Efficient study designs for test of genetic association using sibship data and unrelated cases and controls - PubMed (original) (raw)

. 2006 May;78(5):778-792.

doi: 10.1086/503711. Epub 2006 Mar 20.

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Efficient study designs for test of genetic association using sibship data and unrelated cases and controls

Mingyao Li et al. Am J Hum Genet. 2006 May.

Abstract

Linkage mapping of complex diseases is often followed by association studies between phenotypes and marker genotypes through use of case-control or family-based designs. Given fixed genotyping resources, it is important to know which study designs are the most efficient. To address this problem, we extended the likelihood-based method of Li et al., which assesses whether there is linkage disequilibrium between a disease locus and a SNP, to accommodate sibships of arbitrary size and disease-phenotype configuration. A key advantage of our method is the ability to combine data from different family structures. We consider scenarios for which genotypes are available for unrelated cases, affected sib pairs (ASPs), or only one sibling per ASP. We construct designs that use cases only and others that use unaffected siblings or unrelated unaffected individuals as controls. Different combinations of cases and controls result in seven study designs. We compare the efficiency of these designs when the number of individuals to be genotyped is fixed. Our results suggest that (1) when the disease is influenced by a single gene, the one sibling per ASP-control design is the most efficient, followed by the ASP-control design, and familial cases contribute more association information than singleton cases; (2) when the disease is influenced by multiple genes, familial cases provide more association information than singleton cases, unless the effect of the locus being tested is much smaller than at least one other untested disease locus; and (3) the case-control design can be useful for detecting genes with small effect in the presence of genes with much larger effect. Our findings will be helpful for researchers designing and analyzing complex disease-association studies and will facilitate genotyping resource allocation.

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Figures

Figure 1

Figure 1

Association study designs. The black arrows denote individuals to be genotyped at the candidate SNP. The number of individuals to be genotyped at the SNP is fixed at 1,000 for each study design.

Figure 2

Figure 2

Histograms of ranks for different study designs. Results are based on 2,000 replicates of the corresponding sampling units for each study design. All models have disease prevalence of

_K_=5%

and sibling recurrence risk ratio of

λ_s_=1.02

. Power is assessed at the 1% level. For each disease model in table 2 and at each level of disease-SNP LD (

_r_2=.25

, .50, .75, and 1), the seven study designs are ranked by estimated power.

Figure 3

Figure 3

Comparison of case-control design and one sibling per ASP–control design, under five-locus disease models, when the effect size of the test locus increases and the effect size of the four remaining disease loci are fixed. Results are based on 2,000 replicate data sets. The disease prevalence

_K_=5%

. The disease is influenced by five unlinked disease loci, each with a predisposing-allele frequency of 0.1. The SNP, with a minor-allele frequency of 0.1, is completely linked to the first disease locus, and

_r_2

between the two loci is 0.5. All disease loci follow an additive model, with locus-specific

λ_s_

at the test locus increasing from 1.02 to 1.25 and the locus-specific

λ_s_

for the four remaining disease loci fixed at 1.02. Power is assessed at the 1% level. The solid line is for design with 500 cases (one sibling per ASP) and 500 controls. The dashed line is for design with 500 cases and 500 controls.

Figure 4

Figure 4

Power comparison of case-control design and one sibling per ASP–control design, under multilocus disease models, when the effect size of each disease locus is fixed and the number of disease loci increases. Results are based on 2,000 replicate data sets. The disease prevalence

_K_=5%

. The disease is influenced by L (

2⩽_L_⩽10

) unlinked disease loci, each with a predisposing-allele frequency of 0.1. The SNP, with a minor-allele frequency of 0.1, is completely linked to the first disease locus, and

_r_2

between the two loci is 0.5. All disease loci follow a dominant, an additive, or a recessive model, with locus-specific

λ_s_

at each disease locus fixed at 1.02. Power is assessed at the 1% level. The solid line is for design with 500 cases (one sibling per ASP) and 500 controls. The dashed line is for design with 500 cases and 500 controls.

Figure 5

Figure 5

Power comparison of case-control design and one sibling per ASP–control design, under five-locus disease models, when the effect size of the large-effect background disease locus increases and the effect size of the small-effect disease loci, including the test locus, is fixed. Results are based on 2,000 replicate data sets. The disease prevalence

_K_=5%

. The disease is influenced by five unlinked disease loci, each with a predisposing-allele frequency of 0.1. The SNP, with a minor-allele frequency of 0.1, is completely linked to the first disease locus, and

_r_2

between the two loci is 0.5. All disease loci follow a dominant, an additive, or a recessive model, with locus-specific fixed

λ_s_

at the large effect background disease locus increasing from 1.02 to 1.7 and the locus-specific

λ_s_

for the small effect disease loci, including the testing locus, fixed at 1.02. Power is assessed at the 1% level. The solid line is for design with 500 cases (one sibling per ASP) and 500 controls. The dashed line is for design with 500 cases and 500 controls.

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