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Wang Y, Li Y, Hao M, Liu X, Zhang M, Wang J, Xiong M, Shugart YY, Jin L. Robust Reference Powered Association Test of Genome-Wide Association Studies. Front Genet 2019; 10:319. [PMID: 31024629 PMCID: PMC6465778 DOI: 10.3389/fgene.2019.00319] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2019] [Accepted: 03/21/2019] [Indexed: 12/28/2022] Open
Abstract
Genome-wide association studies (GWASs) have identified abundant genetic susceptibility loci, GWAS of small sample size are far less from meeting the previous expectations due to low statistical power and false positive results. Effective statistical methods are required to further improve the analyses of massive GWAS data. Here we presented a new statistic (Robust Reference Powered Association Test) to use large public database (gnomad) as reference to reduce concern of potential population stratification. To evaluate the performance of this statistic for various situations, we simulated multiple sets of sample size and frequencies to compute statistical power. Furthermore, we applied our method to several real datasets (psoriasis genome-wide association datasets and schizophrenia genome-wide association dataset) to evaluate the performance. Careful analyses indicated that our newly developed statistic outperformed several previously developed GWAS applications. Importantly, this statistic is more robust than naive merging method in the presence of small control-reference differentiation, therefore likely to detect more association signals.
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Affiliation(s)
- Yi Wang
- Ministry of Education Key Laboratory of Contemporary Anthropology, Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Shanghai, China.,Human Phenome Institute, Fudan University, Shanghai, China
| | - Yi Li
- Ministry of Education Key Laboratory of Contemporary Anthropology, Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Shanghai, China.,Human Phenome Institute, Fudan University, Shanghai, China.,Institute of Sixth-Sector Industrialization Research, Fudan University, Shanghai, China
| | - Meng Hao
- Ministry of Education Key Laboratory of Contemporary Anthropology, Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Shanghai, China.,Human Phenome Institute, Fudan University, Shanghai, China
| | - Xiaoyu Liu
- Ministry of Education Key Laboratory of Contemporary Anthropology, Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Shanghai, China.,Human Phenome Institute, Fudan University, Shanghai, China
| | - Menghan Zhang
- Ministry of Education Key Laboratory of Contemporary Anthropology, Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Shanghai, China
| | - Jiucun Wang
- Human Phenome Institute, Fudan University, Shanghai, China.,State Key Laboratory of Genetic Engineering, Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Fudan University, Shanghai, China
| | - Momiao Xiong
- Human Genetics Center, School of Public Health, University of Texas Houston Health Sciences Center, Houston, TX, United States
| | - Yin Yao Shugart
- Human Phenome Institute, Fudan University, Shanghai, China.,State Key Laboratory of Genetic Engineering, Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Fudan University, Shanghai, China
| | - Li Jin
- Human Phenome Institute, Fudan University, Shanghai, China.,State Key Laboratory of Genetic Engineering, Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Fudan University, Shanghai, China
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Zhou S, Shi Z, Cui M, Li J, Ma Z, Shi Y, Zheng Z, Zhang F, Jin T, Geng T, Chen C, Guo Y, Zhou J, Huang S, Guo X, Gao L, Gong P, Gao X, Zhang K. A New Role for LOC101928437 in Non-Syndromic Intellectual Disability: Findings from a Family-Based Association Test. PLoS One 2015; 10:e0135669. [PMID: 26287547 PMCID: PMC4545728 DOI: 10.1371/journal.pone.0135669] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2014] [Accepted: 07/25/2015] [Indexed: 11/19/2022] Open
Abstract
Non-syndromic intellectual disability (NSID) is mental retardation in persons of normal physical appearance who have no recognisable features apart from obvious deficits in intellectual functioning and adaptive ability; however, its genetic etiology of most patients has remained unknown. The main purpose of this study was to fine map and identify specific causal gene(s) by genotyping a NSID family cohort using a panel of markers encompassing a target region reported in a previous work. A total of 139 families including probands, parents and relatives were included in the household survey, clinical examinations and intelligence tests, recruited from the Qinba mountain region of Shannxi province, western China. A collection of 34 tagged single nucleotide polymorphisms (tSNPs) spanning five microsatellite marker (STR) loci were genotyped using an iPLEX Gold assay. The association between tSNPs and patients was analyzed by family-based association testing (FBAT) and haplotype analysis (HBAT). Four markers (rs5974392, rs12164331, rs5929554 and rs3116911) in a block that showed strong linkage disequilibrium within the first three introns of the LOC101928437 locus were found to be significantly associated with NSID (all P<0.01) by the FBAT method for a single marker in additive, dominant and recessive models. The results of haplotype tests of this block also revealed a significant association with NSID (all P<0.05) using 2-window and larger HBAT analyses. These results suggest that LOC101928437 is a novel candidate gene for NSID in Han Chinese individuals of the Qinba region of China. Although the biological function of the gene has not been well studied, knowledge about this gene will provide insights that will increase our understanding of NSID development.
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Affiliation(s)
- Shaohe Zhou
- Key Laboratory of Resource Biology and Biotechnology in Western China (Ministry of Education), College of Life Science, Institute of Population and Health, Northwest University, Xi’an, China
| | - Zhangyan Shi
- Key Laboratory of Resource Biology and Biotechnology in Western China (Ministry of Education), College of Life Science, Institute of Population and Health, Northwest University, Xi’an, China
| | - Meng Cui
- Xi’an Institute of Mental Health, Xi’an, China
| | - Junlin Li
- Key Laboratory of Resource Biology and Biotechnology in Western China (Ministry of Education), College of Life Science, Institute of Population and Health, Northwest University, Xi’an, China
| | - Zhe Ma
- Key Laboratory of Resource Biology and Biotechnology in Western China (Ministry of Education), College of Life Science, Institute of Population and Health, Northwest University, Xi’an, China
| | - Yuanyu Shi
- Key Laboratory of Resource Biology and Biotechnology in Western China (Ministry of Education), College of Life Science, Institute of Population and Health, Northwest University, Xi’an, China
| | - Zijian Zheng
- College of Public Management, Institute of Application Psychology, Northwest University, Xi’an, China
| | - Fuchang Zhang
- Key Laboratory of Resource Biology and Biotechnology in Western China (Ministry of Education), College of Life Science, Institute of Population and Health, Northwest University, Xi’an, China
- College of Public Management, Institute of Application Psychology, Northwest University, Xi’an, China
| | - Tianbo Jin
- School of Life Sciences, Northwest University, Xi’an, Shaanxi, China
- National Engineering Research Center for Miniaturized Detection Systems, Xi’an, Shaanxi, China
| | - Tingting Geng
- School of Life Sciences, Northwest University, Xi’an, Shaanxi, China
- National Engineering Research Center for Miniaturized Detection Systems, Xi’an, Shaanxi, China
| | - Chao Chen
- School of Life Sciences, Northwest University, Xi’an, Shaanxi, China
- National Engineering Research Center for Miniaturized Detection Systems, Xi’an, Shaanxi, China
| | - Yale Guo
- The 2 Affiliated Hospital, Xi’an Jiaotong University, Xi’an, China
| | - Jianping Zhou
- The 2 Affiliated Hospital, Xi’an Jiaotong University, Xi’an, China
| | - Shaoping Huang
- The 2 Affiliated Hospital, Xi’an Jiaotong University, Xi’an, China
| | - Xingli Guo
- School of Computer Science and Technology, Xidian University, Xi'an Shaanxi, China
| | - Lin Gao
- School of Computer Science and Technology, Xidian University, Xi'an Shaanxi, China
| | - Pingyuan Gong
- Laboratory of Medical Molecular Biology, Henan University of Science and Technology, Luoyang, China
| | - Xiaocai Gao
- Key Laboratory of Resource Biology and Biotechnology in Western China (Ministry of Education), College of Life Science, Institute of Population and Health, Northwest University, Xi’an, China
- College of Public Management, Institute of Application Psychology, Northwest University, Xi’an, China
- * E-mail: (XG); (KZ)
| | - Kejin Zhang
- Key Laboratory of Resource Biology and Biotechnology in Western China (Ministry of Education), College of Life Science, Institute of Population and Health, Northwest University, Xi’an, China
- * E-mail: (XG); (KZ)
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Abstract
DNA variants in a 31-kb region of the human major histocompatibility complex, encompassing the tumor necrosis factor (TNF) gene cluster, were surveyed by direct sequencing of 283 unrelated individuals from six Chinese populations. A total of 273 polymorphic sites were identified, with nearly half of them novel. We observed an excess of rare variants and negative values of selection tests of the region, implying either that these populations experienced a historical expansion or that the surveyed region was subjected to natural selection. Different characteristics of the sequence variation in the six populations outline the genetic differentiation between Northern and Southern Chinese populations. The distributions of recombination rates are similar among all the populations, with variation in the magnitude and/or in the fine location of hot spots. Tag single-nucleotide polymorphisms (SNPs) selected from HapMap (Phase II) CHB data accounted for an average of 64% of common SNPs from the six Chinese populations. We also observed a limited transferability of tag SNPs between Chinese populations on the 31-kb region with an excess of untaggable SNPs and ragged linkage disequilibrium blocks. It suggested that the design and interpretation of future association studies should be more cautious, and that a resequencing approach may refine tag SNP selection on Chinese-specific disease mapping.
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Ding X, Weiss S, Raby B, Lange C, Laird NM. Impact of population stratification on family-based association tests with longitudinal measurements. Stat Appl Genet Mol Biol 2009; 8:Article 17. [PMID: 19222384 PMCID: PMC2861319 DOI: 10.2202/1544-6115.1398] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Abstract
Several family-based approaches for testing genetic association with traits obtained from longitudinal or repeated measurement studies have been previously proposed. These approaches utilize the multivariate data more efficiently by using estimated optimal weights to combine univariate tests. We show that these FBAT approaches are still robust against hidden population stratification, but their power can be heavily affected since the estimated weights might provide poor approximation of the true theoretical optimal weights with the presence of population stratification. We introduce a permutation-based approach FBAT-MinP and an equal combination approach FBAT-EW, both of which do not involve the use of estimated weights. Through simulation studies, FBAT-MinP and FBAT-EW are shown to be powerful even in the presence of population stratification, when other approaches may substantially lose their power. An application of these approaches to the Childhood Asthma Management Program (CAMP) study data for testing an association between body mass index and a previously reported candidate SNP is given as an example.
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