Genome-wide Association Studies Datasets Research Articles

Gene-based association tests in family samples using GWAS summary statistics.

Genome-wide association studies (GWAS) have led to rapid growth in detecting genetic variants associated with various phenotypes. Owing to a great number of publicly accessible GWAS summary statistics, and the difficulty in obtaining individual-level genotype data, many existing gene-based association tests have been adapted to require only GWAS summary statistics rather than individual-level data. However, these association tests are restricted to unrelated individuals and thus do not apply to family samples directly. Moreover, due to its flexibility and effectiveness, the linear mixed model has been increasingly utilized in GWAS to handle correlated data, such as family samples. However, it remains unknown how to perform gene-based association tests in family samples using the GWAS summary statistics estimated from the linear mixed model. In this study, we show that, when family size is negligible compared to the total sample size, the diagonal block structure of the kinship matrix makes it possible to approximate the correlation matrix of marginal Z scores by linkage disequilibrium matrix. Based on this result, current methods utilizing summary statistics for unrelated individuals can be directly applied to family data without any modifications. Our simulation results demonstrate that this proposed strategy controls the type 1 error rate well in various situations. Finally, we exemplify the usefulness of the proposed approach with a dental caries GWAS data set.

Identifying novel chemical-related susceptibility genes for five psychiatric disorders through integrating genome-wide association study and tissue-specific 3'aQTL annotation datasets.

The establishment of 3'aQTLs comprehensive database provides an opportunity to help explore the functional interpretation from the genome-wide association study (GWAS) data of psychiatric disorders. In this study, we aim to search novel susceptibility genes, pathways, and related chemicals of five psychiatric disorders via GWAS and 3'aQTLs datasets. The GWAS datasets of five psychiatric disorders were collected from the open platform of Psychiatric Genomics Consortium (PGC, https://www.med.unc.edu/pgc/ ) and iPSYCH ( https://ipsych.dk/ ) (Demontis et al. in Nat Genet 51(1):63-75, 2019; Grove et al. in Nat Genet 51:431-444, 2019; Genomic Dissection of Bipolar Disorder and Schizophrenia in Cell 173: 1705-1715.e1716, 2018; Mullins et al. in Nat Genet 53: 817-829; Howard et al. in Nat Neurosci 22: 343-352, 2019). The 3'untranslated region (3'UTR) alternative polyadenylation (APA) quantitative trait loci (3'aQTLs) summary datasets of 12 brain regions were obtained from another public platform ( https://wlcb.oit.uci.edu/3aQTLatlas/ ) (Cui et al. in Nucleic Acids Res 50: D39-D45, 2022). First, we aligned the GWAS-associated SNPs of psychiatric disorders and datasets of 3'aQTLs, and then, the GWAS-associated 3'aQTLs were identified from the overlap. Second, gene ontology (GO) and pathway analysis was applied to investigate the potential biological functions of matching genes based on the methods provided by MAGMA. Finally, chemical-related gene-set analysis (GSA) was also conducted by MAGMA to explore the potential interaction of GWAS-associated 3'aQTLs and multiple chemicals in the mechanism of psychiatric disorders. A number of susceptibility genes with 3'aQTLs were found to be associated with psychiatric disorders and some of them had brain-region specificity. For schizophrenia (SCZ), HLA-A showed associated with psychiatric disorders in all 12 brain regions, such as cerebellar hemisphere (P = 1.58 × 10-36) and cortex (P = 1.58 × 10-36). GO and pathway analysis identified several associated pathways, such as Phenylpropanoid Metabolic Process (GO:0009698, P = 6.24 × 10-7 for SCZ). Chemical-related GSA detected several chemical-related gene sets associated with psychiatric disorders. For example, gene sets of Ferulic Acid (P = 6.24 × 10-7), Morin (P = 4.47 × 10-2) and Vanillic Acid (P = 6.24 × 10-7) were found to be associated with SCZ. By integrating the functional information from 3'aQTLs, we identified several susceptibility genes and associated pathways especially chemical-related gene sets for five psychiatric disorders. Our results provided new insights to understand the etiology and mechanism of psychiatric disorders.

Genomics Research of Lifetime Depression in the Netherlands: The BIObanks Netherlands Internet Collaboration (BIONIC) Project.

In this cohort profile article we describe the lifetime major depressive disorder (MDD) database that has been established as part of the BIObanks Netherlands Internet Collaboration (BIONIC). Across the Netherlands we collected data on Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) lifetime MDD diagnosis in 132,850 Dutch individuals. Currently, N = 66,684 of these also have genomewide single nucleotide polymorphism (SNP) data. We initiated this project because the complex genetic basis of MDD requires large population-wide studies with uniform in-depth phenotyping. For standardized phenotyping we developed the LIDAS (LIfetime Depression Assessment Survey), which then was used to measure MDD in 11 Dutch cohorts. Data from these cohorts were combined with diagnostic interview depression data from 5 clinical cohorts to create a dataset of N = 29,650 lifetime MDD cases (22%) meeting DSM-5 criteria and 94,300 screened controls. In addition, genomewide genotype data from the cohorts were assembled into a genomewide association study (GWAS) dataset of N = 66,684 Dutch individuals (25.3% cases). Phenotype data include DSM-5-based MDD diagnoses, sociodemographic variables, information on lifestyle and BMI, characteristics of depressive symptoms and episodes, and psychiatric diagnosis and treatment history. We describe the establishment and harmonization of the BIONIC phenotype and GWAS datasets and provide an overview of the available information and sample characteristics. Our next step is the GWAS of lifetime MDD in the Netherlands, with future plans including fine-grained genetic analyses of depression characteristics, international collaborations and multi-omics studies.

Causal Effects of Lipids-Related Metabolites on Androgenic Alopecia: A Mendelian Randomization Study.

To investigate whether increased levels of lipids-related metabolites (LRMs) result in androgenic alopecia (AGA). A two-sample Mendelian randomization (MR) study was designed, and single nucleotide polymorphisms (SNPs) respectively related to nine LRMs were selected from the genome-wide association study (GWAS) dataset. An MR analysis was performed to assess the causal association between LRMs and AGA. Through the fixed-effect inverse variance weighting (IVW) method, MR analysis indicated that Apolipoprotein B (ApoB), low-density lipoprotein (LDL), and very-low-density lipoprotein (VLDL) had a causal relationship with AGA. No obvious heterogeneity or pleiotropy was observed. The risk of AGA increases significantly when the serum levels of ApoB, LDL, and VLDL increase. This causal relationship is solid and free of interference from confounding factors.

Plasma metabolites and risk of myocardial infarction: a bidirectional Mendelian randomization study.

Myocardial infarction (MI) is a critical cardiovascular event with multifaceted etiology, involving several genetic and environmental factors. It is essential to understand the function of plasma metabolites in the development of MI and unravel its complex pathogenesis. This study employed a bidirectional Mendelian randomization (MR) approach to investigate the causal relationships between plasma metabolites and MI risk. We used genetic instruments as proxies for plasma metabolites and MI and conducted MR analyses in both directions to assess the impact of metabolites on MI risk and vice versa. In addition, the large-scale genome-wide association studies datasets was used to identify genetic variants associated with plasma metabolite (1400 metabolites) and MI (20,917 individuals with MI and 440,906 individuals without MI) susceptibility. Inverse variance weighted was the primary method for estimating causal effects. MR estimates are expressed as beta coefficients or odds ratio (OR) with 95% CI. We identified 14 plasma metabolites associated with the occurrence of MI (P < 0.05), among which 8 plasma metabolites [propionylglycine levels (OR = 0.922, 95% CI: 0.881-0.965, P < 0.001), gamma-glutamylglycine levels (OR = 0.903, 95% CI: 0.861-0.948, P < 0.001), hexadecanedioate (C16-DC) levels (OR = 0.941, 95% CI: 0.911-0.973, P < 0.001), pentose acid levels (OR = 0.923, 95% CI: 0.877-0.972, P = 0.002), X-24546 levels (OR = 0.936, 95% CI: 0.902-0.971, P < 0.001), glycine levels (OR = 0.936, 95% CI: 0.909-0.964, P < 0.001), glycine to serine ratio (OR = 0.930, 95% CI: 0.888-0.974, P = 0.002), and mannose to trans-4-hydroxyproline ratio (OR = 0.912, 95% CI: 0.869-0.958, P < 0.001)] were correlated with a decreased risk of MI, whereas the remaining 6 plasma metabolites [1-palmitoyl-2-arachidonoyl-GPE (16:0/20:4) levels (OR = 1.051, 95% CI: 1.018-1.084, P = 0.002), behenoyl dihydrosphingomyelin (d18:0/22:0) levels (OR = 1.076, 95% CI: 1.027-1.128, P = 0.002), 1-stearoyl-2-docosahexaenoyl-GPE (18:0/22:6) levels (OR = 1.067, 95% CI: 1.027-1.109, P = 0.001), alpha-ketobutyrate levels (OR = 1.108, 95% CI: 1.041-1.180, P = 0.001), 5-acetylamino-6-formylamino-3-methyluracil levels (OR = 1.047, 95% CI: 1.019-1.076, P < 0.001), and N-acetylputrescine to (N (1) + N (8))-acetylspermidine ratio (OR = 1.045, 95% CI: 1.018-1.073, P < 0.001)] were associated with an increased risk of MI. Furthermore, we also observed that the mentioned relationships were unaffected by horizontal pleiotropy (P > 0.05). On the contrary, MI did not lead to significant alterations in the levels of the aforementioned 14 plasma metabolites (P > 0.05 for each comparison). Our bidirectional MR study identified 14 plasma metabolites associated with the occurrence of MI, among which 13 plasma metabolites have not been reported previously. These findings provide valuable insights for the early diagnosis of MI and potential therapeutic targets.

Association between psychiatric disorders and glioma risk: evidence from Mendelian randomization analysis

BackgroundObservational studies have explored the association of psychiatric disorders and the risk of brain cancers. However, the causal effect of specific mental illness on glioma remains elusive due to the lack of solid evidence.MethodsWe performed a two-sample bidirectional Mendelian randomization (MR) analysis to explore the causal relationships between 5 common psychiatric disorders (schizophrenia, major depressive disorder, bipolar disorder, autism spectrum disorder, and panic disorder) and glioma. Summary statistics for psychiatric disorders and glioma were extracted from Psychiatric Genomics Consortium (PGC) and 8 genome-wide association study (GWAS) datasets respectively. We calculated the MR estimates for odds ratio of glioma associated with each psychiatric disorder by using inverse-variance weighting (IVW) method. Sensitivity analyses such as weighted median estimator, MR-Egger and MR-PRESSO were leveraged to assess the strength of causal inference.ResultsA total of 30,657 participants of European ancestry were included in this study. After correction for multiple testing, we found that genetically predicted schizophrenia was associated with a statistically significant increase in odds of non-glioblastoma multiforme (non-GBM) (OR = 1.13, 95% CI: 1.03–1.23, P = 0.0096). There is little evidence for the causal relationships between the other 4 psychiatric disorders with the risk of glioma.ConclusionsIn this MR analysis, we revealed an increased risk of non-GBM glioma in individuals with schizophrenia, which gives an insight into the etiology of glioma.

Causal association between 637 human metabolites and ovarian cancer: a mendelian randomization study

BackgroundCurrent evidence suggests a significant association between metabolites and ovarian cancer (OC); however, the causal relationship between the two remains unclear. This study employs Mendelian randomization (MR) to investigate the causal effects between different metabolites and OC.MethodsIn this study, a total of 637 metabolites were selected as the exposure variables from the Genome-wide Association Study (GWAS) database (http://gwas.mrcieu.ac.uk/datasets/). The OC related GWAS dataset (ieu-b-4963) was chosen as the outcome variable. R software and the TwoSampleMR package were utilized for the analysis in this study. MR analysis employed the inverse variance-weighted method (IVW), MR-Egger and weighted median (WM) for regression fitting, taking into consideration potential biases caused by linkage disequilibrium and weak instrument variables. Metabolites that did not pass the tests for heterogeneity and horizontal pleiotropy were considered to have no significant causal effect on the outcome. Steiger’s upstream test was used to determine the causal direction between the exposure and outcome variables.ResultsThe results from IVW analysis revealed that a total of 31 human metabolites showed a significant causal effect on OC (P < 0.05). Among them, 9 metabolites exhibited consistent and stable causal effects, which were confirmed by Steiger’s upstream test (P < 0.05). Among these 9 metabolites, Androsterone sulfate, Propionylcarnitine, 5alpha-androstan-3beta,17beta-diol disulfate, Total lipids in medium VLDL and Concentration of medium VLDL particles demonstrated a significant positive causal effect on OC, indicating that these metabolites promote the occurrence of OC. On the other hand, X-12,093, Octanoylcarnitine, N2,N2-dimethylguanosine, and Cis-4-decenoyl carnitine showed a significant negative causal association with OC, suggesting that these metabolites can inhibit the occurrence of OC.ConclusionsThe study revealed the complex effect of metabolites on OC through Mendelian randomization. As promising biomarkers, these metabolites are worthy of further clinical validation.

Identifying the mediating role of inflammation on the relationship between socioeconomic status and Alzheimer's disease: a Mendelian randomization analysis and mediation analysis.

Observational studies have demonstrated a significant association between socio-economic status (SES) and Alzheimer's disease (AD). Nonetheless, the precise biological mechanisms underlying this association remain unclear. Therefore, we adopted a Mendelian Randomization (MR) approach to investigate the causal relationship between SES and genetic susceptibility to AD, as well as to explore the potential mediation effects of inflammation. Large-scale cohorts based on publicly available genome-wide association study (GWAS) datasets from European populations were employed for conducting the MR study. The primary criterion utilized was the inverse-variance weighting (IVW) model. Heterogeneity and horizontal pleiotropy were assessed. In addition, multivariate MR (MVMR) was utilized to correct the confounders. Moreover, a two-step MR approach was used to evaluate the potential mediating effects of factors on the causal effects between SES and AD. As indicated by the results of the IVW model, educational years (OR = 0.708, 95% CI 0.610-0.821, P < 0.001) and household income (OR = 0.746, 95% CI 0.566-0.982, P = 0.037) was associated with a decreased genetic susceptibility risk for AD. The univariable results showed that the causal effect of educational years on the lower risk of AD remained significant (OR = 0.643, 95% CI 0.467-0.886, P = 0.006). In addition, our findings indicated that C-reactive protein (CRP) played a role in the causal effect of educational years on AD. The proportions of mediation were - 50.08% (95% CI - 92.78; - 7.38%). These findings provided evidence supporting the causal effect of educational attainment lower AD risk, with inflammation playing a mediating role. These findings may inform prevention strategies and interventions directed toward AD. Future studies should explore other plausible biological mechanisms.

Des-Arg(9) bradykinin as a causal metabolite for autism spectrum disorder.

Early diagnosis and therapeutic interventions can greatly enhance the developmental trajectory of children with autism spectrum disorder (ASD). However, the etiology of ASD is not completely understood. The presence of confounding factors from environment and genetics has increased the difficulty of the identification of diagnostic biomarkers for ASD. To estimate and interpret the causal relationship between ASD and metabolite profile, taking into consideration both genetic and environmental influences. A two-sample Mendelian randomization (MR) analysis was conducted using summarized data from large-scale genome-wide association studies (GWAS) including a metabolite GWAS dataset covering 453 metabolites from 7824 European and an ASD GWAS dataset comprising 18381 ASD cases and 27969 healthy controls. Metabolites in plasma were set as exposures with ASD as the main outcome. The causal relationships were estimated using the inverse variant weight (IVW) algorithm. We also performed leave-one-out sensitivity tests to validate the robustness of the results. Based on the drafted metabolites, enrichment analysis was conducted to interpret the association via constructing a protein-protein interaction network with multi-scale evidence from databases including Infinome, SwissTargetPrediction, STRING, and Metascape. Des-Arg(9)-bradykinin was identified as a causal metabolite that increases the risk of ASD (β = 0.262, SE = 0.064, PIVW = 4.64 × 10-5). The association was robust, with no significant heterogeneity among instrument variables (PMR Egger = 0.663, PIVW = 0.906) and no evidence of pleiotropy (P = 0.949). Neuroinflammation and the response to stimulus were suggested as potential biological processes mediating the association between Des-Arg(9) bradykinin and ASD. Through the application of MR, this study provides practical insights into the potential causal association between plasma metabolites and ASD. These findings offer perspectives for the discovery of diagnostic or predictive biomarkers to support clinical practice in treating ASD.

CHEK2 knockout is a therapeutic target for TP53-mutated hepatocellular carcinoma

Currently, there is still a lack of novel and effective drug targets to improve the prognosis of hepatocellular carcinoma (HCC). Additionally, the role of CHEK2 in HCC has not been reported yet. The eQTLgen database and two HCC Genome-Wide Association Study (GWAS) datasets (ieu-b-4953, ICD10 C22.0) were used to find the drug target: CHEK2. Next, Colony, Edu, β-gal, and cell cycle analysis were facilitated to evaluate the role of CHEK2 knockout in HCC. In addition, Nultin-3 was added to evaluate the apoptosis of TP53-mutated HCC cells with CHEK2 knockout. Furthermore, MitoSox, electron microscopy, mitochondrial ATP, and NADH+/NADH levels were assessed in the CHEK2 knockout HCC cells with or without Metformin. Finally, cell-derived tumor xenograft was used to evaluate the role of CHEK2 knockout in vivo. We initially identified a potential drug target, CHEK2, through GWAS data analysis. Furthermore, we observed a significant upregulation of CHEK2 expression in HCC, which was found to be correlated with a poor prognosis. Subsequently, the results indicated that knocking out CHEK2 selectively affects the proliferation, cell cycle, senescence, and apoptosis of TP53-mutant HCC cells. Additionally, the introduction of Nultin-3 further intensified the functional impact on TP53-mutant cells. Then ClusterProfiler results showed high CHEK2 and TP53 mutation group was positively enriched in the mitochondrial ATP pathway. Then we used MitoSox, electron microscopy, mitochondrial ATP, and NADH + /NADH assay and found knockout of CHECK could induce the ATP pathway to inhibit the growth of HCC. Our research introduces a novel drug target for TP53-mutant HCC cells via mitochondrial ATP, addressing the limitation of Nultin-3 as a standalone treatment that does not induce tumor cell death.

Causal relationships between type 1 diabetes mellitus and Alzheimer’s disease and Parkinson’s disease: a bidirectional two-sample Mendelian randomization study

BackgroundPrevious compelling evidence suggests an association between Type 2 diabetes (T2D) and neurodegenerative diseases. However, it remains uncertain whether Type 1 diabetes mellitus (T1DM) exerts a causal influence on the risk of Alzheimer's disease (AD) and Parkinson's disease (PD). Consequently, this study employed a bidirectional two-sample Mendelian Randomization (MR) approach to investigate the causal relationship between T1DM and the genetic susceptibility to AD and PD.MethodsWe utilized large-scale cohorts derived from publicly available genome-wide association study datasets involving European populations to perform MR analyses. The primary analytical method employed was the inverse-variance weighted (IVW) approach. Furthermore, sensitivity analyses, including assessments of heterogeneity and horizontal pleiotropy, were carried out using Cochran's Q, MR-Egger intercept, and MR-PRESSO tests to enhance the robustness of our conclusions.ResultsUsing the IVW-based method, the MR analysis indicated no significant association between genetically determined T1DM and AD (OR = 0.984, 95% CI: 0.958–1.011, p = 0.247). Conversely, T1DM appeared to be associated with a reduced risk of genetic susceptibility to PD (IVW: OR = 0.958, 95% CI: 0.928–0.989, p = 0.001). In the reverse direction, no evidence of reverse causality was observed between AD (OR = 1.010, 95% CI: 0.911–1.116, p = 0.881) or PD (OR = 1.164, 95% CI: 0.686–2.025, p = 0.5202) and T1DM. Additionally, our analysis found no indications of the results being influenced by horizontal pleiotropy.ConclusionThis MR study reveals that T1DM is associated with a reduced genetic susceptibility to PD, whereas no significant genetic susceptibility is observed between T1DM and AD. These findings suggest that T1DM may have a distinct role in the development of neurodegenerative diseases compared to T2D. Further investigations are warranted to elucidate the underlying mechanisms and provide a more comprehensive understanding of this relationship.

Type 1 diabetes, its complications, and non-ischemic cardiomyopathy: a mendelian randomization study of European ancestry

BackgroundType 1 diabetes (T1D) is a significant risk factor for a range of cardiovascular diseases. Nonetheless, the causal relationship between T1D and non-ischemic cardiomyopathy (NICM) remains to be elucidated. Furthermore, the mechanisms responsible for the progression from T1D to NICM have not been definitively characterized.ObjectiveThe aim of this study was to conduct a Mendelian randomization (MR) study to investigate the causal effects of T1D and its complications on the development of NICM. Additionally, this study aimed to conduct a mediation analysis to identify potential mediators within this correlation.MethodsGenetic variants were used as instrumental variables for T1D. The summary data for T1D were obtained from two genome-wide association study datasets. The summary data for T1D with complications and NICM were obtained from the Finnish database. Two-sample MR, multivariable MR and mediation MR were conducted in this study.ResultsThe study revealed a causal association between T1D, T1D with complications, and NICM (with odds ratios of 1.02, 95% CI 1.01–1.04, p = 1.17e-04 and 1.03, 95% CI 1.01–1.05, p = 3.15e-3). Even after adjusting for confounding factors such as body mass index and hypertension, T1D remained statistically significant (with odds ratio of 1.02, 95% CI 1.01–1.04, p = 1.35e-4). Mediation analysis indicated that monokine induced by gamma interferon may play a mediating role in the pathogenesis of T1D-NICM (mediation effect indicated by odds ratio of 1.005, 95% CI 1.001–1.01, p = 4.9e-2).ConclusionThe study demonstrates a causal relationship between T1D, its complications, and NICM. Additionally, monokine induced by gamma interferon may act as a potential mediator in the pathogenesis of T1D-NICM.

Genetically predicted chronic rhinosinusitis and the risk of stroke: a two-sample Mendelian randomization study.

The causal association between chronic rhinosinusitis (CRS) and stroke remains uncertain due to the susceptibility of observational studies to confounding and the possibility of reverse causality. This study aims to examine the potential causal relationship between CRS and the risk of stroke, encompassing various subtypes. In this research, we utilized genome-wide association study (GWAS) data for CRS from FinnGen. We identified significant single-nucleotide polymorphisms (SNPs) associated with CRS and used them as instrumental variables (IVs). GWAS data for any ischemic stroke (AIS), ischemic stroke (IS), large-artery atherosclerotic stroke (LAS), small-vessel strokes (SVS), cardioembolic strokes (CES), intracerebral hemorrhage (ICH), lobar ICH, and non-lobar ICH came from multi-ancestry GWAS datasets. We conducted two-sample Mendelian randomization (MR) analyses using inverse variance weighting (IVW), weighted median, and MR-Egger regression methods to investigate potential causal relationships between CRS and stroke. Both heterogeneity and pleiotropy were evaluated by sensitivity analyses. The IVW analysis revealed no significant associations between CRS and AIS (OR = 0.99, 95% CI [0.93-1.05], p = 0.73), IS (OR = 0.97, 95% CI [0.81-1.17], p = 0.09), SVS (OR = 0.96, 95% CI [0.82-1.12], p = 0.58), LAS (OR = 0.91, 95% CI [0.77-1.08], p = 0.09), CES (OR = 0.97, 95% CI [0.81-1.17], p = 0.79), ICH (OR = 1.28, 95% CI [0.74-2.22], p = 0.28), lobar ICH (OR = 1.22, 95% CI [0.60-2.50], p = 0.28), and non-lobar ICH (OR = 1.25, 95% CI [0.65-2.40], p = 0.79). Sensitivity analysis found no evidence of horizontal pleiotropy. According to genetic evidence, this Mendelian randomization (MR) study does not indicate a causal relationship between CRS and stroke in European populations. However, further studies are necessary to comprehensively evaluate the potential association between CRS and stroke.

Abstract B012: Pedigree and multi-omics analyses implicate nucleotide excision repair genes, XPA, ERCC6, and ERCC8, in susceptibility to breast cancer

Abstract Background: The majority of low- and moderate-risk [odds ratios (OR) ≤ 3] susceptibility genes for peri- and post-menopausal breast cancer (≥50 years of age), particularly those with minor allele frequency (MAF) of &amp;lt;30%, have not yet been identified and/or validated. We used a hypothesis-driven integrative approach to identify breast cancer susceptibility genes by analyzing pedigree and multi-omics (genomics, transcriptomics, mutation landscape) data. Methods: We conducted pedigree analysis in 31 families with DNA repair disorders, xeroderma pigmentosum (XP), trichothiodystrophy (TTD), Cockayne syndrome (CS), XP/TTD and XP/CS. These disorders can be caused by mutations in genes involved in the nucleotide excision repair (NER) pathway such as XPA-XPG, ERCC6 and ERCC8. We estimated the frequency of hereditary breast cancer, defined as the presence of at least two cases of premenopausal breast cancer (diagnosed &amp;lt;50 years of age) or one case of premenopausal breast cancer and one case of ovarian cancer in one side of the family, in our study. Furthermore, we conducted individual analysis of four genome-wide association studies (GWAS) datasets (totaling 2961 cases and 4335 controls), individual and meta-analysis of two gene expression microarray datasets (98 cases and 163 controls), and targeted analysis of The Cancer Genome Atlas (TCGA) (522 breast cancers). All analyses involved invasive ductal breast carcinoma in women diagnosed at ≥50 years of age. Results: In an XP family with causative mutations in XPA, there were four cases of premenopausal breast cancer (including heterozygote mutation carriers), one case of ovarian cancer, and one case of prostate cancer. Comprehensive sequencing failed to identify a BRCA1 or BRCA2 mutation in this family. Overall, 7 of 31 (22.6%) families with DNA repair disorders had a family history that was compatible with hereditary breast cancer. Hence, we selected a panel of NER genes as candidates for association studies with breast cancer. Our candidate-gene association analysis of GWAS datasets suggested an increased risk of breast cancer with ERCC6 (main effect:1.29 ≤ OR ≤ 2.91, 0.005 ≤ p ≤ 0.04, 11.8 ≤ MAF ≤ 29.7%) and implicated its interaction with ERCC8 (joint effect:3.03 ≤ OR ≤ 5.31, 0.01 ≤ pinteraction ≤ 0.03). Our transcriptomics analysis identified significant upregulation of ERCC6 (p=7.95 × 10–6) and ERCC8 (p=4.67 × 10–6) in breast cancer. Our analysis of TCGA breast tumor data showed similar mutation frequency of ERCC6 (1.8%) and ERCC8 (0.3%) as known breast cancer susceptibility genes such as BLM (1.9%) and LSP1 (0.3%). Conclusion: Our integrative analyses suggest that ERCC6 may be a previously unreported low- to moderate-risk breast cancer susceptibility gene, which may also interact with ERCC8. ERCC6 and ERCC8 code for the main components of the transcription-coupled (TC) repair sub-pathway of the NER. TC-NER protects against genotoxic agents, such as free radicals and reactive oxygen species. Our results provide a deeper biologic insight and leads for prevention of common breast cancer. Citation Format: Roxana Moslehi, Amiran Dzutsev. Pedigree and multi-omics analyses implicate nucleotide excision repair genes, XPA, ERCC6, and ERCC8, in susceptibility to breast cancer [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: DNA Damage Repair: From Basic Science to Future Clinical Application; 2024 Jan 9-11; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2024;84(1 Suppl):Abstract nr B012.

Causal analysis between gastroesophageal reflux disease and chronic rhinosinusitis.

Gastroesophageal reflux disease (GERD) and chronic rhinosinusitis (CRS) have been shown to be potentially closely related, but the relationship between these conditions, particularly the possibility of a causal link, is not fully understood. This study used Mendelian randomization (MR) to assess the causal relationship between these two disorders. We extracted genome-wide association study data sets for GERD and CRS from publicly available gene summaries, and used MR to conduct a causal inference analysis. The main robustness test used in this study included MR-Egger regression, a leave-one-out sensitivity test, and multivariate MR (MVMR). GERD increased the risk of developing CRS by 36%, based on the inverse-variance weighted method, a statistically significant association (odds ratio [OR] 1.360, 95% confidence interval [CI] 1.179-1.568, P < 0.001). Other MR assessment methods, such as weighted median, simple mode, and weighted mode, similarly observed a significant increase in the risk of CRS occurrence (OR 1.434, 95% CI 1.186-1.734, P < 0.001; OR 1.927, 95% CI 1.166-3.184, P = 0.013; and OR 1.910, 95% CI 1.222-2.983, P = 0.006, respectively). No significant bias was found in the heterogeneity or pleiotropy tests (P = 0.071 and P = 0.700, respectively). Even after excluding possible mediators using MVMR, GERD appeared to significantly increase the risk of developing CRS (OR 1.013, 95% CI 1.008-1.023, P = 0.002). This study provides new, significant evidence that GERD is genetically associated with a higher incidence rate of CRS. However, further research is needed to elucidate the potential underlying biological mechanisms of this relationship.

Natural and artificial selection of multiple alleles revealed through genomic analyses.

Genome-to-phenome research in agriculture aims to improve crops through in silico predictions. Genome-wide association study (GWAS) is potent in identifying genomic loci that underlie important traits. As a statistical method, increasing the sample quantity, data quality, or diversity of the GWAS dataset positively impacts GWAS power. For more precise breeding, concrete candidate genes with exact functional variants must be discovered. Many post-GWAS methods have been developed to narrow down the associated genomic regions and, ideally, to predict candidate genes and causative mutations (CMs). Historical natural selection and breeding-related artificial selection both act to change the frequencies of different alleles of genes that control phenotypes. With higher diversity and more extensive GWAS datasets, there is an increased chance of multiple alleles with independent CMs in a single causal gene. This can be caused by the presence of samples from geographically isolated regions that arose during natural or artificial selection. This simple fact is a complicating factor in GWAS-driven discoveries. Currently, none of the existing association methods address this issue and need to identify multiple alleles and, more specifically, the actual CMs. Therefore, we developed a tool that computes a score for a combination of variant positions in a single candidate gene and, based on the highest score, identifies the best number and combination of CMs. The tool is publicly available as a Python package on GitHub, and we further created a web-based Multiple Alleles discovery (MADis) tool that supports soybean and is hosted in SoyKB (https://soykb.org/SoybeanMADisTool/). We tested and validated the algorithm and presented the utilization of MADis in a pod pigmentation L1 gene case study with multiple CMs from natural or artificial selection. Finally, we identified a candidate gene for the pod color L2 locus and predicted the existence of multiple alleles that potentially cause loss of pod pigmentation. In this work, we show how a genomic analysis can be employed to explore the natural and artificial selection of multiple alleles and, thus, improve and accelerate crop breeding in agriculture.

Performing highly parallelized and reproducible GWAS analysis on biobank-scale data.

Genome-wide association studies (GWAS) are transforming genetic research and enable the detection of novel genotype-phenotype relationships. In the last two decades, over 60 000 genetic associations across thousands of traits have been discovered using a GWAS approach. Due to increasing sample sizes, researchers are increasingly faced with computational challenges. A reproducible, modular and extensible pipeline with a focus on parallelization is essential to simplify data analysis and to allow researchers to devote their time to other essential tasks. Here we present nf-gwas, a Nextflow pipeline to run biobank-scale GWAS analysis. The pipeline automatically performs numerous pre- and post-processing steps, integrates regression modeling from the REGENIE package and supports single-variant, gene-based and interaction testing. It includes an extensive reporting functionality that allows to inspect thousands of phenotypes and navigate interactive Manhattan plots directly in the web browser. The pipeline is tested using the unit-style testing framework nf-test, a crucial requirement in clinical and pharmaceutical settings. Furthermore, we validated the pipeline against published GWAS datasets and benchmarked the pipeline on high-performance computing and cloud infrastructures to provide cost estimations to end users. nf-gwas is a highly parallelized, scalable and well-tested Nextflow pipeline to perform GWAS analysis in a reproducible manner.

Beverage consumption and facial skin aging: Evidence from Mendelian randomization analysis.

Observational studies have linked coffee, alcohol, tea, and sugar-sweetened beverage (SSB) consumption to facial skin aging. However, confounding factors may influence these studies. The present two-sample Mendelian randomization (MR) investigated the potential causal association between beverage consumption and facial skin aging. The single-nucleotide polymorphisms (SNPs) associated with coffee, alcohol, and tea intake were derived from the IEU project. The SSB-associated SNPs were selected from a genome-wide association study (GWAS). Data on facial skin aging were derived from the largest GWAS involving 16 677 European individuals. The inverse variance-weighted (IVW) was the main MR analysis method, supplemented by other methods (MR-Egger, weighted median, simple mode, and weighted mode). The MR-Egger intercept analysis was used for sensitivity analysis. Moreover, we conducted a replication analysis using data from another GWAS dataset on coffee consumption to validate our findings. Four instrumental variables (IVs) sets were used to examine the causal association between beverage consumption (coffee, alcohol, tea, SSB) and facial skin aging. Our results revealed that genetically predicted higher coffee consumption reduced the risk of facial skin aging (OR: 0.852; 95% CI: 0.753-0.964; p = 0.011, IVW method). The sensitivity analysis confirmed the robustness of the findings, with no evidence of pleiotropy or heterogeneity. The results of replicated MR analysis on coffee consumption were consistent with the initial analysis (OR = 0.997; 95% CI = 0.996-0.999; p = 0.003, IVW method). This study manifests that higher coffee consumption is significantly associated with a reduced risk of facial skin aging. These findings can offer novel strategies for identifying the underlying etiology of facial skin aging.

Tuning parameters for polygenic risk score methods using GWAS summary statistics from training data

Various polygenic risk scores (PRS) methods have been proposed to combine the estimated effects of single nucleotide polymorphisms (SNPs) to predict genetic risks for common diseases, using data collected from genome-wide association studies (GWAS). Some methods require external individual-level GWAS dataset for parameter tuning, posing privacy and security-related concerns. Leaving out partial data for parameter tuning can also reduce model prediction accuracy. In this article, we propose PRStuning, a method that tunes parameters for different PRS methods using GWAS summary statistics from the training data. PRStuning predicts the PRS performance with different parameters, and then selects the best-performing parameters. Because directly using training data effects tends to overestimate the performance in the testing data, we adopt an empirical Bayes approach to shrinking the predicted performance in accordance with the genetic architecture of the disease. Extensive simulations and real data applications demonstrate PRStuning’s accuracy across PRS methods and parameters.

Genome-wide polygenic risk score for rheumatoid arthritis prediction in postmenopausal women.

Rheumatoid arthritis (RA), a common autoimmune disease, exhibits a vital genetic component. Polygenic risk scores (PRS) derived from genome-wide association studies (GWAS) offer potential utility in predicting disease susceptibility. The present study aimed to develop and validate a PRS for predicting RA risk in postmenopausal women. The study developed a novel PRS using 225,000 genetic variants from a GWAS dataset. The PRS was developed in a cohort of 8967 postmenopausal women and validated in an independent cohort of 6269 postmenopausal women. Among the development cohort, approximately 70% were Hispanic and approximately 30% were African American. The testing cohort comprised approximately 50% Hispanic and 50% Caucasian individuals. Stratification according to PRS quintiles revealed a pronounced gradient in RA prevalence and odds ratios. High PRS was significantly associated with increased RA risk in individuals aged 60-70 years, ≥ 70 years, and overweight and obese participants. Furthermore, at age 65 years, individuals in the bottom 5% of the PRS distribution have an absolute risk of RA at 30.6% (95% confidence interval = 18.5%-42.6%). The risk increased to 53.8% (95% confidence interval = 42.8%-64.9%) for those in the top 5% of the PRS distribution. The PRS developed in the present study is significantly associated with RA risk, showing the potential for early screening of RA in postmenopausal women. This work demonstrates the feasibility of personalized medicine in identifying high-risk individuals for RA, indicating the need for further studies to test the utility of PRS in other populations.