Trimming, weighting, and grouping SNPs in human case-control association studies.

By J. Hoh, Anja Wille, J. Ott. Genome Research, 2001

332 citations recorded by the source corpus.

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.

Subfields: Genetics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/f3c8967032b28e6f13a756834ecba0d018d30d77

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Genetics

1. C. Ulrich
2. John D. Potter
3. Rui Jiang
4. Ellen L. Goode
5. J. Li
6. Wenhui Fu
7. Wanwan Tang
8. Xuebing Wu

In Molecular Biology

1. C. Ulrich
2. John D. Potter
3. Rui Jiang
4. Ellen L. Goode
5. J. Li
6. Wenhui Fu
7. Wanwan Tang
8. Xuebing Wu

Citations in the corpus

• Polymorphisms in DNA repair genes and associations with cancer risk. (2002, methodology): “Because of concerns over inflated type I error rates in pathway-wide or genome-wide association studies, methods of statistical analysis seeking to obviate this problem are under development (81).”
• A random forest approach to the detection of epistatic interactions in case-control studies (2009, methodology): “For example, Hoh et al proposed a trimming, weighting, and grouping approach that used the summation of statistics on the basis of single-locus marginal effects and the Hardy-Weinberg equilibrium (HWE) for hypothesis testing [12].”
• Adjusting multiple testing in multilocus analyses using the eigenvalues of a correlation matrix (2005, methodology): “As they are caused by interactions among genes, an increasing number of studies (Hoh et al, 2001; Nelson et al, 2001; Ritchie et al, 2001; Cordell and Clayton, 2002) have employed multilocus methods to detect the causative genes.”
• Gene-Environment-Wide Association Studies: Emerging Approaches (2010, neutral): “Many exploratory methods have been developed for multivariate analysis of high-dimensional data, ranging from standard multiple regression techniques to various machine learning or pattern recognition method”