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Guo, Michael H., Joel N. Hirschhorn, and Andrew Dauber. "Insights and Implications of Genome-Wide Association Studies of Height." Journal of Clinical Endocrinology & Metabolism 103, no. 9 (July 2, 2018): 3155–68. http://dx.doi.org/10.1210/jc.2018-01126.
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Abstract Context In the last decade, genome-wide association studies (GWASs) have catalyzed our understanding of the genetics of height and have identified hundreds of regions of the genome associated with adult height and other height-related body measurements. Evidence Acquisition GWASs related to height were identified via PubMed search and a review of the GWAS catalog. Evidence Synthesis The GWAS results demonstrate that height is highly polygenic: that is, many thousands of genetic variants distributed across the genome each contribute to an individual’s height. These height-associated regions of the genome are enriched for genes in known biological pathways involved in growth, such as fibroblast growth factor signaling, as well as for genes expressed in relevant tissues, such as the growth plate. GWASs can also uncover previously unappreciated biological pathways, such as theSTC2/PAPPA/IGFBP4 pathway. The genes implicated by GWASs are often the same genes that are the genetic causes of Mendelian growth disorders or skeletal dysplasias, and GWAS results can provide complementary information about these disorders. Conclusions Here, we review the rationale behind GWASs and what we have learned from GWASs for height, including how it has enhanced our understanding of the underlying biology of human growth. We also highlight the implications of GWASs in terms of prediction of adult height and our understanding of Mendelian growth disorders.
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Deja-Muylle, Agnieszka, Boris Parizot, Hans Motte, and Tom Beeckman. "Exploiting natural variation in root system architecture via genome-wide association studies." Journal of Experimental Botany 71, no. 8 (January 20, 2020): 2379–89. http://dx.doi.org/10.1093/jxb/eraa029.
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Abstract Root growth and development has become an important research topic for breeders and researchers based on a growing need to adapt plants to changing and more demanding environmental conditions worldwide. Over the last few years, genome-wide association studies (GWASs) became an important tool to identify the link between traits in the field and their genetic background. Here we give an overview of the current literature concerning GWASs performed on root system architecture (RSA) in plants. We summarize which root traits and approaches have been used for GWAS, mentioning their respective success rate towards a successful gene discovery. Furthermore, we zoom in on the current technical hurdles in root phenotyping and GWAS, and discuss future possibilities in this field of research.
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Schmid, Markus, and Jörn Bennewitz. "Invited review: Genome-wide association analysis for quantitative traits in livestock – a selective review of statistical models and experimental designs." Archives Animal Breeding 60, no. 3 (September 29, 2017): 335–46. http://dx.doi.org/10.5194/aab-60-335-2017.
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Abstract. Quantitative or complex traits are controlled by many genes and environmental factors. Most traits in livestock breeding are quantitative traits. Mapping genes and causative mutations generating the genetic variance of these traits is still a very active area of research in livestock genetics. Since genome-wide and dense SNP panels are available for most livestock species, genome-wide association studies (GWASs) have become the method of choice in mapping experiments. Different statistical models are used for GWASs. We will review the frequently used single-marker models and additionally describe Bayesian multi-marker models. The importance of nonadditive genetic and genotype-by-environment effects along with GWAS methods to detect them will be briefly discussed. Different mapping populations are used and will also be reviewed. Whenever possible, our own real-data examples are included to illustrate the reviewed methods and designs. Future research directions including post-GWAS strategies are outlined.
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Azam, Afifah Binti, and Elena Aisha Binti Azizan. "Brief Overview of a Decade of Genome-Wide Association Studies on Primary Hypertension." International Journal of Endocrinology 2018 (January 30, 2018): 1–14. http://dx.doi.org/10.1155/2018/7259704.
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Primary hypertension is widely believed to be a complex polygenic disorder with the manifestation influenced by the interactions of genomic and environmental factors making identification of susceptibility genes a major challenge. With major advancement in high-throughput genotyping technology, genome-wide association study (GWAS) has become a powerful tool for researchers studying genetically complex diseases. GWASs work through revealing links between DNA sequence variation and a disease or trait with biomedical importance. The human genome is a very long DNA sequence which consists of billions of nucleotides arranged in a unique way. A single base-pair change in the DNA sequence is known as a single nucleotide polymorphism (SNP). With the help of modern genotyping techniques such as chip-based genotyping arrays, thousands of SNPs can be genotyped easily. Large-scale GWASs, in which more than half a million of common SNPs are genotyped and analyzed for disease association in hundreds of thousands of cases and controls, have been broadly successful in identifying SNPs associated with heart diseases, diabetes, autoimmune diseases, and psychiatric disorders. It is however still debatable whether GWAS is the best approach for hypertension. The following is a brief overview on the outcomes of a decade of GWASs on primary hypertension.
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Hill, W. David. "Comment on ‘Large-Scale Cognitive GWAS Meta-Analysis Reveals Tissue-Specific Neural Expression and Potential Nootropic Drug Targets’ by Lam et al." Twin Research and Human Genetics 21, no. 2 (March 19, 2018): 84–88. http://dx.doi.org/10.1017/thg.2018.12.
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Intelligence and educational attainment are strongly genetically correlated. This relationship can be exploited by Multi-Trait Analysis of GWAS (MTAG) to add power to Genome-wide Association Studies (GWAS) of intelligence. MTAG allows the user to meta-analyze GWASs of different phenotypes, based on their genetic correlations, to identify association's specific to the trait of choice. An MTAG analysis using GWAS data sets on intelligence and education was conducted by Lam et al. (2017). Lam et al. (2017) reported 70 loci that they described as ‘trait specific’ to intelligence. This article examines whether the analysis conducted by Lam et al. (2017) has resulted in genetic information about a phenotype that is more similar to education than intelligence.
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Walker, Venexia, Sean Harrison, Alice Carter, Dipender Gill, Ioanna Tzoulaki, and Neil Davies. "The consequences of adjustment, correction and selection in genome-wide association studies used for two-sample Mendelian randomization." Wellcome Open Research 6 (May 10, 2021): 103. http://dx.doi.org/10.12688/wellcomeopenres.16752.1.
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Introduction: Genome-wide association studies (GWASs) often adjust for covariates, correct for medication use, or select on medication users. If these summary statistics are used in two-sample Mendelian randomization analyses, estimates may be biased. We used simulations to investigate how GWAS adjustment, correction and selection affects these estimates and performed an analysis in UK Biobank to provide an empirical example. Methods: We simulated six GWASs: no adjustment for a covariate, correction for medication use, or selection on medication users; adjustment only; selection only; correction only; both adjustment and selection; and both adjustment and correction. We then ran two-sample Mendelian randomization analyses using these GWASs to evaluate bias. We also performed equivalent GWASs using empirical data from 306,560 participants in UK Biobank with systolic blood pressure as the exposure and body mass index as the covariate and ran two-sample Mendelian randomization with coronary heart disease as the outcome. Results: The simulation showed that estimates from GWASs with selection can produce biased two-sample Mendelian randomization estimates. Yet, we observed relatively little difference between empirical estimates of the effect of systolic blood pressure on coronary artery disease across the six scenarios. Conclusions: Given the potential for bias from using GWASs with selection on Mendelian randomization estimates demonstrated in our simulation, careful consideration before using this approach is warranted. However, based on our empirical results, using adjusted, corrected or selected GWASs is unlikely to make a large difference to two-sample Mendelian randomization estimates in practice.
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Basile, Kevin J., Matthew E. Johnson, Qianghua Xia, and Struan F. A. Grant. "Genetic Susceptibility to Type 2 Diabetes and Obesity: Follow-Up of Findings from Genome-Wide Association Studies." International Journal of Endocrinology 2014 (2014): 1–13. http://dx.doi.org/10.1155/2014/769671.
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Elucidating the underlying genetic variations influencing various complex diseases is one of the major challenges currently facing clinical genetic research. Although these variations are often difficult to uncover, approaches such as genome-wide association studies (GWASs) have been successful at finding statistically significant associations between specific genomic loci and disease susceptibility. GWAS has been especially successful in elucidating genetic variants that influence type 2 diabetes (T2D) and obesity/body mass index (BMI). Specifically, several GWASs have confirmed that a variant in transcription factor 7-like 2 (TCF7L2) confers risk for T2D, while a variant in fat mass and obesity-associated protein (FTO) confers risk for obesity/BMI; indeed both of these signals are considered the most statistically associated loci discovered for these respective traits to date. The discovery of these two key loci in this context has been invaluable for providing novel insight into mechanisms of heritability and disease pathogenesis. As follow-up studies ofTCF7L2andFTOhave typically lead the way in how to follow up a GWAS discovery, we outline what has been learned from such investigations and how they have implications for the myriad of other loci that have been subsequently reported in this disease context.
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Moll, Matthew, Victoria E. Jackson, Bing Yu, Megan L. Grove, Stephanie J. London, Sina A. Gharib, Traci M. Bartz, et al. "A systematic analysis of protein-altering exonic variants in chronic obstructive pulmonary disease." American Journal of Physiology-Lung Cellular and Molecular Physiology 321, no. 1 (July 1, 2021): L130—L143. http://dx.doi.org/10.1152/ajplung.00009.2021.
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Genome-wide association studies (GWASs) have identified regions associated with chronic obstructive pulmonary disease (COPD). GWASs of other diseases have shown an approximately 10-fold overrepresentation of nonsynonymous variants, despite limited exonic coverage on genotyping arrays. We hypothesized that a large-scale analysis of coding variants could discover novel genetic associations with COPD, including rare variants with large effect sizes. We performed a meta-analysis of exome arrays from 218,399 controls and 33,851 moderate-to-severe COPD cases. All exome-wide significant associations were present in regions previously identified by GWAS. We did not identify any novel rare coding variants with large effect sizes. Within GWAS regions on chromosomes 5q, 6p, and 15q, four coding variants were conditionally significant ( P < 0.00015) when adjusting for lead GWAS single-nucleotide polymorphisms A common gasdermin B ( GSDMB) splice variant (rs11078928) previously associated with a decreased risk for asthma was nominally associated with a decreased risk for COPD [minor allele frequency (MAF) = 0.46, P = 1.8e-4]. Two stop variants in coiled-coil α-helical rod protein 1 ( CCHCR1), a gene involved in regulating cell proliferation, were associated with COPD (both P < 0.0001). The SERPINA1 Z allele was associated with a random-effects odds ratio of 1.43 for COPD (95% confidence interval = 1.17–1.74), though with marked heterogeneity across studies. Overall, COPD-associated exonic variants were identified in genes involved in DNA methylation, cell-matrix interactions, cell proliferation, and cell death. In conclusion, we performed the largest exome array meta-analysis of COPD to date and identified potential functional coding variants. Future studies are needed to identify rarer variants and further define the role of coding variants in COPD pathogenesis.
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Mounier, Ninon, and Zoltán Kutalik. "bGWAS: an R package to perform Bayesian genome wide association studies." Bioinformatics 36, no. 15 (May 29, 2020): 4374–76. http://dx.doi.org/10.1093/bioinformatics/btaa549.
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Abstract Summary Increasing sample size is not the only strategy to improve discovery in Genome Wide Association Studies (GWASs) and we propose here an approach that leverages published studies of related traits to improve inference. Our Bayesian GWAS method derives informative prior effects by leveraging GWASs of related risk factors and their causal effect estimates on the focal trait using multivariable Mendelian randomization. These prior effects are combined with the observed effects to yield Bayes Factors, posterior and direct effects. The approach not only increases power, but also has the potential to dissect direct and indirect biological mechanisms. Availability and implementation bGWAS package is freely available under a GPL-2 License, and can be accessed, alongside with user guides and tutorials, from https://github.com/n-mounier/bGWAS. Supplementary information Supplementary data are available at Bioinformatics online.
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Kong, Yinfei, Pouya Khankhanian, Daniel Himmelstein, Amie E. Hwang, Kristin A. Rand, Dalin Li, David J. Van Den Berg, et al. "Meta-Analysis of Hodgkin Lymphoma and Asthma Genome-Wide Association Scans reveals common variants in GATA3." Blood 124, no. 21 (December 6, 2014): 135. http://dx.doi.org/10.1182/blood.v124.21.135.135.
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Abstract Both Hodgkin lymphoma (HL) and asthma are associated with relative isolation from early childhood infectious exposures (hygiene hypothesis) and a T-helper-2 (Th2)-skewed immune response. To test the hypothesis that there is some component of shared genetic etiology between HL and asthma, we examined the genetic overlap in data from genome-wide association study (GWAS) meta-analyses of the two diseases conducted in populations of European ancestry. The HL GWAS meta-analysis consisted of 1,816 HL cases and 7,877 controls, and the asthma GWAS meta-analysis consisted of 2,088 asthma cases and 2,743 controls. The combined HL-asthma GWAS data resulted in 904,634 common single nucleotide polymorphisms (SNPs) genotyped with both arrays. We observed a total of 9 common SNPs associated with both HL and asthma at p<0.0001, compared to 3 SNPs expected in both data sets at that significance level. Out of 66 genetic risk variants associated with HL at a genome-wide significance level of p<10-8, 5 variants replicated in the asthma GWAS dataset at p < 0.05. In a meta-analysis combining the HL and asthma GWAS data, we found genome-wide significant associations with two correlated SNPs (r2 =0.91) in the Th2 transcription factor gene GATA3 (rs422628, ORHL-asthma meta =1.25, p=3.36 x 10-9 and rs444929, ORHL-asthma meta = 1.26, p=2.09 x 10-8) that were not genome-wide significant in either disease alone (Table 1). The association with one SNP remained genome-wide significant in the meta-analysis combined with the nodular sclerosis subset (rs422628, ORHL-asthma meta =1.32, p=3.75 x 10-9), but not with other HL subtypes. An association with a SNP in IKZF3, a gene involved in B lymphocyte differentiation and proliferation, reached genome-wide significance only in the meta-analysis with the HL subset positive for Epstein-Barr virus (EBV) in the tumor (rs9909593, ORHL-asthma meta = 1.19, p=3.80 x 10-8). In a genetic diseasome analysis based on an ontological analysis of published GWAS data, HL, especially nodular sclerosis HL, was more closely related to asthma than to solid cancers. Recognition of overlap in genetic predisposition to HL and other immune diseases sheds light on the complex etiology of HL and may enable novel diagnostic and therapeutic approaches. Abstract 135. Table 1 Genome-wide significant results of a meta-analysis combining Hodgkin lymphoma (HL) and asthma GWAS1data. HL GWAS1 Asthma GWAS1 Combined GWAS1 SNP2 Chr3 BP4 Gene OR5 P-value6 OR5 P-value6 OR5 P-value6 rs422628a 10 8151415 GATA3 1.24 1.13 x 10-5 1.31 6.39 x10-5 1.25 3.26 x 10-9 rs444929b 10 8150030 GATA3 1.26 3.24x 10-6 1.28 8.90 x 10-4 1.26 2.09 x 10-8 rs422628c 10 8151415 GATA3 1.33 7.77 x 10-6 1.31 6.39 x10-5 1.32 3.75 x 10-9 rs9909593d 17 35223675 IKZF3 1.11 2.60 x 10-1 1.25 5.06 x 10-8 1.19 3.80 x 10-8 1Genome-wide association scan 2Single nucleotide polymorphism 3Chromosome 4Base pair position 5Odds ratio 6P-values are derived from a meta-analysis using a fixed effects model with weights proportional to the square root of the number of cases. aAll Hodgkin lymphoma cases bAll Hodgkin lymphoma cases, SNP association reported in Cozen et al., Nat Comm, 2014 cNodular sclerosis cases only dEBV-positive cases only Disclosures No relevant conflicts of interest to declare.
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Blatt, Marcelo, Alexander Gusev, Yuriy Polyakov, and Shafi Goldwasser. "Secure large-scale genome-wide association studies using homomorphic encryption." Proceedings of the National Academy of Sciences 117, no. 21 (May 12, 2020): 11608–13. http://dx.doi.org/10.1073/pnas.1918257117.
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Genome-wide association studies (GWASs) seek to identify genetic variants associated with a trait, and have been a powerful approach for understanding complex diseases. A critical challenge for GWASs has been the dependence on individual-level data that typically have strict privacy requirements, creating an urgent need for methods that preserve the individual-level privacy of participants. Here, we present a privacy-preserving framework based on several advances in homomorphic encryption and demonstrate that it can perform an accurate GWAS analysis for a real dataset of more than 25,000 individuals, keeping all individual data encrypted and requiring no user interactions. Our extrapolations show that it can evaluate GWASs of 100,000 individuals and 500,000 single-nucleotide polymorphisms (SNPs) in 5.6 h on a single server node (or in 11 min on 31 server nodes running in parallel). Our performance results are more than one order of magnitude faster than prior state-of-the-art results using secure multiparty computation, which requires continuous user interactions, with the accuracy of both solutions being similar. Our homomorphic encryption advances can also be applied to other domains where large-scale statistical analyses over encrypted data are needed.
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Hishida, Asahi, Tomotaka Ugai, Ryosuke Fujii, Masahiro Nakatochi, Michael C. Wu, Hidemi Ito, Isao Oze, et al. "GWAS analysis reveals a significant contribution of PSCA to the risk of Heliobacter pylori-induced gastric atrophy." Carcinogenesis 40, no. 5 (February 8, 2019): 661–68. http://dx.doi.org/10.1093/carcin/bgz016.
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Abstract Although recent genome-wide association studies (GWASs) have identified genetic variants associated with Helicobacter pylori (HP)-induced gastric cancer, few studies have examined the genetic traits associated with the risk of HP-induced gastric precancerous conditions. This study aimed to elucidate genetic variants associated with these conditions using a genome-wide approach. Data from four sites of the Japan Multi-Institutional Collaborative Cohort (J-MICC) Study were used in the discovery phase (Stage I); two datasets from the Hospital-based Epidemiologic Research Program at Aichi Cancer Center 2 (HERPACC2) study were used in the replication phases (Stages II and III) and SKAT (SNP-set Kernel Association Test) and single variant-based GWASs were conducted for the risks of gastric atrophy (GA) and severe GA defined by serum pepsinogen (PG) levels, and PG1 and PG1/2 ratios. In the gene-based SKAT in Stage I, prostate stem cell antigen (PSCA) was significantly associated with the risks of GA and severe GA, and serum PG1/2 level by linear kernel [false discovery rate (FDR) = 0.011, 0.230 and 7.2 × 10−7, respectively]. The single variant-based GWAS revealed that nine PSCA single nucleotide polymorphisms (SNPs) fulfilled the genome-wide significance level (P < 5 × 10−8) for the risks of both GA and severe GA in the combined study, although most of these associations did not reach genome-wide significance in the discovery or validation cohort on their own. GWAS for serum PG1 levels and PG1/2 ratios revealed that the PSCA rs2920283 SNP had a striking P-value of 4.31 × 10−27 for PG1/2 ratios. The present GWAS revealed the genetic locus of PSCA as the most significant locus for the risk of HP-induced GA, which confirmed the recently reported association in Europeans.
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Kuksa, Pavel P., Chien-Yueh Lee, Alexandre Amlie-Wolf, Prabhakaran Gangadharan, Elizabeth E. Mlynarski, Yi-Fan Chou, Han-Jen Lin, et al. "SparkINFERNO: a scalable high-throughput pipeline for inferring molecular mechanisms of non-coding genetic variants." Bioinformatics 36, no. 12 (April 24, 2020): 3879–81. http://dx.doi.org/10.1093/bioinformatics/btaa246.
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Abstract Summary We 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 implementation SparkINFERNO 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
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Liu, Nana, Jiayuan Xu, Huaigui Liu, Shijie Zhang, Miaoxin Li, Yao Zhou, Wen Qin, Mulin Jun Li, and Chunshui Yu. "Hippocampal transcriptome-wide association study and neurobiological pathway analysis for Alzheimer’s disease." PLOS Genetics 17, no. 2 (February 25, 2021): e1009363. http://dx.doi.org/10.1371/journal.pgen.1009363.
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Genome-wide association studies (GWASs) have identified multiple susceptibility loci for Alzheimer’s disease (AD), which is characterized by early and progressive damage to the hippocampus. However, the association of hippocampal gene expression with AD and the underlying neurobiological pathways remain largely unknown. Based on the genomic and transcriptomic data of 111 hippocampal samples and the summary data of two large-scale meta-analyses of GWASs, a transcriptome-wide association study (TWAS) was performed to identify genes with significant associations between hippocampal expression and AD. We identified 54 significantly associated genes using an AD-GWAS meta-analysis of 455,258 individuals; 36 of the genes were confirmed in another AD-GWAS meta-analysis of 63,926 individuals. Fine-mapping models further prioritized 24 AD-related genes whose effects on AD were mediated by hippocampal expression, including APOE and two novel genes (PTPN9 and PCDHA4). These genes are functionally related to amyloid-beta formation, phosphorylation/dephosphorylation, neuronal apoptosis, neurogenesis and telomerase-related processes. By integrating the predicted hippocampal expression and neuroimaging data, we found that the hippocampal expression of QPCTL and ERCC2 showed significant difference between AD patients and cognitively normal elderly individuals as well as correlated with hippocampal volume. Mediation analysis further demonstrated that hippocampal volume mediated the effect of hippocampal gene expression (QPCTL and ERCC2) on AD. This study identifies two novel genes associated with AD by integrating hippocampal gene expression and genome-wide association data and reveals candidate hippocampus-mediated neurobiological pathways from gene expression to AD.
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Dong, Shan-Shan, Yan Guo, and Tie-Lin Yang. "Addressing the Missing Heritability Problem With the Help of Regulatory Features." Evolutionary Bioinformatics 15 (January 2019): 117693431986086. http://dx.doi.org/10.1177/1176934319860861.
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Genome-wide association studies (GWASs) have successfully identified thousands of susceptibility loci for human complex diseases. However, missing heritability is still a challenging problem. Considering most GWAS loci are located in regulatory elements, we recently developed a pipeline named functional disease-associated single-nucleotide polymorphisms (SNPs) prediction (FDSP), to predict novel susceptibility loci for complex diseases based on the interpretation of regulatory features and published GWAS results with machine learning. When applied to type 2 diabetes and hypertension, the predicted susceptibility loci by FDSP were proved to be capable of explaining additional heritability. In addition, potential target genes of the predicted positive SNPs were significantly enriched in disease-related pathways. Our results suggested that taking regulatory features into consideration might be a useful way to address the missing heritability problem. We hope FDSP could offer help for the identification of novel susceptibility loci for complex diseases.
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Kunicki, Thomas J., and Diane J. Nugent. "The genetics of normal platelet reactivity." Blood 116, no. 15 (October 14, 2010): 2627–34. http://dx.doi.org/10.1182/blood-2010-04-262048.
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Abstract Genetic and environmental factors contribute to a substantial variation in platelet function seen among normal persons. Candidate gene association studies represent a valiant effort to define the genetic component in an era where genetic tools were limited, but the single nucleotide polymorphisms identified in those studies need to be validated by more objective, comprehensive approaches, such as genome-wide association studies (GWASs) of quantitative functional traits in much larger cohorts of more carefully selected normal subjects. During the past year, platelet count and mean platelet volume, which indirectly affect platelet function, were the subjects of GWAS. The majority of the GWAS signals were located to noncoding regions, a consistent outcome of all GWAS to date, suggesting a major role for mechanisms that alter phenotype at the level of transcription or posttranscriptional modifications. Of 15 quantitative trait loci associated with mean platelet volume and platelet count, one located at 12q24 is also a risk locus for coronary artery disease. In most cases, the effect sizes of individual quantitative trait loci are admittedly small, but the results of these studies have led to new insight into regulators of hematopoiesis and megakaryopoiesis that would otherwise be unapparent and difficult to define.
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Schlauch, Karen A., Robert W. Read, Vincent C. Lombardi, Gai Elhanan, William J. Metcalf, Anthony D. Slonim, and Joseph J. Grzymski. "A Comprehensive Genome-Wide and Phenome-Wide Examination of BMI and Obesity in a Northern Nevadan Cohort." G3: Genes|Genomes|Genetics 10, no. 2 (December 30, 2019): 645–64. http://dx.doi.org/10.1534/g3.119.400910.
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The aggregation of Electronic Health Records (EHR) and personalized genetics leads to powerful discoveries relevant to population health. Here we perform genome-wide association studies (GWAS) and accompanying phenome-wide association studies (PheWAS) to validate phenotype-genotype associations of BMI, and to a greater extent, severe Class 2 obesity, using comprehensive diagnostic and clinical data from the EHR database of our cohort. Three GWASs of 500,000 variants on the Illumina platform of 6,645 Healthy Nevada participants identified several published and novel variants that affect BMI and obesity. Each GWAS was followed with two independent PheWASs to examine associations between extensive phenotypes (incidence of diagnoses, condition, or disease), significant SNPs, BMI, and incidence of extreme obesity. The first GWAS examines associations with BMI in a cohort with no type 2 diabetics, focusing exclusively on BMI. The second GWAS examines associations with BMI in a cohort that includes type 2 diabetics. In the second GWAS, type 2 diabetes is a comorbidity, and thus becomes a covariate in the statistical model. The intersection of significant variants of these two studies is surprising. The third GWAS is a case vs. control study, with cases defined as extremely obese (Class 2 or 3 obesity), and controls defined as participants with BMI between 18.5 and 25. This last GWAS identifies strong associations with extreme obesity, including established variants in the FTO and NEGR1 genes, as well as loci not yet linked to obesity. The PheWASs validate published associations between BMI and extreme obesity and incidence of specific diagnoses and conditions, yet also highlight novel links. This study emphasizes the importance of our extensive longitudinal EHR database to validate known associations and identify putative novel links with BMI and obesity.
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Liu, Changning, and Zhenyu Xuan. "Prioritization of Cancer-Related Genomic Variants by SNP Association Network." Cancer Informatics 14s2 (January 2015): CIN.S17288. http://dx.doi.org/10.4137/cin.s17288.
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We have developed a general framework to construct an association network of single nucleotide polymorphisms (SNPs) (SNP association network, SAN) based on the functional interactions of genes located in the flanking regions of SNPs. SAN, which was constructed based on protein-protein interactions in the Human Protein Reference Database (HPRD), showed significantly enriched signals in both linkage disequilibrium (LD) and long-range chromatin interaction (Hi-C). We used this network to further develop two methods for predicting and prioritizing disease-associated genes from genome-wide association studies (GWASs). We found that random walk with restart (RWR) using SAN (RWR-SAN) can greatly improve the prediction of lung-cancer-associated genes by comparing RWR with the use of network in HPRD (AUC 0.81 vs 0.66). In a reanalysis of the GWAS dataset of age-related macular degeneration (AMD), SAN could identify more potential AMD-associated genes that were previously ranked lower in the GWAS study. The interactions in SAN could facilitate the study of complex diseases.
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Sakabe, Noboru J., Ivy Aneas, Nicholas Knoblauch, Debora R. Sobreira, Nicole Clark, Cristina Paz, Cynthia Horth, et al. "Transcriptome and regulatory maps of decidua-derived stromal cells inform gene discovery in preterm birth." Science Advances 6, no. 49 (December 2020): eabc8696. http://dx.doi.org/10.1126/sciadv.abc8696.
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While a genetic component of preterm birth (PTB) has long been recognized and recently mapped by genome-wide association studies (GWASs), the molecular determinants underlying PTB remain elusive. This stems in part from an incomplete availability of functional genomic annotations in human cell types relevant to pregnancy and PTB. We generated transcriptome (RNA-seq), epigenome (ChIP-seq of H3K27ac, H3K4me1, and H3K4me3 histone modifications), open chromatin (ATAC-seq), and chromatin interaction (promoter capture Hi-C) annotations of cultured primary decidua-derived mesenchymal stromal/stem cells and in vitro differentiated decidual stromal cells and developed a computational framework to integrate these functional annotations with results from a GWAS of gestational duration in 56,384 women. Using these resources, we uncovered additional loci associated with gestational duration and target genes of associated loci. Our strategy illustrates how functional annotations in pregnancy-relevant cell types aid in the experimental follow-up of GWAS for PTB and, likely, other pregnancy-related conditions.
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Wu, Chong, and Wei Pan. "Integration of methylation QTL and enhancer–target gene maps with schizophrenia GWAS summary results identifies novel genes." Bioinformatics 35, no. 19 (March 8, 2019): 3576–83. http://dx.doi.org/10.1093/bioinformatics/btz161.
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Abstract Motivation Most trait-associated genetic variants identified in genome-wide association studies (GWASs) are located in non-coding regions of the genome and thought to act through their regulatory roles. Results To account for enriched association signals in DNA regulatory elements, we propose a novel and general gene-based association testing strategy that integrates enhancer-target gene pairs and methylation quantitative trait locus data with GWAS summary results; it aims to both boost statistical power for new discoveries and enhance mechanistic interpretability of any new discovery. By reanalyzing two large-scale schizophrenia GWAS summary datasets, we demonstrate that the proposed method could identify some significant and novel genes (containing no genome-wide significant SNPs nearby) that would have been missed by other competing approaches, including the standard and some integrative gene-based association methods, such as one incorporating enhancer-target gene pairs and one integrating expression quantitative trait loci. Availability and implementation Software: wuchong.org/egmethyl.html Supplementary information Supplementary data are available at Bioinformatics online.
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Lee, Taeyeop, Min Kyung Sung, Seulkee Lee, Woojin Yang, Jaeho Oh, Jeong Yeon Kim, Seongwon Hwang, Hyo-Jeong Ban, and Jung Kyoon Choi. "Convolutional neural network model to predict causal risk factors that share complex regulatory features." Nucleic Acids Research 47, no. 22 (October 10, 2019): e146-e146. http://dx.doi.org/10.1093/nar/gkz868.
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Abstract Major progress in disease genetics has been made through genome-wide association studies (GWASs). One of the key tasks for post-GWAS analyses is to identify causal noncoding variants with regulatory function. Here, on the basis of >2000 functional features, we developed a convolutional neural network framework for combinatorial, nonlinear modeling of complex patterns shared by risk variants scattered among multiple associated loci. When applied for major psychiatric disorders and autoimmune diseases, neural and immune features, respectively, exhibited high explanatory power while reflecting the pathophysiology of the relevant disease. The predicted causal variants were concentrated in active regulatory regions of relevant cell types and tended to be in physical contact with transcription factors while residing in evolutionarily conserved regions and resulting in expression changes of genes related to the given disease. We demonstrate some examples of novel candidate causal variants and associated genes. Our method is expected to contribute to the identification and functional interpretation of potential causal noncoding variants in post-GWAS analyses.
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Belsky, Daniel W., and K. Paige Harden. "Phenotypic Annotation: Using Polygenic Scores to Translate Discoveries From Genome-Wide Association Studies From the Top Down." Current Directions in Psychological Science 28, no. 1 (January 9, 2019): 82–90. http://dx.doi.org/10.1177/0963721418807729.
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Genome-wide association studies (GWASs) have identified specific genetic variants associated with complex human traits and behaviors, such as educational attainment, mental disorders, and personality. However, small effect sizes for individual variants, uncertainty regarding the biological function of discovered genotypes, and potential “outside-the-skin” environmental mechanisms leave a translational gulf between GWAS results and scientific understanding that will improve human health and well-being. We propose a set of social, behavioral, and brain-science research activities that map discovered genotypes to neural, developmental, and social mechanisms and call this research program phenotypic annotation. Phenotypic annotation involves (a) elaborating the nomological network surrounding discovered genotypes, (b) shifting focus from individual genes to whole genomes, and (c) testing how discovered genotypes affect life-span development. Phenotypic-annotation research is already advancing the understanding of GWAS discoveries for educational attainment and schizophrenia. We review examples and discuss methodological considerations for psychologists taking up the phenotypic-annotation approach.
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Ustiugova, Alina S., Kirill V. Korneev, Dmitry V. Kuprash, and and Marina A. Afanasyeva. "Functional SNPs in the Human Autoimmunity-Associated Locus 17q12-21." Genes 10, no. 2 (January 23, 2019): 77. http://dx.doi.org/10.3390/genes10020077.
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Genome-wide association studies (GWASes) revealed several single-nucleotide polymorphisms (SNPs) in the human 17q12-21 locus associated with autoimmune diseases. However, follow-up studies are still needed to identify causative SNPs directly mediating autoimmune risk in the locus. We have chosen six SNPs in high linkage disequilibrium with the GWAS hits that showed the strongest evidence of causality according to association pattern and epigenetic data and assessed their functionality in a local genomic context using luciferase reporter system. We found that rs12946510, rs4795397, rs12709365, and rs8067378 influenced the reporter expression level in leukocytic cell lines. The strongest effect visible in three distinct cell types was observed for rs12946510 that is predicted to alter MEF2A/C and FOXO1 binding sites.
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Wang, Zhong, Nating Wang, Zilu Wang, Libo Jiang, Yaqun Wang, Jiahan Li, and Rongling Wu. "HiGwas: how to compute longitudinal GWAS data in population designs." Bioinformatics 36, no. 14 (June 5, 2020): 4222–24. http://dx.doi.org/10.1093/bioinformatics/btaa294.
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Abstract Summary Genome-wide association studies (GWAS), particularly designed with thousands and thousands of single-nucleotide polymorphisms (SNPs) (big p) genotyped on tens of thousands of subjects (small n), are encountered by a major challenge of p ≪ n. Although the integration of longitudinal information can significantly enhance a GWAS’s power to comprehend the genetic architecture of complex traits and diseases, an additional challenge is generated by an autocorrelative process. We have developed several statistical models for addressing these two challenges by implementing dimension reduction methods and longitudinal data analysis. To make these models computationally accessible to applied geneticists, we wrote an R package of computer software, HiGwas, designed to analyze longitudinal GWAS datasets. Functions in the package encompass single SNP analyses, significance-level adjustment, preconditioning and model selection for a high-dimensional set of SNPs. HiGwas provides the estimates of genetic parameters and the confidence intervals of these estimates. We demonstrate the features of HiGwas through real data analysis and vignette document in the package. Availability and implementation https://github.com/wzhy2000/higwas. Contact rwu@phs.psu.edu Supplementary information Supplementary data are available at Bioinformatics online.
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Kumagai, Masahiko, Daiki Nishikawa, Yoshihiro Kawahara, Hironobu Wakimoto, Ryutaro Itoh, Norio Tabei, Tsuyoshi Tanaka, and Takeshi Itoh. "TASUKE+: a web-based platform for exploring GWAS results and large-scale resequencing data." DNA Research 26, no. 6 (September 20, 2019): 445–52. http://dx.doi.org/10.1093/dnares/dsz022.
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Abstract Recent revolutionary advancements in sequencing technologies have made it possible to obtain mass quantities of genome-scale sequence data in a cost-effective manner and have drastically altered molecular biological studies. To utilize these sequence data, genome-wide association studies (GWASs) have become increasingly important. Hence, there is an urgent need to develop a visualization tool that enables efficient data retrieval, integration of GWAS results with diverse information and rapid public release of such large-scale genotypic and phenotypic data. We developed a web-based genome browser TASUKE+ (https://tasuke.dna.affrc.go.jp/), which is equipped with the following functions: (i) interactive GWAS results visualization with genome resequencing data and annotation information, (ii) PCR primer design, (iii) phylogenetic tree reconstruction and (iv) data sharing via the web. GWAS results can be displayed in parallel with polymorphism data, read depths and annotation information in an interactive and scalable manner. Users can design PCR primers for polymorphic sites of interest. In addition, a molecular phylogenetic tree of any region can be reconstructed so that the overall relationship among the examined genomes can be understood intuitively at a glance. All functions are implemented through user-friendly web-based interfaces so that researchers can easily share data with collaborators in remote places without extensive bioinformatics knowledge.
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Broekema, R. V., O. B. Bakker, and I. H. Jonkers. "A practical view of fine-mapping and gene prioritization in the post-genome-wide association era." Open Biology 10, no. 1 (January 2020): 190221. http://dx.doi.org/10.1098/rsob.190221.
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Over the past 15 years, genome-wide association studies (GWASs) have enabled the systematic identification of genetic loci associated with traits and diseases. However, due to resolution issues and methodological limitations, the true causal variants and genes associated with traits remain difficult to identify. In this post-GWAS era, many biological and computational fine-mapping approaches now aim to solve these issues. Here, we review fine-mapping and gene prioritization approaches that, when combined, will improve the understanding of the underlying mechanisms of complex traits and diseases. Fine-mapping of genetic variants has become increasingly sophisticated: initially, variants were simply overlapped with functional elements, but now the impact of variants on regulatory activity and direct variant-gene 3D interactions can be identified. Moreover, gene manipulation by CRISPR/Cas9, the identification of expression quantitative trait loci and the use of co-expression networks have all increased our understanding of the genes and pathways affected by GWAS loci. However, despite this progress, limitations including the lack of cell-type- and disease-specific data and the ever-increasing complexity of polygenic models of traits pose serious challenges. Indeed, the combination of fine-mapping and gene prioritization by statistical, functional and population-based strategies will be necessary to truly understand how GWAS loci contribute to complex traits and diseases.
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Nakayama, Akiyoshi, Masahiro Nakatochi, Yusuke Kawamura, Ken Yamamoto, Hirofumi Nakaoka, Seiko Shimizu, Toshihide Higashino, et al. "Subtype-specific gout susceptibility loci and enrichment of selection pressure on ABCG2 and ALDH2 identified by subtype genome-wide meta-analyses of clinically defined gout patients." Annals of the Rheumatic Diseases 79, no. 5 (April 1, 2020): 657–65. http://dx.doi.org/10.1136/annrheumdis-2019-216644.
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ObjectivesGenome-wide meta-analyses of clinically defined gout were performed to identify subtype-specific susceptibility loci. Evaluation using selection pressure analysis with these loci was also conducted to investigate genetic risks characteristic of the Japanese population over the last 2000–3000 years.MethodsTwo genome-wide association studies (GWASs) of 3053 clinically defined gout cases and 4554 controls from Japanese males were performed using the Japonica Array and Illumina Array platforms. About 7.2 million single-nucleotide polymorphisms were meta-analysed after imputation. Patients were then divided into four clinical subtypes (the renal underexcretion type, renal overload type, combined type and normal type), and meta-analyses were conducted in the same manner. Selection pressure analyses using singleton density score were also performed on each subtype.ResultsIn addition to the eight loci we reported previously, two novel loci, PIBF1 and ACSM2B, were identified at a genome-wide significance level (p<5.0×10–8) from a GWAS meta-analysis of all gout patients, and other two novel intergenic loci, CD2-PTGFRN and SLC28A3-NTRK2, from normal type gout patients. Subtype-dependent patterns of Manhattan plots were observed with subtype GWASs of gout patients, indicating that these subtype-specific loci suggest differences in pathophysiology along patients’ gout subtypes. Selection pressure analysis revealed significant enrichment of selection pressure on ABCG2 in addition to ALDH2 loci for all subtypes except for normal type gout.ConclusionsOur findings on subtype GWAS meta-analyses and selection pressure analysis of gout will assist elucidation of the subtype-dependent molecular targets and evolutionary involvement among genotype, phenotype and subtype-specific tailor-made medicine/prevention of gout and hyperuricaemia.
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Kakuta, Yoichi, Yosuke Kawai, Takeo Naito, Atsushi Hirano, Junji Umeno, Yuta Fuyuno, Zhenqiu Liu, et al. "A Genome-wide Association Study Identifying RAP1A as a Novel Susceptibility Gene for Crohn’s Disease in Japanese Individuals." Journal of Crohn's and Colitis 13, no. 5 (November 29, 2018): 648–58. http://dx.doi.org/10.1093/ecco-jcc/jjy197.
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Abstract Background and Aims Genome-wide association studies [GWASs] of European populations have identified numerous susceptibility loci for Crohn’s disease [CD]. Susceptibility genes differ by ethnicity, however, so GWASs specific for Asian populations are required. This study aimed to clarify the Japanese-specific genetic background for CD by a GWAS using the Japonica array [JPA] and subsequent imputation with the 1KJPN reference panel. Methods Two independent Japanese case/control sets (Tohoku region [379 CD patients, 1621 controls] and Kyushu region [334 CD patients, 462 controls]) were included. GWASs were performed separately for each population, followed by a meta-analysis. Two additional replication sets [254 + 516 CD patients and 287 + 565 controls] were analysed for top hit single nucleotide polymorphisms [SNPs] from novel genomic regions. Results Genotype data of 4 335 144 SNPs from 713 Japanese CD patients and 2083 controls were analysed. SNPs located in TNFSF15 (rs78898421, Pmeta = 2.59 × 10−26, odds ratio [OR] = 2.10), HLA-DQB1 [rs184950714, pmeta = 3.56 × 10−19, OR = 2.05], ZNF365, and 4p14 loci were significantly associated with CD in Japanese individuals. Replication analyses were performed for four novel candidate loci [p <1 × 10−6], and rs488200 located upstream of RAP1A was significantly associated with CD [pcombined = 4.36 × 10−8, OR = 1.31]. Transcriptome analysis of CD4+ effector memory T cells from lamina propria mononuclear cells of CD patients revealed a significant association of rs488200 with RAP1A expression. Conclusions RAP1A is a novel susceptibility locus for CD in the Japanese population.
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Golimbet, V. E., A. K. Golov, and N. V. Kondratyev. "Post-GWAS era in genetics of schizophrenia." V.M. BEKHTEREV REVIEW OF PSYCHIATRY AND MEDICAL PSYCHOLOGY, no. 4-1 (December 9, 2019): 6–7. http://dx.doi.org/10.31363/2313-7053-2019-4-1-6-7.
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Genome-wide association studies (GWASs) discovered multiple genetic variants associated with schizophrenia. Te next step (post-GWAS analysis) is aimed at identifying the causal genetic variants and biological mechanisms underlying the associations with disease risk. Te following strategies are considered: the study of transcriptional regulation in neuronal human cells and the use of epigenomic information for searching for regulatory elements involved in the pathogenesis of schizophrenia. Te frst strategy includes identifcation of neuronal enhancers, mapping of potential target genes and functional confrmation of enhancer-promoter interactions. Te second approach is focused on the identifcation of transcriptional factors, which appear to be master regulators of expression.
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Morisawa, Junji, Takahiro Otani, Jo Nishino, Ryo Emoto, Kunihiko Takahashi, and Shigeyuki Matsui. "Semi-parametric empirical Bayes factor for genome-wide association studies." European Journal of Human Genetics 29, no. 5 (January 25, 2021): 800–807. http://dx.doi.org/10.1038/s41431-020-00800-x.
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AbstractBayes factor analysis has the attractive property of accommodating the risks of both false negatives and false positives when identifying susceptibility gene variants in genome-wide association studies (GWASs). For a particular SNP, the critical aspect of this analysis is that it incorporates the probability of obtaining the observed value of a statistic on disease association under the alternative hypotheses of non-null association. An approximate Bayes factor (ABF) was proposed by Wakefield (Genetic Epidemiology 2009;33:79–86) based on a normal prior for the underlying effect-size distribution. However, misspecification of the prior can lead to failure in incorporating the probability under the alternative hypothesis. In this paper, we propose a semi-parametric, empirical Bayes factor (SP-EBF) based on a nonparametric effect-size distribution estimated from the data. Analysis of several GWAS datasets revealed the presence of substantial numbers of SNPs with small effect sizes, and the SP-EBF attributed much greater significance to such SNPs than the ABF. Overall, the SP-EBF incorporates an effect-size distribution that is estimated from the data, and it has the potential to improve the accuracy of Bayes factor analysis in GWASs.
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Alaraudanjoki, Viivi Karoliina, Salla Koivisto, Paula Pesonen, Minna Männikkö, Jukka Leinonen, Leo Tjäderhane, Marja-Liisa Laitala, Adrian Lussi, and Vuokko Anna-Marketta Anttonen. "Genome-Wide Association Study of Erosive Tooth Wear in a Finnish Cohort." Caries Research 53, no. 1 (June 13, 2018): 49–59. http://dx.doi.org/10.1159/000488208.
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Erosive tooth wear is defined as irreversible loss of dental tissues due to intrinsic or extrinsic acids, exacerbated by mechanical forces. Recent studies have suggested a higher prevalence of erosive tooth wear in males, as well as a genetic contribution to susceptibility to erosive tooth wear. Our aim was to examine erosive tooth wear by performing a genome-wide association study (GWAS) in a sample of the Northern Finland Birth Cohort 1966 (n = 1,962). Erosive tooth wear was assessed clinically using the basic erosive wear examination. A GWAS was performed for the whole sample as well as separately for males and females. We identified one genome-wide significant signal (rs11681214) in the GWAS of the whole sample near the genes PXDN and MYT1L. When the sample was stratified by sex, the strongest genome-wide significant signals were observed in or near the genes FGFR1, C8orf86, CDH4, SCD5, F2R, and ING1. Additionally, multiple suggestive association signals were detected in all GWASs performed. Many of the signals were in or near the genes putatively related to oral environment or tooth development, and some were near the regions considered to be associated with dental caries, such as 2p24, 4q21, and 13q33. Replications of these associations in other samples, as well as experimental studies to determine the biological functions of associated genetic variants, are needed.
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Ming, Jingsi, Tao Wang, and Can Yang. "LPM: a latent probit model to characterize the relationship among complex traits using summary statistics from multiple GWASs and functional annotations." Bioinformatics 36, no. 8 (December 20, 2019): 2506–14. http://dx.doi.org/10.1093/bioinformatics/btz947.
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Abstract Motivation Much effort has been made toward understanding the genetic architecture of complex traits and diseases. In the past decade, fruitful GWAS findings have highlighted the important role of regulatory variants and pervasive pleiotropy. Because of the accumulation of GWAS data on a wide range of phenotypes and high-quality functional annotations in different cell types, it is timely to develop a statistical framework to explore the genetic architecture of human complex traits by integrating rich data resources. Results In this study, we propose a unified statistical approach, aiming to characterize relationship among complex traits, and prioritize risk variants by leveraging regulatory information collected in functional annotations. Specifically, we consider a latent probit model (LPM) to integrate summary-level GWAS data and functional annotations. The developed computational framework not only makes LPM scalable to hundreds of annotations and phenotypes but also ensures its statistically guaranteed accuracy. Through comprehensive simulation studies, we evaluated LPM’s performance and compared it with related methods. Then, we applied it to analyze 44 GWASs with 9 genic category annotations and 127 cell-type specific functional annotations. The results demonstrate the benefits of LPM and gain insights of genetic architecture of complex traits. Availability and implementation The LPM package, all simulation codes and real datasets in this study are available at https://github.com/mingjingsi/LPM. Supplementary information Supplementary data are available at Bioinformatics online.
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Julienne, Hanna, Vincent Laville, Zachary R. McCaw, Zihuai He, Vincent Guillemot, Carla Lasry, Andrey Ziyatdinov, et al. "Multitrait GWAS to connect disease variants and biological mechanisms." PLOS Genetics 17, no. 8 (August 30, 2021): e1009713. http://dx.doi.org/10.1371/journal.pgen.1009713.
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Genome-wide association studies (GWASs) have uncovered a wealth of associations between common variants and human phenotypes. Here, we present an integrative analysis of GWAS summary statistics from 36 phenotypes to decipher multitrait genetic architecture and its link with biological mechanisms. Our framework incorporates multitrait association mapping along with an investigation of the breakdown of genetic associations into clusters of variants harboring similar multitrait association profiles. Focusing on two subsets of immunity and metabolism phenotypes, we then demonstrate how genetic variants within clusters can be mapped to biological pathways and disease mechanisms. Finally, for the metabolism set, we investigate the link between gene cluster assignment and the success of drug targets in randomized controlled trials.
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Julienne, Hanna, Huwenbo Shi, Bogdan Pasaniuc, and Hugues Aschard. "RAISS: robust and accurate imputation from summary statistics." Bioinformatics 35, no. 22 (June 7, 2019): 4837–39. http://dx.doi.org/10.1093/bioinformatics/btz466.
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Abstract Motivation Multi-trait analyses using public summary statistics from genome-wide association studies (GWASs) are becoming increasingly popular. A constraint of multi-trait methods is that they require complete summary data for all traits. Although methods for the imputation of summary statistics exist, they lack precision for genetic variants with small effect size. This is benign for univariate analyses where only variants with large effect size are selected a posteriori. However, it can lead to strong p-value inflation in multi-trait testing. Here we present a new approach that improve the existing imputation methods and reach a precision suitable for multi-trait analyses. Results We fine-tuned parameters to obtain a very high accuracy imputation from summary statistics. We demonstrate this accuracy for variants of all effect sizes on real data of 28 GWAS. We implemented the resulting methodology in a python package specially designed to efficiently impute multiple GWAS in parallel. Availability and implementation The python package is available at: https://gitlab.pasteur.fr/statistical-genetics/raiss, its accompanying documentation is accessible here http://statistical-genetics.pages.pasteur.fr/raiss/. Supplementary information Supplementary data are available at Bioinformatics online.
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Pawińska, Maria. "Dopuszczalność gwałtu na humanoidalnym robocie." Acta Universitatis Lodziensis. Folia Iuridica 86 (March 19, 2019): 9–18. http://dx.doi.org/10.18778/0208-6069.86.02.
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Opracowanie dotyczy problematyki dopuszczalności produkcji, dystrybucji i używania seksrobotów wyposażonych w sztuczną inteligencję, zdolnych do ekspresji osobowości. Największe kontrowersje dotyczą możliwości symulacji gwałtu na kobiecie-robocie oraz podejmowania czynności seksualnych z lalkami wyglądającymi jak dzieci. Autorka przytacza tezy wskazujące zarówno na słuszność ograniczenia, jak i pełnego dopuszczenia rzeczonych przedmiotów do sprzedaży i używania. Analiza dotyczy nie tylko argumentów etycznych, ale również psychologicznych, społecznych i prawniczych.
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Chen, Shih-Pin, Jong-Ling Fuh, Ming-Yi Chung, Ying-Chao Lin, Yi-Chu Liao, Yen-Feng Wang, Chia-Lin Hsu, et al. "Genome-wide association study identifies novel susceptibility loci for migraine in Han Chinese resided in Taiwan." Cephalalgia 38, no. 3 (February 17, 2017): 466–75. http://dx.doi.org/10.1177/0333102417695105.
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Background Susceptibility genes for migraine, despite it being a highly prevalent and disabling neurological disorder, have not been analyzed in Asians by genome-wide association study (GWAS). Methods We conducted a two-stage case-control GWAS to identify susceptibility genes for migraine without aura in Han Chinese residing in Taiwan. In the discovery stage, we genotyped 1005 clinic-based Taiwanese migraine patients and 1053 population-based sex-matched controls using Axiom Genome-Wide CHB Array. In the replication stage, we genotyped 27 single-nucleotide polymorphisms with p < 10−4 in 1120 clinic-based migraine patients and 604 sex-matched normal controls by using Sequenom. Variants at LRP1, TRPM8, and PRDM, which have been replicated in Caucasians, were also genotyped. Results We identified a novel susceptibility locus (rs655484 in DLG2) that reached GWAS significance level for migraine risk in Han Chinese ( p = 1.45 × 10−12, odds ratio [OR] = 2.42), and also another locus (rs3781545in GFRA1) with suggestive significance ( p = 1.27 × 10−7, OR = 1.38). In addition, we observed positive association signals with a similar trend to the associations identified in Caucasian GWASs for rs10166942 in TRPM8 (OR = 1.33, 95% confidence interval [CI] = 1.14–1.54, Ppermutation = 9.99 × 10−5; risk allele: T) and rs1172113 in LRP1 (OR = 1.23, 95% CI = 1.04–1.45, Ppermutation = 2.9 × 10−2; risk allele: T). Conclusion The present study is the first migraine GWAS conducted in Han-Chinese and Asians. The newly identified susceptibility genes have potential implications in migraine pathogenesis. DLG2 is involved in glutamatergic neurotransmission, and GFRA1 encodes GDNF receptors that are abundant in CGRP-containing trigeminal neurons. Furthermore, positive association signals for TRPM8 and LRP1 suggest the possibility for common genetic contributions across ethnicities.
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Голимбет, В. Е., А. К. Голов, Г. Ю. Царапкин, Н. В. Кондратьев, А. С. Товмасян, and Д. А. Абашкин. "Post-GWAS studies of schizophrenia." Nauchno-prakticheskii zhurnal «Medicinskaia genetika», no. 4(213) (April 30, 2020): 5–6. http://dx.doi.org/10.25557/2073-7998.2020.04.5-6.
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Представлены основные направления и полученные результаты проводимых авторами пост-GWAS исследований шизофрении, в том числе эпигенетические исследования, поиск новых регуляторных элементов генома, связанных с патогенезом этого заболевания, создание адекватных клеточных моделей для функциональных исследований. We present the main directions and results of our post-GWAS studies of schizophrenia, including epigenetic studies, the search for enhancers located in schizophrenia GWAS regions and development of adequate cell models for functional studies.
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Lin, Jennie, and Katalin Susztak. "Complexities of Understanding Function from CKD-Associated DNA Variants." Clinical Journal of the American Society of Nephrology 15, no. 7 (June 8, 2020): 1028–40. http://dx.doi.org/10.2215/cjn.15771219.
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Genome-wide association studies (GWASs) have facilitated the unbiased discovery of hundreds of genomic loci associated with CKD and kidney function. The vast majority of disease-associated DNA variants are noncoding. Those that are causal in CKD pathogenesis likely modulate transcription of target genes in a cell type–specific manner. To gain novel biological insights into mechanisms driving the development of CKD, the causal variants (which are usually not the most significant variant reported in a GWAS), their target genes, and causal cell types need to be identified. This functional validation requires a large number of new data sets, complex bioinformatics analyses, and experimental cellular and in vivo studies. Here, we review the basic principles and some of the current approaches being leveraged to assign functional significance to a genotype–phenotype association.
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Antypa, N., A. Drago, and A. Serretti. "Genomewide interaction and enrichment analysis on antidepressant response." Psychological Medicine 44, no. 4 (July 1, 2013): 753–65. http://dx.doi.org/10.1017/s0033291713001554.
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BackgroundGenomewide association studies (GWASs) on antidepressant efficacy have yielded modest results. A possible reason is that response is influenced by other factors, which possibly interact with genetic variation. We used a GWAS model to predict antidepressant response, by including predictors previously known to affect response, such as quality of life (QoL). We also evaluated the association between genes, previously implicated in gene–environment (G × E) interactions, and response using an enrichment analysis.MethodWe examined a sample of 1426 depressed patients from the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) trial: 774 responders, 652 non-responders and 418 865 single nucleotide polymorphisms (SNPs) were analysed. First, in a GWAS model, we investigated whether genetic variations interact with patients' levels of QoL to predict response, after controlling for demographic characteristics, severity and population stratification. Second, we conducted an enrichment analysis exploring whether candidate genes that have emerged from prior G × E interaction studies on depression are associated with treatment response.ResultsThe GWAS model, with QoL as a moderator, yielded one SNP (rs520210) associated with response in the NEDD4L gene (p = 3.64 × 10−8). In the Caucasian sample only, we observed a drop in significance for this SNP. The enrichment analysis showed that SNPs within serotonergic genes contained more significant markers that predicted response, compared with a random set of genes in the genome.ConclusionsOur findings point to possible target genes, which are proposed for further independent replication. Our enrichment analysis provides further support, in a genomewide context, of the role of serotonergic genes in influencing antidepressant response.
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Mishra, Aniket, and Stuart MacGregor. "A Novel Approach for Pathway Analysis of GWAS Data Highlights Role of BMP Signaling and Muscle Cell Differentiation in Colorectal Cancer Susceptibility." Twin Research and Human Genetics 20, no. 1 (January 20, 2017): 1–9. http://dx.doi.org/10.1017/thg.2016.100.
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Genome-wide association studies (GWAS) have revolutionized the field of gene mapping. As the GWAS field matures, it is becoming clear that for many complex traits, a proportion of the missing heritability is attributable to common variants of individually small effect. Detecting these small effects individually can be difficult, and statistical power would be increased if relevant variants could be grouped together for testing. Here, we propose a VEGAS2Pathway approach that aggregates association strength of individual markers into pre-specified biological pathways. It accounts for gene size and linkage disequilibrium between markers using simulations from the multivariate normal distribution. Pathway size is taken into account via a resampling approach. Importantly, since the approach only requires summary data, the method can easily be applied in all GWASs, including meta-analysis, singleton-based, family-based, and DNA-pooling-based designs. This approach is implemented in a user-friendly web page https://vegas2.qimrberghofer.edu.au and a command line tool. The web implementation uses gene-sets from the gene ontology (GO), curated gene-sets from MSigDB (containing canonical pathways and gene-sets from BIOCARTA, REACTOME, KEGG databases), PANTHER, and pathway commons databases, enabling analysis of a wide range of complex traits. We applied this method on a colorectal cancer GWAS meta-analysis data set (10,934 cases, 12,328 controls) from the Genetics and Epidemiology of Colorectal Cancer Consortium (GECCO). We report statistically significant enrichment of association signal for the ‘BMP signaling’ and ‘muscle cell differentiation’ pathways, suggesting a possible role for these pathways onto the risk of colorectal cancer.
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Chen, Lingyan, Yong-Fei Wang, Lu Liu, Adrianna Bielowka, Rahell Ahmed, Huoru Zhang, Phil Tombleson, et al. "Genome-wide assessment of genetic risk for systemic lupus erythematosus and disease severity." Human Molecular Genetics 29, no. 10 (February 20, 2020): 1745–56. http://dx.doi.org/10.1093/hmg/ddaa030.
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Abstract Using three European and two Chinese genome-wide association studies (GWAS), we investigated the performance of genetic risk scores (GRSs) for predicting the susceptibility and severity of systemic lupus erythematosus (SLE), using renal disease as a proxy for severity. We used four GWASs to test the performance of GRS both cross validating within the European population and between European and Chinese populations. The performance of GRS in SLE risk prediction was evaluated by receiver operating characteristic (ROC) curves. We then analyzed the polygenic nature of SLE statistically. We also partitioned patients according to their age-of-onset and evaluated the predictability of GRS in disease severity in each age group. We found consistently that the best GRS in the prediction of SLE used SNPs associated at the level of P < 1e−05 in all GWAS data sets and that SNPs with P-values above 0.2 were inflated for SLE true positive signals. The GRS results in an area under the ROC curve ranging between 0.64 and 0.72, within European and between the European and Chinese populations. We further showed a significant positive correlation between a GRS and renal disease in two independent European GWAS (Pcohort1 = 2.44e−08; Pcohort2 = 0.00205) and a significant negative correlation with age of SLE onset (Pcohort1 = 1.76e−12; Pcohort2 = 0.00384). We found that the GRS performed better in the prediction of renal disease in the ‘later onset’ compared with the ‘earlier onset’ group. The GRS predicts SLE in both European and Chinese populations and correlates with poorer prognostic factors: young age-of-onset and lupus nephritis.
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Okamoto, D., Y. Kakuta, N. Takeo, R. Moroi, M. Kuroha, Y. Kanazawa, S. Hisashi, et al. "P822 Genetic analysis of ulcerative colitis in Japanese individuals using population-specific SNP array." Journal of Crohn's and Colitis 14, Supplement_1 (January 2020): S638—S639. http://dx.doi.org/10.1093/ecco-jcc/jjz203.950.
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Abstract Background Previous genome wide association studies (GWASs) have identified over 200 susceptibility loci for inflammatory bowel diseases (IBD), but studies in non-European population are limited. To clarify the genetic background of ulcerative colitis (UC) in the Japanese population, we conducted GWAS using a population specific SNP array (Japonica Array). Methods Discovery GWAS included 624 UC patients and 2004 healthy controls (HC) and replication study included 1075 UC patients and 419 HCs. We performed GWAS using a Japonica Array and the subsequent imputation with a Japanese population reference panel (referred to as 2KJPN). After GWAS, significant and candidate loci were identified and the representative top SNPs of each region were analysed in replication study and combined analysis. The probability of colectomy between genotypes of rs117506082, the top hit SNP at HLA loci, was analysed using the Kaplan–Meier method. Results In the GWAS, only the HLA loci showed genome wide significant association [rs117506082, p = 6.69E−28, OR=1.29, 95%CI=1.23–1.35]. 7 regions with nominal significance included 2 known loci: IL23R [rs76418789, p = 6.29E−7, OR=0.89, 95%CI=0.85–0.93], IRF8 [rs16940202, p = 1.03E−6, OR=1.07, 95%CI=1.04–1.10] and 5 novel loci: miR-622 [rs9560575, p = 8.23E−7, OR=1.06, 95%CI=1.04–1.09], 14q31 [rs117618617, p = 1.53E−6, OR=1.13, 95%CI=1.09–1.19], KAT6B [rs12260609, p = 1.81E−6, OR=1.06, 95%CI=1.04–1.09], PAX3-CCDC140-SGPP2 [rs7589797, p = 2.87E−6, OR=0.94, 95%CI=0.93–0.97], KCNA2 [rs118020656, p = 4.01E−6, OR=1.12, 95%CI=1.07–1.18]. Combined analysis revealed that the HLA loci [rs117506082, p = 1.10E−23, OR=3.43, 95%CI=2.99–3.83] and IL23R p.G149R [rs76418789, p = 9.03E−11, OR=0.51, 95%CI=0.42–0.63] had a genome wide significant association. The GG genotype of rs117506082 had a significantly lower probability for total colectomy compared with the GA+AA genotype (p = 1.72E−2). Conclusion IL23R p.G149R is a susceptibility locus for UC in Japanese individuals. The GG genotype of rs117506082 at HLA loci, a risk genotype for UC susceptibility, may predict a better clinical course.
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Liu, Yong, Hui Shen, Jonathan Greenbaum, Anqi Liu, Kuan-Jui Su, Li-Shu Zhang, Lei Zhang, et al. "Gene Expression and RNA Splicing Imputation Identifies Novel Candidate Genes Associated with Osteoporosis." Journal of Clinical Endocrinology & Metabolism 105, no. 12 (August 21, 2020): e4742-e4757. http://dx.doi.org/10.1210/clinem/dgaa572.
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Abstract Context Though genome-wide association studies (GWASs) have identified hundreds of genetic variants associated with osteoporosis related traits, such as bone mineral density (BMD) and fracture, it remains a challenge to interpret their biological functions and underlying biological mechanisms. Objective Integrate diverse expression quantitative trait loci and splicing quantitative trait loci data with several powerful GWAS datasets to identify novel candidate genes associated with osteoporosis. Design, Setting, and Participants Here, we conducted a transcriptome-wide association study (TWAS) for total body BMD (TB-BMD) (n = 66 628 for discovery and 7697 for validation) and fracture (53 184 fracture cases and 373 611 controls for discovery and 37 857 cases and 227 116 controls for validation), respectively. We also conducted multi-SNP-based summarized mendelian randomization analysis to further validate our findings. Results In total, we detected 88 genes significantly associated with TB-BMD or fracture through expression or ribonucleic acid splicing. Summarized mendelian randomization analysis revealed that 78 of the significant genes may have potential causal effects on TB-BMD or fracture in at least 1 specific tissue. Among them, 64 genes have been reported in previous GWASs or TWASs for osteoporosis, such as ING3, CPED1, and WNT16, as well as 14 novel genes, such as DBF4B, GRN, TMUB2, and UNC93B1. Conclusions Overall, our findings provide novel insights into the pathogenesis mechanisms of osteoporosis and highlight the power of a TWAS to identify and prioritize potential causal genes.
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Niazi, Yasmeen, Hauke Thomsen, Bozena Smolkova, Ludmila Vodickova, Soňa Vodenkova, Michal Kroupa, Veronika Vymetalkova, et al. "Distinct pathways associated with chromosomal aberration frequency in a cohort exposed to genotoxic compounds compared to general population." Mutagenesis 34, no. 4 (July 2019): 323–30. http://dx.doi.org/10.1093/mutage/gez024.
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Abstract Non-specific structural chromosomal aberrations (CAs) observed in peripheral blood lymphocytes of healthy individuals can be either chromosome-type aberrations (CSAs) or chromatid-type aberrations (CTAs) depending on the stage of cell division they are induced in and mechanism of formation. It is important to study the genetic basis of chromosomal instability as it is a marker of genotoxic exposure and a predictor of cancer risk. For that purpose, we conducted two genome-wide association studies (GWASs) on healthy individuals in the presence and absence of apparent genotoxic exposure from the Czech Republic and Slovakia. The pre-GWAS cytogenetic analysis reported the frequencies of CSA, CTA and total CA (CAtot). We performed both linear and binary logistic regression analysis with an arbitrary cut-off point of 2% for CAtot and 1% for CSA and CTA. Using the statistical threshold of 1.0 × 10−5, we identified five loci with in silico predicted functionality in the reference group and four loci in the exposed group, with no overlap between the associated regions. A meta-analysis on the two GWASs identified further four loci with moderate associations in each of the studies. From the reference group mainly loci within genes related to DNA damage response/repair were identified. Other loci identified from both the reference and exposed groups were found to be involved in the segregation of chromosomes and chromatin modification. Some of the discovered regions in each group were implicated in tumourigenesis and autism.
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Shi, Xingjie, Yuling Jiao, Yi Yang, Ching-Yu Cheng, Can Yang, Xinyi Lin, and Jin Liu. "VIMCO: variational inference for multiple correlated outcomes in genome-wide association studies." Bioinformatics 35, no. 19 (March 9, 2019): 3693–700. http://dx.doi.org/10.1093/bioinformatics/btz167.
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Abstract Motivation In genome-wide association studies (GWASs) where multiple correlated traits have been measured on participants, a joint analysis strategy, whereby the traits are analyzed jointly, can improve statistical power over a single-trait analysis strategy. There are two questions of interest to be addressed when conducting a joint GWAS analysis with multiple traits. The first question examines whether a genetic loci is significantly associated with any of the traits being tested. The second question focuses on identifying the specific trait(s) that is associated with the genetic loci. Since existing methods primarily focus on the first question, this article seeks to provide a complementary method that addresses the second question. Results We propose a novel method, Variational Inference for Multiple Correlated Outcomes (VIMCO) that focuses on identifying the specific trait that is associated with the genetic loci, when performing a joint GWAS analysis of multiple traits, while accounting for correlation among the multiple traits. We performed extensive numerical studies and also applied VIMCO to analyze two datasets. The numerical studies and real data analysis demonstrate that VIMCO improves statistical power over single-trait analysis strategies when the multiple traits are correlated and has comparable performance when the traits are not correlated. Availability and implementation The VIMCO software can be downloaded from: https://github.com/XingjieShi/VIMCO. Supplementary information Supplementary data are available at Bioinformatics online.
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Arning, Astrid, Milan Hiersche, Anika Witten, Gerhard Kurlemann, Karin Kurnik, Daniela Manner, Monika Stoll, and Ulrike Nowak-Göttl. "A genome-wide association study identifies a gene network of ADAMTS genes in the predisposition to pediatric stroke." Blood 120, no. 26 (December 20, 2012): 5231–36. http://dx.doi.org/10.1182/blood-2012-07-442038.
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Abstract Pediatric stroke is a rare but highly penetrant disease with a strong genetic background. Although there are an increasing number of genome-wide association studies (GWASs) for stroke in adults, such studies for stroke of pediatric onset are lacking. Here we report the results of the first GWAS on pediatric stroke using a large cohort of 270 family-based trios. GWAS was performed using the Illumina 370 CNV single nucleotide polymorphisms array and analyzed using the transmission disequilibrium test as implemented in PLINK. An enrichment analysis was performed to identify additional true association signals among lower P value signals and searched for cumulatively associated genes within protein interaction data using dmGWAS. We observed clustering of association signals in 4 genes belonging to one family of metalloproteinases at high (ADAMTS12, P = 2.9 × 10−6; ADAMTS2, P = 8.0 × 10−6) and moderate (ADAMTS13, P = 9.3 × 10−4; ADAMTS17, P = 8.5 × 10−4) significance levels. Over-representation and gene-network analyses highlight the importance of the extracellular matrix in conjunction with members of the phosphoinositide and calcium signaling pathways in the susceptibility for pediatric stroke. Associated extracellular matrix components, such as ADAMTS proteins, in combination with misbalanced coagulation signals as unveiled by gene network analysis suggest a major role of postnatal vascular injury with subsequent thrombus formation as the leading cause of pediatric stroke.
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Montazeri, Zahra, Xue Li, Christine Nyiraneza, Xiangyu Ma, Maria Timofeeva, Victoria Svinti, Xiangrui Meng, et al. "Systematic meta-analyses, field synopsis and global assessment of the evidence of genetic association studies in colorectal cancer." Gut 69, no. 8 (December 9, 2019): 1460–71. http://dx.doi.org/10.1136/gutjnl-2019-319313.
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ObjectiveTo provide an understanding of the role of common genetic variations in colorectal cancer (CRC) risk, we report an updated field synopsis and comprehensive assessment of evidence to catalogue all genetic markers for CRC (CRCgene2).DesignWe included 869 publications after parallel literature review and extracted data for 1063 polymorphisms in 303 different genes. Meta-analyses were performed for 308 single nucleotide polymorphisms (SNPs) in 158 different genes with at least three independent studies available for analysis. Scottish, Canadian and Spanish data from genome-wide association studies (GWASs) were incorporated for the meta-analyses of 132 SNPs. To assess and classify the credibility of the associations, we applied the Venice criteria and Bayesian False-Discovery Probability (BFDP). Genetic associations classified as ‘positive’ and ‘less-credible positive’ were further validated in three large GWAS consortia conducted in populations of European origin.ResultsWe initially identified 18 independent variants at 16 loci that were classified as ‘positive’ polymorphisms for their highly credible associations with CRC risk and 59 variants at 49 loci that were classified as ‘less-credible positive’ SNPs; 72.2% of the ‘positive’ SNPs were successfully replicated in three large GWASs and the ones that were not replicated were downgraded to ‘less-credible’ positive (reducing the ‘positive’ variants to 14 at 11 loci). For the remaining 231 variants, which were previously reported, our meta-analyses found no evidence to support their associations with CRC risk.ConclusionThe CRCgene2 database provides an updated list of genetic variants related to CRC risk by using harmonised methods to assess their credibility.
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Krishna Kumar, Siddharth, Marcus W. Feldman, David H. Rehkopf, and Shripad Tuljapurkar. "Limitations of GCTA as a solution to the missing heritability problem." Proceedings of the National Academy of Sciences 113, no. 1 (December 22, 2015): E61—E70. http://dx.doi.org/10.1073/pnas.1520109113.
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Genome-wide association studies (GWASs) seek to understand the relationship between complex phenotype(s) (e.g., height) and up to millions of single-nucleotide polymorphisms (SNPs). Early analyses of GWASs are commonly believed to have “missed” much of the additive genetic variance estimated from correlations between relatives. A more recent method, genome-wide complex trait analysis (GCTA), obtains much higher estimates of heritability using a model of random SNP effects correlated between genotypically similar individuals. GCTA has now been applied to many phenotypes from schizophrenia to scholastic achievement. However, recent studies question GCTA’s estimates of heritability. Here, we show that GCTA applied to current SNP data cannot produce reliable or stable estimates of heritability. We show first that GCTA depends sensitively on all singular values of a high-dimensional genetic relatedness matrix (GRM). When the assumptions in GCTA are satisfied exactly, we show that the heritability estimates produced by GCTA will be biased and the standard errors will likely be inaccurate. When the population is stratified, we find that GRMs typically have highly skewed singular values, and we prove that the many small singular values cannot be estimated reliably. Hence, GWAS data are necessarily overfit by GCTA which, as a result, produces high estimates of heritability. We also show that GCTA’s heritability estimates are sensitive to the chosen sample and to measurement errors in the phenotype. We illustrate our results using the Framingham dataset. Our analysis suggests that results obtained using GCTA, and the results’ qualitative interpretations, should be interpreted with great caution.
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Akizuki, Shuji, Kazuyoshi Ishigaki, Yuta Kochi, Sze-Ming Law, Keitaro Matsuo, Koichiro Ohmura, Akari Suzuki, et al. "PLD4 is a genetic determinant to systemic lupus erythematosus and involved in murine autoimmune phenotypes." Annals of the Rheumatic Diseases 78, no. 4 (January 24, 2019): 509–18. http://dx.doi.org/10.1136/annrheumdis-2018-214116.
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ObjectivesSystemic lupus erythematosus (SLE) is an autoimmune disease that is characterised by autoantibody production and widespread inflammation damaging many organs. Previous genome-wide association studies (GWASs) have revealed over 80 genetic determinants of SLE, but they collectively explain a fraction of the heritability, and only a few were proven in vivo for the involvement in SLE. We conducted a meta-analysis of SLE GWAS in the Japanese population, followed by functional analyses of a susceptibility gene with use of mutant mice.MethodsWe conducted a meta-analysis of two GWASs comprising a total of 1363 cases and 5536 controls using the 1000 Genome Project data as an imputation reference. Enrichment analyses for functional annotations were conducted. We examined Phospholipase D4 (Pld4) mutant mice to assess functional involvement of a genetic determinant.ResultsWe found a total of 14 significant loci, which included rs2582511 in AHNAK2/PLD4 recently reported in a Chinese study and a novel locus of rs143181706 in MAMLD1 (p=7.9×10−11 and 3.7×10–8, respectively). PLD4 risk allele was associated with anti-dsDNA antibody production. Enrichment analysis of genetic signals revealed involvement of a wide range of immune-related cells and pathways. Pld4 mutant mice revealed remarkably low body weight. The mice demonstrated autoimmune phenotypes compatible with SLE, including splenomegaly and lymphadenopathy, expansion of B cells and hypersecretion of BAFF and production of autoantibodies especially anti-nuclear antibody and anti-dsDNA antibody.Conclusions We found a novel susceptibility gene to SLE. Pld4 mutant mice revealed autoimmune phenotypes suggesting functional involvement of PLD4 with the basics of SLE.
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Mishra, Aniket, and Stuart Macgregor. "VEGAS2: Software for More Flexible Gene-Based Testing." Twin Research and Human Genetics 18, no. 1 (December 18, 2014): 86–91. http://dx.doi.org/10.1017/thg.2014.79.
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Gene-based tests such as versatile gene-based association study (VEGAS) are commonly used following per-single nucleotide polymorphism (SNP) GWAS (genome-wide association studies) analysis. Two limitations of VEGAS were that the HapMap2 reference set was used to model the correlation between SNPs and only autosomal genes were considered. HapMap2 has now been superseded by the 1,000 Genomes reference set, and whereas early GWASs frequently ignored the X chromosome, it is now commonly included. Here we have developed VEGAS2, an extension that uses 1,000 Genomes data to model SNP correlations across the autosomes and chromosome X. VEGAS2 allows greater flexibility when defining gene boundaries. VEGAS2 offers both a user-friendly, web-based front end and a command line Linux version. The online version of VEGAS2 can be accessed through https://vegas2.qimrberghofer.edu.au/. The command line version can be downloaded from https://vegas2.qimrberghofer.edu.au/zVEGAS2offline.tgz. The command line version is developed in Perl, R and shell scripting languages; source code is available for further development.