Genome-wide Association Studies Loci Research Articles

Analysis of 72,469 UK Biobank exomes links rare variants to male-pattern hair loss

Male-pattern hair loss (MPHL) is common and highly heritable. While genome-wide association studies (GWAS) have generated insights into the contribution of common variants to MPHL etiology, the relevance of rare variants remains unclear. To determine the contribution of rare variants to MPHL etiology, we perform gene-based and single-variant analyses in exome-sequencing data from 72,469 male UK Biobank participants. While our population-level risk prediction suggests that rare variants make only a minor contribution to general MPHL risk, our rare variant collapsing tests identified a total of five significant gene associations. These findings provide additional evidence for previously implicated genes (EDA2R, WNT10A) and highlight novel risk genes at and beyond GWAS loci (HEPH, CEPT1, EIF3F). Furthermore, MPHL-associated genes are enriched for genes considered causal for monogenic trichoses. Together, our findings broaden the MPHL-associated allelic spectrum and provide insights into MPHL pathobiology and a shared basis with monogenic hair loss disorders.

Prioritization of risk genes in colorectal cancer by integrative analysis of multi-omics data and gene networks.

Genome-wide association studies (GWASs) have identified over 140 colorectal cancer (CRC)-associated loci; however, target genes at the majority of loci and underlying molecular mechanisms are poorly understood. Here, we utilized a Bayesian approach, integrative risk gene selector (iRIGS), to prioritize risk genes at CRC GWAS loci by integrating multi-omics data. As a result, a total of 105 high-confidence risk genes (HRGs) were identified, which exhibited strong gene dependencies for CRC and enrichment in the biological processes implicated in CRC. Among the 105 HRGs, CEBPB, located at the 20q13.13 locus, acted as a transcription factor playing critical roles in cancer. Our subsequent assays indicated the tumor promoter function of CEBPB that facilitated CRC cell proliferation by regulating multiple oncogenic pathways such as MAPK, PI3K-Akt, and Ras signaling. Next, by integrating a fine-mapping analysis and three independent case-control studies in Chinese populations consisting of 8,039 cases and 12,775 controls, we elucidated that rs1810503, a putative functional variant regulating CEBPB, was associated with CRC risk (OR=0.90, 95%CI=0.86-0.93, P=1.07×10-7). The association between rs1810503 and CRC risk was further validated in three additional multi-ancestry populations consisting of 24,254 cases and 58,741 controls. Mechanistically, the rs1810503 A to T allele change weakened the enhancer activity in an allele-specific manner to decrease CEBPB expression via long-range promoter-enhancer interactions, mediated by the transcription factor, REST, and thus decreased CRC risk. In summary, our study provides a genetic resource and a generalizable strategy for CRC etiology investigation, and highlights the biological implications of CEBPB in CRC tumorigenesis, shedding new light on the etiology of CRC.

Decreased CNNM2 expression in prefrontal cortex affects sensorimotor gating function, cognition, dendritic spine morphogenesis and risk of schizophrenia.

Genome-wide association studies (GWASs) have reported multiple single nucleotide polymorphisms (SNPs) associated with schizophrenia, yet the underlying molecular mechanisms are largely unknown. In this study, we aimed to identify schizophrenia relevant genes showing alterations in mRNA and protein expression associated with risk SNPs at the 10q24.32-33 GWAS locus. We carried out the quantitative trait loci (QTL) and summary data-based Mendelian randomization (SMR) analyses, using the PsychENCODE dorsolateral prefrontal cortex (DLPFC) expression QTL (eQTL) database, as well as the ROSMAP and Banner DLPFC protein QTL (pQTL) datasets. The gene CNNM2 (encoding a magnesium transporter) at 10q24.32-33 was identified to be a robust schizophrenia risk gene, and was highly expressed in human neurons according to single cell RNA-seq (scRNA-seq) data. We further revealed that reduced Cnnm2 in the mPFC of mice led to impaired cognition and compromised sensorimotor gating function, and decreased Cnnm2 in primary cortical neurons altered dendritic spine morphogenesis, confirming the link between CNNM2 and endophenotypes of schizophrenia. Proteomics analyses showed that reduced Cnnm2 level changed expression of proteins associated with neuronal structure and function. Together, these results identify a robust gene in the pathogenesis of schizophrenia.

Integration of eQTL and machine learning to dissect causal genes with pleiotropic effects in genetic regulation networks of seed cotton yield

The dissection of a gene regulatory network (GRN) that complements the genome-wide association study (GWAS) locus and the crosstalk underlying multiple agronomical traits remains a major challenge. In this study, we generate 558 transcriptional profiles of lint-bearing ovules at one day post-anthesis from a selective core cotton germplasm, from which 12,207 expression quantitative trait loci (eQTLs) are identified. Sixty-six known phenotypic GWAS loci are colocalized with 1,090 eQTLs, forming 38 functional GRNs associated predominantly with seed yield. Of the eGenes, 34 exhibit pleiotropic effects. Combining the eQTLs within the seed yield GRNs significantly increases the portion of narrow-sense heritability. The extreme gradient boosting (XGBoost) machine learning approach is applied to predict seed cotton yield phenotypes on the basis of gene expression. Top-ranking eGenes (NF-YB3, FLA2, and GRDP1) derived with pleiotropic effects on yield traits are validated, along with their potential roles by correlation analysis, domestication selection analysis, and transgenic plants.

Genome-wide association analysis of cystatin-C kidney function in continental Africa

Chronic kidney disease is becoming more prevalent in Africa, and its genetic determinants are poorly understood. Creatinine-based estimated glomerular filtration rate (eGFR) is commonly used to estimate kidney function, modelling the excretion of the endogenous biomarker (creatinine). However, eGFR based on creatinine has been shown to inadequately detect individuals with low kidney function in Sub-Saharan Africa, with eGFR based on cystatin-C (eGFRcys) exhibiting significantly superior performance. Therefore, we opted to conduct a GWAS for eGFRcys. Using the Uganda Genomic Resource, we performed a genome-wide association study (GWAS) of eGFRcys in 5877 Ugandans and evaluated replication in independent studies. Subsequently, putative causal variants were screened through Bayesian fine-mapping. Functional annotation of the GWAS loci was performed using Functional Mapping and Annotation (FUMA). Three independent lead single nucleotide polymorphisms (SNPs) (P-value <5×10-8 (based on likelihood ratio test (LRT))) were identified; rs59288815 (ANK3), rs4277141 (OR51B5) and rs911119 (CST3). From fine-mapping, rs59288815 and rs911119 each had a posterior probability of causality of >99%. The rs911119 SNP maps to the cystatin C gene and has been previously associated with eGFRcys among Europeans. With gene-set enrichment analyses of the olfactory receptor family 51 overlapping genes, we identified an association with the G-alpha-S signalling events. Our study found two previously unreported associated SNPs for eGFRcys in continental Africans (rs59288815 and rs4277141) and validated a previously well-established SNP (rs911119) for eGFRcys. The identified gene-set enrichment for the G-protein signalling pathways relates to the capacity of the kidney to readily adapt to an ever-changing environment. Additional GWASs are required to represent the diverse regions in Africa. Wellcome (220740/Z/20/Z).

Colocalization of corneal resistance factor GWAS loci with GTEx e/sQTLs highlights plausible candidate causal genes for keratoconus postnatal corneal stroma weakening.

Background: Genome-wide association studies (GWAS) for corneal resistance factor (CRF) have identified 100s of loci and proved useful to uncover genetic determinants for keratoconus, a corneal ectasia of early-adulthood onset and common indication of corneal transplantation. In the current absence of studies to probe the impact of candidate causal variants in the cornea, we aimed to fill some of this knowledge gap by leveraging tissue-shared genetic effects. Methods: 181 CRF signals were examined for evidence of colocalization with genetic signals affecting steady-state gene transcription and splicing in adult, non-eye, tissues of the Genotype-Tissue Expression (GTEx) project. Expression of candidate causal genes thus nominated was evaluated in single cell transcriptomes from adult cornea, limbus and conjunctiva. Fine-mapping and colocalization of CRF and keratoconus GWAS signals was also deployed to support their sharing causal variants. Results and discussion: 26.5% of CRF causal signals colocalized with GTEx v8 signals and nominated genes enriched in genes with high and specific expression in corneal stromal cells amongst tissues examined. Enrichment analyses carried out with nearest genes to all 181 CRF GWAS signals indicated that stromal cells of the limbus could be susceptible to signals that did not colocalize with GTEx's. These cells might not be well represented in GTEx and/or the genetic associations might have context specific effects. The causal signals shared with GTEx provide new insights into mediation of CRF genetic effects, including modulation of splicing events. Functionally relevant roles for several implicated genes' products in providing tensile strength, mechano-sensing and signaling make the corresponding genes and regulatory variants prime candidates to be validated and their roles and effects across tissues elucidated. Colocalization of CRF and keratoconus GWAS signals strengthened support for shared causal variants but also highlighted many ways into which likely true shared signals could be missed when using readily available GWAS summary statistics.

Machine learning reveals genetic modifiers of the immune microenvironment of cancer

Heritability in the immune tumor microenvironment (iTME) has been widely observed yet remains largely uncharacterized. Here, we developed a machine learning approach to map iTME modifiers within loci from genome-wide association studies (GWASs) for breast cancer (BrCa) incidence. A random forest model was trained on a positive set of immune-oncology (I-O) targets, and then used to assign I-O target probability scores to 1,362 candidate genes in linkage disequilibrium with 155 BrCa GWAS loci. Cluster analysis of the most probable candidates revealed two subfamilies of genes related to effector functions and adaptive immune responses, suggesting that iTME modifiers impact multiple aspects of anticancer immunity. Two of the top ranking BrCa candidates, LSP1 and TLR1, were orthogonally validated as iTME modifiers using BrCa patient biopsies and comparative mapping studies, respectively. Collectively, these data demonstrate a robust and flexible framework for functionally fine-mapping GWAS risk loci to identify translatable therapeutic targets.

Trans-ethnic genomic informed risk assessment for Alzheimer's disease: An International Hundred K+ Cohorts Consortium study.

As a collaboration model between the International HundredK+ Cohorts Consortium (IHCC) and the Davos Alzheimer's Collaborative (DAC), our aim was to develop a trans-ethnic genomic informed risk assessment (GIRA) algorithm for Alzheimer's disease (AD). The GIRA model was created to include polygenic risk score calculated from the AD genome-wide association study loci, the apolipoprotein E haplotypes, and non-genetic covariates including age, sex, and the first three principal components of population substructure. We validated the performance of the GIRA model in different populations. The proteomic study in the participant sites identified proteins related to female infertility and autoimmune thyroiditis and associated with the risk scores of AD. As the initial effort by the IHCC to leverage existing large-scale datasets in a collaborative setting with DAC, we developed a trans-ethnic GIRA for AD with the potential of identifying individuals at high risk of developing AD for future clinical applications.

Genome-Wide Association Study Identifies 4 Novel Risk Loci for Small Intestinal Neuroendocrine Tumors Including a Missense Mutation in LGR5

Small intestinal neuroendocrine tumor (SI-NET) is a rare disease, but its incidence has increased over the past 4 decades. Understanding the genetic risk factors underlying SI-NETs can help in disease prevention and may provide clinically beneficial markers for diagnosis. Here the results of the largest genome-wide association study of SI-NETs performed to date with 405 cases and 614,666 controls are reported. Samples from 307 patients with SI-NETs and 287,137 controls in the FinnGen study were used for the identification of SI-NET risk-associated genetic variants. The results were also meta-analyzed with summary statistics from the UK Biobank (n= 98 patients with SI-NET and n= 327,529 controls). We identified 6 genome-wide significant (P < 5× 10-8) loci associated with SI-NET risk, of which 4 (near SEMA6A, LGR5, CDKAL1, and FERMT2) are novel and 2 (near LTA4H-ELK and in KIF16B) have been reported previously. Interestingly, the top hit (rs200138614; P= 1.80× 10-19) was a missense variant (p.Cys712Phe) in the LGR5 gene, a bona-fide marker of adult intestinal stem cells and a potentiator of canonical WNT signaling. The association was validated in an independent Finnish collection of 70 patients with SI-NETs, as well as in the UK Biobank exome sequence data (n= 92 cases and n= 392,814 controls). Overexpression of LGR5 p.Cys712Phe in intestinal organoids abolished the ability of R-Spondin1 to support organoid growth, indicating that the mutation perturbed R-Spondin-LGR5 signaling. Our study is the largest genome-wide association study to date on SI-NETs and reported 4 new associated genome-wide association study loci, including a novel missense mutation (rs200138614, p.Cys712Phe) in LGR5, a canonical marker of adult intestinal stem cells.

Leveraging polygenic enrichments of gene features to predict genes underlying complex traits and diseases.

Genome-wide association studies (GWASs) are a valuable tool for understanding the biology of complex human traits and diseases, but associated variants rarely point directly to causal genes. In the present study, we introduce a new method, polygenic priority score (PoPS), that learns trait-relevant gene features, such as cell-type-specific expression, to prioritize genes at GWAS loci. Using a large evaluation set of genes with fine-mapped coding variants, we show that PoPS and the closest gene individually outperform other gene prioritization methods, but observe the best overall performance by combining PoPS with orthogonal methods. Using this combined approach, we prioritize 10,642 unique gene-trait pairs across 113 complex traits and diseases with high precision, finding not only well-established gene-trait relationships but nominating new genes at unresolved loci, such as LGR4 for estimated glomerular filtration rate and CCR7 for deep vein thrombosis. Overall, we demonstrate that PoPS provides a powerful addition to the gene prioritization toolbox.

Association Analyses of Predicted Loss-of-Function Variants Prioritized 15 Genes as Blood Pressure Regulators

BackgroundHypertension, clinically defined by elevated blood pressure (BP), is an important cause of mortality and morbidity worldwide. Many risk factors for hypertension are known, including a positive family history, which suggests that genetics contribute to interindividual BP variation. Genome-wide association studies (GWAS) have identified > 1000 loci associated with BP, yet the identity of the genes responsible for these associations remains largely unknown. MethodsTo pinpoint genes that causally affect variation of BP in humans, we analyzed predicted loss-of-function (pLoF) variants in the UK Biobank whole-exome sequencing dataset (n = 454,709 participants, 6% non-European ancestry). We analyzed genetic associations between systolic or diastolic BP (SBP/DBP) and single pLoF variants (additive and recessive genetic models) as well as with the burden of very rare pLoF variants (minor allele frequency [MAF] < 0.01%). ResultsSingle pLoF variants in 10 genes were associated with BP (ANKDD1B, ENPEP, PNCK, BTN3A2, C1orf145 [OBSCN-AS1], CASP9, DBH, KIAA1161 [MYORG], OR4X1, and TMC3). We also found a burden of rare pLoF variants in 5 additional genes associated with BP (TTN, NOS3, FES, SMAD6, COL21A1). Except for PNCK, which is located on the X-chromosome, these genes map near variants previously associated with BP by GWAS, validating the study of pLoF variants to prioritize causal genes at GWAS loci. ConclusionsOur study highlights 15 genes that likely modulate BP in humans, including 5 genes that harbour pLoF variants associated with lower BP.

1473-P: A Data-Driven Cluster Analysis of Renal Complications in Patients with Type 2 Diabetes—From Phenotypic Observation to Genetic Discovery

Diabetic kidney disease (DKD) has a strong genetic component. The marked phenotypic heterogeneity in type 2 diabetes (T2D) limits the power of cross-sectional genome-wide association studies (GWAS) on DKD. We performed a data-driven cluster analysis using the longitudinal electronic health records (EHR) of a prospective cohort to identify subgroups of patients with different patterns of progression of kidney functions. These included 29,416 Chinese patients with T2D recruited in 1995-2018 observed until 2019 (median [IQR] follow-up: 9 [5-12] years). We curated 211 variables including baseline data collected during structured assessment and follow-up EHR data. We applied generalized low rank models (GLRM) to resolve the inter-correlations among variables and reduce the high dimension of the raw dataset. We identified 6 clusters featured by distinct combinations of the 211 variables: 1) DKD-free characterized by stable estimated glomerular filtration rate (eGFR decline &amp;lt;1 ml/min per 1.73 m2 per year) and absence of macroalbuminuria (19.4%); 2) eGFR decline without macroalbuminuria and good glycemic and BP control (26.0%); 3) eGFR decline with albuminuria and high BP (20.1%); 4) eGFR decline with albuminuria and poor glycemic control (14.2%); 5) rapid eGFR decline, albuminuria, high BP and low glucose burden (10.5%); and 6) rapid eGFR decline, macroalbuminuria, high BP and high glucose burden (9.8%). We performed cluster-wise GWAS in 12,358 patients with available genotype data. We found diverse patterns of genetic associations for the 6 DKD clusters reaching genome-wide significance (P GWAS &amp;lt;5×10−8) for cluster 3 and 6. Polygenic risk scores (PRS) using top GWAS loci discriminated risk of end-stage kidney disease with AUC of 0.62 (PRS only) and 0.93 (PRS plus baseline clinical variables). Our novel cluster-GWAS using more precise phenotyping combining baseline and follow-up clinical variables had led to novel genetic discoveries. Disclosure M.Shi: None. J.C.Chan: Board Member; Asia Diabetes Foundation, Consultant; Bayer Inc., Celltrion, Boehringer Ingelheim and Eli Lilly Alliance, Sanofi, Research Support; AstraZeneca, Servier Laboratories, Viatris Inc., Hua Medicine, Merck KGaA, Applied Therapeutics Inc., Lee Powder, Pfizer Inc., Speaker's Bureau; Novartis, Stock/Shareholder; GemVCare Ltd. R.C.Ma: Advisory Panel; AstraZeneca, Merck &amp; Co., Inc., Other Relationship; Bayer Inc., Boehringer-Ingelheim, Research Support; Tricida, Inc., Roche Diagnostics, Novo Nordisk. C.H.Tam: None. E.S.H.Lau: None. A.Yang: None. H.Wu: None. B.Fan: None. A.P.Kong: Advisory Panel; Abbott, Kyowa Kirin Co., Ltd., Speaker's Bureau; Abbott, AstraZeneca, Lilly, Bayer Inc., Boehringer Ingelheim Inc. A.Luk: Research Support; Novo Nordisk, Boehringer-Ingelheim, Bayer Inc., Speaker's Bureau; Eli Lilly and Company. E.Chow: Research Support; Medtronic, Merck KGaA, Speaker's Bureau; Novartis, Bayer Inc., Sanofi. Funding Research Grants Council of the Hong Kong Special Administrative Region (R4012-18); Theme-Based Research Scheme (T12–402/13N)

Lipid-Associated GWAS Loci Predict Antiatherogenic Effects of Rosuvastatin in Patients with Coronary Artery Disease.

We have shown that lipid-associated loci discovered by genome-wide association studies (GWAS) have pleiotropic effects on lipid metabolism, carotid intima-media thickness (CIMT), and CAD risk. Here, we investigated the impact of lipid-associated GWAS loci on the efficacy of rosuvastatin therapy in terms of changes in plasma lipid levels and CIMT. The study comprised 116 CAD patients with hypercholesterolemia. CIMT, total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), and triglycerides (TG) were measured at baseline and after 6 and 12 months of follow-up, respectively. Genotyping of fifteen lipid-associated GWAS loci was performed by the MassArray-4 System. Linear regression analysis adjusted for sex, age, body mass index, and rosuvastatin dose was used to estimate the phenotypic effects of polymorphisms, and p-values were calculated through adaptive permutation tests by the PLINK software, v1.9. Over one-year rosuvastatin therapy, a decrease in CIMT was linked to rs1689800, rs4846914, rs12328675, rs55730499, rs9987289, rs11220463, rs16942887, and rs881844 polymorphisms (Pperm < 0.05). TC change was associated with rs55730499, rs11220463, and rs6065906; LDL-C change was linked to the rs55730499, rs1689800, and rs16942887 polymorphisms; and TG change was linked to polymorphisms rs838880 and rs1883025 (Pperm < 0.05). In conclusion, polymorphisms rs1689800, rs55730499, rs11220463, and rs16942887 were found to be predictive markers for multiple antiatherogenic effects of rosuvastatin in CAD patients.

Dissecting the genetic heterogeneity of gastric cancer.

Gastric cancer (GC) is clinically heterogenous according to location (cardia/non-cardia) and histopathology (diffuse/intestinal). We aimed to characterize the genetic risk architecture of GC according to its subtypes. Another aim was to examine whether cardia GC and oesophageal adenocarcinoma (OAC) and its precursor lesion Barrett's oesophagus (BO), which are all located at the gastro-oesophageal junction (GOJ), share polygenic risk architecture. We did a meta-analysis of ten European genome-wide association studies (GWAS) of GC and its subtypes. All patients had a histopathologically confirmed diagnosis of gastric adenocarcinoma. For the identification of risk genes among GWAS loci we did a transcriptome-wide association study (TWAS) and expression quantitative trait locus (eQTL) study from gastric corpus and antrum mucosa. To test whether cardia GC and OAC/BO share genetic aetiology we also used a European GWAS sample with OAC/BO. Our GWAS consisting of 5816 patients and 10,999 controls highlights the genetic heterogeneity of GC according to its subtypes. We newly identified two and replicated five GC risk loci, all of them with subtype-specific association. The gastric transcriptome data consisting of 361 corpus and 342 antrum mucosa samples revealed that an upregulated expression of MUC1, ANKRD50, PTGER4, and PSCA are plausible GC-pathomechanisms at four GWAS loci. At another risk locus, we found that the blood-group 0 exerts protective effects for non-cardia and diffuse GC, while blood-group A increases risk for both GC subtypes. Furthermore, our GWAS on cardia GC and OAC/BO (10,279 patients, 16,527 controls) showed that both cancer entities share genetic aetiology at the polygenic level and identified two new risk loci on the single-marker level. Our findings show that the pathophysiology of GC is genetically heterogenous according to location and histopathology. Moreover, our findings point to common molecular mechanisms underlying cardia GC and OAC/BO. German Research Foundation (DFG).

Integrating eQTL and GWAS data characterises established and identifies novel migraine risk loci

Migraine—a painful, throbbing headache disorder—is the most common complex brain disorder, yet its molecular mechanisms remain unclear. Genome-wide association studies (GWAS) have proven successful in identifying migraine risk loci; however, much work remains to identify the causal variants and genes. In this paper, we compared three transcriptome-wide association study (TWAS) imputation models—MASHR, elastic net, and SMultiXcan—to characterise established genome-wide significant (GWS) migraine GWAS risk loci, and to identify putative novel migraine risk gene loci. We compared the standard TWAS approach of analysing 49 GTEx tissues with Bonferroni correction for testing all genes present across all tissues (Bonferroni), to TWAS in five tissues estimated to be relevant to migraine, and TWAS with Bonferroni correction that took into account the correlation between eQTLs within each tissue (Bonferroni-matSpD). Elastic net models performed in all 49 GTEx tissues using Bonferroni-matSpD characterised the highest number of established migraine GWAS risk loci (n = 20) with GWS TWAS genes having colocalisation (PP4 > 0.5) with an eQTL. SMultiXcan in all 49 GTEx tissues identified the highest number of putative novel migraine risk genes (n = 28) with GWS differential expression at 20 non-GWS GWAS loci. Nine of these putative novel migraine risk genes were later found to be at and in linkage disequilibrium with true (GWS) migraine risk loci in a recent, more powerful migraine GWAS. Across all TWAS approaches, a total of 62 putative novel migraine risk genes were identified at 32 independent genomic loci. Of these 32 loci, 21 were true risk loci in the recent, more powerful migraine GWAS. Our results provide important guidance on the selection, use, and utility of imputation-based TWAS approaches to characterise established GWAS risk loci and identify novel risk gene loci.

HSPA12A was identified as a key driver in colorectal cancer GWAS loci 10q26.12 and modulated by an enhancer-promoter interaction.

Although genome-wide association studies (GWASs) have identified over 100 colorectal cancer (CRC) risk loci, an understanding of causal genes or risk variants and their biological functions in these loci remain unclear. Recently, genomic loci 10q26.12 with lead SNP rs1665650 was identified as an essential CRC risk loci of Asian populations. However, the functional mechanism of this region has not been fully clarified. Here, we applied an RNA interfering-based on-chip approach to screen for the genes essential for cell proliferation in the CRC risk loci 10q26.12. Notably, HSPA12A had the most significant effect among the identified genes and functioned as a crucial oncogene facilitating cell proliferation. Moreover, we conducted an integrative fine-mapping analysis to identify putative casual variants and further explored their association with CRC risk in a large-scale Chinese population consisting of 4054 cases and 4054 controls and also independently validated in 5208 cases and 20,832 controls from the UK biobank cohort. We identified a risk SNP rs7093835 in the intron of HSPA12A that was significantly associated with an increased risk of CRC (OR 1.23, 95% CI 1.08-1.41, P = 1.92 × 10-3). Mechanistically, the risk variant could facilitate an enhancer-promoter interaction mediated by the transcriptional factor (TF) GRHL1 and ultimately upregulate HSPA12A expression, which provides functional evidence to support our population findings. Collectively, our study reveals the important role of HSPA12A in CRC development and illustrates a novel enhancer-promoter interaction module between HSPA12A and its regulatory elements rs7093835, providing new insights into the etiology of CRC.

Blood cell traits’ GWAS loci colocalization with variation in PU.1 genomic occupancy prioritizes causal noncoding regulatory variants

Genome-wide association studies (GWASs) have uncovered numerous trait-associated loci across the human genome, most of which are located in noncoding regions, making interpretation difficult. Moreover, causal variants are hard to statistically fine-map at many loci because of widespread linkage disequilibrium. To address this challenge, we present a strategy utilizing transcription factor (TF) binding quantitative trait loci (bQTLs) for colocalization analysis to identify trait associations likely mediated by TF occupancy variation and to pinpoint likely causal variants using motif scores. We applied this approach to PU.1 bQTLs in lymphoblastoid cell lines and blood cell trait GWAS data. Colocalization analysis revealed 69 blood cell trait GWAS loci putatively driven by PU.1 occupancy variation. We nominate PU.1 motif-altering variants as the likely shared causal variants at 51 loci. Such integration of TF bQTL data with other GWAS data may reveal transcriptional regulatory mechanisms and causal noncoding variants underlying additional complex traits.

Evaluating the Contribution of Cell Type-Specific Alternative Splicing to Variation in Lipid Levels.

Genome-wide association studies have identified hundreds of loci associated with lipid levels. However, the genetic mechanisms underlying most of these loci are not well-understood. Recent work indicates that changes in the abundance of alternatively spliced transcripts contribute to complex trait variation. Consequently, identifying genetic loci that associate with alternative splicing in disease-relevant cell types and determining the degree to which these loci are informative for lipid biology is of broad interest. We analyze gene splicing in 83 sample-matched induced pluripotent stem cell (iPSC) and hepatocyte-like cell lines (n=166), as well as in an independent collection of primary liver tissues (n=96) to perform discovery of splicing quantitative trait loci (sQTLs). We observe that transcript splicing is highly cell type specific, and the genes that are differentially spliced between iPSCs and hepatocyte-like cells are enriched for metabolism pathway annotations. We identify 1384 hepatocyte-like cell sQTLs and 1455 iPSC sQTLs at a false discovery rate of <5% and find that sQTLs are often shared across cell types. To evaluate the contribution of sQTLs to variation in lipid levels, we conduct colocalization analysis using lipid genome-wide association data. We identify 19 lipid-associated loci that colocalize either with an hepatocyte-like cell expression quantitative trait locus or sQTL. Only 2 loci colocalize with both a sQTL and expression quantitative trait locus, indicating that sQTLs contribute information about genome-wide association studies loci that cannot be obtained by analysis of steady-state gene expression alone. These results provide an important foundation for future efforts that use iPSC and iPSC-derived cells to evaluate genetic mechanisms influencing both cardiovascular disease risk and complex traits in general.

A joint transcriptome-wide association study across multiple tissues identifies candidate breast cancer susceptibility genes

Genome-wide association studies (GWASs) have identified more than 200 genomic loci for breast cancer risk, but specific causal genes in most of these loci have not been identified. In fact, transcriptome-wide association studies (TWASs) of breast cancer performed using gene expression prediction models trained in breast tissue have yet to clearly identify most target genes. To identify candidate genes, we performed a GWAS analysis in a breast cancer dataset from UK Biobank (UKB) and combined the results with the GWAS results of the Breast Cancer Association Consortium (BCAC) by a meta-analysis. Using the summary statistics from the meta-analysis, we performed a joint TWAS analysis that combined TWAS signals from multiple tissues. We used expression prediction models trained in 11 tissues that are potentially relevant to breast cancer from the Genotype-Tissue Expression (GTEx) data. In the GWAS analysis, we identified eight loci distinct from those reported previously. In the TWAS analysis, we identified 309 genes at 108 genomic loci to be significantly associated with breast cancer at the Bonferroni threshold. Of these, 17 genes were located in eight regions that were at least 1 Mb away from published GWAS hits. The remaining TWAS-significant genes were located in 100 known genomic loci from previous GWASs of breast cancer. We found that 21 genes located in known GWAS loci remained statistically significant after conditioning on previous GWAS index variants. Our study provides insights into breast cancer genetics through mapping candidate target genes in a large proportion of known GWAS loci and discovering multiple new loci.

Discovery of target genes and pathways at GWAS loci by pooled single-cell CRISPR screens.

Most variants associated with complex traits and diseases identified by genome-wide association studies (GWAS) map to noncoding regions of the genome with unknown effects. Using ancestrally diverse, biobank-scale GWAS data, massively parallel CRISPR screens, and single-cell transcriptomic and proteomic sequencing, we discovered 124 cis-target genes of 91 noncoding blood trait GWAS loci. Using precise variant insertion through base editing, we connected specific variants with gene expression changes. We also identified trans-effect networks of noncoding loci when cis target genes encoded transcription factors or microRNAs. Networks were themselves enriched for GWAS variants and demonstrated polygenic contributions to complex traits. This platform enables massively parallel characterization of the target genes and mechanisms of human noncoding variants in both cis and trans.