New approaches to population stratification in genome-wide association studies

By Alkes L Price, Noah A. Zaitlen, David Reich, Nick Patterson. 2010

1,230 citations recorded by the source corpus.

Genome-wide association (GWA) studies are an effective approach for identifying genetic variants associated with disease risk. GWA studies can be confounded by population stratification — systematic ancestry differences between cases and controls — which has previously been addressed by methods that infer genetic ancestry. Those methods perform well in data sets in which population structure is the only kind of structure present but are inadequate in data sets that also contain family structure or cryptic relatedness. Here, we review recent progress on methods that correct for stratification while accounting for these additional complexities. systematic ancestry differences between cases and controls — which has previously been addressed by methods that infer genetic ancestry. Those methods perform well in data sets in which population structure is the only kind of structure present but are inadequate in data sets that also contain family structure or cryptic relatedness. Here, we review recent progress on methods that correct for stratification while accounting for these additional complexities.

Subfields: Genetics, Molecular Biology

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Genetics

In Molecular Biology

Citations in the corpus

Opportunities and obstacles for deep learning in biology and medicine (2017, background): “Moreover, real genetic associations found in one population may not hold in other populations with different patterns of linkage disequilibrium (even when population stratification is explicitly controlled for [127]).”
Estimating Individual Admixture Proportions from Next Generation Sequencing Data (2013, methodology): “Various methods can be used to alleviate this problem, for example including the admixture proportions in the association model (Price et al., 2010).”
The confounding effect of cryptic relatedness for environmental risks of systolic blood pressure on cohort studies (2013, background): “First, using the techniques to detect and correct for unrecognized population structure in GWAS, we examined how CR was presented in the sampling.”
The Next-Generation Sequencing Revolution and Its Impact on Genomics (2013, neutral): “…2010; Lin and Tang, 2011; Wu et al., 2011; Zhang et al., 2011]), variants with heterogeneous direction of effect (Han and Pan, 2010; Lin and Tang, 2011; Neale et al., 2011; Wu et al., 2011; Zhang et al., 2011), and selection of the ideal frequency cutoff for rare variants (Price et al., 2010a).”
Identifying targets and agents of selection: innovative methods to evaluate the processes that contribute to local adaptation (2017, background): “Chance associations due to population structure are controlled with pedigrees, a kinship matrix, and/or population membership model covariates (Price et al. 2010).”
An overview of STRUCTURE: applications, parameter settings, and supporting software (2013, methodology): “Finally a further class of model to detect and correct for stratiﬁcation is the mixed model—this approach should perform better when confounding effects are present since it simultaneously addresses population’s stratiﬁcation, family structure, and cryptic relatedness (Price et al., 2010).”
New approaches to disease mapping in admixed populations (2011, neutral): “1a ), which can confound association studies if it is not properly accounted fo”
Genome-partitioning of genetic variation for complex traits using common SNPs (2011, background): “(27,578), 665,163 (24,108) and 593,521 (23,664) autosomal (X-chromosome) SNPs were retained for the HPFS, NHS and ARIC cohorts, respectively, 565,040 (21,858) of which were in common across the three cohorts.”
Genome-wide Efficient Mixed Model Analysis for Association Studies (2012, neutral): “For the derivatives of the log-likelihood and log-restricted likelihood functions (5)–(8), we need to evaluate two types of quantities: trace terms (trace(H−1G), trace(H−1GH−1G), trace(PxG) and trace(PxGPxG)), and vector-matrix-vector (5)”
A high-performance computing toolset for relatedness and principal component analysis of SNP data (2012, methodology): “An actual kinship matrix of individuals can be estimated by eithermethod,which could be used in downstream association analyses (Price et al., 2010).”