Motivation

Genome-wide association studies (GWAS) have identified thousands of regions in the genome that contain genetic variants that increase risk for complex traits and diseases. However, the variants uncovered in GWAS are typically not biologically causal, but rather, correlated to the true causal variant through linkage disequilibrium (LD). To discern the true causal variant(s), a variety of statistical fine-mapping methods have been proposed to prioritize variants for functional validation.

Results

In this work we introduce a new approach, fastPAINTOR, that leverages evidence across correlated traits, as well as functional annotation data, to improve fine-mapping accuracy at pleiotropic risk loci. To improve computational efficiency, we describe an new importance sampling scheme to perform model inference. First, we demonstrate in simulations that by leveraging functional annotation data, fastPAINTOR increases fine-mapping resolution relative to existing methods. Next, we show that jointly modeling pleiotropic risk regions improves fine-mapping resolution compared to standard single trait and pleiotropic fine mapping strategies. We report a reduction in the number of SNPs required for follow-up in order to capture 90% of the causal variants from 23 SNPs per locus using a single trait to 12 SNPs when fine-mapping two traits simultaneously. Finally, we analyze summary association data from a large-scale GWAS of lipids and show that these improvements are largely sustained in real data.

Availability and implementation

The fastPAINTOR framework is implemented in the PAINTOR v3.0 package which is publicly available to the research community http://bogdan.bioinformatics.ucla.edu/software/paintor CONTACT: [email protected] information: Supplementary data are available at Bioinformatics online.

Cancer risk is determined by a complex interplay of genetic and environmental factors. Genome-wide association studies (GWAS) have identified hundreds of common (minor allele frequency [MAF] > 0.05) and less common (0.01 < MAF < 0.05) genetic variants associated with cancer. The marginal effects of most of these variants have been small (odds ratios: 1.1-1.4). There remain unanswered questions on how best to incorporate the joint effects of genes and environment, including gene-environment (G × E) interactions, into epidemiologic studies of cancer. To help address these questions, and to better inform research priorities and allocation of resources, the National Cancer Institute sponsored a "Gene-Environment Think Tank" on January 10-11, 2012. The objective of the Think Tank was to facilitate discussions on (1) the state of the science, (2) the goals of G × E interaction studies in cancer epidemiology, and (3) opportunities for developing novel study designs and analysis tools. This report summarizes the Think Tank discussion, with a focus on contemporary approaches to the analysis of G × E interactions. Selecting the appropriate methods requires first identifying the relevant scientific question and rationale, with an important distinction made between analyses aiming to characterize the joint effects of putative or established genetic and environmental factors and analyses aiming to discover novel risk factors or novel interaction effects. Other discussion items include measurement error, statistical power, significance, and replication. Additional designs, exposure assessments, and analytical approaches need to be considered as we move from the current small number of success stories to a fuller understanding of the interplay of genetic and environmental factors.

BACKGROUND: Although particulate matter (PM) has not been consistently associated with breast cancer risk, two studies have reported harmful associations for breast cancer survival. We examined PM exposures and breast cancer survival in two U.S.-based prospective cohort studies. METHODS: The Nurses Health Study (NHS) and NHSII are cohorts with detailed data on medical history, lifestyle factors, and causes of death. Women with Stage I-III breast cancer (n = 8,936) were followed through June 2014. Residential PM was estimated using spatio-temporal models. We performed Cox regression to estimate hazard ratios (HR) of breast cancer-specific mortality and all-cause mortality for 10 μg/m3 increases in post-diagnosis PM. RESULTS: There were 1,211 breast cancer-specific deaths. Overall, PM was not associated with breast cancer-specific mortality [PM2.5: HR, 1.09; 95% confidence interval (CI), 0.87-1.36; PM2.5-10: HR, 1.03; 95% CI, 0.85-1.24; PM10: HR, 1.05; 95% CI, 0.89-1.24], but was associated with modest increases in all-cause mortality (PM2.5: HR, 1.12; 95% CI, 0.96-1.30; PM2.5-10: HR, 1.12; 95% CI, 1.00-1.24; PM10: HR, 1.09; 95% CI, 1.01-1.18). However, among participants with Stage I disease, PM2.5 was associated with higher breast cancer-specific mortality (HR, 1.64; 95% CI, 1.11-2.43). CONCLUSIONS: PM was not associated with breast cancer-specific death overall; however, higher PM was associated with all-cause mortality. Higher PM2.5 was associated with higher breast cancer-specific mortality among patients with Stage I breast cancer even after adjustment. IMPACT: Studies on ambient PM and breast cancer survival demonstrate that PM2.5 may have broader health effects than previously recognized and warrants further research on breast tumor progression.

Despite its importance, human collective intelligence remains enigmatic. We know what features are predictive of collective intelligence in human groups, but we do not understand the specific mechanisms that lead to the emergence of this distributed information processing ability. In contrast, there is a well-developed literature of experiments that have exposed the mechanisms of collective intelligence in nonhuman animal species. We adapt a recent experiment designed to study collective sensing in groups of fish in order to better understand the mechanisms that may underly the emergence of collective intelligence in human groups. We find that humans in our experiments act at a high level like fish but with two additional behaviors: independent exploration and targeted copying. These distinctively human activities may partially explain the emergence of collective sensing in our task environment at group sizes and on times scales orders of magnitudes smaller than were observed in fish.

Testing for associations in big data faces the problem of multiple comparisons, wherein true signals are difficult to detect on the background of all associations queried. This difficulty is particularly salient in human genetic association studies, in which phenotypic variation is often driven by numerous variants of small effect. The current strategy to improve power to identify these weak associations consists of applying standard marginal statistical approaches and increasing study sample sizes. Although successful, this approach does not leverage the environmental and genetic factors shared among the multiple phenotypes collected in contemporary cohorts. Here we developed covariates for multiphenotype studies (CMS), an approach that improves power when correlated phenotypes are measured on the same samples. Our analyses of real and simulated data provide direct evidence that correlated phenotypes can be used to achieve increases in power to levels often surpassing the power gained by a twofold increase in sample size.

Abstract Background Observational studies and randomized trials have suggested that estrogens and/or progesterone may lower the risk for colorectal cancer. Inherited variation in the sex-hormone genes may be one mechanism by which sex hormones affect colorectal cancer, although data are limited. Method We conducted a comprehensive evaluation of single nucleotide polymorphisms (SNPs) in genes encoding 3 hormone receptors (ESR1, ESR2, PGR) and 5 hormone synthesizers (CYP19A1 and CYP17A1, HSD17B1, HSD17B2, HSD17B4) among 427 women with incident colorectal cancer and 871 matched controls who were Caucasians of European ancestry from 93676 postmenopausal women enrolled in the Women's Health Initiative Observational cohort. A total of 242 haplotype-tagging and functional SNPs in the 8 genes were included for analysis. Unconditional logistic regression with adjustment for age and hysterectomy status was used to estimate odds ratios (ORs) and 95% confidence intervals (CIs). Results We observed a weak association between the CYP17A1 rs17724534 SNP and colorectal cancer risk (OR per risk allele (A) = 1.39, 95% CI = 1.09-1.78, corrected p-value = 0.07). In addition, a suggestive interaction between rs17724534 and rs10883782 in 2 discrete LD blocks of CYP17A1 was observed in relation to colorectal cancer (empirical p value = 0.04). Moreover, one haplotype block of CYP19A1 was associated with colorectal cancer (corrected global p value = 0.02), which likely reflected the association with the tagging SNP, rs1902584, in the block. Conclusion Our findings offer some support for a suggestive association of CYP17A1 and CYP19A1 variants with colorectal cancer risk.

Standard statistical approaches for prioritization of variants for functional testing in fine-mapping studies either use marginal association statistics or estimate posterior probabilities for variants to be causal under simplifying assumptions. Here, we present a probabilistic framework that integrates association strength with functional genomic annotation data to improve accuracy in selecting plausible causal variants for functional validation. A key feature of our approach is that it empirically estimates the contribution of each functional annotation to the trait of interest directly from summary association statistics while allowing for multiple causal variants at any risk locus. We devise efficient algorithms that estimate the parameters of our model across all risk loci to further increase performance. Using simulations starting from the 1000 Genomes data, we find that our framework consistently outperforms the current state-of-the-art fine-mapping methods, reducing the number of variants that need to be selected to capture 90% of the causal variants from an average of 13.3 to 10.4 SNPs per locus (as compared to the next-best performing strategy). Furthermore, we introduce a cost-to-benefit optimization framework for determining the number of variants to be followed up in functional assays and assess its performance using real and simulation data. We validate our findings using a large scale meta-analysis of four blood lipids traits and find that the relative probability for causality is increased for variants in exons and transcription start sites and decreased in repressed genomic regions at the risk loci of these traits. Using these highly predictive, trait-specific functional annotations, we estimate causality probabilities across all traits and variants, reducing the size of the 90% confidence set from an average of 17.5 to 13.5 variants per locus in this data.

Genetic and environmental factors are both known to contribute to susceptibility to complex diseases. Therefore, the study of gene-environment interaction (G×E) has been a focus of research for several years. In this article, select examples of G×E from the literature are described to highlight different approaches and underlying principles related to the success of these studies. These examples can be broadly categorized as studies of single metabolism genes, genes in complex metabolism pathways, ranges of exposure levels, functional approaches and model systems, and pharmacogenomics. Some studies illustrated the success of studying exposure metabolism for which candidate genes can be identified. Moreover, some G×E successes depended on the availability of high-quality exposure assessment and longitudinal measures, study populations with a wide range of exposure levels, and the inclusion of ethnically and geographically diverse populations. In several examples, large population sizes were required to detect G×Es. Other examples illustrated the impact of accurately defining scale of the interactions (i.e., additive or multiplicative). Last, model systems and functional approaches provided insights into G×E in several examples. Future studies may benefit from these lessons learned.

FADS genes encode fatty acid desaturases that are important for the conversion of short chain polyunsaturated fatty acids (PUFAs) to long chain fatty acids. Prior studies indicate that the FADS genes have been subjected to strong positive selection in Africa, South Asia, Greenland, and Europe. By comparing FADS sequencing data from present-day and Bronze Age (5-3k years ago) Europeans, we identify possible targets of selection in the European population, which suggest that selection has targeted different alleles in the FADS genes in Europe than it has in South Asia or Greenland. The alleles showing the strongest changes in allele frequency since the Bronze Age show associations with expression changes and multiple lipid-related phenotypes. Furthermore, the selected alleles are associated with a decrease in linoleic acid and an increase in arachidonic and eicosapentaenoic acids among Europeans; this is an opposite effect of that observed for selected alleles in Inuit from Greenland. We show that multiple SNPs in the region affect expression levels and PUFA synthesis. Additionally, we find evidence for a gene-environment interaction influencing low-density lipoprotein (LDL) levels between alleles affecting PUFA synthesis and PUFA dietary intake: carriers of the derived allele display lower LDL cholesterol levels with a higher intake of PUFAs. We hypothesize that the selective patterns observed in Europeans were driven by a change in dietary composition of fatty acids following the transition to agriculture, resulting in a lower intake of arachidonic acid and eicosapentaenoic acid, but a higher intake of linoleic acid and α-linolenic acid.

Background

DNA damage is an established mediator of carcinogenesis, although genome-wide association studies (GWAS) have identified few significant loci. This cross-cancer site, pooled analysis was performed to increase the power to detect common variants of DNA repair genes associated with cancer susceptibility.

Methods

We conducted a cross-cancer analysis of 60,297 single nucleotide polymorphisms, at 229 DNA repair gene regions, using data from the NCI Genetic Associations and Mechanisms in Oncology (GAME-ON) Network. Our analysis included data from 32 GWAS and 48,734 controls and 51,537 cases across five cancer sites (breast, colon, lung, ovary, and prostate). Because of the unavailability of individual data, data were analyzed at the aggregate level. Meta-analysis was performed using the Association analysis for SubSETs (ASSET) software. To test for genetic associations that might escape individual variant testing due to small effect sizes, pathway analysis of eight DNA repair pathways was performed using hierarchical modeling.

Results

We identified three susceptibility DNA repair genes, RAD51B (P < 5.09 × 10(-6)), MSH5 (P < 5.09 × 10(-6)), and BRCA2 (P = 5.70 × 10(-6)). Hierarchical modeling identified several pleiotropic associations with cancer risk in the base excision repair, nucleotide excision repair, mismatch repair, and homologous recombination pathways.

Conclusions

Only three susceptibility loci were identified, which had all been previously reported. In contrast, hierarchical modeling identified several pleiotropic cancer risk associations in key DNA repair pathways.

Impact

Results suggest that many common variants in DNA repair genes are likely associated with cancer susceptibility through small effect sizes that do not meet stringent significance testing criteria.