Marker selection for genetic case-control association studies.
- Authors
- Pettersson, Fredrik H; Anderson, Carl A; Clarke, Geraldine M; Barrett, Jeffrey C; Cardon, Lon R; Morris, Andrew P; Zondervan, Krina T
- Year
- 2009
- Journal
- Nature protocols
- PMID
- 19390530
- DOI
- 10.1038/nprot.2009.38
- PMCID
- PMC3025519
Association studies can focus on candidate gene(s), a particular genomic region, or adopt a genome-wide association approach, each of which has implications for marker selection. The strategy for marker selection will affect the statistical power of the study to detect a disease association and is a crucial element of study design. The abundant single nucleotide polymorphisms (SNPs) are the markers of choice in genetic case-control association studies. The genotypes of neighboring SNPs are often highly correlated ('in linkage disequilibrium', LD) within a population, which is utilized for selecting specific 'tagSNPs' to serve as proxies for other nearby SNPs in high LD. General guidelines for SNP selection in candidate genes/regions and genome-wide studies are provided in this protocol, along with illustrative examples. Publicly available web-based resources are utilized to browse and retrieve data, and software, such as Haploview and Goldsurfer2, is applied to investigate LD and to select tagSNPs.
View of the candidate genomic region around PPARG using the UCSC genome browser. The plot shows the different versions of the candidate gene and the LD structure in the region as measured in r2 for the CEU HapMap sample.
View of the candidate genomic region around PPARG using the HapMap genome browser. The plot shows the genotyped SNPs in the region and the different versions of the candidate gene together with other information. The ‘GWA studies (NHGRI Catalog)’ track shows SNPs that have previously been found to be associated with a studied trait in published GWA literature.
Genomeview from GoldSurfer2 showing the selection of tagSNPs (track 2) in relation to PPARG and all HapMap SNPs with minor allele frequency > 0.05 (track 1). The SNP, rs1801282, previously implicated in type 2 diabetes, is highlighted near the start of the PPARG gene. LD is measured in r2.
| Name | Type |
|---|---|
| 1000 Genomes Project | cohort |
| cancer | phenotype |
| candidate genes | cohort |
| candidate gene study | cohort |
| cases | cohort |
| CEPH (Utah Residents with Northern and Western European Ancestry) local | cohort |
| CEU | cohort |
| CHB | cohort |
| CNV | variant |
| common genetic variation local | phenotype |
| complex diseases | phenotype |
| Continuous quantitative traits local | phenotype |
| controls | cohort |
| copy number variation | variant |
| dbSNP | cohort |
| disease | phenotype |
| disease susceptibility | phenotype |
| ENCODE project | cohort |
| Ensembl | drug |
| European ancestry | cohort |
| Four populations local | cohort |
| gene | gene |
| GeneSNPs local | drug |
| genomic variation local | phenotype |
| GoldSurfer2 local | drug |
| GWA panel local | cohort |
| GWA study | cohort |
| HapMap | cohort |
| HapMap local | drug |
| Human Genome Project | cohort |
| intergenic region local | variant |
| intron local | variant |
| JPT | cohort |
| marker | variant |
| mutations | variant |
| phenotype | phenotype |
| population | cohort |
| PPARG | gene |
| publicly available controls local | cohort |
| rs1801282 | variant |
| SeattleSNPs local | drug |
| Seven populations local | cohort |
| SNP | cohort |
| SNP Consortium local | cohort |
| study cohort | cohort |
| tagSNP | variant |
| type 2 diabetes | phenotype |
| UCSC Genome Browser | drug |
| variation local | variant |
| Wellcome Trust case control consortium | cohort |
| WTCCC project local | cohort |
| Yoruba in Ibadan local | cohort |
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| An Interdisciplinary Approach to Studying Gene-Environment Interactions: From Twin Studies to Gene Identification and Back. | Dick DM | — | 2011 | → |
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| Human studies on genetics of the age at natural menopause: a systematic review. | Voorhuis M et al. | — | 2010 | → |
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