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BACKGROUND: While polygenic risk scores hold significant promise in estimating an individual's risk of developing a complex trait such as obesity, their application in the clinic has, to date, been limited by a lack of data from non-European populations. As a collaboration model of the International Hundred K+ Cohorts Consortium (IHCC), we endeavored to develop a globally applicable trans-ethnic PRS for body mass index (BMI) through this relatively new international effort. METHODS: The polygenic risk score (PRS) model was developed, trained and tested at the Center for Applied Genomics (CAG) of The Children's Hospital of Philadelphia (CHOP) based on a BMI meta-analysis from the GIANT consortium. The validated PRS models were subsequently disseminated to the participating sites. Scores were generated by each site locally on their cohorts and summary statistics returned to CAG for final analysis. RESULTS: We show that in the absence of a well powered trans-ethnic GWAS from which to derive marker SNPs and effect estimates for PRS, trans-ethnic scores can be generated from European ancestry GWAS using Bayesian approaches such as LDpred, by adjusting the summary statistics using trans-ethnic linkage disequilibrium reference panels. The ported trans-ethnic scores outperform population specific-PRS across all non-European ancestry populations investigated including East Asians and three-way admixed Brazilian cohort. CONCLUSIONS: Here we show that for a truly polygenic trait such as BMI adjusting the summary statistics of a well powered European ancestry study using trans-ethnic LD reference results in a score that is predictive across a range of ancestries including East Asians and three-way admixed Brazilians.

Large-scale genome-wide association studies (GWAS) have produced a rich resource of genetic data over the past decade, urging the need to develop computational and statistical methods that analyze these data. This dissertation presents four statistical methods that model the correlation structure between genetic variants and its effect on GWAS summary association statistics to help understand the genetic basis of complex human traits and diseases.The first method employs the multivariate Bernoulli distribution to model haplotype data, allowing for higher-order interactions among genetic variants, and shows better accuracy in predicting DNase I hypersensitivity status.The second method partitions heritability into small regions on the genome using GWAS summary statistics data, while accounting for complex correlation structures among genetic variants, and uncovers the genetic architectures of complex human traits and diseases.Extending the second method into pairs of traits, the third method partitions genetic correlation into small genomic regions using GWAS summary statistics data, and provides insights into the shared genetic basis between pairs of traits.Finally, the fourth method dissects population-specific and shared causal genetic variants of complex traits in two continental populations, using GWAS summary statistics data obtained from samples of different ethnicities, and reveals differences in genetic architectures of two continental populations.

Most polygenic risk score (PRS)models have been based on data from populations of European origins (accounting for the majority of the large genomics datasets, e.g. >78% in the UK Biobank and >85% in the GTEx project). Although several large-scale Asian biobanks were initiated (e.g. Japanese, Korean, Han Chinese biobanks), most other Asian countries have little or near-zero genomics data. To implement PRS models for under-represented populations, we explored transfer learning approaches, assuming that information from existing large datasets can compensate for the small sample size that can be feasibly obtained in developing countries, like Vietnam. Here, we benchmark 13 common PRS methods in meta-population strategy (combining individual genotype data from multiple populations) and multi-population strategy (combining summary statistics from multiple populations). Our results highlight the complementarity of different populations and the choice of methods should depend on the target population. Based on these results, we discussed a set of guidelines to help users select the best method for their datasets. We developed a robust and comprehensive software to allow for benchmarking comparisons between methods and proposed a computational framework for improving PRS performance in a dataset with a small sample size. This work is expected to inform the development of genomics applications in under-represented populations. PRSUP framework is available at: https://github.com/BiomedicalMachineLearning/VGP

Growing research suggests that posttraumatic stress disorder (PTSD) may be a risk factor for poor cardiovascular health, and yet our understanding of who might be at greatest risk of adverse cardiovascular outcomes after trauma is limited. In this study, we conducted the first examination of the individual and synergistic contributions of PTSD symptoms and blood pressure genetics to continuous blood pressure levels. We harnessed the power of the Psychiatric Genomics Consortium-PTSD Physical Health Working Group and investigated these associations across 11 studies of 72,224 trauma-exposed individuals of European (n = 70,870) and African (n = 1,354) ancestry. Genetic contributions to blood pressure were modeled via polygenic scores (PGS) for systolic blood pressure (SBP) and diastolic blood pressure (DBP) that were derived from a prior trans-ethnic blood pressure genome-wide association study (GWAS). Results of trans-ethnic meta-analyses revealed significant main effects of the PGS on blood pressure levels [SBP: β = 2.83, standard error (SE) = 0.06, p < 1E-20; DBP: β = 1.32, SE = 0.04, p < 1E-20]. Significant main effects of PTSD symptoms were also detected for SBP and DBP in trans-ethnic meta-analyses, though there was significant heterogeneity in these results. When including data from the largest contributing study – United Kingdom Biobank – PTSD symptoms were negatively associated with SBP levels (β = −1.46, SE = 0.44, p = 9.8E-4) and positively associated with DBP levels (β = 0.70, SE = 0.26, p = 8.1E-3). However, when excluding the United Kingdom Biobank cohort in trans-ethnic meta-analyses, there was a nominally significant positive association between PTSD symptoms and SBP levels (β = 2.81, SE = 1.13, p = 0.01); no significant association was observed for DBP (β = 0.43, SE = 0.78, p = 0.58). Blood pressure PGS did not significantly moderate the associations between PTSD symptoms and blood pressure levels in meta-analyses. Additional research is needed to better understand the extent to which PTSD is associated with high blood pressure and how genetic as well as contextual factors may play a role in influencing cardiovascular risk.

Uterine fibroids affect up to 77% of women by menopause and account for up to $34 billion in healthcare costs each year. Although fibroid risk is heritable, genetic risk for fibroids is not well understood. We conducted a two-stage case-control meta-analysis of genetic variants in European and African ancestry women with and without fibroids classified by a previously published algorithm requiring pelvic imaging or confirmed diagnosis. Women from seven electronic Medical Records and Genomics (eMERGE) network sites (3,704 imaging-confirmed cases and 5,591 imaging-confirmed controls) and women of African and European ancestry from UK Biobank (UKB, 5,772 cases and 61,457 controls) were included in the discovery genome-wide association study (GWAS) meta-analysis. Variants showing evidence of association in Stage I GWAS (P < 1 × 10-5) were targeted in an independent replication sample of African and European ancestry individuals from the UKB (Stage II) (12,358 cases and 138,477 controls). Logistic regression models were fit with genetic markers imputed to a 1000 Genomes reference and adjusted for principal components for each race- and site-specific dataset, followed by fixed-effects meta-analysis. Final analysis with 21,804 cases and 205,525 controls identified 326 genome-wide significant variants in 11 loci, with three novel loci at chromosome 1q24 (sentinel-SNP rs14361789; P = 4.7 × 10-8), chromosome 16q12.1 (sentinel-SNP rs4785384; P = 1.5 × 10-9) and chromosome 20q13.1 (sentinel-SNP rs6094982; P = 2.6 × 10-8). Our statistically significant findings further support previously reported loci including SNPs near WT1, TNRC6B, SYNE1, BET1L, and CDC42/WNT4. We report evidence of ancestry-specific findings for sentinel-SNP rs10917151 in the CDC42/WNT4 locus (P = 1.76 × 10-24). Ancestry-specific effect-estimates for rs10917151 were in opposite directions (P-Het-between-groups = 0.04) for predominantly African (OR = 0.84) and predominantly European women (OR = 1.16). Genetically-predicted gene expression of several genes including LUZP1 in vagina (P = 4.6 × 10-8), OBFC1 in esophageal mucosa (P = 8.7 × 10-8), NUDT13 in multiple tissues including subcutaneous adipose tissue (P = 3.3 × 10-6), and HEATR3 in skeletal muscle tissue (P = 5.8 × 10-6) were associated with fibroids. The finding for HEATR3 was supported by SNP-based summary Mendelian randomization analysis. Our study suggests that fibroid risk variants act through regulatory mechanisms affecting gene expression and are comprised of alleles that are both ancestry-specific and shared across continental ancestries.

BACKGROUND AND AIMS: Recent meta-analysis of genome-wide association studies have identified over 241 inflammatory bowel disease susceptibility loci. However, the known variants only account for a fraction of inflammatory bowel disease heritability. To identify additional susceptibility loci, we performed a trans-ethnic meta-analysis as well as an Asian-specific meta-analysis, using all published Immunochip association results of inflammatory bowel disease. METHODS: An inverse-variance fixed-effects meta-analysis was carried out across Korean and East Asian Immunochip datasets of 4156 cases and 4904 controls [Asian ancestry]. A trans-ethnic meta-analysis of inflammatory bowel disease was performed together with the European datasets of 38 155 cases and 48 485 controls genotyped on the immunochip using a Bayesian approach, Meta-Analysis of Trans-ethnic Association studies [MANTRA]. RESULTS: We identified seven novel associations, including three novel susceptibility loci at MYO10-BASP1, PPP2R3C/KIAA0391/PSMA6/NFKB1A and LRRK1 as well as four novel secondary associations within previously known loci at NCF4, TSPAN32, CIITA and VANGL2. The new loci further implicate alterations in B cell biology in Crohn’s disease pathogenesis. The effects of five loci were universal across European and Asian ancestries, whereas the NCF4 and CIITA loci showed significant heterogeneity between European and East Asian populations. In addition, 103 previously known IBD loci showed supporting evidence of association with nominal significance [p < 0.05] in Asians. CONCLUSIONS: Our findings of new loci not previously associated with IBD support the importance of studying inflammatory bowel disease genetics in diverse populations.

The genetic basis of earlobe attachment has been a matter of debate since the early 20th century, such that geneticists argue both for and against polygenic inheritance. Recent genetic studies have identified a few loci associated with the trait, but large-scale analyses are still lacking. Here, we performed a genome-wide association study of lobe attachment in a multiethnic sample of 74,660 individuals from four cohorts (three with the trait scored by an expert rater and one with the trait self-reported). Meta-analysis of the three expert-rater-scored cohorts revealed six associated loci harboring numerous candidate genes, including EDAR, SP5, MRPS22, ADGRG6 (GPR126), KIAA1217, and PAX9. The large self-reported 23andMe cohort recapitulated each of these six loci. Moreover, meta-analysis across all four cohorts revealed a total of 49 significant (p < 5 × 10−8) loci. Annotation and enrichment analyses of these 49 loci showed strong evidence of genes involved in ear development and syndromes with auricular phenotypes. RNA sequencing data from both human fetal ear and mouse second branchial arch tissue confirmed that genes located among associated loci showed evidence of expression. These results provide strong evidence for the polygenic nature of earlobe attachment and offer insights into the biological basis of normal and abnormal ear development. Fil: Shaffer, John R.. University of Pittsburgh; Estados Unidos Fil: Li, Jinxi. University of Chinese Academy of Sciences; China Fil: Lee, Myoung Keun. University of Pittsburgh; Estados Unidos Fil: Roosenboom, Jasmien. University of Pittsburgh; Estados Unidos Fil: Orlova, Ekaterina. University of Pittsburgh; Estados Unidos Fil: Adhikari, Kaustabh. Colegio Universitario de Londres; Reino Unido Fil: Gallo, Carla. Universidad Peruana Cayetano Heredia; Perú Fil: Poletti, Giovanni. Universidad Peruana Cayetano Heredia; Perú Fil: Schuler Faccini, Lavinia. Universidade Federal do Rio Grande do Sul; Brasil Fil: Bortolini, Maria Catira. Universidade Federal do Rio Grande do Sul; Brasil Fil: Canizales Quinteros, Samuel. Universidad Nacional Autónoma de México; México Fil: Rothhammer, Francisco. Universidad de Tarapacá; Chile Fil: Bedoya, Gabriel. Universidad de Antioquia; Colombia Fil: González José, Rolando. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Centro Nacional Patagónico. Instituto Patagónico de Ciencias Sociales y Humanas; Argentina Fil: Pfeffer, Paige E.. Saint Louis University; Estados Unidos Fil: Wollenschlaeger, Christopher A.. University of Pittsburgh; Estados Unidos Fil: Hecht, Jacqueline T.. University of Texas; Estados Unidos Fil: Wehby, George. University of Iowa; Estados Unidos Fil: Moreno, Lina M.. University of Iowa; Estados Unidos Fil: Ding, Anan. University of Chinese Academy of Sciences; China Fil: Jin, Li. University of Chinese Academy of Sciences; China. Fudan University; China Fil: Yang, Yajun. Fudan University; China Fil: Carlson, Jenna C.. University of Pittsburgh; Estados Unidos Fil: Leslie, Elizabeth J.. University of Pittsburgh; Estados Unidos Fil: Feingold, Eleanor. University of Pittsburgh; Estados Unidos Fil: Marazita, Mary L.. University of Pittsburgh; Estados Unidos Fil: Hinds, David A.. 899 West Evelyn Avenue; Estados Unidos Fil: Cox, Timothy C.. Seattle Children’s Research Institute; Estados Unidos. University of Washington; Estados Unidos. Monash University; Australia Fil: Wang, Sijia. University of Chinese Academy of Sciences; China. Fudan University; China Fil: Ruiz Linares, Andrés. Colegio Universitario de Londres; Reino Unido. Fudan University; China. Aix-Marseille University; Francia Fil: Weinberg, Seth M.. University of Pittsburgh; Estados Unidos