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Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization
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A benchmark study on current GWAS models in admixed populations.

Zikun Yang1,2, Basilio Cieza1,2, Dolly Reyes-Dumeyer1,2,3

  • 1Taub Institute for Research on Alzheimer's Disease and the Aging Brain, College of Physicians and Surgeons, Columbia University, 630 West 168th Street, New York, NY 10032, USA.

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Summary

This study benchmarks genome-wide association study (GWAS) tools in admixed populations. Tractor showed promise for ancestry-specific variants, while SAIGE controlled errors better with unbalanced cases.

Keywords:
GWASadmixturebenchmark

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Area of Science:

  • Population Genetics
  • Statistical Genomics
  • Bioinformatics

Background:

  • Genome-wide association studies (GWAS) are crucial for identifying genetic variants associated with diseases.
  • Genetic admixture presents challenges for GWAS due to heterogeneous allele frequencies and varying effect sizes.
  • Existing GWAS models require consistent evaluation in admixed populations.

Purpose of the Study:

  • To evaluate the performance of popular genome-wide association study (GWAS) models in the context of genetic admixture.
  • To compare the accuracy and power of generalized linear mixed model associated test (GMMAT), scalable and accurate implementation of generalized mixed model (SAIGE), and Tractor.
  • To assess model performance across varying minor allele frequencies (MAFs), case-control ratios, sample sizes, and ancestry proportions.

Main Methods:

  • Generated a synthetic cohort (N=19,234) simulating two-way admixture (Native American and European ancestry) and a binary phenotype.
  • Benchmarked GMMAT, SAIGE, and Tractor using inflation factors and power calculations under diverse genetic and phenotypic scenarios.
  • Validated model performance on a real Peruvian cohort (N=249) with small sample sizes and admixed ancestry.

Main Results:

  • SAIGE demonstrated superior control of type-I error rates, particularly with unbalanced case-control ratios, in the synthetic cohort.
  • Tractor exhibited the highest power for detecting ancestry-specific causal variants but showed reduced power with limited effect size heterogeneity.
  • In the Peruvian cohort, Tractor identified two suggestive loci associated with Native American ancestry.

Conclusions:

  • The study highlights best practices and limitations of current GWAS tools for admixed populations.
  • Incorporating local ancestry information enhances GWAS power but requires careful consideration of complex factors like sample size and allele frequency heterogeneity.
  • Tractor shows potential for fine-mapping ancestry-specific associations in admixed cohorts.