Genome-wide Association Studies Associations Research Articles

SparkINFERNO: a scalable high-throughput pipeline for inferring molecular mechanisms of non-coding genetic variants.

SummaryWe report Spark-based INFERence of the molecular mechanisms of NOn-coding genetic variants (SparkINFERNO), a scalable bioinformatics pipeline characterizing non-coding genome-wide association study (GWAS) association findings. SparkINFERNO prioritizes causal variants underlying GWAS association signals and reports relevant regulatory elements, tissue contexts and plausible target genes they affect. To achieve this, the SparkINFERNO algorithm integrates GWAS summary statistics with large-scale collection of functional genomics datasets spanning enhancer activity, transcription factor binding, expression quantitative trait loci and other functional datasets across more than 400 tissues and cell types. Scalability is achieved by an underlying API implemented using Apache Spark and Giggle-based genomic indexing. We evaluated SparkINFERNO on large GWASs and show that SparkINFERNO is more than 60 times efficient and scales with data size and amount of computational resources.Availability and implementationSparkINFERNO runs on clusters or a single server with Apache Spark environment, and is available at https://bitbucket.org/wanglab-upenn/SparkINFERNO or https://hub.docker.com/r/wanglab/spark-inferno.Contact lswang@pennmedicine.upenn.edu Supplementary information Supplementary data are available at Bioinformatics online

Characterisation of genetic regulatory effects for osteoporosis risk variants in human osteoclasts

BackgroundOsteoporosis is a complex disease with a strong genetic contribution. A recently published genome-wide association study (GWAS) for estimated bone mineral density (eBMD) identified 1103 independent genome-wide significant association signals. Most of these variants are non-coding, suggesting that regulatory effects may drive many of the associations. To identify genes with a role in osteoporosis, we integrate the eBMD GWAS association results with those from our previous osteoclast expression quantitative trait locus (eQTL) dataset.ResultsWe identify sixty-nine significant cis-eQTL effects for eBMD GWAS variants after correction for multiple testing. We detect co-localisation of eBMD GWAS and osteoclast eQTL association signals for 21 of the 69 loci, implicating a number of genes including CCR5, ZBTB38, CPE, GNA12, RIPK3, IQGAP1 and FLCN. Summary-data-based Mendelian Randomisation analysis of the eBMD GWAS and osteoclast eQTL datasets identifies significant associations for 53 genes, with TULP4 presenting as a strong candidate for pleiotropic effects on eBMD and gene expression in osteoclasts. By performing analysis using the GARFIELD software, we demonstrate significant enrichment of osteoporosis risk variants among high-confidence osteoclast eQTL across multiple GWAS P value thresholds. Mice lacking one of the genes of interest, the apoptosis/necroptosis gene RIPK3, show disturbed bone micro-architecture and increased osteoclast number, highlighting a new biological pathway relevant to osteoporosis.ConclusionWe utilise a unique osteoclast eQTL dataset to identify a number of potential effector genes for osteoporosis risk variants, which will help focus functional studies in this area.

Integrative Omics Approach to Identifying Genes Associated With Atrial Fibrillation.

Rationale: GWAS (Genome-Wide Association Studies) have identified hundreds of genetic loci associated with atrial fibrillation (AF). However, these loci explain only a small proportion of AF heritability. Objective: To develop an approach to identify additional AF-related genes by integrating multiple omics data. Methods and Results: Three types of omics data were integrated: (1) summary statistics from the AFGen 2017 GWAS; (2) a whole blood EWAS (Epigenome-Wide Association Study) of AF; and (3) a whole blood TWAS (Transcriptome-Wide Association Study) of AF. The variant-level GWAS results were collapsed into gene-level associations using fast set-based association analysis. The CpG-level EWAS results were also collapsed into gene-level associations by an adapted SNP-set Kernel Association Test approach. Both GWAS and EWAS gene-based associations were then meta-analyzed with TWAS using a fixed-effects model weighted by the sample size of each data set. A tissue-specific network was subsequently constructed using the NetWAS (Network-Wide Association Study). The identified genes were then compared with the AFGen 2018 GWAS that contained more than triple the number of AF cases compared with AFGen 2017 GWAS. We observed that the multiomics approach identified many more relevant AF-related genes than using AFGen 2018 GWAS alone (1931 versus 206 genes). Many of these genes are involved in the development and regulation of heart- and muscle-related biological processes. Moreover, the gene set identified by multiomics approach explained much more AF variance than those identified by GWAS alone (10.4% versus 3.5%). Conclusions: We developed a strategy to integrate multiple omics data to identify AF-related genes. Our integrative approach may be useful to improve the power of traditional GWAS, which might be particularly useful for rare traits and diseases with limited sample size.

Transcriptome-wide association study of multiple myeloma identifies candidate susceptibility genes

BackgroundWhile genome-wide association studies (GWAS) of multiple myeloma (MM) have identified variants at 23 regions influencing risk, the genes underlying these associations are largely unknown. To identify candidate causal genes at these regions and search for novel risk regions, we performed a multi-tissue transcriptome-wide association study (TWAS).ResultsGWAS data on 7319 MM cases and 234,385 controls was integrated with Genotype-Tissue Expression Project (GTEx) data assayed in 48 tissues (sample sizes, N = 80–491), including lymphocyte cell lines and whole blood, to predict gene expression. We identified 108 genes at 13 independent regions associated with MM risk, all of which were in 1 Mb of known MM GWAS risk variants. Of these, 94 genes, located in eight regions, had not previously been considered as a candidate gene for that locus.ConclusionsOur findings highlight the value of leveraging expression data from multiple tissues to identify candidate genes responsible for GWAS associations which provide insight into MM tumorigenesis. Among the genes identified, a number have plausible roles in MM biology, notably APOBEC3C, APOBEC3H, APOBEC3D, APOBEC3F, APOBEC3G, or have been previously implicated in other malignancies. The genes identified in this TWAS can be explored for follow-up and validation to further understand their role in MM biology.

Population Structure of the World Vegetable Center Mungbean Mini Core Collection and Genome-Wide Association Mapping of Loci Associated with Variation of Seed Coat Luster

Mungbean [Vigna radiata (L.) R. Wilczek var. radiata] is an important legume crop for South East Asia. Smallholder farmers rely on this crop to diversify income streams and enrich soils through N-fixation. To produce new varieties that stabilize mungbean productivity under biotic and abiotic stresses, breeders need to take advantage of genomic tools. Genome-Wide Association Studies (GWAS) are an effective strategy to associate agronomic traits with underlying genes. A key factor to avoid false associations in GWAS is a thorough understanding of the population structure. In this study, we analyzed the population structure of the World Vegetable Center’s (WorldVeg) mungbean mini core collection and performed a GWAS to identify loci associated with seed coat luster (SCL) utilizing SNP markers. The mini core collection consisted of 297 most genetically diverse accessions selected from 6700 global accessions held by the WorldVeg genebank. Genotyping by Sequencing (GBS) produced a total of 24,870 Single Nucleotide Polymorphism (SNP) markers. After filtering, 5288 highly polymorphic SNPs were used for analysis. Population structure analysis predicted four sub-populations based on the non-parametric Wilcoxon test. Linkage disequilibrium (LD) was found to decline to half of the maximal r2 value at approximately 350 kb; and a pilot GWAS study investigating seed coat luster (SCL) revealed two loci in chromosome 5 significantly associated with SCL.

Dyslexia associated functional variants in Europeans are not associated with dyslexia in Chinese.

Genome-wide association studies (GWAS) of developmental dyslexia (DD) often used European samples and identified only a handful associations with moderate or weak effects. This study aims to identify DD functional variants by integrating the GWAS associations with tissue-specific functional data and test the variants in a Chinese DD study cohort named READ. We colocalized associations from nine DD related GWAS with expression quantitative trait loci (eQTL) derived from brain tissues and identified two eSNPs rs349045 and rs201605. Both eSNPs had supportive evidence of chromatin interactions observed in human hippocampus tissues and their respective target genes ZNF45 and DNAH9 both had lower expression in brain tissues in schizophrenia patients than controls. In contrast, an eSNP rs4234898 previously identified based on eQTL from the lymphoblastic cell lines of dyslexic children had no chromatin interaction with its target gene SLC2A3 in hippocampus tissues and SLC2A3 expressed higher in the schizophrenia patients than controls. We genotyped the three eSNPs in the READ cohort of 372 cases and 354 controls and discovered only weak associations in rs201605 and rs4234898 with three DD symptoms (p < .05). The lack of associations could be due to low power in READ but could also implicate different etiology of DD in Chinese.

Genetic associations of breast and prostate cancer are enriched for regulatory elements identified in disease-related tissues.

Although genome-wide association studies (GWAS) have identified hundreds of risk loci for breast and prostate cancer, only a few studies have characterized the GWAS association signals across functional genomic annotations with a particular focus on single nucleotide polymorphisms (SNPs) located in DNA regulatory elements. In this study, we investigated the enrichment pattern of GWAS signals for breast and prostate cancer in genomic functional regions located in normal tissue and cancer cell lines. We quantified the overall enrichment of SNPs with breast and prostate cancer association p values < 1 × 10-8 across regulatory categories. We then obtained annotations for DNaseI hypersensitive sites (DHS), typical enhancers, and super enhancers across multiple tissue types, to assess if significant GWAS signals were selectively enriched in annotations found in disease-related tissue. Finally, we quantified the enrichment of breast and prostate cancer SNP heritability in regulatory regions, and compared the enrichment pattern of SNP heritability with GWAS signals. DHS, typical enhancers, and super enhancers identified in the breast cancer cell line MCF-7 were observed with the highest enrichment of genome-wide significant variants for breast cancer. For prostate cancer, GWAS signals were mostly enriched in DHS and typical enhancers identified in the prostate cancer cell line LNCaP. With progressively stringent GWAS p value thresholds, an increasing trend of enrichment was observed for both diseases in DHS, typical enhancers, and super enhancers located in disease-related tissue. Results from heritability enrichment analysis supported the selective enrichment pattern of functional genomic regions in disease-related cell lines for both breast and prostate cancer. Our results suggest the importance of studying functional annotations identified in disease-related tissues when characterizing GWAS results, and further demonstrate the role of germline DNA regulatory elements from disease-related tissue in breast and prostate carcinogenesis.

Genetic analyses of diverse populations improves discovery for complex traits.

Genome-wide association studies (GWAS) have laid the foundation for investigations into the biology of complex traits, drug development and clinical guidelines. However, the majority of discovery efforts are based on data from populations of European ancestry1-3. In light of the differential genetic architecture that is known to exist between populations, bias in representation can exacerbate existing disease and healthcare disparities. Critical variants may be missed if they have a low frequency or are completely absent in European populations, especially as the field shifts its attention towards rare variants, which are more likely to be population-specific4-10. Additionally, effect sizes and their derived risk prediction scores derived in one population may not accurately extrapolate to other populations11,12. Here we demonstrate the value of diverse, multi-ethnic participants in large-scale genomic studies. The Population Architecture using Genomics and Epidemiology (PAGE) study conducted a GWAS of 26 clinical and behavioural phenotypes in 49,839non-European individuals. Using strategies tailored for analysis of multi-ethnic and admixed populations, we describe a framework for analysing diverse populations, identify 27 novel loci and 38 secondary signals at known loci, as well as replicate 1,444 GWAS catalogue associations across these traits. Our data show evidence of effect-size heterogeneity across ancestries for published GWAS associations, substantial benefits for fine-mapping using diverse cohorts and insights into clinical implications. In the United States-where minority populations have a disproportionately higher burden of chronic conditions13-the lack of representation of diverse populations in genetic research will result in inequitable access to precision medicine for those with the highest burden of disease. We strongly advocate for continued, large genome-wide efforts in diverse populations to maximize genetic discovery and reduce health disparities.

A Convergent Study of Genetic Variants Associated With Crohn's Disease: Evidence From GWAS, Gene Expression, Methylation, eQTL and TWAS.

Crohn’s Disease (CD) is one of the predominant forms of inflammatory bowel disease (IBD). A combination of genetic and non-genetic risk factors have been reported to contribute to the development of CD. Many high-throughput omics studies have been conducted to identify disease associated risk variants that might contribute to CD, such as genome-wide association studies (GWAS) and next generation sequencing studies. A pressing need remains to prioritize and characterize candidate genes that underlie the etiology of CD. In this study, we collected a comprehensive multi-dimensional data from GWAS, gene expression, and methylation studies and generated transcriptome-wide association study (TWAS) data to further interpret the GWAS association results. We applied our previously developed method called mega-analysis of Odds Ratio (MegaOR) to prioritize CD candidate genes (CDgenes). As a result, we identified consensus sets of CDgenes (62–235 genes) based on the evidence matrix. We demonstrated that these CDgenes were significantly more frequently interact with each other than randomly expected. Functional annotation of these genes highlighted critical immune-related processes such as immune response, MHC class II receptor activity, and immunological disorders. In particular, the constitutive photomorphogenesis 9 (COP9) signalosome related genes were found to be significantly enriched in CDgenes, implying a potential role of COP9 signalosome involved in the pathogenesis of CD. Finally, we found some of the CDgenes shared biological functions with known drug targets of CD, such as the regulation of inflammatory response and the leukocyte adhesion to vascular endothelial cell. In summary, we identified highly confident CDgenes from multi-dimensional evidence, providing insights for the understanding of CD etiology.

The genetics of depression: successful genome-wide association studies introduce new challenges

The recent successful genome-wide association studies (GWASs) for depression have yielded more than 80 replicated loci and brought back the excitement that had evaporated during the years of negative GWAS findings. The identified loci provide anchors to explore their relevance for depression, but this comes with new challenges. Using the watershed model of genotype–phenotype relationships as a conceptual aid and recent genetic findings on other complex phenotypes, we discuss why it took so long and identify seven future challenges. The biggest challenge involves the identification of causal mechanisms since GWAS associations merely flag genomic regions without a direct link to underlying biological function. Furthermore, the genetic association with the index phenotype may also be part of a more extensive causal pathway (e.g., from variant to comorbid condition) or be due to indirect influences via intermediate traits located in the causal pathways to the final outcome. This challenge is highly relevant for depression because even its narrow definition of major depressive disorder captures a heterogeneous set of phenotypes which are often measured by even more broadly defined operational definitions consisting of a few questions (minimal phenotyping). Here, Mendelian randomization and future discovery of additional genetic variants for depression and related phenotypes will be of great help. In addition, reduction of phenotypic heterogeneity may also be worthwhile. Other challenges include detecting rare variants, determining the genetic architecture of depression, closing the “heritability gap”, and realizing the potential for personalized treatment. Along the way, we identify pertinent open questions that, when addressed, will advance the field.

Identification of Functional Variants in the FAM13A Chronic Obstructive Pulmonary Disease Genome-Wide Association Study Locus by Massively Parallel Reporter Assays.

The identification of causal variants responsible for disease associations from genome-wide association studies (GWASs) facilitates functional understanding of the biological mechanisms by which those genetic variants influence disease susceptibility. We aim to identify causal variants in or near the FAM13A (family with sequence similarity member 13A) GWAS locus associated with chronic obstructive pulmonary disease (COPD). We used an integrated approach featuring conditional genetic analysis, massively parallel reporter assays (MPRAs), traditional reporter assays, chromatin conformation capture assays, and clustered regularly interspaced short palindromic repeats (CRISPR)-based gene editing to characterize COPD-associated regulatory variants in the FAM13A region in human bronchial epithelial cell lines. Conditional genetic association suggests the presence of two independent COPD association signals in FAM13A. MPRAs identified 45 regulatory variants within FAM13A, among which six variants were prioritized for further investigation. Three COPD-associated variants demonstrated significant allele-specific activity in reporter assays. One of three variants, rs2013701, was tested in the endogenous genomic context by CRISPR-based genome editing that confirmed its allele-specific effects on FAM13A expression and on cell proliferation, providing functional characterization for this COPD-associated variant. The human GWAS association near FAM13A may contain independent association signals. MPRAs identified multiple functional variants in this region, including rs2013701, a putative COPD-causing variant with allele-specific regulatory activity.

A case-control genome wide association study of substance use disorder (SUD) identifies novel variants on chromosome 7p14.1 in patients from the United Arab Emirates (UAE).

Genome wide association studies (GWASs) have provided insights into the molecular basis of the disorder in different population. This study presents the first GWAS of substance use disorder (SUD) in patients from the United Arab Emirates (UAE). The aim was to identify genetic association(s) that may provide insights into the molecular basis of the disorder. The GWAS discovery cohort consisted of 512 (250 cases and 262 controls) male participants from the UAE. Controls with no prior history of SUD were available from the Emirates family registry. The replication cohort consisted of 520 (415 cases and 105 controls) Australian male Caucasian participants. The GWAS discovery samples were genotyped for 4.6 million single nucleotide polymorphism (SNP). The replication cohort was genotyped using TaqMan assay. The GWAS association analysis identified three potential SNPs rs118129027 (p-value = 6.24 × 10-8 ), rs74477937 (p-value = 8.56 × 10-8 ) and rs78707086 (p-value = 8.55 × 10-8 ) on ch7p14.1, that did not meet the GWAS significance threshold but were highly suggestive. In the replication cohort, the association of the three top SNPs did not reach statistical significance. In a meta-analysis of the discovery and the replication cohorts, there were no strengthen evidence for association of the three SNPs. The top identified rs118129027 overlaps with a regulatory factor (enhancer) region that targets three neighboring genes LOC105375237, LOC105375240, and YAE1D1. The YAE1D1, which represents a potential locus that is involved in regulating translation initiation pathway. Novel associations that require further confirmation were identified, suggesting a new insight to the genetic basis of SUD.

Molecular insights into genome-wide association studies of chronic kidney disease-defining traits

Genome-wide association studies (GWAS) have identified >100 loci of chronic kidney disease-defining traits (CKD-dt). Molecular mechanisms underlying these associations remain elusive. Using 280 kidney transcriptomes and 9958 gene expression profiles from 44 non-renal tissues we uncover gene expression partners (eGenes) for 88.9% of CKD-dt GWAS loci. Through epigenomic chromatin segmentation analysis and variant effect prediction we annotate functional consequences to 74% of these loci. Our colocalisation analysis and Mendelian randomisation in >130,000 subjects demonstrate causal effects of three eGenes (NAT8B, CASP9 and MUC1) on estimated glomerular filtration rate. We identify a common alternative splice variant in MUC1 (a gene responsible for rare Mendelian form of kidney disease) and observe increased renal expression of a specific MUC1 mRNA isoform as a plausible molecular mechanism of the GWAS association signal. These data highlight the variants and genes underpinning the associations uncovered in GWAS of CKD-dt.

Phenome-wide association studies across large population cohorts support drug target validation

Phenome-wide association studies (PheWAS) have been proposed as a possible aid in drug development through elucidating mechanisms of action, identifying alternative indications, or predicting adverse drug events (ADEs). Here, we select 25 single nucleotide polymorphisms (SNPs) linked through genome-wide association studies (GWAS) to 19 candidate drug targets for common disease indications. We interrogate these SNPs by PheWAS in four large cohorts with extensive health information (23andMe, UK Biobank, FINRISK, CHOP) for association with 1683 binary endpoints in up to 697,815 individuals and conduct meta-analyses for 145 mapped disease endpoints. Our analyses replicate 75% of known GWAS associations (P < 0.05) and identify nine study-wide significant novel associations (of 71 with FDR < 0.1). We describe associations that may predict ADEs, e.g., acne, high cholesterol, gout, and gallstones with rs738409 (p.I148M) in PNPLA3 and asthma with rs1990760 (p.T946A) in IFIH1. Our results demonstrate PheWAS as a powerful addition to the toolkit for drug discovery.

The association between sixteen genome-wide association studies-related allergic diseases loci and childhood allergic rhinitis in a Chinese Han population

BackgroundAllergic rhinitis (AR) is one of the most common respiratory diseases in children. It is caused by a combination of genetic and environmental factors. Moderate-to-severe AR decreases the quality of life and social performance in children. ObjectiveTo investigate whether polymorphisms in previously published genome-wide association studies (GWAS) allergic disease loci are associated with childhood AR and the severity of AR symptoms in a Chinese Han population. MethodsA case-control study was conducted in 503 pediatric patients with AR and 393 control Chinese school-aged subjects. AR severity was classified as mild or moderate-to-severe according to the AR and its Impact on Asthma (ARIA) guidelines. Overall, 16 tagged single-nucleotide polymorphisms (tSNPs) of published GWAS associations with allergic diseases were selected. All subjects were genotyped and analyzed for the 16 selected tSNPs using the improved multiplex ligation detection reaction (iMLDR) technique. ResultsBoth rs7130588_AG and rs7927894_CT genotypes in EMSY region were associated with a significantly increased risk of AR (1.75-fold and 1.50-fold) compared to the AA and CC genotypes, respectively, specific to moderate-to-severe AR. The difference of rs7130588 genotypes in cases vs. controls was still statistically significant under the additive model after multiple test correction to adjust the false discovery rate (FDR) with an adjusted odds ratio of 1.818 and 95% confidence interval (CI) of 1.240–2.664 (PFDR = 0.0349). The rs7130588_G allele was only associated with a high risk of moderate-to-severe AR (1.85-fold, Page and gender_adjustment = 0.003). The rs2227284_TG genotype at the IL4 locus was significantly associated with a 0.65-fold decreased risk of AR compared to the TT genotype. The protective effect of the rs2227284_G allele was also found in different severity of AR. Haplotype analysis showed a significantly increased AR risk associated with the haplotype G-T-T (rs7130588-rs2155219-rs7927894) and a protective effect with the haplotype C-G-C (rs2243250-rs2227284-s2243290). ConclusionsThe loci in EMSY and IL4 can be considered as a hereditary marker for childhood AR. The rs7130588_G allele seems to predispose only to moderate-to-severe AR, though other mechanisms are also likely to be involved.

Abstract 236: Identification of novel cancer target genes by combining data from the cancer genome-wide association studies (GWAS), regulatory DNA elements and The Cancer Genome Atlas (TCGA)

Abstract Unlike tumors that develop because of somatic genetic alterations such as frequent coding mutations or chromosomal deletions, amplifications or translocations, some tumors are addicted to expression of certain genes for their sustained proliferation and survival - for example, LIM domain only 1 (LMO1) expression in neuroblastoma (NB). Numerous genome-wide association studies (GWAS) have identified significant associations between germline single-nucleotide polymorphisms (SNPs), many in the non-coding genome, and cancer. A robust genome-wide association has been reported previously between an intronic LMO1 SNP and NB susceptibility. By further sequencing and epigenetic fine mapping of the SNP locus, it was demonstrated that the causal SNP is part of a transcription factor binding site within an enhancer element, regulates expression of LMO1 and in turn increases the tumorigenic potential. To identify additional examples of regulatory SNPs as cancer drivers, we overlaid published genome-wide significant cancer associations with active chromatin marks from Encyclopedia of DNA Elements and searched for SNPs that resided within gene regulatory elements. To map these SNPs to candidate genes and determine direction of effect, we co-localized GWAS signals with expression quantitative trait (eQTL) signals from the Genotype-Tissue Expression (GTEx) Consortium database. Lastly, we checked for gene amplification and/or overexpression of the mapped genes in the Cancer Genome Atlas (TCGA) data to identify a set of target genes that not only exhibit significant cancer association in GWAS, but also have evidence for epigenetic regulation and propensity for amplification and/or overexpression in tumors. We identified more than 25 novel cancer-target pairs with strong germline, regulatory and somatic evidence. A look up through synthetic lethality screen data available in-house suggested that several of these targets are self-lethal, further underscoring their importance for cancer cell proliferation and survival. Additional in vitro experiments are being planned to further validate the targets. Citation Format: Diptee A. Kulkarni, Karl Guo, Junping Jing, Mugdha Khaladkar, Kijoung Song, Coco Dong, David Cooper, Benjamin Schwartz. Identification of novel cancer target genes by combining data from the cancer genome-wide association studies (GWAS), regulatory DNA elements and The Cancer Genome Atlas (TCGA) [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 236.

Abstract 5319: Genetic regulation of DNA methylation in breast cancer

Abstract Aberrant DNA methylation plays an important role in breast cancer initiation and progression. It is believed that changes in DNA methylation occur early in breast cancer development or even in normal tissue before tumor occurrence, and these changes could be a useful source for early detection, prognosis, and targeted therapy. DNA methylation is tissue-specific and is influenced by inherited genetic variations in the human genome. Genome-wide association studies (GWAS) have identified more than 600 single nucleotide polymorphisms (SNPs) associated with breast cancer risk and outcome. However, how these germline genetic variants influence DNA methylation in breast tissue, especially in normal breast tissue, remains largely unknown. We performed a methylation quantitative trait loci (meQTL) analysis to examine the association between the GWAS identified breast cancer risk loci and DNA methylation in 48 women with paired blood and breast tissue samples. Genotyping of SNPs was performed using blood-derived DNA and the Illumina HumanOmni 2.5-4v1_D array chip. Genome-wide DNA methylation was measured using breast tissue derived genomic DNA and the new Illumina TruSeq Methyl Capture EPIC sequencing technology. We found that GWAS-identified breast cancer risk loci were associated with DNA methylation sites close to genes enriched in pathways related to cell differentiation and morphogenesis, DNA damage, cell movement, and hormonal signaling pathways. We identified more cis-acting than trans-acting meQTLs, and the peak enrichment for meQTLs was in close proximity to transcription start sites. We further compared our results with those from the meQTL studies conducted with lymphoblastoid cell lines and peripheral blood samples. We found more than 60% of the identified association between genetic variants and DNA methylation in our study are tissue-type specific. Our study may provide a post-GWAS functional characterization of breast cancer risk loci and shed light on epigenetic mechanisms that underlie the observed GWAS association. Citation Format: Nan Lin, Aditi Shendre, JinPeng Liu, Chi Wang, Yunlong Liu, Chunyan He. Genetic regulation of DNA methylation in breast cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 5319.

Multitrait Genome-Wide Association Analysis of Macronutrient Intake Identifies 12 Novel Loci for Dietary Intake and Unravels Genetic Overlap with Lifestyle Traits and Cardiometabolic Diseases

Suboptimal diets are associated with increased risk of type 2 diabetes, cardiovascular disease and cancer. Genome-wide association studies (GWAS) for individual macronutrients have facilitated the discovery of two relevant loci (FGF-21, FTO), however a single-nutrient approach may preclude the discovery of additional relevant loci. Here, we conducted a multi-trait macronutrient GWAS for carbohydrate, fat, and protein in the UK Biobank to factor in the significant correlation among macronutrient intake. In the UK Biobank, dietary data were collected from over 210,000 individuals aged 37-74 using the Oxford WebQ, a web-based 24 hour diet recall. For multi-trait GWAS, we combined GWAS association evidence of ∼11m imputed variants in participants of European ancestry (n =192,005) for percent of carbohydrate, fat, and protein adjusted for age and sex by applying the CPASSOC package. We identified 14 independent genome-wide significant (Pυ x 10-8) loci for macronutrient intake. We discovered loci associated with lipid metabolism (APOE, GCKR, SLC39A8, PPP1R3B, MLXIPL), alcohol intake (ADH1B, KLB), and new candidate genes (CCDC171, PSMD7, ARL17B, METTL4), and replicated previously associated loci with dietary intake (FGF-21 and FTO). Of the 14 signals, 12 replicated (P003C;0.0036) in multi-trait analyses using previous GWAS on dietary intake from the CHARGE Consortium (n =91,114). Tissue enrichment analysis across 53 tissues in GTEx revealed enrichment of gene expression in the cerebellum. Genome-wide genetic correlations across 234 traits with publicly available GWAS data indicated shared biological links between dietary intake and lifestyle factors (smoking and education) as well as type 2 diabetes and coronary artery disease (P&amp;lt;2.14x10-4). The present findings expand our understanding of the genetic architecture of dietary intake and the shared genetic links with related traits. Disclosure H.S. Dashti: None. J. Merino: None. J.M. Lane: None. J.C. Florez: Consultant; Self; Intarcia Therapeutics, Inc.. Consultant; Spouse/Partner; Santen. M.K. Rutter: Consultant; Self; Novo Nordisk Inc.. Speaker's Bureau; Self; Novo Nordisk Inc.. Consultant; Self; Ascensia Diabetes Care, Roche Diabetes Care Health and Digital Solutions. R. Saxena: None.

Landscape of Conditional eQTL in Dorsolateral Prefrontal Cortex and Co-localization with Schizophrenia GWAS

Causal genes and variants within genome-wide association study (GWAS) loci can be identified by integrating GWAS statistics with expression quantitative trait loci (eQTL) and determining which variants underlie both GWAS and eQTL signals. Most analyses, however, consider only the marginal eQTL signal, rather than dissect this signal into multiple conditionally independent signals for each gene. Here we show that analyzing conditional eQTL signatures, which could be important under specific cellular or temporal contexts, leads to improved fine mapping of GWAS associations. Using genotypes and gene expression levels from post-mortem human brain samples (n = 467) reported by the CommonMind Consortium (CMC), we find that conditional eQTL are widespread; 63% of genes with primary eQTL also have conditional eQTL. In addition, genomic features associated with conditional eQTL are consistent with context-specific (e.g., tissue-, cell type-, or developmental time point-specific) regulation of gene expression. Integrating the 2014 Psychiatric Genomics Consortium schizophrenia (SCZ) GWAS and CMC primary and conditional eQTL data reveals 40 loci with strong evidence for co-localization (posterior probability > 0.8), including six loci with co-localization of conditional eQTL. Our co-localization analyses support previously reported genes, identify novel genes associated with schizophrenia risk, and provide specific hypotheses for their functional follow-up.

Integration of Enhancer-Promoter Interactions with GWAS Summary Results Identifies Novel Schizophrenia-Associated Genes and Pathways.

It remains challenging to boost statistical power of genome-wide association studies (GWASs) to identify more risk variants or loci that can account for "missing heritability." Furthermore, since most identified variants are not in gene-coding regions, a biological interpretation of their function is largely lacking. On the other hand, recent biotechnological advances have made it feasible to experimentally measure the three-dimensional organization of the genome, including enhancer-promoter interactions in high resolutions. Due to the well-known critical roles of enhancer-promoter interactions in regulating gene expression programs, such data have been applied to link GWAS risk variants to their putative target genes, gaining insights into underlying biological mechanisms. However, their direct use in GWAS association testing is yet to be exploited. Here we propose integrating enhancer-promoter interactions into GWAS association analysis to both boost statistical power and enhance interpretability. We demonstrate that through an application to two large-scale schizophrenia (SCZ) GWAS summary data sets, the proposed method could identify some novel SCZ-associated genes and pathways (containing no significant SNPs). For example, after the Bonferroni correction, for the larger SCZ data set with 36,989 cases and 113,075 controls, our method applied to the gene body and enhancer regions identified 27 novel genes and 11 novel KEGG pathways to be significant, all missed by the transcriptome-wide association study (TWAS) approach. We conclude that our proposed method is potentially useful and is complementary to TWAS and other standard gene- and pathway-based methods.