scholarly journals Improved score statistics for meta-analysis in single-variant and gene-level association studies

2018 ◽  
Vol 42 (4) ◽  
pp. 333-343 ◽  
Author(s):  
Jingjing Yang ◽  
Sai Chen ◽  
Gonçalo Abecasis ◽  
Keyword(s):  
Association Studies ◽  
Meta Analysis ◽  
Score Statistics ◽  
Gene Level

scholarly journals Improved Score Statistics for Meta-analysis in Single-variant and Gene-level Association Studies

2017 ◽  
Author(s):  
Jingjing Yang ◽  
Sai Chen ◽  
Gonçalo Abecasis ◽  
Keyword(s):  
Power Loss ◽  
Association Studies ◽  
Meta Analysis ◽  
Genetic Association Studies ◽  
Age Related Macular Degeneration ◽  
Joint Analysis ◽  
Analysis Methods ◽  
Age Related ◽  
Score Statistics ◽  
Gene Level

AbstractMeta-analysis is now an essential tool for genetic association studies, allowing these to combine large studies and greatly accelerating the pace of genetic discovery. Although the standard meta-analysis methods perform equivalently as the more cumbersome joint analysis under ideal settings, they result in substantial power loss under unbalanced settings with various case-control ratios. Here, we investigate why the standard meta-analysis methods lose power under unbalanced settings, and further propose a novel meta-analysis method that performs as efficiently as joint analysis under general settings. Our proposed method can accurately approximate the score statistics obtainable by joint analysis, for both linear and logistic regression models, with and without covariates. In addition, we propose a novel approach to adjust for population stratification by correcting for known population structures through minor allele frequencies (MAFs). In the simulated gene-level association studies under unbalanced settings, our method recovered up to 85% power loss caused by the standard method. We further showed the power gain of our method in gene-level association studies with 26 unbalanced real studies of Age-related Macular Degeneration (AMD). In addition, we took the meta-analysis of three studies of type 2 diabetes (T2D) as an example to discuss the challenges of meta-analyzing multi-ethnic samples. In summary, we propose improved single-variant score statistics in meta-analysis, requiring “accurate” population-specific MAFs for multi-ethnic studies. These improved score statistics can be used to construct both single-variant and gene-level association studies, providing a useful framework for ensuring well-powered, convenient, cross-study analyses.

Genome-Wide Association Studies Identify Two Novel Loci Conferring Susceptibility to Diabetic Retinopathy in Japanese Patients with Type 2 Diabetes

2021 ◽  
Author(s):  
Minako Imamura ◽  
Atsushi Takahashi ◽  
Masatoshi Matsunami ◽  
Momoko Horikoshi ◽  
Minoru Iwata ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Diabetic Retinopathy ◽  
Association Studies ◽  
Meta Analysis ◽  
Japanese Patients ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
Stage 1

Abstract Several reports have suggested that genetic susceptibility contributes to the development and progression of diabetic retinopathy. We aimed to identify genetic loci that confer susceptibility to diabetic retinopathy in Japanese patients with type 2 diabetes. We analysed 5 790 508 single nucleotide polymorphisms (SNPs) in 8880 Japanese patients with type 2 diabetes, 4839 retinopathy cases and 4041 controls, as well as 2217 independent Japanese patients with type 2 diabetes, 693 retinopathy cases, and 1524 controls. The results of these two genome-wide association studies (GWAS) were combined with an inverse variance meta-analysis (Stage-1), followed by de novo genotyping for the candidate SNP loci (p < 1.0 × 10−4) in an independent case–control study (Stage-2, 2260 cases and 723 controls). After combining the association data (Stage-1 and -2) using meta-analysis, the associations of two loci reached a genome-wide significance level: rs12630354 near STT3B on chromosome 3, p = 1.62 × 10−9, odds ratio (OR) = 1.17, 95% confidence interval (CI) 1.11–1.23, and rs140508424 within PALM2 on chromosome 9, p = 4.19 × 10−8, OR = 1.61, 95% CI 1.36–1.91. However, the association of these two loci were not replicated in Korean, European, or African American populations. Gene-based analysis using Stage-1 GWAS data identified a gene-level association of EHD3 with susceptibility to diabetic retinopathy (p = 2.17 × 10−6). In conclusion, we identified two novel SNP loci, STT3B and PALM2, and a novel gene, EHD3, that confers susceptibility to diabetic retinopathy; however, further replication studies are required to validate these associations.

Life Course Adiposity and Alzheimer’s Disease: A Mendelian Randomization Study

2021 ◽  
pp. 1-10
Author(s):  
Xian Li ◽  
Yan Tian ◽  
Yu-Xiang Yang ◽  
Ya-Hui Ma ◽  
Xue-Ning Shen ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Life Course ◽  
Mendelian Randomization ◽  
Association Studies ◽  
Meta Analysis ◽  
Genome Wide Association Studies ◽  
Fat Percentage ◽  
Regression Methods ◽  
Weighted Median

Background: Several studies showed that life course adiposity was associated with Alzheimer’s disease (AD). However, the underlying causality remains unclear. Objective: We aimed to examine the causal relationship between life course adiposity and AD using Mendelian randomization (MR) analysis. Methods: Instrumental variants were obtained from large genome-wide association studies (GWAS) for life course adiposity, including birth weight (BW), childhood body mass index (BMI), adult BMI, waist circumference (WC), waist-to-hip ratio (WHR), and body fat percentage (BFP). A meta-analysis of GWAS for AD including 71,880 cases and 383,378 controls was used in this study. MR analyses were performed using inverse variance weighted (IVW), weighted median, and MR-Egger regression methods. We calculated odds ratios (ORs) per genetically predicted standard deviation (1-SD) unit increase in each trait for AD. Results: Genetically predicted 1-SD increase in adult BMI was significantly associated with higher risk of AD (IVW: OR = 1.03, 95% confidence interval [CI] = 1.01–1.05, p = 2.7×10–3) after Bonferroni correction. The weighted median method indicated a significant association between BW and AD (OR = 0.94, 95% CI = 0.90–0.98, p = 1.8×10–3). We also found suggestive associations of AD with WC (IVW: OR = 1.03, 95% CI = 1.00–1.07, p = 0.048) and WHR (weighted median: OR = 1.04, 95% CI = 1.00–1.07, p = 0.029). No association was detected of AD with childhood BMI and BFP. Conclusion: Our study demonstrated that lower BW and higher adult BMI had causal effects on increased AD risk.

scholarly journals A meta-analysis of genome-wide association studies for average daily gain and lean meat percentage in two Duroc pig populations

BMC Genomics ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Shenping Zhou ◽  
Rongrong Ding ◽  
Fanming Meng ◽  
Xingwang Wang ◽  
Zhanwei Zhuang ◽  
...  
Keyword(s):  
Candidate Genes ◽  
Growth And Development ◽  
Genetic Architecture ◽  
Association Studies ◽  
Meta Analysis ◽  
Average Daily Gain ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
Daily Gain

Abstract Background Average daily gain (ADG) and lean meat percentage (LMP) are the main production performance indicators of pigs. Nevertheless, the genetic architecture of ADG and LMP is still elusive. Here, we conducted genome-wide association studies (GWAS) and meta-analysis for ADG and LMP in 3770 American and 2090 Canadian Duroc pigs. Results In the American Duroc pigs, one novel pleiotropic quantitative trait locus (QTL) on Sus scrofa chromosome 1 (SSC1) was identified to be associated with ADG and LMP, which spans 2.53 Mb (from 159.66 to 162.19 Mb). In the Canadian Duroc pigs, two novel QTLs on SSC1 were detected for LMP, which were situated in 3.86 Mb (from 157.99 to 161.85 Mb) and 555 kb (from 37.63 to 38.19 Mb) regions. The meta-analysis identified ten and 20 additional SNPs for ADG and LMP, respectively. Finally, four genes (PHLPP1, STC1, DYRK1B, and PIK3C2A) were detected to be associated with ADG and/or LMP. Further bioinformatics analysis showed that the candidate genes for ADG are mainly involved in bone growth and development, whereas the candidate genes for LMP mainly participated in adipose tissue and muscle tissue growth and development. Conclusions We performed GWAS and meta-analysis for ADG and LMP based on a large sample size consisting of two Duroc pig populations. One pleiotropic QTL that shared a 2.19 Mb haplotype block from 159.66 to 161.85 Mb on SSC1 was found to affect ADG and LMP in the two Duroc pig populations. Furthermore, the combination of single-population and meta-analysis of GWAS improved the efficiency of detecting additional SNPs for the analyzed traits. Our results provide new insights into the genetic architecture of ADG and LMP traits in pigs. Moreover, some significant SNPs associated with ADG and/or LMP in this study may be useful for marker-assisted selection in pig breeding.

scholarly journals UTMOST, a single and cross-tissue TWAS (Transcriptome Wide Association Study), reveals new ASD (Autism Spectrum Disorder) associated genes

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Cristina Rodriguez-Fontenla ◽  
Angel Carracedo
Keyword(s):  
In Silico ◽  
Association Studies ◽  
Meta Analysis ◽  
Neurodevelopmental Disorder ◽  
Gastrointestinal Symptoms ◽  
Tissue Expression ◽  
Autism Spectrum ◽  
Biological Characterization ◽  
Large Size ◽  
Body Tissues

AbstractAutism spectrum disorders (ASD) is a complex neurodevelopmental disorder that may significantly impact on the affected individual’s life. Common variation (SNPs) could explain about 50% of ASD heritability. Despite this fact and the large size of the last GWAS meta-analysis, it is believed that hundreds of risk genes in ASD have yet to be discovered. New tools, such as TWAS (Transcriptome Wide Association Studies) which integrate tissue expression and genetic data, are a great approach to identify new ASD susceptibility genes. The main goal of this study is to use UTMOST with the publicly available summary statistics from the largest ASD GWAS meta-analysis as genetic input. In addition, an in silico biological characterization for the novel associated loci was performed. Our results have shown the association of 4 genes at the brain level (CIPC, PINX1, NKX2-2, and PTPRE) and have highlighted the association of NKX2-2, MANBA, ERI1, and MITF at the gastrointestinal level. The gastrointestinal associations are quite relevant given the well-established but unexplored relationship between ASD and gastrointestinal symptoms. Cross-tissue analysis has shown the association of NKX2-2 and BLK. UTMOST-associated genes together with their in silico biological characterization seems to point to different biological mechanisms underlying ASD etiology. Thus, it would not be restricted to brain tissue and it will involve the participation of other body tissues such as the gastrointestinal.

scholarly journals Family-Based Quantitative Trait Meta-Analysis Implicates Rare Noncoding Variants in DENND1A in Polycystic Ovary Syndrome

2019 ◽  
Vol 104 (9) ◽  
pp. 3835-3850 ◽  
Author(s):  
Matthew Dapas ◽  
Ryan Sisk ◽  
Richard S Legro ◽  
Margrit Urbanek ◽  
Andrea Dunaif ◽  
...  
Keyword(s):  
Polycystic Ovary Syndrome ◽  
Quantitative Trait ◽  
Rare Variants ◽  
Polycystic Ovary ◽  
Association Studies ◽  
Meta Analysis ◽  
Premenopausal Women ◽  
Endocrine Disorders ◽  
Genome Wide Association Studies ◽  
Ovary Syndrome

AbstractContextPolycystic ovary syndrome (PCOS) is among the most common endocrine disorders of premenopausal women, affecting 5% to15% of this population depending on the diagnostic criteria applied. It is characterized by hyperandrogenism, ovulatory dysfunction, and polycystic ovarian morphology. PCOS is highly heritable, but only a small proportion of this heritability can be accounted for by the common genetic susceptibility variants identified to date.ObjectiveThe objective of this study was to test whether rare genetic variants contribute to PCOS pathogenesis.Design, Patients, and MethodsWe performed whole-genome sequencing on DNA from 261 individuals from 62 families with one or more daughters with PCOS. We tested for associations of rare variants with PCOS and its concomitant hormonal traits using a quantitative trait meta-analysis.ResultsWe found rare variants in DENND1A (P = 5.31 × 10−5, adjusted P = 0.039) that were significantly associated with reproductive and metabolic traits in PCOS families.ConclusionsCommon variants in DENND1A have previously been associated with PCOS diagnosis in genome-wide association studies. Subsequent studies indicated that DENND1A is an important regulator of human ovarian androgen biosynthesis. Our findings provide additional evidence that DENND1A plays a central role in PCOS and suggest that rare noncoding variants contribute to disease pathogenesis.

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