Calculation and use of the Hardy–Weinberg model in association studies

Vanderbilt University, Nashville, Tennessee, USA.
Current protocols in human genetics / editorial board, Jonathan L. Haines ... [et al.] 05/2008; Chapter 1:Unit 1.18. DOI: 10.1002/0471142905.hg0118s57
Source: PubMed

ABSTRACT Hardy-Weinberg equilibrium (HWE) is an important tool for understanding population structure. If certain assumptions are met, genotype and allele frequencies can be estimated from one generation to the next. In genetic association studies, HWE principles have been applied to detect genotyping error and disease susceptibility loci. The focus of this unit is to review the key principles and assumptions of HWE. There is a brief discussion on how the significance of HWE is tested, and a review of current applications of HWE in association studies. The applications discussed include estimating penetrance, evaluating genotyping errors, testing for population stratification, and testing for association.

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    • "Recently, much emphasis has been given to tests that aim to evaluate if a population is in HWE. While historically the HWE formula was developed to examine the transmission of alleles in a population from one generation to the next, usage of HWE concepts has expanded in human diseases studies to detect genotyping error and disease susceptibility (association): see Ryckman and Williams (2008) for interesting discussion. Understanding how HWE testing can be used in the process of disease gene discovery or genotyping error is becoming increasingly important as the number of SNPs in studies increases to the hundreds. "
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    ABSTRACT: Hardy-Weinberg Equilibrium (HWE) is an important genetic property that populations should have whenever they are not observing adverse situations as complete lack of panmixia, excess of mutations, excess of selection pressure, etc. HWE for decades has been evaluated; both frequentist and Bayesian methods are in use today. While historically the HWE formula was developed to examine the transmission of alleles in a population from one generation to the next, use of HWE concepts has expanded in human diseases studies to detect genotyping error and disease susceptibility (association); Ryckman and Williams (2008). Most analyses focus on trying to answer the question of whether a population is in HWE. They do not try to quantify how far from the equilibrium the population is. In this paper, we propose the use of a simple disequilibrium coefficient to a locus with two alleles. Based on the posterior density of this disequilibrium coefficient, we show how one can conduct a Bayesian analysis to verify how far from HWE a population is. There are other coefficients introduced in the literature and the advantage of the one introduced in this paper is the fact that, just like the standard correlation coefficients, its range is bounded and it is symmetric around zero (equilibrium) when comparing the positive and the negative values. To test the hypothesis of equilibrium, we use a simple Bayesian significance test, the Full Bayesian Significance Test (FBST); see Pereira, Stern and Wechsler (2008) for a complete review. The disequilibrium coefficient proposed provides an easy and efficient way to make the analyses, especially if one uses Bayesian statistics. A routine in R programs (R Development Core Team, 2009) that implements the calculations is provided for the readers.
    Statistical Applications in Genetics and Molecular Biology 01/2011; 10(1):22-22. DOI:10.2202/1544-6115.1636 · 1.52 Impact Factor
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    ABSTRACT: Only Apolipoprotein E polymorphisms have been consistently associated with the risk of late-onset Alzheimer disease (LOAD), but they represent only a minority of the underlying genetic effect. To identify additional LOAD risk loci, we performed a genome-wide association study (GWAS) on 492 LOAD cases and 498 cognitive controls using Illumina's HumanHap550 beadchip. An additional 238 cases and 220 controls were used as a validation data set for single-nucleotide polymorphisms (SNPs) that met genome-wide significance. To validate additional associated SNPs (p < 0.0001) and nominally associated candidate genes, we imputed SNPs from our GWAS using a previously published LOAD GWAS(1) and the IMPUTE program. Association testing was performed with the Cochran-Armitage trend test and logistic regression, and genome-wide significance was determined with the False Discovery Rate-Beta Uniform Mixture method. Extensive quality-control methods were performed at both the sample and the SNP level. The GWAS confirmed the known APOE association and identified association with a 12q13 locus at genome-wide significance; the 12q13 locus was confirmed in our validation data set. Four additional highly associated signals (1q42, 4q28, 6q14, 19q13) were replicated with the use of the imputed data set, and six candidate genes had SNPs with nominal association in both the GWAS and the joint imputated data set. These results help to further define the genetic architecture of LOAD.
    The American Journal of Human Genetics 01/2009; 84(1):35-43. DOI:10.1016/j.ajhg.2008.12.008 · 10.99 Impact Factor
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