A multiple testing correction method for genetic association studies using correlated single nucleotide polymorphisms

2008 ◽  
Vol 32 (4) ◽  
pp. 361-369 ◽  
Author(s):  
Xiaoyi Gao ◽  
Joshua Starmer ◽  
Eden R. Martin
2002 ◽  
Vol 30 (Supplement) ◽  
pp. A132
Author(s):  
Robert C Barber ◽  
Fernando A Rivera-Chavez ◽  
Herbert T Wheeler ◽  
Gina M Whitney ◽  
Dixie L Peters-Hybki ◽  
...  

2017 ◽  
Vol 2017 ◽  
pp. 1-5 ◽  
Author(s):  
Lijun Wu ◽  
Liwang Gao ◽  
Xiaoyuan Zhao ◽  
Meixian Zhang ◽  
Jianxin Wu ◽  
...  

Purpose. Genome-wide association studies have found two obesity-related single-nucleotide polymorphisms (SNPs), rs17782313 near the melanocortin-4 receptor (MC4R) gene and rs6265 near the brain-derived neurotrophic factor (BDNF) gene, but the associations of both SNPs with other obesity-related traits are not fully described, especially in children. The aim of the present study is to investigate the associations between the SNPs and adiponectin that has a regulatory role in glucose and lipid metabolism. Methods. We examined the associations of the SNPs with adiponectin in Beijing Child and Adolescent Metabolic Syndrome (BCAMS) study. A total of 3503 children participated in the study. Results. The SNP rs6265 was significantly associated with adiponectin under an additive model (P=0.02 and 0.024, resp.) after adjustment for age, gender, and BMI or obesity statuses. The SNP rs17782313 was significantly associated with low adiponectin under a recessive model. No statistical significance was found between the two SNPs and low adiponectin after correction for multiple testing. Conclusion. We demonstrate for the first time that the SNP rs17782313 near MC4R and the SNP rs6265 near BDNF are associated with adiponectin in Chinese children. These novel findings provide important evidence that adiponectin possibly mediates MC4R and BDNF involved in obesity.


2021 ◽  
Author(s):  
Aubrey Annis ◽  
Anita Pandit ◽  
Jonathon LeFaive ◽  
Sarah Gagliano Taliun ◽  
Lars Fritsche ◽  
...  

Abstract Biobanks housing genetic and phenotypic data for thousands of individuals introduce new opportunities and challenges for genetic association studies. Association testing across many phenotypes increases the multiple-testing burden and correlation between phenotypes makes appropriate multiple-testing correction uncertain. Moreover, analysis including low-frequency variants results in inflated type I error due to the much larger number of tests and the elevated importance of each individual minor allele carrier in those tests. Here we demonstrate that standard Bonferroni and permutation-based methods for multiple testing correction are inadequate for a holistic analysis of biobank data because ideal significance thresholds vary across datasets and minor allele frequencies. We propose a single-iteration permutation method that is computationally feasible and provides false discovery rate (FDR) estimates tailored to individual datasets and variant frequencies. Each dataset’s unique FDR estimates provide customized levels of confidence for association results and enable informed interpretation of genetic association studies across the phenome.


2021 ◽  
Vol 12 ◽  
Author(s):  
Xiaonan Ding ◽  
Yan Mei ◽  
Zhi Mao ◽  
Lingling Long ◽  
Qiuxia Han ◽  
...  

IgA nephropathy is the most prevalent primary glomerulonephritis worldwide, with identical immunopathological characteristics caused by multiple etiologies as well as influenced by geographical and ethnical factors. To elucidate the role of immunologic and inflammatory mechanisms in the susceptibility to IgA nephropathy, we explored single nucleotide polymorphisms of related molecules in the immune pathways. We searched the PubMed database for studies that involved all gene variants of molecules in the 20 immunologic and inflammatory pathways selected from the Kyoto Encyclopedia of Genes and Genomes database. The odds ratios with their corresponding 95% confidence intervals in six genetic models (allele model, dominant model, homozygote model, heterozygote model, overdominant model, and recessive model) were summarized using fixed or random effect models. Subgroup analysis was conducted based on different ethnicities with generalized odds ratios. Heterogeneity was evaluated using the Q and I2 tests. Begg’s funnel plot and Egger’s linear regression test were used to evaluating possible publication bias among the included studies, and sensitivity analysis was used to test the stability of the overall results. A total of 45 studies met our selection criteria and eight related genetic association studies were retrieved, including 320 single-nucleotide polymorphisms from 20 candidate pathways, ranging from 2000 to 2021. A total of 28,994 healthy people versus 20,600 IgA nephropathy patients were enrolled. Upon meta-analyzed results that TGFB1 (rs1800469, rs1982073, rs1800471), IL-1B (rs1143627), IL-18 (rs1946518), and TLR1 (rs5743557) showed effect with or without ethnicity difference. And 10 variants presented stable and robust related to IgA nephropathy. This research showed that genetic variants are related to the immunologic and inflammatory effects of IgA nephropathy pathogenesis. The meta-analysis results supported the previous researches, and may help deepen the understanding of pathogenesis and explore new targets for IgA nephropathy-specific immunotherapy.


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