Genome-wide association studies (GWAS) have been successful in identifying common genetic variants contributing to disease risk. However, nearly all of these studies have been conducted in populations of European ancestry. It is important to include other populations, because GWAS in Europeans are unlikely to detect risk variants that are common only in non-European populations. In the United States, the majority of individuals of non-European ancestry belong to admixed populations (e.g. African Americans or Latinos) that inherit ancestry from more than one continental population. Existing GWAS methods for admixed populations are inadequate, because they do not incorporate both SNP association and admixture association signals. Thus, if existing methods are applied, analyses will not be fully powered and important variants will be missed. For diseases with known population differences-such as cardiovascular disease in African Americans and asthma in Latinos-the need to develop methods that combine these signals is particularly pressing, because admixture association signals are likely to be particularly important. Here, we propose to develop a complete set of methods and software to combine SNP and admixture association signals in GWAS in admixed populations, while addressing questions such as imputation and choosing SNPs for replication. Our goal is to make fully powered association studies in populations of mixed ancestry as practical as studies in populations of homogeneous ancestry. In addition to African Americans, we will also develop methods for complex admixed populations (e.g. Latinos) that inherit ancestry from three or more continental populations, and for related individuals from admixed populations. Our methods research will be driven by empirical data, including over 10,000 African American samples and 2,400 Latino samples that will be genome-scanned by the CARe consortium, the Jackson Heart Study, and the Multiethnic Cohort Study. Our work will be applicable not only to GWAS in admixed populations, but also to meta-analyses of European and admixed populations, as well as future resequencing-based studies.
Genome-wide association studies (GWAS), an approach in which the genomes of both diseased and healthy individuals are scanned to identify genes affecting disease risk, have thus far been primarily restricted to populations of European ancestry. It is important to extend these studies to other populations, such as admixed populations (African Americans and Latinos) that inherit ancestry from multiple continental groups, but existing statistical methods for conducting GWAS in admixed populations are inadequate due to the complexities posed by chromosomal segments of distinct continental ancestry. In this proposal, we will use empirical genetic data sets to develop statistical methods and software to fill this gap.
| Giambartolomei, Claudia; Zhenli Liu, Jimmy; Zhang, Wen et al. (2018) A Bayesian framework for multiple trait colocalization from summary association statistics. Bioinformatics 34:2538-2545 |
| Johnson, Ruth; Shi, Huwenbo; Pasaniuc, Bogdan et al. (2018) A unifying framework for joint trait analysis under a non-infinitesimal model. Bioinformatics 34:i195-i201 |
| Freund, Malika Kumar; Burch, Kathryn S; Shi, Huwenbo et al. (2018) Phenotype-Specific Enrichment of Mendelian Disorder Genes near GWAS Regions across 62 Complex Traits. Am J Hum Genet 103:535-552 |
| Franceschini, Nora; Giambartolomei, Claudia; de Vries, Paul S et al. (2018) GWAS and colocalization analyses implicate carotid intima-media thickness and carotid plaque loci in cardiovascular outcomes. Nat Commun 9:5141 |
| Galinsky, Kevin J; Reshef, Yakir A; Finucane, Hilary K et al. (2018) Estimating cross-population genetic correlations of causal effect sizes. Genet Epidemiol : |
| Roytman, Megan; Kichaev, Gleb; Gusev, Alexander et al. (2018) Methods for fine-mapping with chromatin and expression data. PLoS Genet 14:e1007240 |
| Loh, Po-Ru; Kichaev, Gleb; Gazal, Steven et al. (2018) Mixed-model association for biobank-scale datasets. Nat Genet 50:906-908 |
| Mak, Angel C Y; White, Marquitta J; Eckalbar, Walter L et al. (2018) Whole-Genome Sequencing of Pharmacogenetic Drug Response in Racially Diverse Children with Asthma. Am J Respir Crit Care Med 197:1552-1564 |
| Mancuso, Nicholas; Gayther, Simon; Gusev, Alexander et al. (2018) Large-scale transcriptome-wide association study identifies new prostate cancer risk regions. Nat Commun 9:4079 |
| Palamara, Pier Francesco; Terhorst, Jonathan; Song, Yun S et al. (2018) High-throughput inference of pairwise coalescence times identifies signals of selection and enriched disease heritability. Nat Genet 50:1311-1317 |
Showing the most recent 10 out of 75 publications
