Estimation of genotype relative risks from pedigree data by retrospective likelihoods

A microRNA Transcriptome-wide Association Study of Prostate Cancer Risk

Large genome-wide association studies have identified hundreds of single-nucleotide polymorphisms associated with increased risk of prostate cancer (PrCa), and many of these risk loci is presumed to confer regulatory effects on gene expression. While eQTL studies of long RNAs has yielded many potential risk genes, the relationship between PrCa risk genetics and microRNA expression dysregulation is understudied. We performed an microRNA transcriptome-wide association study of PrCa risk using small RNA sequencing and genome-wide genotyping data from N = 441 normal prostate epithelium tissue samples along with N = 411 prostate adenocarcinoma tumor samples from the Cancer Genome Atlas (TCGA). Genetically regulated expression prediction models were trained for all expressed microRNAs using the FUSION TWAS software. TWAS for PrCa risk was performed with both sets of models using single-SNP summary statistics from the recent PRACTICAL consortium PrCa case-control OncoArray GWAS meta-analysis. A total of 613 and 571 distinct expressed microRNAs were identified in the normal and tumor tissue datasets, respectively (overlap: 480). Among these, 79 (13%) normal tissue microRNAs demonstrated significant cis-heritability (median cis-h2 = 0.15, range: 0.03–0.79) for model training. Similar results were obtained from TCGA tumor samples, with 48 (9%) microRNA expression models successfully trained (median cis-h2 = 0.14, range: 0.06–0.60). Using normal tissue models, we identified two significant TWAS microRNA associations with PrCa risk: over-expression of mir-941 family microRNAs (PTWAS = 2.9E-04) and reduced expression of miR-3617-5p (PTWAS = 1.0E-03). The TCGA tumor TWAS also identified a significant association with miR-941 overexpression (PTWAS = 9.7E-04). Subsequent finemapping of the TWAS results using a multi-tissue database indicated limited evidence of causal status for each microRNA with PrCa risk (posterior inclusion probabilities <0.05). Future work will examine downstream regulatory effects of microRNA dysregulation as well as microRNA-mediated risk mechanisms via competing endogenous RNA relationships.

Cumulative risks of colorectal cancer in Han Chinese patients with Lynch syndrome in Taiwan

Patients with Lynch syndrome have a high risk of colorectal cancer (CRC). In this study, we estimated the age- and sex-specific cumulative risks of CRC in Han Chinese patients with Lynch syndrome caused by the pathogenic germline mutations in MLH1 or MSH2 in Taiwan. Based on 321 mutation carriers and 419 non-mutation carriers from 75 pedigrees collected in an Amsterdam criteria family registry in Taiwan, the age- and sex-specific cumulative risks of CRC in male carriers of mutation in MLH1 and MSH2 at the age of 70 years were 60.3% (95% confidence interval (CI) = 31.1%–89.9%) and 76.7% (95% CI = 37.2%–99.0%), respectively. For females, the cumulative risks of CRC at the age of 70 were estimated to be 30.6% (95% CI = 14.3%–57.7%) and 49.3% (95% CI = 21.9%–84.5%) in the carriers of MLH1 and MSH2 germline mutations, respectively. In conclusion, the cumulative risks of CRC at the age of 70 in the Han Chinese patients is higher in mutation carriers than non-mutation carriers and male mutation carriers have a higher cumulative risk of developing CRC than the female mutation carriers.

An expanded variant list and assembly annotation identifies multiple novel coding and noncoding genes for prostate cancer risk using a normal prostate tissue eQTL data set

Prostate cancer (PrCa) is highly heritable; 284 variants have been identified to date that are associated with increased prostate cancer risk, yet few genes contributing to its development are known. Expression quantitative trait loci (eQTL) studies link variants with affected genes, helping to determine how these variants might regulate gene expression and may influence prostate cancer risk. In the current study, we performed eQTL analysis on 471 normal prostate epithelium samples and 249 PrCa-risk variants in 196 risk loci, utilizing RNA sequencing transcriptome data based on ENSEMBL gene definition and genome-wide variant data. We identified a total of 213 genes associated with known PrCa-risk variants, including 141 protein-coding genes, 16 lncRNAs, and 56 other non-coding RNA species with differential expression. Compared to our previous analysis, where RefSeq was used for gene annotation, we identified an additional 130 expressed genes associated with known PrCa-risk variants. We detected an eQTL signal for more than half (n = 102, 52%) of the 196 loci tested; 52 (51%) of which were a Group 1 signal, indicating high linkage disequilibrium (LD) between the peak eQTL variant and the PrCa-risk variant (r2>0.5) and may help explain how risk variants influence the development of prostate cancer.

Network-directed cis-mediator analysis of normal prostate tissue expression profiles reveals downstream regulatory associations of prostate cancer susceptibility loci

Large-scale genome-wide association studies have identified multiple single-nucleotide polymorphisms associated with risk of prostate cancer. Many of these genetic variants are presumed to be regulatory in nature; however, follow-up expression quantitative trait loci (eQTL) association studies have to-date been restricted largely to cis-acting associations due to study limitations. While trans-eQTL scans suffer from high testing dimensionality, recent evidence indicates most trans-eQTL associations are mediated by cis-regulated genes, such as transcription factors. Leveraging a data-driven gene co-expression network, we conducted a comprehensive cis-mediator analysis using RNA-Seq data from 471 normal prostate tissue samples to identify downstream regulatory associations of previously identified prostate cancer risk variants. We discovered multiple trans-eQTL associations that were significantly mediated by cis-regulated transcripts, four of which involved risk locus 17q12, proximal transcription factor HNF1B, and target trans-genes with known HNF response elements (MIA2, SRC, SEMA6A, KIF12). We additionally identified evidence of cis-acting down-regulation of MSMB via rs10993994 corresponding to reduced co-expression of NDRG1. The majority of these cis-mediator relationships demonstrated trans-eQTL replicability in 87 prostate tissue samples from the Gene-Tissue Expression Project. These findings provide further biological context to known risk loci and outline new hypotheses for investigation into the etiology of prostate cancer.

Power in the phenotypic extremes: a simulation study of power in discovery and replication of rare variants

Next-generation sequencing technologies are making it possible to study the role of rare variants in human disease. Many studies balance statistical power with cost-effectiveness by (a) sampling from phenotypic extremes and (b) utilizing a two-stage design. Two-stage designs include a broad-based discovery phase and selection of a subset of potential causal genes/variants to be further examined in independent samples. We evaluate three parameters: first, the gain in statistical power due to extreme sampling to discover causal variants; second, the informativeness of initial (Phase I) association statistics to select genes/variants for follow-up; third, the impact of extreme and random sampling in (Phase 2) replication. We present a quantitative method to select individuals from the phenotypic extremes of a binary trait, and simulate disease association studies under a variety of sample sizes and sampling schemes. First, we find that while studies sampling from extremes have excellent power to discover rare variants, they have limited power to associate them to phenotype—suggesting high false-negative rates for upcoming studies. Second, consistent with previous studies, we find that the effect sizes estimated in these studies are expected to be systematically larger compared with the overall population effect size; in a well-cited lipids study, we estimate the reported effect to be twofold larger. Third, replication studies require large samples from the general population to have sufficient power; extreme sampling could reduce the required sample size as much as fourfold. Our observations offer practical guidance for the design and interpretation of studies that utilize extreme sampling.

Factors determining penetrance in familial atypical haemolytic uraemic syndrome

BACKGROUND

Inherited abnormalities of complement are found in ∼60% of patients with atypical haemolytic uraemic syndrome (aHUS). Such abnormalities are not fully penetrant. In this study, we have estimated the penetrance of the disease in three families with a CFH mutation (c.3643C>G; p. Arg1215Gly) in whom a common lineage is probable. 25 individuals have been affected with aHUS with three peaks of incidence-early childhood (n=6), early adulthood (n=11) and late adulthood (n=8). Eighteen individuals who have not developed aHUS carry the mutation.

METHODS

We estimated penetrance at the ages of 4, 27, 60 and 70 years as both a binary and a survival trait using MLINK and Mendel. We genotyped susceptibility factors in CFH, CD46 and CFHR1 in affected and unaffected carriers.

RESULTS AND CONCLUSIONS

We found that the estimates of penetrance at the age of 4 years ranged from <0.01 to 0.10, at the age of 27 years from 0.16 to 0.29, at the age of 60 years from 0.39 to 0.51 and at the age of 70 years from 0.44 to 0.64. We found that the CFH haplotype on the allele not carrying the CFH mutation had a significant effect on disease penetrance. In this family, we did not find that the CD46 haplotypes had a significant effect on penetrance.

Efficient generalized least squares method for mixed population and family-based samples in genome-wide association studies

Genome-wide association studies (GWAS) that draw samples from multiple studies with a mixture of relationship structures are becoming more common. Analytical methods exist for using mixed-sample data, but few methods have been proposed for the analysis of genotype-by-environment (G×E) interactions. Using GWAS data from a study of sarcoidosis susceptibility genes in related and unrelated African Americans, we explored the current analytic options for genotype association testing in studies using both unrelated and family-based designs. We propose a novel method-generalized least squares (GLX)-to estimate both SNP and G×E interaction effects for categorical environmental covariates and compared this method to generalized estimating equations (GEE), logistic regression, the Cochran-Armitage trend test, and the WQLS and MQLS methods. We used simulation to demonstrate that the GLX method reduces type I error under a variety of pedigree structures. We also demonstrate its superior power to detect SNP effects while offering computational advantages and comparable power to detect G×E interactions versus GEE. Using this method, we found two novel SNPs that demonstrate a significant genome-wide interaction with insecticide exposure-rs10499003 and rs7745248, located in the intronic and 3' UTR regions of the FUT9 gene on chromosome 6q16.1.

Estimating single nucleotide polymorphism associations using pedigree data: applications to breast cancer

BACKGROUND

Pedigrees with multiple genotyped family members have been underutilised in breast cancer (BC) genetic-association studies. We developed a pedigree-based analytical framework to characterise single-nucleotide polymorphism (SNP) associations with BC risk using data from 736 BC families ascertained through multiple affected individuals. On average, eight family members had been genotyped for 24 SNPs previously associated with BC.

METHODS

Breast cancer incidence was modelled on the basis of SNP effects and residual polygenic effects. Relative risk (RR) estimates were obtained by maximising the retrospective likelihood (RL) of observing the family genotypes conditional on all disease phenotypes. Models were extended to assess parent-of-origin effects (POEs).

RESULTS

Thirteen SNPs were significantly associated with BC under the pedigree RL approach. This approach yielded estimates consistent with those from large population-based studies. Logistic regression models ignoring pedigree structure generally gave larger RRs and association P-values. SNP rs3817198 in LSP1, previously shown to exhibit POE, yielded maternal and paternal RR estimates that were similar to those previously reported (paternal RR=1.12 (95% confidence interval (CI): 0.99-1.27), P=0.081, one-sided P=0.04; maternal RR=0.94 (95% CI: 0.84-1.06), P=0.33). No other SNP exhibited POE.

CONCLUSION

Our pedigree-based methods provide a valuable and efficient tool for characterising genetic associations with BC risk or other diseases and can complement population-based studies.

Multiple genetic variant association testing by collapsing and kernel methods with pedigree or population structured data

Searching 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.

Disease-specific prospective family study cohorts enriched for familial risk

Most common diseases demonstrate familial aggregation; the ratio of the risk for relatives of affected people to the risk for relatives of unaffected people (the familial risk ratio)) > 1. This implies there are underlying genetic and/or environmental risk factors shared by relatives. The risk gradient across this underlying 'familial risk profile', which can be predicted from family history and measured familial risk factors, is typically strong. Under a multiplicative model, the ratio of the risk for people in the upper 25% of familial risk to the risk for those in the lower 25% (the inter-quartile risk gradient) is an order of magnitude greater than the familial risk ratio. If familial risk ratio = 2 for first-degree relatives, in terms of familial risk profile: (a) people in the upper quartile will be at more than 20 times the risk of those in the lower quartile; and (b) about 90% of disease will occur in people above the median. Historically, therefore, epidemiology has compared cases with controls dissimilar for underlying familial risk profile. Were gene-environment and gene-gene interactions to exist, environmental and genetic effects could be stronger for people with increased familial risk profile. Studies in which controls are better matched to cases for familial risk profile might be more informative, especially if both cases and controls are over-sampled for increased familial risk. Prospective family study cohort (ProF-SC) designs involving people across a range of familial risk profile provide such a resource for epidemiological, genetic, behavioural, psycho-social and health utilisation research. The prospective aspect gives credibility to risk estimates. The familial aspect allows family-based designs, matching for unmeasured factors, adjusting for underlying familial risk profile, and enhanced cohort maintenance.