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Detecting rare variant associations by identity-by-descent mapping in case-control studies.

Browning SR, Thompson EA - Genetics (2012)

Bottom Line: In those data we find that we can detect association only with the HLA region using IBD mapping.Overall, our results suggest that IBD mapping may have higher power than association analysis of SNP data when multiple rare causal variants are clustered within a gene.However, for outbred populations, very large sample sizes may be required for genome-wide significance unless the causal variants have strong effects.

View Article: PubMed Central - PubMed

Affiliation: Department of Biostatistics, University of Washington, Seattle, Washington 98195, USA. sguy@uw.edu

ABSTRACT
Identity-by-descent (IBD) mapping tests whether cases share more segments of IBD around a putative causal variant than do controls. These segments of IBD can be accurately detected from genome-wide SNP data. We investigate the power of IBD mapping relative to that of SNP association testing for genome-wide case-control SNP data. Our focus is particularly on rare variants, as these tend to be more recent and hence more likely to have recent shared ancestry. We simulate data from both large and small populations and find that the relative performance of IBD mapping and SNP association testing depends on population demographic history and the strength of selection against causal variants. We also present an IBD mapping analysis of a type 1 diabetes data set. In those data we find that we can detect association only with the HLA region using IBD mapping. Overall, our results suggest that IBD mapping may have higher power than association analysis of SNP data when multiple rare causal variants are clustered within a gene. However, for outbred populations, very large sample sizes may be required for genome-wide significance unless the causal variants have strong effects.

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Permutation P-values for the IBD test in the WTCCC type 1 diabetes data. P-values were calculated at every tenth marker along the autosomes. The smallest possible P-value from the 5,000,000 permutations (2 × 10−7) is shown by the black horizontal line. The genome-wide significance level determined by 1000 permutations (6 × 10−6) is shown by the blue horizontal line.
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fig3: Permutation P-values for the IBD test in the WTCCC type 1 diabetes data. P-values were calculated at every tenth marker along the autosomes. The smallest possible P-value from the 5,000,000 permutations (2 × 10−7) is shown by the black horizontal line. The genome-wide significance level determined by 1000 permutations (6 × 10−6) is shown by the blue horizontal line.

Mentions: Figure 3 shows the unadjusted P-values. Because of the limited number of permutations, the smallest achievable P-value is 2 × 10−7 = 1/(5 × 106). The HLA region is clearly significant; however, this is not surprising given the extremely strong signal in this region. A region on chromosome 2 is almost significant (genome-wide adjusted P-value 0.20). A recent review (Baker and Steck 2011) lists two known associations with type 1 diabetes on chromosome 2. These are IFIH1 at 2q24.2 and CTLA4 (cytotoxic T lymphocyte associated antigen 4) at 2q33.2. The closer of these is IFIH1, which is 103 Mb away from IBD signal. The closest gene to the IBD signal is BCL11A (B-cell CCL/lymphoma 11A), which is 1.0 Mb away from the location of the smallest P-value on chromosome 2. BCL11A has been suggestively associated with type 2 diabetes (Zeggini et al. 2008) and affects pancreatic β-cell function (Simonis-Bik et al. 2010).


Detecting rare variant associations by identity-by-descent mapping in case-control studies.

Browning SR, Thompson EA - Genetics (2012)

Permutation P-values for the IBD test in the WTCCC type 1 diabetes data. P-values were calculated at every tenth marker along the autosomes. The smallest possible P-value from the 5,000,000 permutations (2 × 10−7) is shown by the black horizontal line. The genome-wide significance level determined by 1000 permutations (6 × 10−6) is shown by the blue horizontal line.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3316661&req=5

fig3: Permutation P-values for the IBD test in the WTCCC type 1 diabetes data. P-values were calculated at every tenth marker along the autosomes. The smallest possible P-value from the 5,000,000 permutations (2 × 10−7) is shown by the black horizontal line. The genome-wide significance level determined by 1000 permutations (6 × 10−6) is shown by the blue horizontal line.
Mentions: Figure 3 shows the unadjusted P-values. Because of the limited number of permutations, the smallest achievable P-value is 2 × 10−7 = 1/(5 × 106). The HLA region is clearly significant; however, this is not surprising given the extremely strong signal in this region. A region on chromosome 2 is almost significant (genome-wide adjusted P-value 0.20). A recent review (Baker and Steck 2011) lists two known associations with type 1 diabetes on chromosome 2. These are IFIH1 at 2q24.2 and CTLA4 (cytotoxic T lymphocyte associated antigen 4) at 2q33.2. The closer of these is IFIH1, which is 103 Mb away from IBD signal. The closest gene to the IBD signal is BCL11A (B-cell CCL/lymphoma 11A), which is 1.0 Mb away from the location of the smallest P-value on chromosome 2. BCL11A has been suggestively associated with type 2 diabetes (Zeggini et al. 2008) and affects pancreatic β-cell function (Simonis-Bik et al. 2010).

Bottom Line: In those data we find that we can detect association only with the HLA region using IBD mapping.Overall, our results suggest that IBD mapping may have higher power than association analysis of SNP data when multiple rare causal variants are clustered within a gene.However, for outbred populations, very large sample sizes may be required for genome-wide significance unless the causal variants have strong effects.

View Article: PubMed Central - PubMed

Affiliation: Department of Biostatistics, University of Washington, Seattle, Washington 98195, USA. sguy@uw.edu

ABSTRACT
Identity-by-descent (IBD) mapping tests whether cases share more segments of IBD around a putative causal variant than do controls. These segments of IBD can be accurately detected from genome-wide SNP data. We investigate the power of IBD mapping relative to that of SNP association testing for genome-wide case-control SNP data. Our focus is particularly on rare variants, as these tend to be more recent and hence more likely to have recent shared ancestry. We simulate data from both large and small populations and find that the relative performance of IBD mapping and SNP association testing depends on population demographic history and the strength of selection against causal variants. We also present an IBD mapping analysis of a type 1 diabetes data set. In those data we find that we can detect association only with the HLA region using IBD mapping. Overall, our results suggest that IBD mapping may have higher power than association analysis of SNP data when multiple rare causal variants are clustered within a gene. However, for outbred populations, very large sample sizes may be required for genome-wide significance unless the causal variants have strong effects.

Show MeSH
Related in: MedlinePlus