Calculation and use of the Hardy–Weinberg model in association studies
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.
- "Therefore, any deviation of genotype distribution from HWE may indicate a population in flux, such as non-random mating and immigration. In genetic research, however, such deviations may also be caused by random fluctuations in samples included into the study, bias in samples collected or included into the research project, presence of samples from different ethnicities (where the frequency of the alleles differ from each other, which is called as population stratification; see section entitled Population substructure of the patient cohort investigated), and technical errors in genotyping (such as underdetection of an allele due to poor primer binding, errors in DNA sampling, DNA contamination by other sources, and errors in interpretation of the genotype [26,27]). In genetic research, HWE has been mostly used to address handling, sampling, and genotyping errors and thus many experts use deviations from HWE as a flag to indicate that the genotyping method may need additional scrutiny. "
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ABSTRACT: Analysis of genetic polymorphisms may help identify putative prognostic markers and determine the biological basis of variable prognosis in patients. However, in contrast to other variables commonly used in the prognostic studies, there are special considerations when studying genetic polymorphisms. For example, variable inheritance patterns (recessive, dominant, codominant, and additive genetic models) need to be explored to identify the specific genotypes associated with the outcome. In addition, several characteristics of genetic polymorphisms, such as their minor allele frequency and linkage disequilibrium among multiple polymorphisms, and the population substructure of the cohort investigated need to be accounted for in the analyses. In addition, in cancer research due to the genomic differences between the tumor and non-tumor DNA, differences in the genetic information obtained using these tissues need to be carefully assessed in prognostic studies. In this article, we review these and other considerations specific to genetic polymorphism by focusing on genetic prognostic studies in cancer.
BMC Medicine 06/2013; 11(1):149. DOI:10.1186/1741-7015-11-149 · 7.25 Impact Factor
Available from: Carlos Alberto de Bragança Pereira
- "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 · 1.13 Impact Factor
Available from: unsworks.unsw.edu.au
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