Multi-ancestry fine-mapping improves precision to identify causal genes in transcriptome-wide association studies

Zeyun Lu; Shyamalika Gopalan; Dong Yuan; David V Conti; Bogdan Pasaniuc; Alexander Gusev; Nicholas Mancuso

doi:10.1016/j.ajhg.2022.07.002

Multi-ancestry fine-mapping improves precision to identify causal genes in transcriptome-wide association studies

Am J Hum Genet. 2022 Aug 4;109(8):1388-1404. doi: 10.1016/j.ajhg.2022.07.002.

Authors

Zeyun Lu¹, Shyamalika Gopalan², Dong Yuan³, David V Conti⁴, Bogdan Pasaniuc⁵, Alexander Gusev⁶, Nicholas Mancuso⁷

Affiliations

¹ Biostatistics Division, Department of Population and Public Health Sciences, University of Southern California, Los Angeles, CA, USA. Electronic address: zeyunlu@usc.edu.
² Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA; Department of Evolutionary Anthropology, Duke University, Durham, NC, USA.
³ Biostatistics Division, Department of Population and Public Health Sciences, University of Southern California, Los Angeles, CA, USA.
⁴ Biostatistics Division, Department of Population and Public Health Sciences, University of Southern California, Los Angeles, CA, USA; Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
⁵ Bioinformatics Interdepartmental Program, University of California, Los Angeles, Los Angeles, CA, USA; Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA; Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA; Department of Computational Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.
⁶ Division of Population Sciences, Dana-Farber Cancer Institute & Harvard Medical School, Boston, MA, USA; Division of Genetics, Brigham & Women's Hospital, Boston, MA, USA; Program in Medical and Population Genetics, The Broad Institute, Cambridge, MA, USA.
⁷ Biostatistics Division, Department of Population and Public Health Sciences, University of Southern California, Los Angeles, CA, USA; Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA; Quantitative and Computational Biology, University of Southern California, Los Angeles, CA, USA. Electronic address: nicholas.mancuso@med.usc.edu.

Abstract

Transcriptome-wide association studies (TWASs) are a powerful approach to identify genes whose expression is associated with complex disease risk. However, non-causal genes can exhibit association signals due to confounding by linkage disequilibrium (LD) patterns and eQTL pleiotropy at genomic risk regions, which necessitates fine-mapping of TWAS signals. Here, we present MA-FOCUS, a multi-ancestry framework for the improved identification of genes underlying traits of interest. We demonstrate that by leveraging differences in ancestry-specific patterns of LD and eQTL signals, MA-FOCUS consistently outperforms single-ancestry fine-mapping approaches with equivalent total sample sizes across multiple metrics. We perform TWASs for 15 blood traits using genome-wide summary statistics (average n_EA = 511 k, n_AA = 13 k) and lymphoblastoid cell line eQTL data from cohorts of primarily European and African continental ancestries. We recapitulate evidence demonstrating shared genetic architectures for eQTL and blood traits between the two ancestry groups and observe that gene-level effects correlate 20% more strongly across ancestries than SNP-level effects. Lastly, we perform fine-mapping using MA-FOCUS and find evidence that genes at TWAS risk regions are more likely to be shared across ancestries than they are to be ancestry specific. Using multiple lines of evidence to validate our findings, we find that gene sets produced by MA-FOCUS are more enriched in hematopoietic categories than alternative approaches (p = 2.36 × 10^-15). Our work demonstrates that including and appropriately accounting for genetic diversity can drive more profound insights into the genetic architecture of complex traits.

Keywords: GWAS; TWAS; gene fine-mapping; multi-ancestry; statistical genetics.

Publication types

Research Support, Non-U.S. Gov't
Research Support, N.I.H., Extramural

MeSH terms

Genome-Wide Association Study*
Humans
Linkage Disequilibrium
Multifactorial Inheritance / genetics
Polymorphism, Single Nucleotide / genetics
Transcriptome* / genetics

Abstract

Publication types

MeSH terms

Grants and funding