Quality control and conduct of genome-wide association meta-analyses.
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ABSTRACT:
SUBMITTER: Winkler TW
PROVIDER: S-EPMC4083217 | biostudies-literature | 2014 May
REPOSITORIES: biostudies-literature
Publications
Quality control and conduct of genome-wide association meta-analyses.
Winkler Thomas W TW Day Felix R FR Croteau-Chonka Damien C DC Wood Andrew R AR Locke Adam E AE Mägi Reedik R Ferreira Teresa T Fall Tove T Graff Mariaelisa M Justice Anne E AE Luan Jian'an J Gustafsson Stefan S Randall Joshua C JC Vedantam Sailaja S Workalemahu Tsegaselassie T Kilpeläinen Tuomas O TO Scherag André A Esko Tonu T Kutalik Zoltán Z Heid Iris M IM Loos Ruth J F RJ
Nature protocols 20140424 5
Rigorous organization and quality control (QC) are necessary to facilitate successful genome-wide association meta-analyses (GWAMAs) of statistics aggregated across multiple genome-wide association studies. This protocol provides guidelines for (i) organizational aspects of GWAMAs, and for (ii) QC at the study file level, the meta-level across studies and the meta-analysis output level. Real-world examples highlight issues experienced and solutions developed by the GIANT Consortium that has cond ...[more]
Similar Datasets
Project description:This paper provides details on the necessary steps to assess and control data in genome wide association studies (GWAS) using genotype information on a large number of genetic markers for large number of individuals. Due to varied study designs and genotyping platforms between multiple sites/projects as well as potential genotyping errors, it is important to ensure high quality data. Scripts and directions are provided to facilitate others in this process.
| S-EPMC5007749 | biostudies-other
Project description:Meta-analysis is an increasingly popular tool for combining multiple genome-wide association studies in a single analysis to identify associations with small effect sizes. The effect sizes between studies in a meta-analysis may differ and these differences, or heterogeneity, can be caused by many factors. If heterogeneity is observed in the results of a meta-analysis, interpreting the cause of heterogeneity is important because the correct interpretation can lead to a better understanding of the disease and a more effective design of a replication study. However, interpreting heterogeneous results is difficult. The standard approach of examining the association p-values of the studies does not effectively predict if the effect exists in each study. In this paper, we propose a framework facilitating the interpretation of the results of a meta-analysis. Our framework is based on a new statistic representing the posterior probability that the effect exists in each study, which is estimated utilizing cross-study information. Simulations and application to the real data show that our framework can effectively segregate the studies predicted to have an effect, the studies predicted to not have an effect, and the ambiguous studies that are underpowered. In addition to helping interpretation, the new framework also allows us to develop a new association testing procedure taking into account the existence of effect.
| S-EPMC3291559 | biostudies-literature
Project description:Genome-wide association studies (GWAS) are being conducted at an unprecedented rate in population-based cohorts and have increased our understanding of the pathophysiology of complex disease. Regardless of context, the practical utility of this information will ultimately depend upon the quality of the original data. Quality control (QC) procedures for GWAS are computationally intensive, operationally challenging, and constantly evolving. Here we enumerate some of the challenges in QC of GWAS data and describe the approaches that the electronic MEdical Records and Genomics (eMERGE) network is using for quality assurance in GWAS data, thereby minimizing potential bias and error in GWAS results. We discuss common issues associated with QC of GWAS data, including data file formats, software packages for data manipulation and analysis, sex chromosome anomalies, sample identity, sample relatedness, population substructure, batch effects, and marker quality. We propose best practices and discuss areas of ongoing and future research.
| S-EPMC3066182 | biostudies-literature
Project description:Conduct disorder (CD) is one of the most prevalent childhood psychiatric conditions, and is associated with a number of serious concomitant and future problems. CD symptomatology is known to have a considerable genetic component, with heritability estimates in the range of 50%. Despite this, there is a relative paucity of studies aimed at identifying genes involved in the susceptibility to CD. In this study, we report results from a genome-wide association study of CD symptoms. CD symptoms were retrospectively reported by a psychiatric interview among a sample of cases and controls, in which cases met the criteria for alcohol dependence. Our primary phenotype was the natural log transformation of the number of CD symptoms that were endorsed, with data available for 3963 individuals who were genotyped on the Illumina Human 1M beadchip array. Secondary analyses are presented for case versus control status, in which caseness was established as endorsing three or more CD symptoms (N = 872 with CD and N = 3091 without CD). We find four markers that meet the criteria for genome-wide significance (P<5 × 10(-8)) with the CD symptom count, two of which are located in the gene C1QTNF7 (C1q and tumor necrosis factor-related protein 7). There were six additional SNPs in the gene that yielded converging evidence of association. These data provide the first evidence of a specific gene that is associated with CD symptomatology. None of the top signals resided in traditional candidate genes, underscoring the importance of a genome-wide approach for identifying novel variants involved in this serious childhood disorder.
| S-EPMC3580835 | biostudies-literature
Project description:BackgroundMeta-analysis is the systematic and quantitative synthesis of effect sizes and the exploration of their diversity across different studies. Meta-analyses are increasingly applied to synthesize data from genome-wide association (GWA) studies and from other teams that try to replicate the genetic variants that emerge from such investigations. Between-study heterogeneity is important to document and may point to interesting leads.Methodology/principal findingsTo exemplify these issues, we used data from three GWA studies on type 2 diabetes and their replication efforts where meta-analyses of all data using fixed effects methods (not incorporating between-study heterogeneity) have already been published. We considered 11 polymorphisms that at least one of the three teams has suggested as susceptibility loci for type 2 diabetes. The I2 inconsistency metric (measuring the amount of heterogeneity not due to chance) was different from 0 (no detectable heterogeneity) for 6 of the 11 genetic variants; inconsistency was moderate to very large (I2 = 32-77%) for 5 of them. For these 5 polymorphisms, random effects calculations incorporating between-study heterogeneity revealed more conservative p-values for the summary effects compared with the fixed effects calculations. These 5 associations were perused in detail to highlight potential explanations for between-study heterogeneity. These include identification of a marker for a correlated phenotype (e.g. FTO rs8050136 being associated with type 2 diabetes through its effect on obesity); differential linkage disequilibrium across studies of the identified genetic markers with the respective culprit polymorphisms (e.g., possibly the case for CDKAL1 polymorphisms or for rs9300039 and markers in linkage disequilibrium, as shown by additional studies); and potential bias. Results were largely similar, when we treated the discovery and replication data from each GWA investigation as separate studies.SignificanceBetween-study heterogeneity is useful to document in the synthesis of data from GWA investigations and can offer valuable insights for further clarification of gene-disease associations.
| S-EPMC1950790 | biostudies-literature
Project description:ObjectivePostpartum depression (PPD) is a common subtype of major depressive disorder (MDD) that is more heritable, yet is understudied in psychiatric genetics. The authors conducted meta-analyses of genome-wide association studies (GWASs) to investigate the genetic architecture of PPD.MethodMeta-analyses were conducted on 18 cohorts of European ancestry (17,339 PPD cases and 53,426 controls), one cohort of East Asian ancestry (975 cases and 3,780 controls), and one cohort of African ancestry (456 cases and 1,255 controls), totaling 18,770 PPD cases and 58,461 controls. Post-GWAS analyses included 1) single-nucleotide polymorphism (SNP)-based heritability ([Formula: see text]), 2) genetic correlations between PPD and other phenotypes, and 3) enrichment of the PPD GWAS findings in 27 human tissues and 265 cell types from the mouse central and peripheral nervous system.ResultsNo SNP achieved genome-wide significance in the European or the trans-ancestry meta-analyses. The [Formula: see text] of PPD was 0.14 (SE=0.02). Significant genetic correlations were estimated for PPD with MDD, bipolar disorder, anxiety disorders, posttraumatic stress disorder, insomnia, age at menarche, and polycystic ovary syndrome. Cell-type enrichment analyses implicate inhibitory neurons in the thalamus and cholinergic neurons within septal nuclei of the hypothalamus, a pattern that differs from MDD.ConclusionsWhile more samples are needed to reach genome-wide levels of significance, the results presented confirm PPD as a polygenic and heritable phenotype. There is also evidence that despite a high correlation with MDD, PPD may have unique genetic components. Cell enrichment results suggest GABAergic neurons, which converge on a common mechanism with the only medication approved by the U.S. Food and Drug Administration for PPD (brexanolone).
| S-EPMC11163373 | biostudies-literature
Project description:BACKGROUND: Genetic improvement of beef quality will benefit both producers and consumers, and can be achieved by selecting animals that carry desired quantitative trait nucleotides (QTN), which result from intensive searches using genetic markers. This paper presents a genome-wide association approach utilizing single nucleotide polymorphisms (SNP) in the Illumina BovineSNP50 BeadChip to seek genomic regions that potentially harbor genes or QTN underlying variation in carcass quality of beef cattle.This study used 747 genotyped animals, mainly crossbred, with phenotypes on twelve carcass quality traits, including hot carcass weight (HCW), back fat thickness (BF), Longissimus dorsi muscle area or ribeye area (REA), marbling scores (MRB), lean yield grade by Beef Improvement Federation formulae (BIFYLD), steak tenderness by Warner-Bratzler shear force 7-day post-mortem (LM7D) as well as body composition as determined by partial rib (IMPS 103) dissection presented as a percentage of total rib weight including body cavity fat (BDFR), lean (LNR), bone (BNR), intermuscular fat (INFR), subcutaneous fat (SQFR), and total fat (TLFR). RESULTS: At the genome wide level false discovery rate (FDR?<?10%), eight SNP were found significantly associated with HCW. Seven of these SNP were located on Bos taurus autosome (BTA) 6. At a less stringent significance level (P?<?0.001), 520 SNP were found significantly associated with mostly individual traits (473 SNP), and multiple traits (47 SNP). Of these significant SNP, 48 were located on BTA6, and 22 of them were in association with hot carcass weight. There were 53 SNP associated with percentage of rib bone, and 12 of them were on BTA20. The rest of the significant SNP were scattered over other chromosomes. They accounted for 1.90 - 5.89% of the phenotypic variance of the traits. A region of approximately 4 Mbp long on BTA6 was found to be a potential area to harbor candidate genes influencing growth. One marker on BTA25 accounting for 2.67% of the variation in LM7D may be worth further investigation for the improvement of beef tenderness. CONCLUSION: This study provides useful information to further assist the identification of chromosome regions and subsequently genes affecting carcass quality traits in beef cattle. It also revealed many SNP that acted pleiotropically to affect carcass quality. This knowledge is important in selecting subsets of SNP to improve the performance of beef cattle.
| S-EPMC3827924 | biostudies-literature
Project description:SummaryQuality control (QC) of genome wide association study (GWAS) result files has become increasingly difficult due to advances in genomic technology. The main challenges include continuous increases in the number of polymorphic genetic variants contained in recent GWASs and reference panels, the rising number of cohorts participating in a GWAS consortium, and inclusion of new variant types. Here, we present GWASinspector, a flexible R package for comprehensive QC of GWAS results. This package is compatible with recent imputation reference panels, handles insertion/deletion and multi-allelic variants, provides extensive QC reports and efficiently processes big data files. Reference panels covering three human genome builds (NCBI36, GRCh37 and GRCh38) are available. GWASinspector has a user friendly design and allows easy set-up of the QC pipeline through a configuration file. In addition to checking and reporting on individual files, it can be used in preparation of a meta-analysis by testing for systemic differences between studies and generating cleaned, harmonized GWAS files. Comparison with existing GWAS QC tools shows that the main advantages of GWASinspector are its ability to more effectively deal with insertion/deletion and multi-allelic variants and its relatively low memory use.Availability and implementationOur package is available at The Comprehensive R Archive Network (CRAN): https://CRAN.R-project.org/package=GWASinspector. Reference datasets and a detailed tutorial can be found at the package website at http://gwasinspector.com/.Supplementary informationSupplementary data are available at Bioinformatics online.
| S-EPMC8034536 | biostudies-literature
Project description:BackgroundMaternal and inherited (ie, case) genetic factors likely contribute to the pathogenesis of congenital heart defects, but it is unclear whether individual common variants confer a large risk.Methods and resultsTo evaluate the relationship between individual common maternal/inherited genotypes and risk for heart defects, we conducted genome-wide association studies in 5 cohorts. Three cohorts were recruited at the Children's Hospital of Philadelphia: 670 conotruncal heart defect (CTD) case-parent trios, 317 left ventricular obstructive tract defect (LVOTD) case-parent trios, and 406 CTD cases (n=406) and 2976 pediatric controls. Two cohorts were recruited through the Pediatric Cardiac Genomics Consortium: 355 CTD trios and 192 LVOTD trios. We also conducted meta-analyses using the genome-wide association study results from the CTD cohorts, the LVOTD cohorts, and from the combined CTD and LVOTD cohorts. In the individual genome-wide association studies, several genome-wide significant associations (P≤5×10-8) were observed. In our meta-analyses, 1 genome-wide significant association was detected: the case genotype for rs72820264, an intragenetic single-nucleotide polymorphism associated with LVOTDs (P=2.1×10-8).ConclusionsWe identified 1 novel candidate region associated with LVOTDs and report on several additional regions with suggestive evidence for association with CTD and LVOTD. These studies were constrained by the relatively small samples sizes and thus have limited power to detect small to moderate associations. Approaches that minimize the multiple testing burden (eg, gene or pathway based) may, therefore, be required to uncover common variants contributing to the risk of these relatively rare conditions.
| S-EPMC5539991 | biostudies-literature
Project description:Genome-wide scans of nucleotide variation in human subjects are providing an increasing number of replicated associations with complex disease traits. Most of the variants detected have small effects and, collectively, they account for a small fraction of the total genetic variance. Very large sample sizes are required to identify and validate findings. In this situation, even small sources of systematic or random error can cause spurious results or obscure real effects. The need for careful attention to data quality has been appreciated for some time in this field, and a number of strategies for quality control and quality assurance (QC/QA) have been developed. Here we extend these methods and describe a system of QC/QA for genotypic data in genome-wide association studies (GWAS). This system includes some new approaches that (1) combine analysis of allelic probe intensities and called genotypes to distinguish gender misidentification from sex chromosome aberrations, (2) detect autosomal chromosome aberrations that may affect genotype calling accuracy, (3) infer DNA sample quality from relatedness and allelic intensities, (4) use duplicate concordance to infer SNP quality, (5) detect genotyping artifacts from dependence of Hardy-Weinberg equilibrium test P-values on allelic frequency, and (6) demonstrate sensitivity of principal components analysis to SNP selection. The methods are illustrated with examples from the "Gene Environment Association Studies" (GENEVA) program. The results suggest several recommendations for QC/QA in the design and execution of GWAS.
| S-EPMC3061487 | biostudies-literature