Ancestry‐specific association mapping in admixed populations

Abstract

During the last decade genome‐wide association studies have proven to be a powerful approach to identifying disease‐causing variants. However, for admixed populations, most current methods for association testing are based on the assumption that the effect of a genetic variant is the same regardless of its ancestry. This is a reasonable assumption for a causal variant but may not hold for the genetic variants that are tested in genome‐wide association studies, which are usually not causal. The effects of noncausal genetic variants depend on how strongly their presence correlate with the presence of the causal variant, which may vary between ancestral populations because of different linkage disequilibrium patterns and allele frequencies. Motivated by this, we here introduce a new statistical method for association testing in recently admixed populations, where the effect size is allowed to depend on the ancestry of a given allele. Our method does not rely on accurate inference of local ancestry, yet using simulations we show that in some scenarios it gives a substantial increase in statistical power to detect associations. In addition, the method allows for testing for difference in effect size between ancestral populations, which can be used to help determine if a given genetic variant is causal. We demonstrate the usefulness of the method on data from the Greenlandic population.

The derived allele of a novel intergenic variant at chromosome 11 associates with lower body mass index and a favorable metabolic phenotype in Greenlanders

“…The Greenlandic population is an admixture of Inuit and Europeans, and we applied the asaMap method [19] to estimate the effect size of the BMI-associated variant in each ancestry component of the study population, and to compare the contribution from each ancestry component to the association. With asaMap, we ran a linear regression applying an additive model adjusted for age, sex, cohort, and the first 10 principal components to account for the relatedness and population structure.…”

“…For comparison, in the 1000 genomes project the observed frequency of the rs4936356 G-allele in the British (GBR) and Europeans from Utah (CEU) populations was 9.2% and 6.6%, respectively. Analyses estimating the effect in each ancestry component of Greenlanders separately, applying the Asamap method [19], suggested that the observed effect on BMI was mainly driven by the Inuit compared to the European component (beta SD (SE), -0.16 SD (0.04), p = 2.9x10 -6 vs. -0.03 SD (0.13), p = 0.77); however, the effect did not differ significantly between the two population components (p = 0.36). Novel genetic locus associated with metabolic health in Greenlanders…”

Ancestry deconvolution and partial polygenic score can improve susceptibility predictions in recently admixed individuals

“…Most of the times, associated SNPs are thus merely correlated with a phenotype and are not really causal 3 . Furthermore, as environment interactions, Linkage Disequilibrium patterns, allele frequencies and rare polymorphisms are often population specific, effect sizes might be in part population dependent 3,8,9 . This may lead PS to exhibit a directional bias and a lower predictivity in individuals from populations not closely related to the one where the GWAS study was performed [10][11][12][13][14] or even from the same population as it was shown for UK and Finnish cohorts [15][16][17] .…”

Genomics and the Acute Respiratory Distress Syndrome: Current and Future Directions

“…Other approaches will provide complementary views to genetic associations studies in ARDS (Table 1) and elucidate new risks factors to be considered for PRS. Among them, admixture mapping analyses for allocating disease loci by leveraging the regional differences in the ancestry blocks across the genome of recently admixed populations and their correlations (co-inheritance) with disease loci [104] and whole genome sequencing (WGS) studies, will allow us to assess rare variants and other types of genetic variation beyond single nucleotide polymorphisms. Admixture mapping is a hypothesis-free approach that associates with a reduced proportion of false positive findings despite having a reduced penalty of statistical significance compared to GWAS.…”