New approaches to population stratification in genome wide association studies

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. There are several methods to correct for population stratification Price Zaitlen Reich Patterson 2010.

GWAS can be confounded by population stratificationsystematic ancestry differences between cases and controlswhich has previously been addressed by methods that infer genetic ancestry.

New approaches to population stratification in genome wide association studies. 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. Genome-wide association studies GWAS are an effective approach for identifying genetic variants associated to disease risk.

GWAS can be confounded by population stratificationsystematic ancestry differences between cases and controlswhich has previously been addressed by methods that infer genetic ancestry. Those methods perform well in data sets in which population structure is. 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. Genome-wide association studies GWAS are an effective approach for identifying genetic variants associated to disease risk. GWAS can be confounded by population stratificationsystematic ancestry differences between cases and controlswhich has previously been addressed by methods that infer genetic ancestry.

Those methods perform well in data sets in which. 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. New approaches to population stratification in genome-wide association studies. Nature Reviews Genetics 2010.

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.

Therefore testing and controlling for the presence of population stratification is an essential QC step. There are several methods to correct for population stratification Price Zaitlen Reich Patterson 2010. In this tutorial we illustrate a method that is incorporated in PLINK.

The multidimensional scaling MDS approach. This method calculates the genomewide average. Population stratification PS is a primary consideration in studies of genetic determinants of human traits.

Failure to control for PS may lead to confounding causing a study to fail for lack of significant results or resources to be wasted following falsepositive signals. Here historical and current approaches for addressing PS when performing genetic association studies in human populations. Since GC method can be useful for controlling spurious association caused by relatedness in the data investigators proposed a PCA double GC method to control the spurious association findings in meta-analysis as follows.

1 to perform PCA adjustment for population stratification in the individual study association analysis followed by a GC correction on the genome-wide results 2 to perform a GC correction on the combined statistics over all studies. Genome-wide association studies GWAS are a powerful tool for investigators to examine the human genome to detect genetic risk factors reveal the genetic architecture of diseases and open up new opportunities for treatment and prevention. However despite its successes GWAS have not been able to identify genetic loci that are effective classifiers of disease limiting their value.

Here I give an overview of statistical approaches to population association studies including preliminary analyses HardyWeinberg equilibrium testing inference of phase and missing data and. Although genome-wide association studies GWASs have identified numerous loci associated with complex traits imprecise modeling of the genetic relatedness within study samples may cause substantial inflation of test statistics and possibly spurious associations. Variance component approaches such as efficient mixed-model association EMMA can correct for a wide range of sample.

New approaches to population stratification in genome-wide association studies Alkes L. Zaitlen David Reich and Nick Patterson Abstract Genome-wide association GWA studies are an effective approach for identifying genetic variants associated with disease risk. The most common factor that can bias the results of genetic association studies refers to differences in frequency of candidate genetic variants in the study cohort due to sub-populations with different ancestry.

In the presence of population stratification the significant associations could be exclusively due to the different prevalence of genetic variants in various ethnic and racial sub-groups. 2011 Population Stratification Analysis in Genome-Wide Association Studies. Eds Mathematical Approaches to Polymer Sequence Analysis and Related Problems.

Springer New York NY. When applied to human data GWA studies compare the DNA of participants having varying phenotypes for a particular trait or disease. These participants may be people with a disease and similar people without the disease or they may be people with different phenotypes for a particular trait for example blood pressure.

This approach is known as phenotype-first in which the participants are classified. Several main approaches exist to account for population stratification in GWAS. Genomic Control Principal Component Analysis PCA based methods Regression models and Meta-Analyses.