eleazar eskin ucla
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Increasing Power in Association Studies by using Linkage Disequilibrium Structure and Molecular Function as Prior Information. Eleazar Eskin UCLA. Motivation. Whole genome association study How to perform multiple hypothesis correction To increase statistical power - PowerPoint PPT PresentationTRANSCRIPT
Increasing Power in Association Studies by using Linkage Disequilibrium Structure and
Molecular Function as Prior Information
Eleazar EskinUCLA
Motivation
• Whole genome association study• How to perform multiple hypothesis
correction – To increase statistical power
• Incorporate prior information on molecular function of associated loci
• Information on linkage disequilibrium structure
Main idea
• Traditional method– Use a single significance threshold
• In practice, markers are not identical
• Set a different threshold at each marker, which reflects both intrinsic (e.g. LD, allele freq.) and extrinsic information on the markers
Standard Association Study
• M markers in N cases and N controls
• fi = minor allele frequency at marker i
• True case/control allele frequency• Marker d: casual variant with a relative
risk
dd
dd
dd
fp
ff
fp
)1(
ii pp /
Standard Association Study
• Test statistic~ N(
,1)
• Power at a single marker (probability of detecting an association with N individuals at p-value or significance threshold t
Multiple Hypothesis correction
• Fix the false positive rate at each marker so that the total false positive rate is α
• Bonferroni correction– ti= α/M
• Expected power:where ci is the probability of marker i to be causal
Probability of rejecting the correct null hypothesis
Multi-Threshold Association
• Allow a different threshold ti for each marker
• Power:
with adjusted false positive rate
• Goal: set values for ti to maximize the power subject to the constraints
Maximizing the Power
• Gradient at each marker will be equal at the optimal point
• Given a value of gradient, solve for the threshold at each marker to achieve that gradient
• Do binary search over the gradient until thresholds sum to α
Maximizing Power for Proxies
• In practice, markers are tags for causal variation• Given K variants, assign each potential causal
variation vk to the best marker i
• The effective non-centrality parameter is reduced by a factor of |rki| where rki is the correlation coefficient between variant k and marker i.
• If vk is causal, the power function when observing proxy marker i is ),||,( NrtP kkis
Maximizing Power for Proxies
• Each variant k has a prob of being causal ck
• The total power captured by each marker i
• The total power of the association study
ik Tv kkiskiim NNrtPcNTtP ),||,(),,(
M
i Tvkkisk
M
iiimM
ik
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Candidate Gene study
• 1000 cases and controls over ENCODE regions using markers in Affymetrix 500k genechip
Robustness over relative risks
Whole Genome Association
• Assumption– Each SNP is equally likely to be causal with
relative risk of 2
• Power for traditional study and multi-threshold association for 2,614,057 SNPs– avg: 0.593 / 0.610– Avg over power in [0.1, 0.9]: 0.568 / 0.615
Impact of extrinsic information
1. cSNPs are more likely to be involved in disease2. Add information on se of genes which are more
likely to be involved in specific disease
• 30,700 cSNPs in HapMap contributes to 20% of the disease causing variation
• Cancer Gene Census: 363 genes in which mutations have been implicated in cancer. 20% of causal variation is assumed in these genes