Association Studies in Candidate Genes: Strategies to Select SNPs to Be Tested

Cousin, Emmanuelle; Génin, Emmanuelle; Macé, Sandrine; Ricard, Sylvain; Chansac, Céline; Zompo, Maria Del; Deleuze, Jean Fran ois

doi:10.1159/000073200

Cited by 26 publications

(26 citation statements)

References 19 publications

(22 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Tag SNPs are selected by utilizing the correlation structure between the SNPs, referred to as linkage disequilibrium (LD). Tag SNP selection under different criteria has been very well investigated (Cousin et al 2003(Cousin et al , 2006Carlson et al 2004;Stram 2004Stram , 2005Lin and Altman 2004;de Bakker et al 2005;Halperin et al 2005;Pardi et al 2005;Qin et al 2006;Saccone et al 2006;Santana et al 2010).…”

mentioning

confidence: 99%

Increasing Power of Genome-Wide Association Studies by Collecting Additional Single-Nucleotide Polymorphisms

2011

View full text Add to dashboard Cite

Genome-wide association studies (GWASs) have been effectively identifying the genomic regions associated with a disease trait. In a typical GWAS, an informative subset of the single-nucleotide polymorphisms (SNPs), called tag SNPs, is genotyped in case/control individuals. Once the tag SNP statistics are computed, the genomic regions that are in linkage disequilibrium (LD) with the most significantly associated tag SNPs are believed to contain the causal polymorphisms. However, such LD regions are often large and contain many additional polymorphisms. Following up all the SNPs included in these regions is costly and infeasible for biological validation. In this article we address how to characterize these regions cost effectively with the goal of providing investigators a clear direction for biological validation. We introduce a follow-up study approach for identifying all untyped associated SNPs by selecting additional SNPs, called follow-up SNPs, from the associated regions and genotyping them in the original case/control individuals. We introduce a novel SNP selection method with the goal of maximizing the number of associated SNPs among the chosen follow-up SNPs. We show how the observed statistics of the original tag SNPs and human genetic variation reference data such as the HapMap Project can be utilized to identify the follow-up SNPs. We use simulated and real association studies based on the HapMap data and the Wellcome Trust Case Control Consortium to demonstrate that our method shows superior performance to the correlation-and distancebased traditional follow-up SNP selection approaches. Our method is publicly available at http://genetics. cs.ucla.edu/followupSNPs.

show abstract

mentioning

confidence: 99%

Increasing Power of Genome-Wide Association Studies by Collecting Additional Single-Nucleotide Polymorphisms

2011

View full text Add to dashboard Cite

show abstract

“…Choosing an informative subset of SNPs, termed tag-SNPs, under various criteria has been extensively investigated (Stram, 2004;de Bakker et al, 2005;Stram, 2005;Cousin et al, 2003Cousin et al, , 2006Halperin et al, 2005;Lin and Altman, 2004;Pardi et al, 2005;Qin et al, 2006;Saccone et al, 2006;Carlson et al, 2004;Santana et al, 2010). The main goal of these methods is to reduce the cost of GWASs by genotyping a subset of the SNPs yet collect as much information as possible on the remaining SNPs.…”

Section: Comparison To Traditional Tag-snp-based Association Testingmentioning

confidence: 99%

Efficiently Identifying Significant Associations in Genome-Wide Association Studies

Kostem

Eskin

2013

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Over the past several years, genome-wide association studies (GWAS) have implicated hundreds of genes in common disease. More recently, the GWAS approach has been utilized to identify regions of the genome that harbor variation affecting gene expression or expression quantitative trait loci (eQTLs). Unlike GWAS applied to clinical traits, where only a handful of phenotypes are analyzed per study, in eQTL studies, tens of thousands of gene expression levels are measured, and the GWAS approach is applied to each gene expression level. This leads to computing billions of statistical tests and requires substantial computational resources, particularly when applying novel statistical methods such as mixed models. We introduce a novel two-stage testing procedure that identifies all of the significant associations more efficiently than testing all the single nucleotide polymorphisms (SNPs). In the first stage, a small number of informative SNPs, or proxies, across the genome are tested. Based on their observed associations, our approach locates the regions that may contain significant SNPs and only tests additional SNPs from those regions. We show through simulations and analysis of real GWAS datasets that the proposed two-stage procedure increases the computational speed by a factor of 10. Additionally, efficient implementation of our software increases the computational speed relative to the state-of-the-art testing approaches by a factor of 75.

show abstract

“…Many different algorithms were suggested for identifying haplotype blocks and for picking tagSNPs, and prior comparisons of some of these methods suggest that the number of haplotype blocks, the tagSNPs selected to characterize the blocks, and the haplotype block boundaries can differ substantially between methods [Zhang et al, 2002a;Cousin et al, 2003;Schwartz et al, 2003]. …”

Section: Identifying and Using Haplotype Tagsnps In Linkage And Assocmentioning

confidence: 99%

Haplotypes and haplotype-tagging single-nucleotide polymorphism: Presentation Group 8 of Genetic Analysis Workshop 14

Beckmann¹,

Ziegler²,

Duggal³

et al. 2005

Genet. Epidemiol.

View full text Add to dashboard Cite

Moderately dense maps of single-nucleotide polymorphism (SNP) markers across the human genome for both the simulated data set and data from the Collaborative Study of the Genetics of Alcoholism were available at Genetic Analysis Workshop 14 for the first time. This allowed examination of various novel and existing methods for haplotype analyses. Three contributors applied Mantel statistics in different ways for both linkage and association analysis by using the shared length between two haplotypes at a marker locus as a measure of genetic similarity. The results indicate that haplotype-sharing based on Mantel statistics can be a powerful approach and needs further methodological evaluation. Four contributors investigated haplotype-tagging SNP (htSNP) selection procedures, two contributors examined the use of multilocus haplotypes compared to single loci in association tests, and two contributors compared the accuracy of various methods for reconstructing haplotypes and estimating haplotype frequencies for both pedigree data and data from unrelated individuals. For all three different tasks, software packages and procedures gave similar results in regions of high linkage disequilibrium (LD). However, they were not as consistent in regions of moderate to low LD. One coalescence-based approach for estimating haplotype frequencies, coupled with a Markov chain Monte Carlo technique, outperformed the other haplotype frequency estimation methods in regions of low LD. In conclusion, regardless of the task, results were similar in chromosomal regions of high LD. However, based on the differing results observed here, methodological improvements are required for chromosomal regions of low to moderate LD.

show abstract

Association Studies in Candidate Genes: Strategies to Select SNPs to Be Tested

Cited by 26 publications

References 19 publications

Increasing Power of Genome-Wide Association Studies by Collecting Additional Single-Nucleotide Polymorphisms

Increasing Power of Genome-Wide Association Studies by Collecting Additional Single-Nucleotide Polymorphisms

Efficiently Identifying Significant Associations in Genome-Wide Association Studies

Haplotypes and haplotype-tagging single-nucleotide polymorphism: Presentation Group 8 of Genetic Analysis Workshop 14

Contact Info

Product

Resources

About