Extension of the Haseman–Elston method to multiple alleles and multiple loci: theory and practice for candidate genes

Stoesz, Marcia Regier; COHEN, J. C.; Mooser, Vincent; Marcovina, S. M.; Guerra, Raissa

doi:10.1046/j.1469-1809.1997.6130263.x

Cited by 8 publications

(6 citation statements)

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“…Looking at all possible pairs of marker loci in the genome and evaluating the significance level of each pair may not be the answer because of the high number of tests required (Dupuis et al 1995), although, for a small number of candidate marker loci, this method does seem to have merit (Cordell et al 1995). Conditional approaches, in which a new locus is searched for, given good evidence for an existing locus or set of loci, appear more promising (Dupuis et al 1995;Cordell et al 2000).In addition to a small number of multilocus approaches (Stoesz et al 1997;Blangero et al 2000), an intriguing method has recently been proposed to allow for the joint analysis of multiple marker loci (Nelson et al 2001). This combinatorial partitioning method (CPM) works by evaluating all possible partitions of marker loci and retaining only those partitions fulfilling certain optimality criteria.…”

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confidence: 99%

Trimming, Weighting, and Grouping SNPs in Human Case-Control Association Studies

Hoh¹,

Wille²,

Ott³

2001

Genome Res.

287

318

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The search for genes underlying complex traits has been difficult and often disappointing. The main reason for these difficulties is that several genes, each with rather small effect, might be interacting to produce the trait. Therefore, we must search the whole genome for a good chance to find these genes. Doing this with tens of thousands of SNP markers, however, greatly increases the overall probability of false-positive results, and current methods limiting such error probabilities to acceptable levels tend to reduce the power of detecting weak genes. Investigating large numbers of SNPs inevitably introduces errors (e.g., in genotyping), which will distort analysis results. Here we propose a simple strategy that circumvents many of these problems. We develop a set-association method to blend relevant sources of information such as allelic association and Hardy-Weinberg disequilibrium. Information is combined over multiple markers and genes in the genome, quality control is improved by trimming, and an appropriate testing strategy limits the overall false-positive rate. In contrast to other available methods, our method to detect association to sets of SNP markers in different genes in a real data application has shown remarkable success.The current emphasis on searching for disease susceptibility genes is carried out by association to tens of thousands of SNP markers (Collins et al. 1998). Such association analyses may be carried out in a variety of data designs, for example, by testing for differences in SNP allele frequencies between affected and unaffected individuals (case-control studies), or by comparing whether a SNP allele is transmitted to an affected offspring more or less often than expected by chance (the transmission disequilibrium test, TDT; Spielman and Ewens 1996). Because complex traits presumably arise from multiple interacting genes located throughout the genome, it would be appropriate to search for sets of marker loci in different genes and to analyze these markers jointly rather than testing each marker in isolation. Forming haplotypes over multiple neighboring markers in one gene can increase the power of gene mapping studies (Fallin et al. 2001), as can scan statistics ; but these methods only work locally in a given genomic region.Most current approaches essentially evaluate one SNP marker at a time, that is, by focusing on its marginal effect on disease. Those SNPs with a significant association to disease are taken to be close to or within susceptibility genes. Testing each SNP for association with disease leads to a locus-specific probability of a false-positive result (type I error). Such a type I error can easily be inflated when large numbers of SNPs are tested simultaneously and treated independently (Risch and Merikangas 1996); the problems involving such multiple testing and its effect on the genomewide type I error are the subject of a presently ongoing debate (Lin et al. 2001). For genomewide linkage analysis, appropriate measures have been developed to keep this problem u...

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confidence: 99%

Trimming, Weighting, and Grouping SNPs in Human Case-Control Association Studies

Hoh¹,

Wille²,

Ott³

2001

Genome Res.

287

318

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“…It is not possible within the regression analysis to make inferences about a residual polygenic component because it is confounded with the individual-specific residual error. However, an ad hoc method to account for a residual polygenic effect when estimating the variance contribution by a candidate gene has been proposed by Stoesz et al [1997]. In light of these limitations it is thus interesting, and perhaps surprising, that the HE method detected the APOE gene.…”

Section: Discussionmentioning

confidence: 99%

“…The original presentation of the HE procedure assumed g to be biallelic, but this assumption may be relaxed to higher degrees of polymorphism [Stoesz et al, 1997]. Letting Û 2 g denote the additive component of the genetic variance at g, and Û 2 ‰ the variance of the difference of the residual error between two siblings (e 1 -e 2 ), Haseman and Elston [1972] showed that…”

Section: He Methodsmentioning

confidence: 99%

“…The procedure is based on simple genetic principles and does not depend on specifying a genetic model for penetrance. Extensions of the Haseman and Elston (HE) method include the use of arbitrary pairs of relatives [Olson and Wijsman, 1993;, allowing for multiple loci [Stoesz et al, 1997;Tiwari and Elston, 1997], and multipoint mapping [Fulker et al, 1995]. Because the HE method is genetically robust and requires only simple regression to implement, it is appealing in practice; however, other methods have been shown to have higher power [Risch and Zhang, 1995;Amos et al, 1996].…”

Section: Introductionmentioning

confidence: 99%

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Testing for Linkage underRobust Genetic Models

Guerra

Wan²,

Jia

et al. 1999

Hum Hered

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Robust genetic models are used to assess linkage between a quantitative trait and genetic variation at a specific locus using allele-sharing data. Little is known about the relative performance of different possible significance tests under these models. Under the robust variance components model approach there are several alternatives: standard Wald and likelihood ratio tests, a quasilikelihood Wald test, and a Monte Carlo test. This paper reports on the relative performance (significance level and power) of the robust sibling pair test and the different alternatives under the robust variance components model. Simulations show that (1) for a fixed sample size of nuclear families, the variance components model approach is more powerful than the robust sibling pair approach; (2) when the number of nuclear families is at least ∼100 and heritability at the trait locus is moderate to high (>0.20) all tests based on the variance components model are equally effective; (3) when the number of nuclear families is less than ∼100 or heritability at the trait locus is low (<0.20), on balance, the Monte Carlo test provides the best power and is the most valid. The different testing procedures are applied to determine which are able to detect the known association between low density lipoprotein cholesterol and the common genotypes at the locus encoding apolipoprotein E. Results from this application show that the robust sibling pair method may be more effective in practice than that indicated by simulations.

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“…Assume that the trait X follows the model X = µ + g + e, where µ is an overall mean, g is the effect of a candidate locus which may have multiple alleles (Stoesz et al 1997), and e represents a normally distributed residual with zero mean and variance σ 2 . Let x 1 and x 2 denote the trait phenotypes of two siblings and y = (x 1 −x 2 ) 2 .…”

Section: Methodsmentioning

confidence: 99%

Statistically robust approaches for sib‐pair linkage analysis

Wang¹,

Guerra²,

Cohen³

1998

Annals of Human Genetics

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summaryMany traits that distinguish one individual from another, such as height or weight, are clearly heritable and yet vary continuously in populations. Continuous, heritable variation in trait levels presumably reflects the segregation of multiple genes, but elucidation of the genetic architecture of quantitative traits has been limited. Haseman & Elston (1972) developed a genetically robust method (HE) for detecting linkage to quantitative trait loci using sib-pairs. The method is based on a simple linear regression of the squared sib-pairs trait difference on the proportion of alleles shared identical by descent at a marker locus. Linkage is detected by a negative slope which has been traditionally assessed by a standard t-test. Wan, have shown that the standard t-test is robust to the violations of the stochastic assumptions underlying the test. In practice, however, the standard t-test, based on least-squares regression, is sensitive to outliers. The presence of outliers in the data can lead to false positive and false negative linkage results. Accordingly we have developed and evaluated a statistically robust procedure for the HE approach to linkage. The procedure is based on robust regression. Simulation studies show that this robust procedure has greater power than the standard t-test in the presence of outliers, and has similar power to the standard t-test in the absence of outliers. This robust procedure also shows greater power than rank-based approaches either in the absence or presence of outliers. To illustrate the methods using real data we reanalyse data from two lipoprotein systems that motivated this work.

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Extension of the Haseman–Elston method to multiple alleles and multiple loci: theory and practice for candidate genes

Cited by 8 publications

References 0 publications

Trimming, Weighting, and Grouping SNPs in Human Case-Control Association Studies

Trimming, Weighting, and Grouping SNPs in Human Case-Control Association Studies

Testing for Linkage underRobust Genetic Models

Statistically robust approaches for sib‐pair linkage analysis

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