Three genetic loci for lung cancer risk have been identified by genome-wide association studies (GWAS), but inherited susceptibility to specific histologic types of lung cancer is not well established. We conducted a GWAS of lung cancer and its major histologic types, genotyping 515,922 single-nucleotide polymorphisms (SNPs) in 5739 lung cancer cases and 5848 controls from one population-based case-control study and three cohort studies. Results were combined with summary data from ten additional studies, for a total of 13,300 cases and 19,666 controls of European descent. Four studies also provided histology data for replication, resulting in 3333 adenocarcinomas (AD), 2589 squamous cell carcinomas (SQ), and 1418 small cell carcinomas (SC). In analyses by histology, rs2736100 (TERT), on chromosome 5p15.33, was associated with risk of adenocarcinoma (odds ratio [OR]=1.23, 95% confidence interval [CI]=1.13-1.33, p=3.02x10(-7)), but not with other histologic types (OR=1.01, p=0.84 and OR=1.00, p=0.93 for SQ and SC, respectively). This finding was confirmed in each replication study and overall meta-analysis (OR=1.24, 95% CI=1.17-1.31, p=3.74x10(-14) for AD; OR=0.99, p=0.69 and OR=0.97, p=0.48 for SQ and SC, respectively). Other previously reported association signals on 15q25 and 6p21 were also refined, but no additional loci reached genome-wide significance. In conclusion, a lung cancer GWAS identified a distinct hereditary contribution to adenocarcinoma.

We propose in this paper a unified approach for testing the association between rare variants and phenotypes in sequencing association studies. This approach maximizes power by adaptively using the data to optimally combine the burden test and the nonburden sequence kernel association test (SKAT). Burden tests are more powerful when most variants in a region are causal and the effects are in the same direction, whereas SKAT is more powerful when a large fraction of the variants in a region are noncausal or the effects of causal variants are in different directions. The proposed unified test maintains the power in both scenarios. We show that the unified test corresponds to the optimal test in an extended family of SKAT tests, which we refer to as SKAT-O. The second goal of this paper is to develop a small-sample adjustment procedure for the proposed methods for the correction of conservative type I error rates of SKAT family tests when the trait of interest is dichotomous and the sample size is small. Both small-sample-adjusted SKAT and the optimal unified test (SKAT-O) are computationally efficient and can easily be applied to genome-wide sequencing association studies. We evaluate the finite sample performance of the proposed methods using extensive simulation studies and illustrate their application using the acute-lung-injury exome-sequencing data of the National Heart, Lung, and Blood Institute Exome Sequencing Project.

Genome-wide association studies have successfully identified a subset of common variants associated with lung cancer risk. However, these variants explain only a fraction of lung cancer heritability. It has been proposed that low-frequency or rare variants might have strong effects and contribute to the missing heritability. To assess the role of low-frequency or rare variants in lung cancer development, we analyzed exome chips representing 1,348 lung cancer subjects and 1,998 control subjects during the discovery stage and subsequently evaluated promising associations in an additional 4,699 affected subjects and 4,915 control subjects during the replication stages. Single-variant and gene-based analyses were carried out for coding variants with a minor allele frequency less than 0.05. We identified three low-frequency missense variants in BAT2 (rs9469031, c.1544C>T [p.Pro515Leu]; odds ratio [OR] = 0.55, p = 1.28 × 10(-10)), FKBPL (rs200847762, c.410C>T [p.Pro137Leu]; OR = 0.25, p = 9.79 × 10(-12)), and BPIFB1 (rs6141383, c.850G>A [p.Val284Met]; OR = 1.72, p = 1.79 × 10(-7)); these variants were associated with lung cancer risk. rs9469031 in BAT2 and rs6141383 in BPIFB1 were also associated with the age of onset of lung cancer (p = 0.001 and 0.006, respectively). BAT2 and FKBPL at 6p21.33 and BPIFB1 at 20q11.21 were differentially expressed in lung tumors and paired normal tissues. Gene-based analysis revealed that FKBPL, in which two independent variants were identified, might account for the association with lung cancer risk at 6p21.33. Our results highlight the important role low-frequency variants play in lung cancer susceptibility and indicate that candidate genes at 6p21.33 and 20q11.21 are potentially biologically relevant to lung carcinogenesis.

There is increasing interest in the joint analysis of multiple phenotypes in genome-wide association studies (GWASs), especially for the analysis of multiple secondary phenotypes in case-control studies and in detecting pleiotropic effects. Multiple phenotypes often measure the same underlying trait. By taking advantage of similarity across phenotypes, one could potentially gain statistical power in association analysis. Because continuous phenotypes are likely to be measured on different scales, we propose a scaled marginal model for testing and estimating the common effect of single-nucleotide polymorphism (SNP) on multiple secondary phenotypes in case-control studies. This approach improves power in comparison to individual phenotype analysis and traditional multivariate analysis when phenotypes are positively correlated and measure an underlying trait in the same direction (after transformation) by borrowing strength across outcomes with a one degree of freedom (1-DF) test and jointly estimating outcome-specific scales along with the SNP and covariate effects. To account for case-control ascertainment bias for the analysis of multiple secondary phenotypes, we propose weighted estimating equations for fitting scaled marginal models. This weighted estimating equation approach is robust to departures from normality of continuous multiple phenotypes and the misspecification of within-individual correlation among multiple phenotypes. Statistical power improves when the within-individual correlation is correctly specified. We perform simulation studies to show the proposed 1-DF common effect test outperforms several alternative methods. We apply the proposed method to investigate SNP associations with smoking behavior measured with multiple secondary smoking phenotypes in a lung cancer case-control GWAS and identify several SNPs of biological interest.

Height is a classic complex trait with common variants in a growing list of genes known to contribute to the phenotype. Using a genecentric genotyping array targeted toward cardiovascular-related loci, comprising 49,320 SNPs across approximately 2000 loci, we evaluated the association of common and uncommon SNPs with adult height in 114,223 individuals from 47 studies and six ethnicities. A total of 64 loci contained a SNP associated with height at array-wide significance (p < 2.4 × 10(-6)), with 42 loci surpassing the conventional genome-wide significance threshold (p < 5 × 10(-8)). Common variants with minor allele frequencies greater than 5% were observed to be associated with height in 37 previously reported loci. In individuals of European ancestry, uncommon SNPs in IL11 and SMAD3, which would not be genotyped with the use of standard genome-wide genotyping arrays, were strongly associated with height (p < 3 × 10(-11)). Conditional analysis within associated regions revealed five additional variants associated with height independent of lead SNPs within the locus, suggesting allelic heterogeneity. Although underpowered to replicate findings from individuals of European ancestry, the direction of effect of associated variants was largely consistent in African American, South Asian, and Hispanic populations. Overall, we show that dense coverage of genes for uncommon SNPs, coupled with large-scale meta-analysis, can successfully identify additional variants associated with a common complex trait.