Genomic summary statistics have been widely used to address various scientific questions in genetic and genomic research. Applications that involve multiple genetic variants, such as conditional analysis, variant set and gene-based tests, heritability and genetic correlation estimation, also require correlation or linkage disequilibrium (LD) information between genetic variants, often obtained from an external reference panel. While these methods usually have good performance for common variants in populations of only European ancestry, in practice, it is usually difficult to find external reference panels that accurately represent the LD structure for isolated, underrepresented or admixed populations, as well as rare genetic variants from whole genome sequencing (WGS) studies, limiting their applications to European populations. To maximize the applicability of summary statistics-based methods and make them equally beneficial to all human populations, we have developed StocSum, a novel reference-panel-free statistical framework for generating, managing, and analyzing stochastic summary statistics using random matrix algorithms. Using two cohorts from the Trans-Omics for Precision Medicine Program, we demonstrate the accuracy of StocSum-based LD measures as compared to those directly computed from individual-level genotype data, in European-, African-, and Hispanic/Latino-Americans. We also show that for admixed populations such as African- and Hispanic/Latino-Americans, LD measures computed from external reference panels perform much worse, even if all ancestry populations are included in those reference panels. As a reference-panel-free framework, StocSum will facilitate sharing and utilization of genomic summary statistics from WGS studies, especially for isolated, underrepresented and admixed populations.

African populations demonstrate exceptional genetic and phenotypic diversity, due in part to their varied environments, lifestyles, and demographic history. We conducted genetic and phenotypic association analyses in 6,965 geographically and ethnically diverse Sub-Saharan African individuals (6,280 with whole-genome sequences from the NIH TOPMed consortium and 685 with genotypes from Illumina arrays), using 15 cardiometabolic phenotypes (range 686-6,854 individuals/trait). Each phenotype had at least one ethnicity with significantly differing mean values compared to the remaining cohort, such as short stature in the Baka rainforest hunter-gatherers of Cameroon, and high adiposity in the Herero pastoralists of Botswana. An analysis of ethnicity-sex interactions revealed several ethnic groups with significant sexual dimorphism for at least one cardiometabolic phenotype, such as Herero women having markedly higher body mass index than men. Comparison between the African cohort and African ancestry UK Biobank (UKBB) individuals showed the latter have higher mean values than any of the 53 African ethnic groups for multiple cardiometabolic measurements, including low density lipoprotein cholesterol (LDL), body fat percentage (BFP), and systolic blood pressure. We also found that phenotype-phenotype correlations differ between the UKBB and African cohort, as well as between African ethnicities. For example, BFP and LDL had low correlation in the UKBB (R=0.04) but showed a range of correlation among African groups, from R = 0.00 in the Maasai pastoralists of eastern Africa to R = 0.43 in the Agaw agriculturalists of Ethiopia. Genome-wide association analyses identified 76 significantly associated loci (p<5.0x10-8), with 14 passing a more stringent empirical threshold (p<3.0x10-9), including APOE and APOC1 loci for various blood lipids, PCSK9 for LDL, and CETP for high density lipoprotein cholesterol (HDL), as well as novel loci. Set-based rare variant analyses for loss-of-function variants found 12 gene-phenotype associations replicating known associations with PCSK9 and APOE for LDL and total cholesterol and uncovering several novel gene-trait associations for adiposity traits and HDL. Ongoing analyses include phenotype associations with subsistence and genetically inferred ancestry, replication of genetic associations, and gene-set enrichment. In total, these results offer insights into the genetic and phenotypic landscape of cardiometabolic traits in African populations. This work was supported by grant numbers: ADA 1–19-VSN-02, NIH grants 1R35GM134957, R01DK104339, and R01AR076241, and 1X01HL139409-01.

Non-alcoholic fatty liver disease (NAFLD) is the most common cause of chronic liver disease in the US. Notably, disease prevalence differs greatly by race/ethnicity, with the highest prevalence in those of Hispanic and Asian ancestry, and lowest prevalence in those of African ancestry. To date, studies have identified common variants associated with NAFLD in predominantly European American population. We have conducted the largest-to-date multi-ancestry whole genome sequencing (WGS) association study to identify rare variants that promote NAFLD.

Study-, ethnic/race- and sex-stratified association analyses were conducted in six cohorts with imaging-measured hepatic steatosis using SAIGEgds adjusted for age, sex (where appropriate), alcoholic drinks per week and principal component estimates of admixture. Stratified results were meta-analyzed overall and male- and female-only using a fixed-effects meta-analysis in METAL. Cochran’s Q test and the I2 metric were used to identify and quantify heterogeneity.

The overall meta-analysis included 16,664 individuals representing European, African American, Hispanic and Chinese American ancestries with imaging-measured hepatic steatosis. The overall meta-analysis identified three variants significantly associated (P<5E-08) with NAFLD, i.e. PNPLA3, HAPLN4, PPP1R3B. An additional 12 variants trended toward association (P<5E-07). The sex-stratified meta-analysis revealed that the PNPLA3 and PPP1R3B variants were more strongly associated in females. In addition, variants in HAPLN4 and TMTM120B were identified in male- and female-only analyses, respectively.

In a large, multiethnic analysis of imaging-measured hepatic steatosis, we replicated loci previously associated with NAFLD and identified new sex-specific loci. Several variants were trending toward association and will benefit from ongoing analyses to include 6492 additional samples with imaging measured steatosis.

Background: Non-alcoholic fatty liver disease (NAFLD) is the most common cause of chronic liver disease in the US. Notably, disease prevalence differs greatly by race/ethnicity, with the highest prevalence in those of Hispanic and Asian ancestry, and the lowest prevalence in those of African ancestry. To date, studies have identified common variants associated with NAFLD in predominantly European or American populations. We have conducted the largest-to-date multi-ancestry whole genome sequencing (WGS) association
study to identify rare variants that promote NAFLD in the NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium.

Methods: Study- and ethnic/race-stratified association analyses were conducted in six cohorts with imaging-measured hepatic steatosis using SAIGEgds adjusted for age, sex, alcoholic drinks per week, and principal component estimates of admixture. Stratified results were meta-analyzed for Hispanic Ancestry, non-Hispanic European Ancestry, non-Hispanic African
Ancestry, and non-Hispanic Chinese Ancestry individuals and for an overall analysis using a fixed-effects meta-analysis in METAL. Cochran’s Q test and the I2 metric were used to identify and quantify heterogeneity.

Results: The meta-analysis included 16,664 individuals with imaging measured hepatic steatosis. Of these, 9,443 were of European Ancestry, 5,918 were of African Ancestry, 937 were of Hispanic Ancestry and 366 were of Chinese Ancestry. The ethnic/race-stratified meta-analysis identified six variants significantly associated (P<=5E-08) with NAFLD, i.e. European Ancestry
(n=2), African Ancestry (n=4), including variants in/near PNPLA3, TM6SF2, PPP1R3B, LINC01684, and SLC2A1. An additional 15 variants trended toward association (P<=5E-07) i.e. European Ancestry (n=1), African Ancestry (n=11), Hispanic Ancestry (n=2), and Chinese Ancestry (n=1) with NAFLD.

Conclusions: In a large, multiethnic analysis of imaging-measured hepatic steatosis, we replicated loci previously associated with NAFLD and identified possible new race-specific loci. Several variants were trending toward association and will benefit from ongoing analyses to include 6,492 additional samples.

Background: Obesity is a global public health crisis associated with high morbidity and mortality rates. Previous genome-wide association studies (GWAS) investigating body mass index (BMI) have primarily relied on imputed data from European individuals. Consequently, most BMI risk variants identified to date are common and in primarily European ancestry populations exhibiting small effect sizes, leaving low and rare variants with potentially large effects largely unexplored.
Methods: This study leveraged whole-genome sequencing (WGS) data from 88,873 participants from the Trans-Omics for Precision Medicine (TOPMed) Program, of which 51% belonged to non-European population groups. We performed GWAS of BMI adjusting for age, age2, sex, study, population group, principal components, sequencing center, sequencing phase, and project (TOPMed vs. Centers for Common Disease Genomics). We subsequently conducted replication analyses, stepwise conditional analyses, rare variant (MAF ≤ 1%) aggregate association analyses, and fine-mapping.
Results: We discovered a total of 18 BMI-associated signals (P < 5 × 10-9), including a novel low-frequency single nucleotide polymorphism (SNP), rs111490516, in MTMR3 that was common in individuals of African descent (minor allele frequency [MAF] = 13% in African and Barbadian population groups). We successfully replicated this novel SNP and observed directionally consistent associations across replication studies of similar background. In the meta-analysis of 198,621 individuals from both discovery and replication studies, the estimated effect of rs111490516 was 0.037 kg/m2 with a SE of 0.006 (P = 4.19 × 10-9). Using our diverse study population, we additionally identified two potentially novel secondary signals in known BMI loci (rs2206277 in TFAP2B and rs3838785 in BDNF) in our conditional analyses and pinpointed two likely causal variants in the POC5 (rs2307111, posterior probability [PP] = 0.99) and DMD loci (rs1379871, PP = 1.00) in our fine-mapping analyses. Finally, we successfully replicated previous gene-based associations with the well-known MC4R gene (P = 8.47 × 10-8) by aggregating 111 alleles across 37 sites within coding regions, enhancers, and promoters.
Conclusion: Our work shows the benefits of linking WGS in diverse cohorts for discovering new variants and genes that confer risk for obesity. Ultimately, our study brings us one step closer to understanding the complex genetic underpinnings of obesity, translating these leads into mechanistic insights, and developing targeted preventions and interventions to address this global public health challenge.