McDonnell, Shannon K

ABSTRACTRare variants (RVs) have been shown to be significant contributors to complex disease risk. By definition, these variants have very low minor allele frequencies and traditional single‐marker methods for statistical analysis are underpowered for typical sequencing study sample sizes. Multimarker burden‐type approaches attempt to identify aggregation of RVs across case‐control status by analyzing relatively small partitions of the genome, such as genes. However, it is generally the case that the aggregative measure would be a mixture of causal and neutral variants, and these omnibus tests do not directly provide any indication of which RVs may be driving a given association. Recently, Bayesian variable selection approaches have been proposed to identify RV associations from a large set of RVs under consideration. Although these approaches have been shown to be powerful at detecting associations at the RV level, there are often computational limitations on the total quantity of RVs under consideration and compromises are necessary for large‐scale application. Here, we propose a computationally efficient alternative formulation of this method using a probit regression approach specifically capable of simultaneously analyzing hundreds to thousands of RVs. We evaluate our approach to detect causal variation on simulated data and examine sensitivity and specificity in instances of high RV dimensionality as well as apply it to pathway‐level RV analysis results from a prostate cancer (PC) risk case‐control sequencing study. Finally, we discuss potential extensions and future directions of this work.

Abstract Background: Unnecessary intervention and overtreatment of indolent disease are common challenges in clinical management of prostate cancer. Improved tools to distinguish lethal from indolent disease are critical. Methods: We performed a genome-wide survival analysis of cause-specific death in 24,023 prostate cancer patients (3,513 disease-specific deaths) from the PRACTICAL and BPC3 consortia. Top findings were assessed for replication in a Norwegian cohort (CONOR). Results: We observed no significant association between genetic variants and prostate cancer survival. Conclusions: Common genetic variants with large impact on prostate cancer survival were not observed in this study. Impact: Future studies should be designed for identification of rare variants with large effect sizes or common variants with small effect sizes. Cancer Epidemiol Biomarkers Prev; 24(11); 1796–800. ©2015 AACR.

Abstract A genome-wide association study (GWAS) of prostate cancer in Kaiser Permanente health plan members (7,783 cases, 38,595 controls; 80.3% non-Hispanic white, 4.9% African-American, 7.0% East Asian, and 7.8% Latino) revealed a new independent risk indel rs4646284 at the previously identified locus 6q25.3 that replicated in PEGASUS (N = 7,539) and the Multiethnic Cohort (N = 4,679) with an overall P = 1.0 × 10−19 (OR, 1.18). Across the 6q25.3 locus, rs4646284 exhibited the strongest association with expression of SLC22A1 (P = 1.3 × 10−23) and SLC22A3 (P = 3.2 × 10−52). At the known 19q13.33 locus, rs2659124 (P = 1.3 × 10−13; OR, 1.18) nominally replicated in PEGASUS. A risk score of 105 known risk SNPs was strongly associated with prostate cancer (P < 1.0 × 10−8). Comparing the highest to lowest risk score deciles, the OR was 6.22 for non-Hispanic whites, 5.82 for Latinos, 3.77 for African-Americans, and 3.38 for East Asians. In non-Hispanic whites, the 105 risk SNPs explained approximately 7.6% of disease heritability. The entire GWAS array explained approximately 33.4% of heritability, with a 4.3-fold enrichment within DNaseI hypersensitivity sites (P = 0.004). Significance: Taken together, our findings of independent risk variants, ethnic variation in existing SNP replication, and remaining unexplained heritability have important implications for further clarifying the genetic risk of prostate cancer. Our findings also suggest that there may be much promise in evaluating understudied variation, such as indels and ethnically diverse populations. Cancer Discov; 5(8); 878–91. ©2015 AACR. This article is highlighted in the In This Issue feature, p. 783

Purpose Liver injury is a serious adverse event leading to permanent discontinuation of lapatinib in affected patients. This study aimed to validate previously associated major histocompatibility complex (MHC) variants as predictors of risk of liver injury by using a large, randomized, placebo-controlled trial of lapatinib in human epidermal growth factor receptor 2–positive, early-stage breast cancer (Tykerb Evaluation After Chemotherapy [TEACH]: Lapatinib Versus Placebo In Women With Early-Stage Breast Cancer). Patients and Methods The frequency of ALT elevation cases was compared among four MHC variants in 1,194 patients randomly assigned to lapatinib. Cumulative ALT elevation time courses during treatment were also compared between carriers and noncarriers of specified MHC variants. Results In lapatinib-treated patients, there was a significant difference in ALT case incidence between HLA carriers and noncarriers. The highly correlated alleles HLA-DRB1*07:01 and HLA-DQA1*02:01 (study frequency, 22.4%) were associated with ALT elevation (odds ratio, 14) between cases (n = 37) and controls (n = 1,071). These associations strengthened at higher ALT elevation thresholds and in Hy's Law cases. In lapatinib-treated patients, the overall risk for National Cancer Institute-Common Terminology Criteria for Adverse Events grade 3 ALT elevation (> 5× upper limit of normal) was 2.1%; HLA allele carriers had an increased risk of 7.7%; in noncarriers, risk was reduced to 0.5%, comparable to ALT elevation for all patients receiving placebo. The increase in ALT case incidence in the lapatinib arm showed no evidence of plateau during 1 year of lapatinib treatment. Conclusion These results validate HLA-DRB1*07:01 allele carriage as a predictor of increased risk of lapatinib-induced liver injury and implicate an immune pathology. The HLA association could support clinical management of patients experiencing hepatotoxicity during lapatinib treatment.

ABSTRACTSearching for rare genetic variants associated with complex diseases can be facilitated by enriching for diseased carriers of rare variants by sampling cases from pedigrees enriched for disease, possibly with related or unrelated controls. This strategy, however, complicates analyses because of shared genetic ancestry, as well as linkage disequilibrium among genetic markers. To overcome these problems, we developed broad classes of “burden” statistics and kernel statistics, extending commonly used methods for unrelated case‐control data to allow for known pedigree relationships, for autosomes and the X chromosome. Furthermore, by replacing pedigree‐based genetic correlation matrices with estimates of genetic relationships based on large‐scale genomic data, our methods can be used to account for population‐structured data. By simulations, we show that the type I error rates of our developed methods are near the asymptotic nominal levels, allowing rapid computation of P‐values. Our simulations also show that a linear weighted kernel statistic is generally more powerful than a weighted “burden” statistic. Because the proposed statistics are rapid to compute, they can be readily used for large‐scale screening of the association of genomic sequence data with disease status.

Abstract Background: Colorectal cancer (CRC) in densely affected families without Lynch Syndrome may be due to mutations in undiscovered genetic loci. Familial linkage analyses have yielded disparate results; the use of exome sequencing in coding regions may identify novel segregating variants. Methods: We completed exome sequencing on 40 affected cases from 16 multicase pedigrees to identify novel loci. Variants shared among all sequenced cases within each family were identified and filtered to exclude common variants and single-nucleotide variants (SNV) predicted to be benign. Results: We identified 32 nonsense or splice-site SNVs, 375 missense SNVs, 1,394 synonymous or noncoding SNVs, and 50 indels in the 16 families. Of particular interest are two validated and replicated missense variants in CENPE and KIF23, which are both located within previously reported CRC linkage regions, on chromosomes 1 and 15, respectively. Conclusions: Whole-exome sequencing identified DNA variants in multiple genes. Additional sequencing of these genes in additional samples will further elucidate the role of variants in these regions in CRC susceptibility. Impact: Exome sequencing of familial CRC cases can identify novel rare variants that may influence disease risk. Cancer Epidemiol Biomarkers Prev; 22(7); 1239–51. ©2013 AACR.

Summary A recent meta‐analysis of three genome‐wide association studies of chronic lymphocytic leukaemia ( CLL ) identified two common variants at the 6p21.31 locus that are associated with CLL risk. To verify and further explore the association of these variants with other non‐ H odgkin lymphoma ( NHL ) subtypes, we genotyped 1196 CLL cases, 1699 NHL cases, and 2410 controls. We found significant associations between the 6p21.31 variants and CLL risk (rs210134: P = 0·01; rs210142: P = 6·8 × 10 −3 ). These variants also showed a trend towards association with some of the other NHL subtypes. Our results validate the prior work and support specific genetic pathways for risk among NHL subtypes.