Bayesian Variable Selection to identify QTL affecting a simulated quantitative trait

Background Recent developments in genetic technology and methodology enable accurate detection of QTL and estimation of breeding values, even in individuals without phenotypes. The QTL-MAS workshop offers the opportunity to test different methods to perform a genome-wide association study on simulated data with a QTL structure that is unknown beforehand. The simulated data contained 3,220 individuals: 20 sires and 200 dams with 3,000 offspring. All individuals were genotyped, though only 2,000 offspring were phenotyped for a quantitative trait. QTL affecting the simulated quantitative trait were identified and breeding values of individuals without phenotypes were estimated using Bayesian Variable Selection, a multi-locus SNP model in association studies. Results Estimated heritability of the simulated quantitative trait was 0.30 (SD = 0.02). Mean posterior probability of SNP modelled having a large effect ( pˆi) was 0.0066 (95%HPDR: 0.0014-0.0132). Mean posterior probability of variance of second distribution was 0.409 (95%HPDR: 0.286-0.589). The genome-wide association analysis resulted in 14 significant and 43 putative SNP, comprising 7 significant QTL on chromosome 1, 2 and 3 and putative QTL on all chromosomes. Assigning single or multiple QTL to significant SNP was not obvious, especially for SNP in the same region that were more or less in LD. Correlation between the simulated and estimated breeding values of 1,000 offspring without phenotypes was 0.91. Conclusions Bayesian Variable Selection using thousands of SNP was successfully applied to genome-wide association analysis of a simulated dataset with unknown QTL structure. Simulated QTL with Mendelian inheritance were accurately identified, while imprinted and epistatic QTL were only putatively detected. The correlation between simulated and estimated breeding values of offspring without phenotypes was high

Use of the gamma method for self-contained gene-set analysis of SNP data

Gene-set analysis (GSA) evaluates the overall evidence of association between a phenotype and all genotyped single nucleotide polymorphisms (SNPs) in a set of genes, as opposed to testing for association between a phenotype and each SNP individually. We propose using the Gamma Method (GM) to combine gene-level P-values for assessing the significance of GS association. We performed simulations to compare the GM with several other self-contained GSA strategies, including both one-step and two-step GSA approaches, in a variety of scenarios. We denote a ‘one-step' GSA approach to be one in which all SNPs in a GS are used to derive a test of GS association without consideration of gene-level effects, and a ‘two-step' approach to be one in which all genotyped SNPs in a gene are first used to evaluate association of the phenotype with all measured variation in the gene and then the gene-level tests of association are aggregated to assess the GS association with the phenotype. The simulations suggest that, overall, two-step methods provide higher power than one-step approaches and that combining gene-level P-values using the GM with a soft truncation threshold between 0.05 and 0.20 is a powerful approach for conducting GSA, relative to the competing approaches assessed. We also applied all of the considered GSA methods to data from a pharmacogenomic study of cisplatin, and obtained evidence suggesting that the glutathione metabolism GS is associated with cisplatin drug response

Identifying rare variants using a Bayesian regression approach

Recent advances in next-generation sequencing technologies have made it possible to generate large amounts of sequence data with rare variants in a cost-effective way. Statistical methods that test variants individually are underpowered to detect rare variants, so it is desirable to perform association analysis of rare variants by combining the information from all variants. In this study, we use a Bayesian regression method to model all variants simultaneously to identify rare variants in a data set from Genetic Analysis Workshop 17. We studied the association between the quantitative risk traits Q1, Q2, and Q4 and the single-nucleotide polymorphisms and identified several positive single-nucleotide polymorphisms for traits Q1 and Q2. However, the model also generated several apparent false positives and missed many true positives, suggesting that there is room for improvement in this model

Clinical characteristics of ovarian cancer classified by BRCA1, BRCA2, and RAD51C status.

We evaluated homologous recombination deficient (HRD) phenotypes in epithelial ovarian cancer (EOC) considering BRCA1, BRCA2, and RAD51C in a large well-annotated patient set. We evaluated EOC patients for germline deleterious mutations (n = 899), somatic mutations (n = 279) and epigenetic alterations (n = 482) in these genes using NGS and genome-wide methylation arrays. Deleterious germline mutations were identified in 32 (3.6%) patients for BRCA1, in 28 (3.1%) for BRCA2 and in 26 (2.9%) for RAD51C. Ten somatically sequenced patients had deleterious alterations, six (2.1%) in BRCA1 and four (1.4%) in BRCA2. Fifty two patients (10.8%) had methylated BRCA1 or RAD51C. HRD patients with germline or somatic alterations in any gene were more likely to be high grade serous, have an earlier diagnosis age and have ovarian and/or breast cancer family history. The HRD phenotype was most common in high grade serous EOC. Identification of EOC patients with an HRD phenotype may help tailor specific therapies.This work was supported by National Institutes of Health grants R01-CA122443, P50-CA136393, P30-CA15083, and the Fred C. and Katherine B. Andersen Foundation. We thank Gary Kenney, M.D. for pathology review of tumor tissue. We thank Craig Luccarini, Caroline Baynes from University of Cambridge for assisting our sample sequencing

Gene-Based Tests of Association

Genome-wide association studies (GWAS) are now used routinely to identify SNPs associated with complex human phenotypes. In several cases, multiple variants within a gene contribute independently to disease risk. Here we introduce a novel Gene-Wide Significance (GWiS) test that uses greedy Bayesian model selection to identify the independent effects within a gene, which are combined to generate a stronger statistical signal. Permutation tests provide p-values that correct for the number of independent tests genome-wide and within each genetic locus. When applied to a dataset comprising 2.5 million SNPs in up to 8,000 individuals measured for various electrocardiography (ECG) parameters, this method identifies more validated associations than conventional GWAS approaches. The method also provides, for the first time, systematic assessments of the number of independent effects within a gene and the fraction of disease-associated genes housing multiple independent effects, observed at 35%–50% of loci in our study. This method can be generalized to other study designs, retains power for low-frequency alleles, and provides gene-based p-values that are directly compatible for pathway-based meta-analysis

Different genes interact with particulate matter and tobacco smoke exposure in affecting lung function decline in the general population

BACKGROUND: Oxidative stress related genes modify the effects of ambient air pollution or tobacco smoking on lung function decline. The impact of interactions might be substantial, but previous studies mostly focused on main effects of single genes. OBJECTIVES: We studied the interaction of both exposures with a broad set of oxidative-stress related candidate genes and pathways on lung function decline and contrasted interactions between exposures. METHODS: For 12679 single nucleotide polymorphisms (SNPs), change in forced expiratory volume in one second (FEV(1)), FEV(1) over forced vital capacity (FEV(1)/FVC), and mean forced expiratory flow between 25 and 75% of the FVC (FEF(25-75)) was regressed on interval exposure to particulate matter >10 microm in diameter (PM10) or packyears smoked (a), additive SNP effects (b), and interaction terms between (a) and (b) in 669 adults with GWAS data. Interaction p-values for 152 genes and 14 pathways were calculated by the adaptive rank truncation product (ARTP) method, and compared between exposures. Interaction effect sizes were contrasted for the strongest SNPs of nominally significant genes (p(interaction)>0.05). Replication was attempted for SNPs with MAF<10% in 3320 SAPALDIA participants without GWAS. RESULTS: On the SNP-level, rs2035268 in gene SNCA accelerated FEV(1)/FVC decline by 3.8% (p(interaction) = 2.5x10(-6)), and rs12190800 in PARK2 attenuated FEV1 decline by 95.1 ml p(interaction) = 9.7x10(-8)) over 11 years, while interacting with PM10. Genes and pathways nominally interacting with PM10 and packyears exposure differed substantially. Gene CRISP2 presented a significant interaction with PM10 (p(interaction) = 3.0x10(-4)) on FEV(1)/FVC decline. Pathway interactions were weak. Replications for the strongest SNPs in PARK2 and CRISP2 were not successful. CONCLUSIONS: Consistent with a stratified response to increasing oxidative stress, different genes and pathways potentially mediate PM10 and tobac smoke effects on lung function decline. Ignoring environmental exposures would miss these patterns, but achieving sufficient sample size and comparability across study samples is challengin

Inherited variants in regulatory T cell genes and outcome of ovarian cancer.

Although ovarian cancer is the most lethal of gynecologic malignancies, wide variation in outcome following conventional therapy continues to exist. The presence of tumor-infiltrating regulatory T cells (Tregs) has a role in outcome of this disease, and a growing body of data supports the existence of inherited prognostic factors. However, the role of inherited variants in genes encoding Treg-related immune molecules has not been fully explored. We analyzed expression quantitative trait loci (eQTL) and sequence-based tagging single nucleotide polymorphisms (tagSNPs) for 54 genes associated with Tregs in 3,662 invasive ovarian cancer cases. With adjustment for known prognostic factors, suggestive results were observed among rarer histological subtypes; poorer survival was associated with minor alleles at SNPs in RGS1 (clear cell, rs10921202, p = 2.7×10(-5)), LRRC32 and TNFRSF18/TNFRSF4 (mucinous, rs3781699, p = 4.5×10(-4), and rs3753348, p = 9.0×10(-4), respectively), and CD80 (endometrioid, rs13071247, p = 8.0×10(-4)). Fo0r the latter, correlative data support a CD80 rs13071247 genotype association with CD80 tumor RNA expression (p = 0.006). An additional eQTL SNP in CD80 was associated with shorter survival (rs7804190, p = 8.1×10(-4)) among all cases combined. As the products of these genes are known to affect induction, trafficking, or immunosuppressive function of Tregs, these results suggest the need for follow-up phenotypic studies

Contribution of Germline Mutations in the RAD51B, RAD51C, and RAD51D Genes to Ovarian Cancer in the Population

PURPOSE: The aim of this study was to estimate the contribution of deleterious mutations in the RAD51B, RAD51C, and RAD51D genes to invasive epithelial ovarian cancer (EOC) in the population and in a screening trial of individuals at high risk of ovarian cancer. PATIENTS AND METHODS: The coding sequence and splice site boundaries of the three RAD51 genes were sequenced and analyzed in germline DNA from a case-control study of 3,429 patients with invasive EOC and 2,772 controls as well as in 2,000 unaffected women who were BRCA1/BRCA2 negative from the United Kingdom Familial Ovarian Cancer Screening Study (UK_FOCSS) after quality-control analysis. RESULTS: In the case-control study, we identified predicted deleterious mutations in 28 EOC cases (0.82%) compared with three controls (0.11%; P < .001). Mutations in EOC cases were more frequent in RAD51C (14 occurrences, 0.41%) and RAD51D (12 occurrences, 0.35%) than in RAD51B (two occurrences, 0.06%). RAD51C mutations were associated with an odds ratio of 5.2 (95% CI, 1.1 to 24; P = .035), and RAD51D mutations conferred an odds ratio of 12 (95% CI, 1.5 to 90; P = .019). We identified 13 RAD51 mutations (0.65%) in unaffected UK_FOCSS participants (RAD51C, n = 7; RAD51D, n = 5; and RAD51B, n = 1), which was a significantly greater rate than in controls (P < .001); furthermore, RAD51 mutation carriers were more likely than noncarriers to have a family history of ovarian cancer (P < .001). CONCLUSION: These results confirm that RAD51C and RAD51D are moderate ovarian cancer susceptibility genes and suggest that they confer levels of risk of EOC that may warrant their use alongside BRCA1 and BRCA2 in routine clinical genetic testing

Germline whole exome sequencing and large-scale replication identifies FANCM as a likely high grade serous ovarian cancer susceptibility gene

We analyzed whole exome sequencing data in germline DNA from 412 high grade serous ovarian cancer (HGSOC) cases from The Cancer Genome Atlas Project and identified 5,517 genes harboring a predicted deleterious germline coding mutation in at least one HGSOC case. Gene-set enrichment analysis showed enrichment for genes involved in DNA repair (p = 1.8x10(-3)). Twelve DNA repair genes - APEX1, APLF, ATX, EME1, FANCL, FANCM, MAD2L2, PARP2, PARP3, POLN, RAD54L and SMUG1 - were prioritized for targeted sequencing in up to 3,107 HGSOC cases, 1,491 cases of other epithelial ovarian cancer (EOC) subtypes and 3,368 unaffected controls of European origin. We estimated mutation prevalence for each gene and tested for associations with disease risk. Mutations were identified in both cases and controls in all genes except MAD2L2, where we found no evidence of mutations in controls. In FANCM we observed a higher mutation frequency in HGSOC cases compared to controls (29/3,107 cases, 0.96 percent; 13/3,368 controls, 0.38 percent; P = 0.008) with little evidence for association with other subtypes (6/1,491, 0.40 percent; P = 0.82). The relative risk of HGSOC associated with deleterious FANCM mutations was estimated to be 2.5 (95% CI 1.3 - 5.0; P = 0.006). In summary, whole exome sequencing of EOC cases with large-scale replication in case-control studies has identified FANCM as a likely novel susceptibility gene for HGSOC, with mutations associated with a moderate increase in risk. These data may have clinical implications for risk prediction and prevention approaches for high-grade serous ovarian cancer in the future and a significant impact on reducing disease mortality