rare variants using family-specific linkage information

Genome-wide association studies have been successful in identifying common variants for common complex traits in recent years. However, common variants have generally failed to explain substantial proportions of the trait heritabilities. Rare variants, structural variations, and gene-gene and gene-environment interactions, among others, have been suggested as potential sources of the so-called missing heritability. With the advent of exome-wide and whole-genome next-generation sequencing technologies, finding rare variants in functionally important sites (e.g., protein-coding regions) becomes feasible. We investigate the role of linkage information to select families enriched for rare variants using the simulated Genetic Analysis Workshop 17 data. In each replicate of simulated phenotypes Q1 and Q2 on 697 subjects in 8 extended pedigrees, we select one pedigree with the largest family-specific LOD score. Across all 200 replications, we compare the probability that rare causal alleles will be carried in the selected pedigree versus a randomly chosen pedigree. One example of successful enrichment was exhibited for gene VEGFC. The causal variant had minor allele frequency of 0.0717% in the simulated unrelated individuals and explained about 0.1% of the phenotypic variance. However, it explained 7.9% of the phenotypic variance in the eight simulated pedigrees and 23.8% in the family that carried the minor allele. The carrier’s family was selected in all 200 replications. Thus our results show that family-specific linkage information is useful for selecting families for sequencing, thus ensuring that rare functional variants are segregating in the sequencing samples. Background Genome-wide association studies (GWAS) have achieved great success in recent years, with about 4,000 single-nucleotide polymorphisms (SNPs) found to be associated with hundreds of common complex traits [1]. However, substantial proportions of their heritabilities have not been accounted for. The socalled missing heritability [2] represents the dark matter of the genetic architecture of complex traits. With the ongoing efforts of many larger mega-consortia, additional genetic variants will likely be discovered through the pooling of statistical evidence from more samples. However, common variants discovered through GWAS have small effect sizes [1]. Finding the missing heritability is one of the most challenging tasks for genetic dissection of complex traits. Rare variants, structural variations, gene-gene interaction, and gene-environment interaction, among others, have been suggested as potential sources to be tapped [2]. Rare variants are likely to have much larger effects [3-5]. Recently, evidence from simulation studies has suggested the presence of synthetic association [6]; that is, rare causal variants may be stochastically associated more often with one allele of the common variants identified through GWAS. Hence rare variants that are poorly correlated with common variants may partly explain the missing heritability. With the advent of exome-wide and whole-genome next-generation sequencing technologies [7], finding rare and functional variants becomes feasible [8]. In this work, we investigate a strategic approach for studying rare variants in families. A real example of our approach has been demonstrated recently [9]. Using the Genetic Analysis Workshop 17 (GAW17) simulated data based on genotype data from the 1,000 Genomes Project * Correspondence: gang@wubios.wustl. edu Division of Biostatistics, Washington University School of Medicine, 660 South Euclid Avenue, St. Louis, MO 63110, USA Shi et al. BMC Proceedings 2011, 5(Suppl 9):S82 http://www.biomedcentral. com/1753-6561/5/S9/S82 © 2011 Shi et al; licensee BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. [10], we investigate the utility of family-specific linkage information for identifying families that are enriched for rare variants. We had the GAW17 simulation answers [11] when conducting the analyses. Methods We used the simulated family data set provided by GAW17, which consists of 697 subjects in 8 extended pedigrees. The 202 founders were chosen from 697 unrelated individuals participating in the 1000 Genomes Project, and their 495 descendants were simulated. See the GAW17 answers [11] for more details about the family data simulation. In total, 200 replications of simulated traits were available for analysis, including three quantitative traits (Q1, Q2, Q4) and one dichotomous trait (Affected). Our analyses were limited to quantitative phenotypes Q1 and Q2. We first adjusted the phenotypes Q1 and Q2 for age, sex, and smoking effects and used the standardized residuals in the analysis. We conducted variance component linkage analysis on Q1 and Q2. Family-specific logarithm of odds (LOD) scores were computed using QTLtrends[12] using the identity-by-descent information provided by GAW17. The QTLtrends program was developed to conduct variance component linkage analysis with interactions; we used a regular variance component model without interactions for this analysis. We analyzed each of the 200 replications; in each replication, we selected one pedigree that had the largest family-specific LOD score. To evaluate the efficacy of enriching rare causal variants using linkage information, we compared the relative frequency among the 200 replications of selecting the correct family (carrying the causal minor allele) with the probability that a randomly selected family will carry the causal minor allele. To save computational burden, we conducted analyses on only 9 and 13 causal genes used for simulating phenotypes Q1 and Q2, respectively. The variants in gene FLT4 for Q1 and in genes INSIG1 and RARB for Q2 are monomorphic and thus do not contribute to any phenotypic variation; hence they were deemed noncausal in our evaluations. Analyses on these three genes were actually carried out under the null hypothesis. We also computed effect sizes of the causal genes in terms of phenotypic variances explained within each family and in the total sample. The effect of each variant for each subject was computed from the number of rare alleles and effect (b) provided in the answers, assuming an additive model; the effect of each gene was the sum of all causal variants in it. The percentage of explained phenotypic variance was computed from the average ratio of the variance of the gene effect to the total phenotypic variance from 200 replications. Results In Table 1 we show the counts of rare alleles of causal genes for Q1 and Q2 separately in the eight pedigrees. According to the GAW17 answers, phenotype Q1 was influenced by 39 SNPs in 9 genes and Q2 was affected by 72 SNPs in 13 genes. Because linkage analysis is known to have low genomic resolution and our linkage analyses were conducted at the level of genes (not SNPs), we summarize the numbers of rare alleles by gene. Genes having a single nonmonomorphic causal SNP are indicated with an asterisk in the table. Not all families carry rare alleles of the causal SNPs. For instance, the rare allele of the causal SNP C14S1734 in gene HIF1A exists in only one founder of family 5, and the rare allele of SNP C6S2981 in gene VEGFA exists only in families 1, 2, and 7. In those families whose founder(s) carry rare alleles, some of the rare alleles were transmitted to descendants and some were not. For example, the founder in family 3 did not transmit a copy of the rare allele of the SNP C1S3181 in gene ELAVL4 to any descendants. On the other hand, 30 descendants in family 7 inherited the rare allele of the causal SNP C4S4935 in gene VEGFC from one common Table 1 Distribution of causal minor alleles of Q1 and Q2 in the eight simulated pedigrees