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Review
. 2019 Oct 17;179(3):589-603.
doi: 10.1016/j.cell.2019.08.051. Epub 2019 Oct 10.

Genome-wide Association Studies in Ancestrally Diverse Populations: Opportunities, Methods, Pitfalls, and Recommendations

Affiliations
Review

Genome-wide Association Studies in Ancestrally Diverse Populations: Opportunities, Methods, Pitfalls, and Recommendations

Roseann E Peterson et al. Cell. .

Abstract

Genome-wide association studies (GWASs) have focused primarily on populations of European descent, but it is essential that diverse populations become better represented. Increasing diversity among study participants will advance our understanding of genetic architecture in all populations and ensure that genetic research is broadly applicable. To facilitate and promote research in multi-ancestry and admixed cohorts, we outline key methodological considerations and highlight opportunities, challenges, solutions, and areas in need of development. Despite the perception that analyzing genetic data from diverse populations is difficult, it is scientifically and ethically imperative, and there is an expanding analytical toolbox to do it well.

Keywords: GWAS; admixed populations; ancestry; complex disease; cross-ancestry; diversity; population genetics; psychiatry; trans-ancestry; trans-ethnic.

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Figures

Figure 1.
Figure 1.. Diversity in GWAS of psychiatric disorders compared to global diversity.
Participant numbers were extracted from the largest consortium publication(s) for each psychiatric disorder and are shown as fractions of the total sample size for each disorder. Note: Sample sizes are given in parentheses. Numbers reflect cases and controls combined. MD=major depression (490,999), SCZ=schizophrenia (205,661), PTSD=post-traumatic stress disorder (188,932), BIP=bipolar disorder (51,710), ADHD=attention deficit hyperactivity disorder (55,230), AUT=autism (46,350), AD=alcohol dependence (52,848), AN=anorexia (14,477). *For schizophrenia, the African American samples from an earlier publication (2009, International Schizophrenia Consortium) were not included in the most recent PGC schizophrenia publication (2014). Ancestry information for each participant was based on principal components analysis of genetic data. See Supplemental Table S1 for consortium studies and references.
Figure 2:
Figure 2:. Flow chart for quality control, imputation, and association analysis in diverse population samples.
This flowchart depicts the general analysis framework for genome-wide association studies of participants with diverse ancestral backgrounds. Note: boxes with red headers indicate analyses done in samples with diverse ancestral backgrounds and blue denotes analysis done within samples in major population groups. The left path shows a strategy for the stratified meta-analysis approach and the right path shows steps for the joint mixed model approach [see Supplemental Table 2 for more detailed quality control (QC) considerations].

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