Gene-based partial least-squares approaches for detecting rare variant associations with complex traits

Genome-wide association studies are largely based on single-nucleotide polymorphisms and rest on the common disease/common variants (single-nucleotide polymorphisms) hypothesis. However, it has been argued in the last few years and is well accepted now that rare variants are valuable for studying common diseases. Although current genome-wide association studies have successfully discovered many genetic variants that are associated with common diseases, detecting associated rare variants remains a great challenge. Here, we propose two partial least-squares approaches to aggregate the signals of many single-nucleotide polymorphisms (SNPs) within a gene to reveal possible genetic effects related to rare variants. The availability of the 1000 Genomes Project offers us the opportunity to evaluate the effectiveness of these two gene-based approaches. Compared to results from a SNP-based analysis, the proposed methods were able to identify some (rare) SNPs that were missed by the SNP-based analysis

Blocking approach for identification of rare variants in family-based association studies.

With the advent of next-generation sequencing technology, rare variant association analysis is increasingly being conducted to identify genetic variants associated with complex traits. In recent years, significant effort has been devoted to develop powerful statistical methods to test such associations for population-based designs. However, there has been relatively little development for family-based designs although family data have been shown to be more powerful to detect rare variants. This study introduces a blocking approach that extends two popular family-based common variant association tests to rare variants association studies. Several options are considered to partition a genomic region (gene) into "independent" blocks by which information from SNVs is aggregated within a block and an overall test statistic for the entire genomic region is calculated by combining information across these blocks. The proposed methodology allows different variants to have different directions (risk or protective) and specification of minor allele frequency threshold is not needed. We carried out a simulation to verify the validity of the method by showing that type I error is well under control when the underlying null hypothesis and the assumption of independence across blocks are satisfied. Further, data from the Genetic Analysis Workshop [Formula: see text] are utilized to illustrate the feasibility and performance of the proposed methodology in a realistic setting

SNV base-pair locations for two-causal genes: (a) RRAS: the average distance between two successive SNVs is kb with a maximum distance of kb; (b) PRKCB1: the average distance between two SNVs is kb with a maximum distance of kb.

<p>SNV base-pair locations for two-causal genes: (a) RRAS: the average distance between two successive SNVs is kb with a maximum distance of kb; (b) PRKCB1: the average distance between two SNVs is kb with a maximum distance of kb.</p

200 replicate results: Boxplots of the area under the ROC curves for replicates using <i>fbatv0</i>, <i>fbatv1</i>, and <i>rbPDT</i> and <i>rbFBAT</i> with four options.

<p>200 replicate results: Boxplots of the area under the ROC curves for replicates using <i>fbatv0</i>, <i>fbatv1</i>, and <i>rbPDT</i> and <i>rbFBAT</i> with four options.</p

List of p-values for six causal genes for different options with a variety of partitioning choices.

<p>List of p-values for six causal genes for different options with a variety of partitioning choices.</p

Type 1 error and power results for the simulation based on a data set with families.

<p>Type 1 error and power results for the simulation based on a data set with families.</p

<i>rbPDT</i> results using different blocking options: (a) Option 1 with block size 2, 3, 4, 5, or 6 kb; (b) Option 2 with number of blocks 2, 3, 5, 7, or 10; (c) Option 3 with blocks including 2, 3, 5, 7, or 10 SNVs; (d) Option 4 with number of blocks 2, 3, 5, 7, or 10.

<p><i>rbPDT</i> results using different blocking options: (a) Option 1 with block size 2, 3, 4, 5, or 6 kb; (b) Option 2 with number of blocks 2, 3, 5, 7, or 10; (c) Option 3 with blocks including 2, 3, 5, 7, or 10 SNVs; (d) Option 4 with number of blocks 2, 3, 5, 7, or 10.</p

200 replicate results: ROC curves for <i>fbatv0</i>, <i>fbatv1</i>, (a) <i>rbPDT</i> and <i>rbFBAT</i> with Option 1; (b) <i>rbPDT</i> and <i>rbFBAT</i> with Option 2; (c) <i>rbPDT</i>and <i>rbFBAT</i> with Option 3; (d) <i>rbPDT</i> and <i>rbFBAT</i> with Option 4.

<p>200 replicate results: ROC curves for <i>fbatv0</i>, <i>fbatv1</i>, (a) <i>rbPDT</i> and <i>rbFBAT</i> with Option 1; (b) <i>rbPDT</i> and <i>rbFBAT</i> with Option 2; (c) <i>rbPDT</i>and <i>rbFBAT</i> with Option 3; (d) <i>rbPDT</i> and <i>rbFBAT</i> with Option 4.</p