Biology subjects

Simard, J.

Publications and source records attributed to Simard, J..

5 recordsLinked to original sources

bioRxiv2020

Pleiotropy-guided transcriptome imputation from normal and tumor tissues identifies new candidate susceptibility genes for breast and ovarian cancer

Familial, genome-wide association (GWAS), and sequencing studies and genetic correlation analyses have progressively unraveled the shared or pleiotropic germline genetics of breast and ovarian cancer. In this study, we aimed to leverage this shared germline genetics to improve the power of transcriptome-wide association studies (TWAS) to identify candidate breast cancer and ovarian cancer susceptibility genes. We built gene expression prediction models using the PrediXcan method in 681 breast and 295 ovarian tumors from The Cancer Genome Atlas and 211 breast and 99 ovarian normal tissue samples from the Genotype-Tissue Expression project and integrated these with GWAS meta-analysis data from the Breast Cancer Association Consortium (122,977 cases/105,974 controls) and the Ovarian Cancer Association Consortium (22,406 cases/40,941 controls). The integration was achieved through novel application of a pleiotropy-guided conditional/conjunction false discovery rate approach for the first time in the setting of a TWAS. This identified 14 new candidate breast cancer susceptibility genes spanning 11 genomic regions and 8 new candidate ovarian cancer susceptibility genes spanning 5 genomic regions at conjunction FDR < 0.05 that were > 1 Mb away from known breast and/or ovarian cancer susceptibility loci. We also identified 38 candidate breast cancer susceptibility genes and 17 candidate ovarian cancer susceptibility genes at conjunction FDR < 0.05 at known breast and/or ovarian susceptibility loci. Overlaying candidate causal risk variants identified by GWAS fine mapping onto expression prediction models for genes at known loci suggested that the association for 55% of these genes was driven by the underlying GWAS signal. SignificanceThe 22 new genes identified by our cross-cancer analysis represent promising candidates that further elucidate the role of the transcriptome in mediating germline breast and ovarian cancer risk.

genetics↗

bioRxiv2019

Assessment of Polygenic Architecture and Risk Prediction based on Common Variants Across Fourteen Cancers

We analyzed summary-level data from genome-wide association studies (GWAS) of European ancestry across fourteen cancer sites to estimate the number of common susceptibility variants (polygenicity) contributing to risk, as well as the distribution of their associated effect sizes. All cancers evaluated showed polygenicity, involving at a minimum thousands of independent susceptibility variants. For some malignancies, particularly chronic lymphoid leukemia (CLL) and testicular cancer, there are a larger proportion of variants with larger effect sizes than those for other cancers. In contrast, most variants for lung and breast cancers have very small associated effect sizes. For different cancer sites, we estimate a wide range of GWAS sample sizes, required to explain 80% of GWAS heritability, varying from 60,000 cases for CLL to over 1,000,000 cases for lung cancer. The maximum relative risk achievable for subjects at the 99th risk percentile of underlying polygenic risk scores, compared to average risk, ranges from 12 for testicular to 2.5 for ovarian cancer. We show that polygenic risk scores have substantial potential for risk stratification for relatively common cancers such as breast, prostate and colon, but limited potential for other cancer sites because of modest heritability and lower disease incidence.

genetics↗

bioRxiv2019

DSNetwork: An integrative approach to visualize predictions of variants’ deleteriousness

One of the most challenging tasks of the post-genome-wide association studies (GWAS) research era is the identification of functional variants among those associated with a trait for an observed GWAS signal. Several methods have been developed to evaluate the potential functional implications of genetic variants. Each of these tools has its own scoring system which forces users to become acquainted with each approach to interpret their results. From an awareness of the amount of work needed to analyze and integrate results for a single locus, we proposed a flexible and versatile approach designed to help the prioritization of variants by aggregating the predictions of their potential functional implications. This approach has been made available through a web interface called DSNetwork which acts as a single-point of entry to almost 60 reference predictors for both coding and non-coding variants and displays predictions in an easy-to-interpret visualization. We confirmed the usefulness of our methodology by successfully identifying functional variants in four breast cancer susceptibility loci. DSNetwork is an integrative web application implemented through the Shiny framework and available at: http://romix.genome.ulaval.ca/dsnetwork.\n\nAuthor summaryOver the past years, GWAS have enabled the identification of numerous susceptibility loci associated with complex traits (https://www.ebi.ac.uk/gwas/). However, many of those signals contain hundreds or even thousands of significantly associated variants among which only a few are really responsible of the phenotype. Substantial efforts have been made in the development of prediction methods to prioritize variants within GWAS-associated regions to go from statistical associations, to the identification of functional variants modulating gene expression, in order to ultimately gain insight into disease pathophysiology. Unfortunately, these numerous prediction tools generate contradictory predictions rendering the interpretation of results challenging. Some tools such as VEP [McLaren et al., 2016] report their scores using a color scheme, thus acknowledging the need to assist the user in the interpretation of predictor results. Nonetheless, the multiplication of approaches can often result in an extensive amount of data that is hard to synthesize. Aware of the challenge of evaluating the potential deleteriousness of variants in the context of fine mapping analyses, we created a customizable visualization approach that was implemented it in the decision support tool called DSNetwork for Decision Support Network. This tool enables quick access to gold standard and new predictors for both coding and non-coding variants through an easily interpretable visualization of these predictions for a set of variants.

bioinformatics↗

bioRxiv2018

The association between weight at birth and breast cancer risk revisited using Mendelian randomisation

Observational studies suggest that higher birth weight (BW) is associated with increased risk of breast cancer in adult life. We conducted a two-sample Mendelian randomisation (MR) study to assess whether this association is causal. Sixty independent single nucleotide polymorphisms (SNPs) known to be associated at P < 5 x 10-8 with BW were used to construct (1) a 41-SNP instrumental variable (IV) for univariable MR after removing SNPs with pleiotropic associations with other breast cancer risk factors and (2) a 49-SNP IV for multivariable MR after filtering SNPs for data availability. BW predicted by the 41-SNP IV was not associated with overall breast cancer risk in inverse-variance weighted (IVW) univariable MR analysis of genetic association data from 122,977 breast cancer cases and 105,974 controls (odds ratio = 0.86 per 500 g higher BW; 95% confidence interval: 0.73--1.01). Sensitivity analyses using four alternative methods and three alternative IVs, including an IV with 59 of the 60 BW-associated SNPs, yielded similar results. Multivariable MR adjusting for the effects of the 49-SNP IV on birth length, adult height, adult body mass index, age at menarche, and age at menopause using IVW and MR-Egger methods provided estimates consistent with univariable analyses. Results were also similar when all analyses were repeated after restricting to estrogen receptor-positive or -negative breast cancer cases. Point estimates of the odds ratios from most analyses performed indicated an inverse relationship between genetically-predicted BW and breast cancer. Thus, there is little evidence from MR to suggest that the previously observed association between higher BW and increased risk of breast cancer in adult life is causal.

epidemiology↗

bioRxiv2018

The association between low-density lipoprotein cholesterol predicted by HMGCR genetic variants and breast cancer risk may be mediated by body mass index

Orho-Melander et al. recently reported that lower low-density lipoprotein cholesterol (LDLC) as predicted by the T-allele of the variant rs12916 in HMGCR is associated with a decreased risk of developing breast cancer [odds ratio (OR) = 0.89; 95% confidence interval (CI): 0.82-0.96].1 This analysis was embedded in a wider Mendelian randomization (MR) study performed using genotype data from a prospective cohort of 26,589 individuals that included 16,022 women and 1176 incident breast cancer cases. HMGCR encodes 3-hydroxy-3-methylglutaryl-coenzyme A reductase, the enzyme inhibited by statins. The T-allele of rs12916 is associated with reduced HMGCR expression and therefore, in principle, its effects should be analogous to the effects of lifelong statin administration starting at birth.2 The MR study of Orho-Melander et al. also found that a genome-wide LDLC score based on 32 independent LDLC-associated single nucleotide polymorphisms (SNPs) was not associated with breast cancer. In light of this finding, they suggest that the protective effect of the rs12916 T-allele on breast cancer may either be specific to LDLC lowering via genetic inhibition of HMGCR or be the result of a distinct mechanism that is regulated by rs12916 and HMGCR.

epidemiology↗