Genome-wide association studies in diverse populations

By Noah A. Rosenberg, Lucy Huang, E. Jewett, Zachary A. Szpiech, Ivana Janković, Michael Boehnke. 2010

591 citations recorded by the source corpus.

Genome-wide association (GWA) studies have identified a large number of SNPs associated with disease phenotypes. As most GWA studies have been performed in populations of European descent, this Review examines the issues involved in extending the consideration of GWA studies to diverse worldwide populations. Although challenges exist with issues such as imputation, admixture and replication, investigation of a greater diversity of populations could make substantial contributions to the goal of mapping the genetic determinants of complex diseases for the human population as a whole.

Subfields: Genetics, Molecular Biology

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Citations in the corpus

Rare-variant association analysis: study designs and statistical tests. (2014, background): “122,123 In these populations, direct sequencing might be more attractive for ﬁne-mapping and association studies.”
New approaches to disease mapping in admixed populations (2011, unknown): “, but the advent of genome-wide association studies (GWASs) has led to new techniques for local ancestry inference and raises some additional challenges, such as combining SNP and admixture association signals, optimizing genotype imputation and fine-mapping causal variant”
Stacks: an analysis tool set for population genomics (2013, background): “…Mapping (Barchi et al. 2011, 2012; Chutimanitsakun et al. 2011; Pfender et al. 2011; Houston et al. 2012; King et al. 2012), and a promising avenue for the use of GBS studies may be genome-wide association studies (GWAS) in natural populations (Rosenberg et al. 2010; Balding 2006; Luo et al. 2011).”
Genotype Imputation with Thousands of Genomes (2011, background): “It is possible that Beagle could produce better results with different settings, although we tried varying both the model-fitting parameters (niterations, nsamples) and the model-building parameters (scale, shift) without observing a meaningful change in accuracy (data not shown).”
Sequence variants in SLC16A11 are a common risk factor for type 2 diabetes in Mexico (2013, background): “Sample size for each tissue (n): pancreas (5); adipose, brain, colon, liver, skeletal muscle and thyroid (3); adrenal, fetal brain, breast, heart, kidney, lung, placenta, prostate, small intestine, spleen, testes, thymus and trachea (2); bladder, cervix, oesophagus, fetal liver, ovary, salivary glan”