Evaluation of a LASSO regression approach on the unrelated samples of Genetic Analysis Workshop 17

Abstract

The Genetic Analysis Workshop 17 data we used comprise 697 unrelated individuals genotyped at 24,487 single-nucleotide polymorphisms (SNPs) from a mini-exome scan, using real sequence data for 3,205 genes annotated by the 1000 Genomes Project and simulated phenotypes. We studied 200 sets of simulated phenotypes of trait Q2. An important feature of this data set is that most SNPs are rare, with 87% of the SNPs having a minor allele frequency less than 0.05. For rare SNP detection, in this study we performed a least absolute shrinkage and selection operator (LASSO) regression and F tests at the gene level and calculated the generalized degrees of freedom to avoid any selection bias. For comparison, we also carried out linear regression and the collapsing method, which sums the rare SNPs, modified for a quantitative trait and with two different allele frequency thresholds. The aim of this paper is to evaluate these four approaches in this mini-exome data and compare their performance in terms of power and false positive rates. In most situations the LASSO approach is more powerful than linear regression and collapsing methods. We also note the difficulty in determining the optimal threshold for the collapsing method and the significant role that linkage disequilibrium plays in detecting rare causal SNPs. If a rare causal SNP is in strong linkage disequilibrium with a common marker in the same gene, power will be much improved.

Figure 1

Power to detect 13 causal genes at the significance levels of 0.01 and 1.6 × 10^–5 in 200 replicates. The x-axis indicates the 13 genes sorted in decreasing order of the power of the F_LASSO test, and the y-axis indicates the corresponding power. The power is shown as solid black lines for the significance level 0.01 and as red dashed lines for the significance 1.6 × 10^–5.

Figure 2

Linkage disequilibrium plot for genes SIRT1 and VLDLR. Linkage disequilibrium plots generated from Haploview. The values of R² are shown in each cell. The color code in the Haploview plot follows the standard color scheme for Haploview: white, |D′| < 1, LOD < 2; shades of pink/red, |D′| < 1, LOD ≥ 2; blue, | D′| = 1, LOD < 2; red, |D′| = 1, LOD ≥ 2.