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Genet Epidemiol. 2018 Nov 25. doi: 10.1002/gepi.22173. [Epub ahead of print]

Estimating cross-population genetic correlations of causal effect sizes.

Author information

1
Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts.
2
Takeda Oncology, Cambridge, Massachusetts.
3
Department of Epidemiology, Harvard School of Public Health, Boston, Massachusetts.
4
Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, Massachusetts.
5
Schmidt Fellows Program, Broad Institute of MIT and Harvard, Cambridge, Massachusetts.
6
Department of Medicine, University of California, San Francisco, San Francisco, California.
7
Verily Life Sciences, South San Francisco, California.

Abstract

Recent studies have examined the genetic correlations of single-nucleotide polymorphism (SNP) effect sizes across pairs of populations to better understand the genetic architectures of complex traits. These studies have estimated ρ g , the cross-population correlation of joint-fit effect sizes at genotyped SNPs. However, the value of ρ g depends both on the cross-population correlation of true causal effect sizes ( ρ b ) and on the similarity in linkage disequilibrium (LD) patterns in the two populations, which drive tagging effects. Here, we derive the value of the ratio ρ g / ρ b as a function of LD in each population. By applying existing methods to obtain estimates of ρ g , we can use this ratio to estimate ρ b . Our estimates of ρ b were equal to 0.55 ( SE = 0.14) between Europeans and East Asians averaged across nine traits in the Genetic Epidemiology Research on Adult Health and Aging data set, 0.54 ( SE = 0.18) between Europeans and South Asians averaged across 13 traits in the UK Biobank data set, and 0.48 ( SE = 0.06) and 0.65 ( SE = 0.09) between Europeans and East Asians in summary statistic data sets for type 2 diabetes and rheumatoid arthritis, respectively. These results implicate substantially different causal genetic architectures across continental populations.

KEYWORDS:

genetic architecture; genetic correlation; multiethnic

PMID:
30474154
DOI:
10.1002/gepi.22173

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