Identifying genes associated with disease outcomes using joint sparse canonical correlation analysis-An application in renal clear cell carcinoma.

Somatic changes like copy number aberrations (CNAs) and epigenetic alterations like methylation have pivotal effects on disease outcomes and prognosis in cancer, by regulating gene expressions, that drive critical biological processes. To identify potential biomarkers and molecular targets and understand how they impact disease outcomes, it is important to identify key groups of CNAs, the associated methylation, and the gene expressions they impact, through a joint integrative analysis. Here, we propose a novel analysis pipeline, the joint sparse canonical correlation analysis (jsCCA), an extension of sCCA, to effectively identify an ensemble of CNAs, methylation sites and gene (expression) components in the context of disease endpoints, especially tumor characteristics. Our approach detects potentially orthogonal gene components that are highly correlated with sets of methylation sites which in turn are correlated with sets of CNA sites. It then identifies the genes within these components that are associated with the outcome. Further, we aggregate the effect of each gene expression set on tumor stage by constructing "gene component scores" and test its interaction with traditional risk factors. Analyzing clinical and genomic data on 515 renal clear cell carcinoma (ccRCC) patients from the TCGA-KIRC, we found eight gene components to be associated with methylation sites, regulated by groups of proximally located CNA sites. Association analysis with tumor stage at diagnosis identified a novel association of expression of ASAH1 gene trans-regulated by methylation of several genes including SIX5 and by CNAs in the 10q25 region including TCF7L2. Further analysis to quantify the overall effect of gene sets on tumor stage, revealed that two of the eight gene components have significant interaction with smoking in relation to tumor stage. These gene components represent distinct biological functions including immune function, inflammatory responses, and hypoxia-regulated pathways. Our findings suggest that jsCCA analysis can identify interpretable and important genes, regulatory structures, and clinically consequential pathways. Such methods are warranted for comprehensive analysis of multimodal data especially in cancer genomics.

Multi-ancestry genome-wide association study of kidney cancer identifies 63 susceptibility regions.

Here, in a multi-ancestry genome-wide association study meta-analysis of kidney cancer (29,020 cases and 835,670 controls), we identified 63 susceptibility regions (50 novel) containing 108 independent risk loci. In analyses stratified by subtype, 52 regions (78 loci) were associated with clear cell renal cell carcinoma (RCC) and 6 regions (7 loci) with papillary RCC. Notably, we report a variant common in African ancestry individuals ( rs7629500 ) in the 3' untranslated region of VHL, nearly tripling clear cell RCC risk (odds ratio 2.72, 95% confidence interval 2.23-3.30). In cis-expression quantitative trait locus analyses, 48 variants from 34 regions point toward 83 candidate genes. Enrichment of hypoxia-inducible factor-binding sites underscores the importance of hypoxia-related mechanisms in kidney cancer. Our results advance understanding of the genetic architecture of kidney cancer, provide clues for functional investigation and enable generation of a validated polygenic risk score with an estimated area under the curve of 0.65 (0.74 including risk factors) among European ancestry individuals.

Genome-wide Association Study of Bladder Cancer Reveals New Biological and Translational Insights.

BACKGROUND: Genomic regions identified by genome-wide association studies (GWAS) for bladder cancer risk provide new insights into etiology. OBJECTIVE: To identify new susceptibility variants for bladder cancer in a meta-analysis of new and existing genome-wide genotype data. DESIGN, SETTING, AND PARTICIPANTS: Data from 32 studies that includes 13,790 bladder cancer cases and 343,502 controls of European ancestry were used for meta-analysis. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSES: Log-additive associations of genetic variants were assessed using logistic regression models. A fixed-effects model was used for meta-analysis of the results. Stratified analyses were conducted to evaluate effect modification by sex and smoking status. A polygenic risk score (PRS) was generated on the basis of known and novel susceptibility variants and tested for interaction with smoking. RESULTS AND LIMITATIONS: Multiple novel bladder cancer susceptibility loci (6p.22.3, 7q36.3, 8q21.13, 9p21.3, 10q22.1, 19q13.33) as well as improved signals in three known regions (4p16.3, 5p15.33, 11p15.5) were identified, bringing the number of independent markers at genome-wide significance (p < 5 × 10-8) to 24. The 4p16.3 (FGFR3/TACC3) locus was associated with a stronger risk for women than for men (p-interaction = 0.002). Bladder cancer risk was increased by interactions between smoking status and genetic variants at 8p22 (NAT2; multiplicative p value for interaction [pM-I] = 0.004), 8q21.13 (PAG1; pM-I = 0.01), and 9p21.3 (LOC107987026/MTAP/CDKN2A; pM-I = 0.02). The PRS based on the 24 independent GWAS markers (odds ratio per standard deviation increase 1.49, 95% confidence interval 1.44-1.53), which also showed comparable results in two prospective cohorts (UK Biobank, PLCO trial), revealed an approximately fourfold difference in the lifetime risk of bladder cancer according to the PRS (e.g., 1st vs 10th decile) for both smokers and nonsmokers. CONCLUSIONS: We report novel loci associated with risk of bladder cancer that provide clues to its biological underpinnings. Using 24 independent markers, we constructed a PRS to stratify lifetime risk. The PRS combined with smoking history, and other established risk factors, has the potential to inform future screening efforts for bladder cancer. PATIENT SUMMARY: We identified new genetic markers that provide biological insights into the genetic causes of bladder cancer. These genetic risk factors combined with lifestyle risk factors, such as smoking, may inform future preventive and screening strategies for bladder cancer.

Sparse canonical correlation to identify breast cancer related genes regulated by copy number aberrations.

BACKGROUND: Copy number aberrations (CNAs) in cancer affect disease outcomes by regulating molecular phenotypes, such as gene expressions, that drive important biological processes. To gain comprehensive insights into molecular biomarkers for cancer, it is critical to identify key groups of CNAs, the associated gene modules, regulatory modules, and their downstream effect on outcomes. METHODS: In this paper, we demonstrate an innovative use of sparse canonical correlation analysis (sCCA) to effectively identify the ensemble of CNAs, and gene modules in the context of binary and censored disease endpoints. Our approach detects potentially orthogonal gene expression modules which are highly correlated with sets of CNA and then identifies the genes within these modules that are associated with the outcome. RESULTS: Analyzing clinical and genomic data on 1,904 breast cancer patients from the METABRIC study, we found 14 gene modules to be regulated by groups of proximally located CNA sites. We validated this finding using an independent set of 1,077 breast invasive carcinoma samples from The Cancer Genome Atlas (TCGA). Our analysis of 7 clinical endpoints identified several novel and interpretable regulatory associations, highlighting the role of CNAs in key biological pathways and processes for breast cancer. Genes significantly associated with the outcomes were enriched for early estrogen response pathway, DNA repair pathways as well as targets of transcription factors such as E2F4, MYC, and ETS1 that have recognized roles in tumor characteristics and survival. Subsequent meta-analysis across the endpoints further identified several genes through the aggregation of weaker associations. CONCLUSIONS: Our findings suggest that sCCA analysis can aggregate weaker associations to identify interpretable and important genes, modules, and clinically consequential pathways.

Genome-Wide Association Study of Pelvic Organ Prolapse Using the Michigan Genomics Initiative.

OBJECTIVES: The aim of this study was to (1) replicate previously identified genetic variants significantly associated with pelvic organ prolapse and (2) identify new genetic variants associated with pelvic organ prolapse using a genome-wide association study. METHODS: Using our institution's database linking genetic and clinical data, we identified 1,329 women of European ancestry with an International Classification of Diseases, Ninth Revision (ICD-9)/ICD-10 code for prolapse, 767 of whom also had Current Procedural Terminology (CPT)/ICD-9/ICD-10 procedure codes for prolapse surgery, and 16,383 women of European ancestry older than 40 years without a prolapse diagnosis code as controls. Patients were genotyped using the Illumina HumanCoreExome chip and imputed to the Haplotype Reference Consortium. We tested 20 million single nucleotide polymorphisms (SNPs) for association with pelvic organ prolapse adjusting for relatedness, age, chip version, and 4 principal components. We compared our results with 18 previously identified genome-wide significant SNPs from the UK Biobank, Commun Biol (2020;3:129), and Obstet Gynecol (2011;118:1345-1353). RESULTS: No variants achieved genome-wide significance (P = 5 × 10-8). However, we replicated 4 SNPs with biologic plausibility at nominal significance (P ≤ 0.05): rs12325192 (P = 0.002), rs9306894 (P = 0.05), rs1920568 (P = 0.034), and rs1247943 (P = 0.041), which were all intergenic and nearest the genes SALL1, GDF7, TBX5, and TBX5, respectively. CONCLUSIONS: Our replication of 4 biologically plausible previously reported SNPs provides further evidence for a genetic contribution to prolapse, specifically that rs12325192, rs9306894, rs1920568, and rs1247943 may contribute to susceptibility for prolapse. These and previously reported associations that have not yet been replicated should be further explored in larger, more diverse cohorts, perhaps through meta-analysis.

Heritability of the Fibromyalgia Phenotype Varies by Age.

OBJECTIVE: Many studies suggest a strong familial component to fibromyalgia (FM). However, those studies have nearly all been confined to individuals with primary FM, i.e., FM without any other accompanying disorder. The current 2011 and 2016 criteria for diagnosing FM construct a score using a combination of the number of painful body sites and the severity of somatic symptoms (FM score). This study was undertaken to estimate the genetic heritability of the FM score across sex and age groups to identify subgroups of individuals with greater heritability, which may help in the design of future genetic studies. METHODS: We collected data on 26,749 individuals of European ancestry undergoing elective surgery at the University of Michigan (Michigan Genomics Initiative study). We estimated the single-nucleotide polymorphism-based heritability of FM score by age and sex categories using genome-wide association study data and a linear mixed-effects model. RESULTS: Overall, the FM score had an estimated heritability of 13.9% (SE 2.9%) (P = 1.6 × 10-7 ). Estimated FM score heritability was highest in individuals ≤50 years of age (23.5%; SE 7.9%) (P = 3.0 ×10-4 ) and lowest in individuals >60 years of age (7.5%; SE 8.1%) (P = 0.41). These patterns remained the same when we analyzed FM as a case-control phenotype. Even though women had an ~30% higher average FM score than men across age categories, FM score heritability did not differ significantly by sex. CONCLUSION: Younger individuals appear to have a much stronger genetic component to the FM score than older individuals. Older individuals may be more likely to have what was previously called "secondary FM." Regardless of the cause, these results have implications for future genetic studies of FM and associated conditions.