Anna AE | 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 | Leach, Irene Mateo | 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 | Stancáková, 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, Loïc | Zhang, Weihua | Afzal, Uzma | Ärnlöv, Johan | Arscott, Gillian M | Bandinelli, Stefania | Barrett, Amy | Bellis, Claire | Bennett, Amanda J | Berne, Christian | Blüher, Matthias | Bolton, Jennifer L | Böttcher, 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 SF | Dörr, Marcus | Eklund, Niina | Eury, Elodie | Folkersen, Lasse | Garcia, Melissa E | Geller, Frank | Giedraitis, Vilmantas | Go, Alan S | Grallert, Harald | Grammer, Tanja B | Gräßler, Jürgen | Grönberg, 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, Åsa | Jolley, Jennifer | Juliusdottir, Thorhildur | Junttila, Juhani | Kho, Abel N | Kinnunen, Leena | Klopp, Norman | Kocher, Thomas | Kratzer, Wolfgang | Lichtner, Peter | Lind, Lars | Lindström, Jaana | Lobbens, Stéphane | Lorentzon, Mattias | Lu, Yingchang | Lyssenko, Valeriya | Magnusson, Patrik KE | 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 | Müller, Gabriele | Müller-Nurasyid, Martina | Musk, Arthur W | Narisu, Narisu | Nauck, Matthias | Nolte, Ilja M | Nöthen, 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 | Sundström, Johan | Swertz, Morris A | Syvänen, 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 VA | 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 JL | 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 | Ferrières, 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 JP | 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 | Lehtimäki, Terho | Lupoli, Sara | Madden, Pamela AF | Männistö, Satu | Manunta, Paolo | Marette, André | 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, DC | Rice, Treva K | Ritchie, Marylyn | Rudan, Igor | Salomaa, Veikko | Samani, Nilesh J | Saramies, Jouko | Sarzynski, Mark A | Schwarz, Peter EH | Sebert, Sylvain | Sever, Peter | Shuldiner, Alan R | Sinisalo, Juha | Steinthorsdottir, Valgerdur | Stolk, Ronald P | Tardif, Jean-Claude | Tönjes, Anke | Tremblay, Angelo | Tremoli, Elena | Virtamo, Jarmo | Vohl, Marie-Claude | Amouyel, Philippe | Asselbergs, Folkert W | Assimes, Themistocles L | Bochud, Murielle | Boehm, Bernhard O | Boerwinkle, Eric | Bottinger, Erwin P | Bouchard, Claude | Cauchi, Stéphane | Chambers, John C | Chanock, Stephen J | Cooper, Richard S | de Bakker, Paul IW | 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 | März, Winfried | Melbye, Mads | Moebus, Susanne | Munroe, Patricia B | Njølstad, Inger | Oostra, Ben A | Palmer, Colin NA | Pedersen, Nancy L | Perola, Markus | Pérusse, 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 | Völzke, 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, Inês | 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, André G | van Duijn, Cornelia M | Franke, Lude | Willer, Cristen J | Price, Alkes L. | Lettre, Guillaume | Loos, Ruth JF | 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
Nature genetics  2014;46(11):1173-1186.
Using genome-wide data from 253,288 individuals, we identified 697 variants at genome-wide significance that together explain one-fifth of heritability for adult height. By testing different numbers of variants in independent studies, we show that the most strongly associated ~2,000, ~3,700 and ~9,500 SNPs explained ~21%, ~24% and ~29% of phenotypic variance. Furthermore, all common variants together captured the majority (60%) of heritability. The 697 variants clustered in 423 loci 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/beta-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.
doi:10.1038/ng.3097
PMCID: PMC4250049  PMID: 25282103
19.  Defining the role of common variation in the genomic and biological architecture of adult human height 
Wood, Andrew R | Esko, Tonu | Yang, Jian | Vedantam, Sailaja | Pers, Tune H | Gustafsson, Stefan | Chu, Audrey Y | Estrada, Karol | Luan, Jian’an | Kutalik, Zoltán | 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 | Mägi, Reedik | Mihailov, Evelin | Porcu, Eleonora | Randall, Joshua C | Scherag, André | Vinkhuyzen, Anna AE | 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 | Leach, Irene Mateo | 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 | Stancáková, 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, Loïc | Zhang, Weihua | Afzal, Uzma | Ärnlöv, Johan | Arscott, Gillian M | Bandinelli, Stefania | Barrett, Amy | Bellis, Claire | Bennett, Amanda J | Berne, Christian | Blüher, Matthias | Bolton, Jennifer L | Böttcher, 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 SF | Dörr, Marcus | Eklund, Niina | Eury, Elodie | Folkersen, Lasse | Garcia, Melissa E | Geller, Frank | Giedraitis, Vilmantas | Go, Alan S | Grallert, Harald | Grammer, Tanja B | Gräßler, Jürgen | Grönberg, 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, Åsa | Jolley, Jennifer | Juliusdottir, Thorhildur | Junttila, Juhani | Kho, Abel N | Kinnunen, Leena | Klopp, Norman | Kocher, Thomas | Kratzer, Wolfgang | Lichtner, Peter | Lind, Lars | Lindström, Jaana | Lobbens, Stéphane | Lorentzon, Mattias | Lu, Yingchang | Lyssenko, Valeriya | Magnusson, Patrik KE | 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 | Müller, Gabriele | Müller-Nurasyid, Martina | Musk, Arthur W | Narisu, Narisu | Nauck, Matthias | Nolte, Ilja M | Nöthen, 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 | Sundström, Johan | Swertz, Morris A | Syvänen, 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 VA | 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 JL | 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 | Ferrières, 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 JP | 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 | Lehtimäki, Terho | Lupoli, Sara | Madden, Pamela AF | Männistö, Satu | Manunta, Paolo | Marette, André | 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, DC | Rice, Treva K | Ritchie, Marylyn | Rudan, Igor | Salomaa, Veikko | Samani, Nilesh J | Saramies, Jouko | Sarzynski, Mark A | Schwarz, Peter EH | Sebert, Sylvain | Sever, Peter | Shuldiner, Alan R | Sinisalo, Juha | Steinthorsdottir, Valgerdur | Stolk, Ronald P | Tardif, Jean-Claude | Tönjes, Anke | Tremblay, Angelo | Tremoli, Elena | Virtamo, Jarmo | Vohl, Marie-Claude | Amouyel, Philippe | Asselbergs, Folkert W | Assimes, Themistocles L | Bochud, Murielle | Boehm, Bernhard O | Boerwinkle, Eric | Bottinger, Erwin P | Bouchard, Claude | Cauchi, Stéphane | Chambers, John C | Chanock, Stephen J | Cooper, Richard S | de Bakker, Paul IW | 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 | März, Winfried | Melbye, Mads | Moebus, Susanne | Munroe, Patricia B | Njølstad, Inger | Oostra, Ben A | Palmer, Colin NA | Pedersen, Nancy L | Perola, Markus | Pérusse, 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 | Völzke, 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, Inês | 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, André G | van Duijn, Cornelia M | Franke, Lude | Willer, Cristen J | Price, Alkes L. | Lettre, Guillaume | Loos, Ruth JF | 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
Nature genetics  2014;46(11):1173-1186.
Using genome-wide data from 253,288 individuals, we identified 697 variants at genome-wide significance that together explain one-fifth of heritability for adult height. By testing different numbers of variants in independent studies, we show that the most strongly associated ~2,000, ~3,700 and ~9,500 SNPs explained ~21%, ~24% and ~29% of phenotypic variance. Furthermore, all common variants together captured the majority (60%) of heritability. The 697 variants clustered in 423 loci 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/beta-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.
doi:10.1038/ng.3097
PMCID: PMC4250049  PMID: 25282103
21.  Integrating Functional Data to Prioritize Causal Variants in Statistical Fine-Mapping Studies 
PLoS Genetics  2014;10(10):e1004722.
Standard statistical approaches for prioritization of variants for functional testing in fine-mapping studies either use marginal association statistics or estimate posterior probabilities for variants to be causal under simplifying assumptions. Here, we present a probabilistic framework that integrates association strength with functional genomic annotation data to improve accuracy in selecting plausible causal variants for functional validation. A key feature of our approach is that it empirically estimates the contribution of each functional annotation to the trait of interest directly from summary association statistics while allowing for multiple causal variants at any risk locus. We devise efficient algorithms that estimate the parameters of our model across all risk loci to further increase performance. Using simulations starting from the 1000 Genomes data, we find that our framework consistently outperforms the current state-of-the-art fine-mapping methods, reducing the number of variants that need to be selected to capture 90% of the causal variants from an average of 13.3 to 10.4 SNPs per locus (as compared to the next-best performing strategy). Furthermore, we introduce a cost-to-benefit optimization framework for determining the number of variants to be followed up in functional assays and assess its performance using real and simulation data. We validate our findings using a large scale meta-analysis of four blood lipids traits and find that the relative probability for causality is increased for variants in exons and transcription start sites and decreased in repressed genomic regions at the risk loci of these traits. Using these highly predictive, trait-specific functional annotations, we estimate causality probabilities across all traits and variants, reducing the size of the 90% confidence set from an average of 17.5 to 13.5 variants per locus in this data.
Author Summary
Genome-wide association studies (GWAS) have successfully identified numerous regions in the genome that harbor genetic variants that increase risk for various complex traits and diseases. However, it is generally the case that GWAS risk variants are not themselves causally affecting the trait, but rather, are correlated to the true causal variant through linkage disequilibrium (LD). Plausible causal variants are identified in fine-mapping studies through targeted sequencing followed by prioritization of variants for functional validation. In this work, we propose methods that leverage two sources of independent information, the association strength and genomic functional location, to prioritize causal variants. We demonstrate in simulations and empirical data that our approach reduces the number of SNPs that need to be selected for follow-up to identify the true causal variants at GWAS risk loci.
doi:10.1371/journal.pgen.1004722
PMCID: PMC4214605  PMID: 25357204
22.  Replication and fine mapping of asthma-associated loci in individuals of African ancestry 
Human genetics  2013;132(9):1039-1047.
Asthma originates from genetic and environmental factors with about half the risk of disease attributable to heritable causes. Genome-wide association studies, mostly in populations of European ancestry, have identified numerous asthma-associated single nucleotide polymorphisms (SNPs). Studies in populations with diverse ancestries allow both for identification of robust associations that replicate across ethnic groups and for improved resolution of associated loci due to different patterns of linkage disequilibrium between ethnic groups. Here we report on an analysis of 745 African-American subjects with asthma and 3,238 African-American control subjects from the Candidate Gene Association Resource (CARe) Consortium, including analysis of SNPs imputed using 1,000 Genomes reference panels and adjustment for local ancestry. We show strong evidence that variation near RAD50/IL13, implicated in studies of European ancestry individuals, replicates in individuals largely of African ancestry. Fine mapping in African ancestry populations also refined the variants of interest for this association. We also provide strong or nominal evidence of replication at loci near ORMDL3/GSDMB, IL1RLML18R1, and 10pl4, all previously associated with asthma in European or Japanese populations, but not at the PYHIN1 locus previously reported in studies of African-American samples. These results improve the understanding of asthma genetics and further demonstrate the utility of genetic studies in populations other than those of largely European ancestry.
doi:10.1007/s00439-013-1310-7
PMCID: PMC3975655  PMID: 23666277
23.  Advantages and pitfalls in the application of mixed model association methods 
Nature genetics  2014;46(2):100-106.
Mixed linear models are emerging as a method of choice for conducting genetic association studies in humans and other organisms. The advantages of mixed linear model association (MLMA) include preventing false-positive associations due to population or relatedness structure, and increasing power by applying a correction that is specific to this structure. An underappreciated point is that MLMA can also increase power in studies without sample structure, by implicitly conditioning on associated loci other than the candidate locus. Numerous variations on the standard MLMA approach have recently been published, with a focus on reducing computational cost. These advances provide researchers applying MLMA methods with many options to choose from, but we caution that MLMA methods are still subject to potential pitfalls. Here, we describe and quantify the advantages and pitfalls of MLMA methods as a function of study design, and provide recommendations for the application of these methods in practical settings.
doi:10.1038/ng.2876
PMCID: PMC3989144  PMID: 24473328
24.  Pitfalls of predicting complex traits from SNPs 
Nature reviews. Genetics  2013;14(7):507-515.
The success of genome-wide association studies has led to increasing interest in making predictions of complex trait phenotypes including disease from genotype data. Rigorous assessment of the value of predictors is critical before implementation. Here we discuss some of the limitations and pitfalls of prediction analysis and show how naïve implementations can lead to severe bias and misinterpretation of results.
doi:10.1038/nrg3457
PMCID: PMC4096801  PMID: 23774735
25.  Improved ancestry inference using weights from external reference panels 
Bioinformatics  2013;29(11):1399-1406.
Motivation: Inference of ancestry using genetic data is motivated by applications in genetic association studies, population genetics and personal genomics. Here, we provide methods and software for improved ancestry inference using genome-wide single nucleotide polymorphism (SNP) weights from external reference panels. This approach makes it possible to leverage the rich ancestry information that is available from large external reference panels, without the administrative and computational complexities of re-analyzing the raw genotype data from the reference panel in subsequent studies.
Results: We extensively validate our approach in multiple African American, Latino American and European American datasets, making use of genome-wide SNP weights derived from large reference panels, including HapMap 3 populations and 6546 European Americans from the Framingham Heart Study. We show empirically that our approach provides much greater accuracy than either the prevailing ancestry-informative marker (AIM) approach or the analysis of genome-wide target genotypes without a reference panel. For example, in an independent set of 1636 European American genome-wide association study samples, we attained prediction accuracy (R2) of 1.000 and 0.994 for the first two principal components using our method, compared with 0.418 and 0.407 using 150 published AIMs or 0.955 and 0.003 by applying principal component analysis directly to the target samples. We finally show that the higher accuracy in inferring ancestry using our method leads to more effective correction for population stratification in association studies.
Availability: The SNPweights software is available online at http://www.hsph.harvard.edu/faculty/alkes-price/software/.
Contact: aprice@hsph.harvard.edu or cychen@mail.harvard.edu.
Supplementary information: Supplementary data are available at Bioinformatics online.
doi:10.1093/bioinformatics/btt144
PMCID: PMC3661048  PMID: 23539302

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