Abstract 56: Fine-Mapping of Metabochip Lipid Regions in Global Populations Identifies Signals Unique to Hispanic Descent Populations and Refines Previously Identified Lipid Loci
Authors: Zubair Niha , Graff Mariaelisa , Lin Danyu , Manichaikul Ani , Chen Ida , Stahl Eli , Lu Kevin , Cheng Iona , Haiman Christopher , Crawford Dana , Dumitrescu Logan , Buzkova Petra , Buyske Steven , Fornage Myriam , North Kari , Kooperberg Charles , Carty Cara 
Published: July 2021

INTRODUCTION:
Genome wide association studies (GWAS) have identified over 150 loci associated with lipids traits. The majority of these GWAS were performed in European Americans (EA); no large-scale studies exist for Hispanic descent populations. Additionally, in many cases, the genetic architecture of these trait-influencing loci remains largely unknown. To address these gaps in knowledge, we performed one of the most ethnically diverse fine-mapping genetic studies on HDL-C, LDL-C, and triglycerides (TG) to-date.

HYPOTHESIS:
Here we aimed to identify variants with the strongest association at each locus, detect population-specific signals, and refine previously identified EA GWAS loci.

METHODS:
We used Metabochip data from African American (AA, ~21,000), Hispanic American (HA, ~20,000), Asian (AS, ~2,000), and Native American (NA, ~550) participants from the Population Architecture using Genomics and Epidemiology (PAGE) Study. We applied multiple linear regression models and assumed an additive mode of inheritance to test for association between genotypes and HDL-C, LDL-C, or log-transformed TG levels; lipid levels were corrected for lipid-lowering medication use. Model covariates included age, sex, and principal components of ancestry. We first conducted a meta-analysis within each ethnic group separately and then performed a combined trans-ethnic fixed effects meta-analysis. Significance was defined as p < 1 x 10
-6
; equivalent to 0.05/ the mean number of variants at each Metabochip lipid locus.

RESULTS:
For HDL-C, 19 loci significantly associated in the trans-ethnic meta-analysis; the top signals at 5 of these loci, APOB, LIPC, STARD3, LIPG, and APOC1, have not been reported in EA. We identified a signal unique to HA at APOA5. In addition, we refined the set of candidate functional variants at PPP1R3B, LPL, and PLTP.

For LDL-C, 16 loci significantly associated in the trans-ethnic meta-analysis; the top signals at 5 of these loci, PCSK9, APOB, APOA5, CLIP2, and APOC1, have not been reported in EA. We identified a signal unique to HA at SLC22A1. In addition, we refined the set of candidate functional variants at TIMD4 and LDLR.

For TG, 15 loci significantly associated in the trans-ethnic meta-analysis; the top signals at 3 of these loci, APOB, APOA5, and LIPC, have not been reported in EA. In addition, we refined the set of candidate functional variants at ANGPTL3, MLXIPL, PPP1R3B, and LPL.

CONCLUSIONS:
By taking advantage of the genetic architecture of ethnically diverse populations, we identified novel lipid-influencing variants in HA and refined the set of candidate functional variants at GWAS lipid loci. Anticipated conditional analyses will provide further insight into secondary and ethnic-specific signals. Our results can guide the creation of more informed risk models, which can then be used for targeted prevention efforts, especially for underrepresented populations.