Hardy–Weinberg quality control

Gomes, Isabela Bronchtein; Collins, Andrew; Lonjou, Christine; Ns, Thomas; Wilkinson, James; Watson, Mark L.; Morton, N.E.

doi:10.1046/j.1469-1809.1999.6360535.x

Cited by 113 publications

(100 citation statements)

References 14 publications

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“…Chi-square analysis was used to test for deviation of genotype frequencies from Hardy-Weinberg expectations for biallelic systems. The deviation of genotype frequencies from Hardy-Weinberg expectations for the IL-1RN (VNTR) variant was tested using G 2 and performing the permutation test of HardyWeinberg proportion for multiple alleles following the method of Gomes et al 30 We also examined two-SNP haplotypes for association with disease by scoring individuals for the two haplotypes they possess (from the set 1-1, 1-2, 2-1, 2-2), such that each individual had a score that summed to 2. For doubly heterozygous individuals, we scored in probability from the allele frequencies predicted by the method of Hill.…”

Section: Methodsmentioning

confidence: 99%

“…LD analysis in a case-control study on GAP C Scapoli et al the Hardy-Weinberg test, 30 ensured that the controls marker typing showed no deviation from Hardy-Weinberg frequencies under the likelihood ratio Shannon test of w 2 (Table 1). However, the TIC polymorphism showed a marked deviation from HWE and was omitted from further LD analysis.…”

Section: Il-1b(+3953)mentioning

confidence: 99%

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Linkage disequilibrium analysis of case–control data: an application to generalized aggressive periodontitis

et al. 2004

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Several studies have shown a role for the involvement of interleukin (IL)-1 gene cluster polymorphisms in the risk of periodontal diseases. In the present study, we tested polymorphisms, derived from genes of the IL-1 cluster, for association with generalized aggressive periodontitis (GAP) through both allelic association and by constructing a linkage disequilibrium (LD) map of the 2q13-14 disease candidate region. The IL-1RN (VNTR) genotype distribution observed was significantly different in GAP and control subjects (P ¼ 0.019). We also observed some evidence for an association between GAP and the IL-1B þ 3953 polymorphism (P ¼ 0.039). The pattern of association in the region, represented as an LD map, identifies a recombination hot area between the IL-1B þ 3953 and IL-1B À511 polymorphisms. Multilocus modelling of association with disease gives a location for the peak association at the IL-1B þ 3953 marker, although support for the peak is not significant. Haplotype analysis identifies a IL-1B þ 3953 -IL-1B À511 haplotype as having the lowest P-value in the region. Recognition of the presence of a recombination hot area between the IL-1B þ 3953 and IL-1B À511 polymorphisms will have an important bearing on future efforts to develop higher resolution SNP analysis in this region for both this and other diseases for which this cluster is implicated.

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Section: Methodsmentioning

confidence: 99%

Section: Il-1b(+3953)mentioning

confidence: 99%

Linkage disequilibrium analysis of case–control data: an application to generalized aggressive periodontitis

et al. 2004

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“…Rohlfs and Weir (2008) derived the distribution of the P-value for the exact probability test for HW in the case of two alleles and used this information to correct the bias. This consideration can be important when it is necessary to make specific decisions on the basis of the evidence against HW proportions (Gomes et al 1999;Salanti et al 2005;Zou and Donner 2006). On the other hand, for most situations where no immediate decision is required, one can follow the advice of Yates (1984), who recommended for discrete data that researchers simply report the calculated P-value itself without worrying about whether it lies above or below an arbitrary cutoff point.…”

Section: Discussionmentioning

confidence: 99%

Exact Tests for Hardy–Weinberg Proportions

Engels

2009

Genetics

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Exact conditional tests are often required to evaluate statistically whether a sample of diploids comes from a population with Hardy-Weinberg proportions or to confirm the accuracy of genotype assignments. This requirement is especially common when the sample includes multiple alleles and sparse data, thus rendering asymptotic methods, such as the common x 2 -test, unreliable. Such an exact test can be performed using the likelihood ratio as its test statistic rather than the more commonly used probability test. Conceptual advantages in using the likelihood ratio are discussed. A substantially improved algorithm is described to permit the performance of a full-enumeration exact test on sample sizes that are too large for previous methods. An improved Monte Carlo algorithm is also proposed for samples that preclude full enumeration. These algorithms are about two orders of magnitude faster than those currently in use. Finally, methods are derived to compute the number of possible samples with a given set of allele counts, a useful quantity for evaluating the feasibility of the full enumeration procedure. Software implementing these methods, ExactoHW, is provided. W HEN studying the genetics of a population, one of the first questions to be asked is whether the genotype frequencies fit Hardy-Weinberg (HW) expectations. They will fit HW if the population is behaving like a single randomly mating unit without intense viability selection acting on the sampled loci. In addition, testing for HW proportions is often used for quality control in genotyping, as the test is sensitive to misclassifications or undetected null alleles. Traditionally, geneticists have relied on test statistics with asymptotic x 2 -distributions to test for goodness-of-fit with respect to HW proportions. However, as pointed out by several authors (Elston and Forthofer 1977;Emigh 1980;Louis and Dempster 1987;Hernandez and Weir 1989;Guo and Thompson 1992;Chakraborty and Zhong 1994;Rousset and Raymond 1995;Aoki 2003;Maiste and Weir 2004;Wigginton et al. 2005;Kang 2008;Rohlfs and Weir 2008), these asymptotic tests quickly become unreliable when samples are small or when rare alleles are involved. The latter situation is increasingly common as techniques for detecting large numbers of alleles become widely used. Moreover, loci with large numbers of alleles are intentionally selected for use in DNA identification techniques (e.g., Weir 1992). The result is often sparse-matrix data for which the asymptotic methods cannot be trusted.A solution to this problem is to use an exact test (Levene 1949;Haldane 1954) analogous to Fisher's exact test for independence in a 2 3 2 contingency table and its generalization to rectangular tables (Freeman and Halton 1951). In this approach, one considers only potential outcomes that have the same allele frequencies as observed, thus greatly reducing the number of outcomes that must be analyzed. One then identifies all such outcomes that deviate from the HW null hypothesis by at least as much the observed sample...

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“…Finally, a probable explanation for deviation from HWE is genotyping error. 1,37,39,40 HWE deviation may be the strongest and most straightforward hint that genotyping may need to be repeated and double-checked.…”

Section: Discussionmentioning

confidence: 99%

Hardy–Weinberg equilibrium in genetic association studies: an empirical evaluation of reporting, deviations, and power

et al. 2005

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We evaluated the testing and reporting of Hardy -Weinberg equilibrium (HWE) in recent genetic association studies, detected how frequently HWE was violated and estimated the power for HWE testing in this literature. Genetic association studies published in 2002 in Nature Genetics, American Journal of Human Genetics, and American Journal of Medical Genetics were assessed. Data were analyzed on 239 biallelic associations using 154 distinct genotype distribution data sets where HWE could be tested. Any information on HWE was given only for 150 (62.8%) associations (92 (59.7%) data sets). Reanalysis of the data showed significant deviation from HWE in the disease-free controls of 20 associations (13 data sets), but only four of them (two data sets) were admitted in the published articles. Another four deviations (in two data sets) were observed in the combined sample of cases and controls of studies where both cases and controls were diseased, and none were reported in the papers. In all six tested multiallelic associations (six data sets), there was violation of HWE, but this was not admitted in the published articles. Power calculations showed that most studies conforming to HWE simply were largely underpowered to detect HWE deviation; for example, power to detect an inbreeding of magnitude F ¼ 0.10 exceeded 80% in only 11 (7%) of the data sets being tested. This empirical evidence suggests that, even in high profile genetics journals, testing and reporting for HWE is often neglected and deviations are rarely admitted in the published reports. Moreover, power is limited for HWE testing in most current genetic association studies.

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Hardy–Weinberg quality control

Cited by 113 publications

References 14 publications

Linkage disequilibrium analysis of case–control data: an application to generalized aggressive periodontitis

Linkage disequilibrium analysis of case–control data: an application to generalized aggressive periodontitis

Exact Tests for Hardy–Weinberg Proportions

Hardy–Weinberg equilibrium in genetic association studies: an empirical evaluation of reporting, deviations, and power

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