Family-based association tests for genomewide association scans.
With millions of single-nucleotide polymorphisms (SNPs) identified and characterized, genomewide association studies have begun to identify susceptibility genes for complex traits and diseases. These studies involve the characterization and analysis of very-high-resolution SNP genotype data for hundreds or thousands of individuals. We describe a computationally efficient approach to testing association between SNPs and quantitative phenotypes, which can be applied to whole-genome association scans. In addition to observed genotypes, our approach allows estimation of missing genotypes, resulting in substantial increases in power when genotyping resources are limited. We estimate missing genotypes probabilistically using the Lander-Green or Elston-Stewart algorithms and combine high-resolution SNP genotypes for a subset of individuals in each pedigree with sparser marker data for the remaining individuals. We show that power is increased whenever phenotype information for ungenotyped individuals is included in analyses and that high-density genotyping of just three carefully selected individuals in a nuclear family can recover >90% of the information available if every individual were genotyped, for a fraction of the cost and experimental effort. To aid in study design, we evaluate the power of strategies that genotype different subsets of individuals in each pedigree and make recommendations about which individuals should be genotyped at a high density. To illustrate our method, we performed genomewide association analysis for 27 gene-expression phenotypes in 3-generation families (Centre d'Etude du Polymorphisme Humain pedigrees), in which genotypes for ~860,000 SNPs in 90 grandparents and parents are complemented by genotypes for ~6,700 SNPs in a total of 168 individuals. In addition to increasing the evidence of association at 15 previously identified cis-acting associated alleles, our genotype-inference algorithm allowed us to identify associated alleles at 4 cis-acting loci that were missed when analysis was restricted to individuals with the high-density SNP data. Our genotype-inference algorithm and the proposed association tests are implemented in software that is available for free.
Chen
Wei-Min
WM
Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA. wmchen@virginia.edu
Abecasis
Goncalo R
GR
D000429:Alcohol Oxidoreductases / Q000235:genetics; D002675:Child, Preschool; D003198:Computer Simulation; D004268:DNA-Binding Proteins / Q000235:genetics; D030541:Databases, Genetic; D005260:Female; D020022:Genetic Predisposition to Disease; D005820:Genetic Testing / Q000379:methods*; D015894:Genome, Human; D005838:Genotype; D006801:Humans; D008297:Male; D010375:Pedigree; D020641:Polymorphism, Single Nucleotide / Q000235:genetics; D035781:Siblings
D004268:DNA-Binding Proteins; D000429:Alcohol Oxidoreductases; C096794:C-terminal binding protein