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A powerful parent-of-origin effects test for qualitative traits on X chromosome in general pedigrees. BMC Bioinformatics 2018; 19:8. [PMID: 29304743 PMCID: PMC5756386 DOI: 10.1186/s12859-017-2001-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2016] [Accepted: 12/18/2017] [Indexed: 11/10/2022] Open
Abstract
Background Genomic imprinting is one of the well-known epigenetic factors causing the association between traits and genes, and has generally been examined by detecting parent-of-origin effects of alleles. A lot of methods have been proposed to test for parent-of-origin effects on autosomes based on nuclear families and general pedigrees. Although these parent-of-origin effects tests on autosomes have been available for more than 15 years, there has been no statistical test developed to test for parent-of-origin effects on X chromosome, until the parental-asymmetry test on X chromosome (XPAT) and its extensions were recently proposed. However, these methods on X chromosome are only applicable to nuclear families and thus are not suitable for general pedigrees. Results In this article, we propose the pedigree parental-asymmetry test on X chromosome (XPPAT) statistic to test for parent-of-origin effects in the presence of association, which can accommodate general pedigrees. When there are missing genotypes in some pedigrees, we further develop the Monte Carlo pedigree parental-asymmetry test on X chromosome (XMCPPAT) to test for parent-of-origin effects, by inferring the missing genotypes given the observed genotypes based on a Monte Carlo estimation. An extensive simulation study has been carried out to investigate the type I error rates and the powers of the proposed tests. Our simulation results show that the proposed methods control the size well under the null hypothesis of no parent-of-origin effects. Moreover, XMCPPAT substantially outperforms the existing tests and has a much higher power than XPPAT which only uses complete nuclear families (with both parents) from pedigrees. We also apply the proposed methods to analyze rheumatoid arthritis data for their practical use. Conclusions The proposed XPPAT and XMCPPAT test statistics are valid and powerful in detecting parent-of-origin effects on X chromosome for qualitative traits based on general pedigrees and thus are recommended. Electronic supplementary material The online version of this article (10.1186/s12859-017-2001-5) contains supplementary material, which is available to authorized users.
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Li JL, Wang P, Fung WK, Zhou JY. Generalized disequilibrium test for association in qualitative traits incorporating imprinting effects based on extended pedigrees. BMC Genet 2017; 18:90. [PMID: 29037145 PMCID: PMC5644153 DOI: 10.1186/s12863-017-0560-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2017] [Accepted: 10/04/2017] [Indexed: 11/14/2022] Open
Abstract
BACKGROUND For dichotomous traits, the generalized disequilibrium test with the moment estimate of the variance (GDT-ME) is a powerful family-based association method. Genomic imprinting is an important epigenetic phenomenon and currently, there has been increasing interest of incorporating imprinting to improve the test power of association analysis. However, GDT-ME does not take imprinting effects into account, and it has not been investigated whether it can be used for association analysis when the effects indeed exist. RESULTS In this article, based on a novel decomposition of the genotype score according to the paternal or maternal source of the allele, we propose the generalized disequilibrium test with imprinting (GDTI) for complete pedigrees without any missing genotypes. Then, we extend GDTI and GDT-ME to accommodate incomplete pedigrees with some pedigrees having missing genotypes, by using a Monte Carlo (MC) sampling and estimation scheme to infer missing genotypes given available genotypes in each pedigree, denoted by MCGDTI and MCGDT-ME, respectively. The proposed GDTI and MCGDTI methods evaluate the differences of the paternal as well as maternal allele scores for all discordant relative pairs in a pedigree, including beyond first-degree relative pairs. Advantages of the proposed GDTI and MCGDTI test statistics over existing methods are demonstrated by simulation studies under various simulation settings and by application to the rheumatoid arthritis dataset. Simulation results show that the proposed tests control the size well under the null hypothesis of no association, and outperform the existing methods under various imprinting effect models. The existing GDT-ME and the proposed MCGDT-ME can be used to test for association even when imprinting effects exist. For the application to the rheumatoid arthritis data, compared to the existing methods, MCGDTI identifies more loci statistically significantly associated with the disease. CONCLUSIONS Under complete and incomplete imprinting effect models, our proposed GDTI and MCGDTI methods, by considering the information on imprinting effects and all discordant relative pairs within each pedigree, outperform all the existing test statistics and MCGDTI can recapture much of the missing information. Therefore, MCGDTI is recommended in practice.
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Affiliation(s)
- Jian-Long Li
- State Key Laboratory of Organ Failure Research, Ministry of Education, and Guangdong Provincial Key Laboratory of Tropical Disease Research, Department of Biostatistics, School of Public Health, Southern Medical University, Guangzhou, China
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Peng Wang
- State Key Laboratory of Organ Failure Research, Ministry of Education, and Guangdong Provincial Key Laboratory of Tropical Disease Research, Department of Biostatistics, School of Public Health, Southern Medical University, Guangzhou, China
| | - Wing Kam Fung
- Department of Statistics and Actuarial Science, The University of Hong Kong, Hong Kong, China
| | - Ji-Yuan Zhou
- State Key Laboratory of Organ Failure Research, Ministry of Education, and Guangdong Provincial Key Laboratory of Tropical Disease Research, Department of Biostatistics, School of Public Health, Southern Medical University, Guangzhou, China
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Zhou JY, You XP, Yang R, Fung WK. Detection of imprinting effects for qualitative traits on X chromosome based on nuclear families. Stat Methods Med Res 2016; 27:2329-2343. [PMID: 27920363 DOI: 10.1177/0962280216680243] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Methods for detecting imprinting effects have been developed primarily for autosomal markers. However, no method is available in the literature to test for imprinting effects on X chromosome. Therefore, it is necessary to suggest methods for detecting such imprinting effects. In this article, the parental-asymmetry test on X chromosome (XPAT) is first developed to test for imprinting for qualitative traits in the presence of association, based on family trios each with both parents and their affected daughter. Then, we propose 1-XPAT to deal with parent-daughter pairs, each with one parent and his/her affected daughter. By simultaneously considering family trios and parent-daughter pairs, C-XPAT (the combined test statistic of XPAT and 1-XPAT) is constructed to test for imprinting. Further, we extend the proposed methods to accommodate complete (with both parents) and incomplete (with one parent) nuclear families having multiple daughters of which at least one is affected. Simulation results demonstrate that the proposed methods control the size well, irrespective of the inbreeding coefficient in females being zero or non-zero. By incorporating incomplete nuclear families, C-XPAT is more powerful than XPAT using only complete nuclear families. For practical use, these proposed methods are applied to analyse the rheumatoid arthritis data and Turner's syndrome data.
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Affiliation(s)
- Ji-Yuan Zhou
- 1 State Key Laboratory of Organ Failure Research, Ministry of Education, and Guangdong Provincial Key Laboratory of Tropical Disease Research, Department of Biostatistics, School of Public Health, Southern Medical University, China
| | - Xiao-Ping You
- 2 Zhujiang Hospital, Southern Medical University, China
| | - Ran Yang
- 3 Department of Statistics and Actuarial Science, The University of Hong Kong, Hong Kong
| | - Wing Kam Fung
- 3 Department of Statistics and Actuarial Science, The University of Hong Kong, Hong Kong
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Fung WK, Yu K, Yang Y, Zhou JY. Efficient Monte Carlo evaluation of resampling-based hypothesis tests with applications to genetic epidemiology. Stat Methods Med Res 2016; 27:1437-1450. [PMID: 27507290 DOI: 10.1177/0962280216661876] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Monte Carlo evaluation of resampling-based tests is often conducted in statistical analysis. However, this procedure is generally computationally intensive. The pooling resampling-based method has been developed to reduce the computational burden but the validity of the method has not been studied before. In this article, we first investigate the asymptotic properties of the pooling resampling-based method and then propose a novel Monte Carlo evaluation procedure namely the n-times pooling resampling-based method. Theorems as well as simulations show that the proposed method can give smaller or comparable root mean squared errors and bias with much less computing time, thus can be strongly recommended especially for evaluating highly computationally intensive hypothesis testing procedures in genetic epidemiology.
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Affiliation(s)
- Wing K Fung
- 1 Department of Statistics and Actuarial Science, The University of Hong Kong, Hong Kong
| | - Kexin Yu
- 1 Department of Statistics and Actuarial Science, The University of Hong Kong, Hong Kong
| | - Yingrui Yang
- 1 Department of Statistics and Actuarial Science, The University of Hong Kong, Hong Kong
| | - Ji-Yuan Zhou
- 2 State Key Laboratory of Organ Failure Research, Ministry of Education; Guangdong Provincial Key Laboratory of Tropical Research, Department of Biostatistics, School of Public Health, Southern Medical University, China
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Zheng Y, Wang C, Zhang H, Shao C, Gao LH, Li SS, Yu WJ, He JW, Fu WZ, Hu YQ, Li M, Liu YJ, Zhang ZL. Polymorphisms in Wnt signaling pathway genes are associated with peak bone mineral density, lean mass, and fat mass in Chinese male nuclear families. Osteoporos Int 2016; 27:1805-15. [PMID: 26733379 DOI: 10.1007/s00198-015-3457-7] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/25/2015] [Accepted: 12/10/2015] [Indexed: 10/22/2022]
Abstract
UNLABELLED Our objective was to investigate the associations between polymorphisms in Wnt pathway genes and peak bone mineral density (BMD) and body composition in young Chinese men. Our study identified that WNT5B and CTNNBL1 for both BMD and body composition, and WNT4 and CTNNB1 gene polymorphisms contribute to the variation in BMD and body composition in young Chinese men, respectively. INTRODUCTION Our objective was to investigate the associations between polymorphisms in WNT4, WNT5B, WNT10B, WNT16, CTNNB1, and CTNNBL1 genes and peak bone mineral density (BMD), lean mass (LM), and fat mass (FM) in young Chinese men. METHODS Using SNPscan(TM) kits, 51 single-nucleotide polymorphisms (SNPs) located in the 6 genes were genotyped in a total of 1214 subjects from 399 Chinese nuclear families. BMD, total lean mass (TLM), and total fat mass (TFM) were measured using dual energy X-ray absorptiometry (DXA). The associations between the 51 SNPs and peak BMD and body composition [including the TLM, percentage lean mass (PLM), TFM, percentage fat mass (PFM), and the body mass index (BMI)] were analyzed through quantitative transmission disequilibrium tests (QTDTs). RESULTS For peak BMD, we found significant within-family associations of rs2240506, rs7308793, and rs4765830 in the WNT5B gene and rs10917157 in the WNT4 gene with the lumbar spine BMD (all P < 0.05). We detected an association of rs11830202, rs3809269, rs1029628, and rs6489301 in the WNT5B gene and rs2293303 in the CTNNB1 gene with body composition (all P < 0.05). For the CTNNBL1 gene, six SNPs (rs6126098, rs6091103, rs238303, rs6067647, rs8126174, and rs4811144) were associated with peak BMD of the lumbar spine, femoral neck, or total hip (all P < 0.05). Furthermore, two of the six SNPs (rs8126174 and rs4811144) were associated with body composition. CONCLUSIONS This study identified WNT5B and CTNNBL1 for peak BMD and body composition in males from the Han Chinese ethnic group, and the results suggest a site-specific gene regulation. The WNT4 and CTNNB1 gene polymorphisms contribute to the variation in peak BMD and body composition, respectively.
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Affiliation(s)
- Y Zheng
- Metabolic Bone Disease and Genetic Research Unit, Department of Osteoporosis and Bone Diseases, Shanghai Key Clinical Center for Metabolic Disease, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, 600 Yi-Shan Road, Shanghai, 200233, People's Republic of China
- Department of Endocrinology, Yueqing Hospital Affiliated with Wenzhou Medical University, 318 Qing-Yuan Road, Yueqing, Zhejiang, 325600, People's Republic of China
| | - C Wang
- Metabolic Bone Disease and Genetic Research Unit, Department of Osteoporosis and Bone Diseases, Shanghai Key Clinical Center for Metabolic Disease, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, 600 Yi-Shan Road, Shanghai, 200233, People's Republic of China
| | - H Zhang
- Metabolic Bone Disease and Genetic Research Unit, Department of Osteoporosis and Bone Diseases, Shanghai Key Clinical Center for Metabolic Disease, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, 600 Yi-Shan Road, Shanghai, 200233, People's Republic of China
| | - C Shao
- Metabolic Bone Disease and Genetic Research Unit, Department of Osteoporosis and Bone Diseases, Shanghai Key Clinical Center for Metabolic Disease, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, 600 Yi-Shan Road, Shanghai, 200233, People's Republic of China
| | - L-H Gao
- Metabolic Bone Disease and Genetic Research Unit, Department of Osteoporosis and Bone Diseases, Shanghai Key Clinical Center for Metabolic Disease, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, 600 Yi-Shan Road, Shanghai, 200233, People's Republic of China
| | - S-S Li
- Metabolic Bone Disease and Genetic Research Unit, Department of Osteoporosis and Bone Diseases, Shanghai Key Clinical Center for Metabolic Disease, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, 600 Yi-Shan Road, Shanghai, 200233, People's Republic of China
| | - W-J Yu
- Metabolic Bone Disease and Genetic Research Unit, Department of Osteoporosis and Bone Diseases, Shanghai Key Clinical Center for Metabolic Disease, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, 600 Yi-Shan Road, Shanghai, 200233, People's Republic of China
| | - J-W He
- Metabolic Bone Disease and Genetic Research Unit, Department of Osteoporosis and Bone Diseases, Shanghai Key Clinical Center for Metabolic Disease, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, 600 Yi-Shan Road, Shanghai, 200233, People's Republic of China
| | - W-Z Fu
- Metabolic Bone Disease and Genetic Research Unit, Department of Osteoporosis and Bone Diseases, Shanghai Key Clinical Center for Metabolic Disease, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, 600 Yi-Shan Road, Shanghai, 200233, People's Republic of China
| | - Y-Q Hu
- Metabolic Bone Disease and Genetic Research Unit, Department of Osteoporosis and Bone Diseases, Shanghai Key Clinical Center for Metabolic Disease, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, 600 Yi-Shan Road, Shanghai, 200233, People's Republic of China
| | - M Li
- Metabolic Bone Disease and Genetic Research Unit, Department of Osteoporosis and Bone Diseases, Shanghai Key Clinical Center for Metabolic Disease, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, 600 Yi-Shan Road, Shanghai, 200233, People's Republic of China
| | - Y-J Liu
- Metabolic Bone Disease and Genetic Research Unit, Department of Osteoporosis and Bone Diseases, Shanghai Key Clinical Center for Metabolic Disease, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, 600 Yi-Shan Road, Shanghai, 200233, People's Republic of China
| | - Z-L Zhang
- Metabolic Bone Disease and Genetic Research Unit, Department of Osteoporosis and Bone Diseases, Shanghai Key Clinical Center for Metabolic Disease, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, 600 Yi-Shan Road, Shanghai, 200233, People's Republic of China.
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Abstract
Genomic imprinting is a genetic phenomenon in which certain alleles are differentially expressed in a parent-of-origin-specific manner, and plays an important role in the study of complex traits. For a diallelic marker locus in human, the parentalasymmetry tests Q-PAT(c) with any constant c were developed to detect parent-of-origin effects for quantitative traits. However, these methods can only be applied to deal with nuclear families and thus are not suitable for extended pedigrees. In this study, by making no assumption about the distribution of the quantitative trait, we first propose the pedigree parentalasymmetry tests Q-PPAT(c) with any constant c for quantitative traits to test for parent-of-origin effects based on nuclear families with complete information from general pedigree data, in the presence of association between marker alleles under study and quantitative traits. When there are any genotypes missing in pedigrees, we utilize Monte Carlo (MC) sampling and estimation and develop the Q-MCPPAT(c) statistics to test for parent-of-origin effects. Various simulation studies are conducted to assess the performance of the proposed methods, for different sample sizes, genotype missing rates, degrees of imprinting effects and population models. Simulation results show that the proposed methods control the size well under the null hypothesis of no parent-of-origin effects and Q-PPAT(c) are robust to population stratification. In addition, the power comparison demonstrates that Q-PPAT(c) and Q-MCPPAT(c) for pedigree data are much more powerful than Q-PAT(c) only using two-generation nuclear families selected from extended pedigrees.
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A powerful association test for qualitative traits incorporating imprinting effects using general pedigree data. J Hum Genet 2014; 60:77-83. [PMID: 25518739 DOI: 10.1038/jhg.2014.109] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2014] [Revised: 11/19/2014] [Accepted: 11/24/2014] [Indexed: 11/08/2022]
Abstract
For qualitative traits and diallelic marker loci, the pedigree disequilibrium test (PDT) based on general pedigrees and its extension (Monte Carlo PDT (MCPDT)) for dealing with missing genotypes are simple and powerful tests for association. There is an increasing interest of incorporating imprinting into association analysis. However, PDT and MCPDT do not take account of the information on imprinting effects in the analysis, which may reduce their test powers when the effects are present. On the other hand, the transmission disequilibrium test with imprinting (TDTI*) combines imprinting into the mapping of association variants. However, TDTI* only accommodates two-generation nuclear families and thus is not suitable for extended pedigrees. In this article, we first extend PDT to incorporate imprinting and propose PDTI for complete pedigrees (no missing genotypes). To fully utilize pedigrees with missing genotypes, we further develop the Monte Carlo PDTI (MCPDTI) statistic based on Monte Carlo sampling and estimation. Both PDTI and MCPDTI are derived in a two-stage framework. Simulation study shows that PDTI and MCPDTI control the size well under the null hypothesis of no association and are more powerful than PDT and TDTI* (based on a sample of nuclear families randomly selecting from pedigrees) when imprinting effects exist.
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Li X, Sui Y, Liu T, Wang J, Li Y, Lin Z, Hegarty J, Koltun WA, Wang Z, Wu R. A model for family-based case-control studies of genetic imprinting and epistasis. Brief Bioinform 2013; 15:1069-79. [PMID: 23887693 DOI: 10.1093/bib/bbt050] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
Abstract
Genetic imprinting, or called the parent-of-origin effect, has been recognized to play an important role in the formation and pathogenesis of human diseases. Although the epigenetic mechanisms that establish genetic imprinting have been a focus of many genetic studies, our knowledge about the number of imprinting genes and their chromosomal locations and interactions with other genes is still scarce, limiting precise inference of the genetic architecture of complex diseases. In this article, we present a statistical model for testing and estimating the effects of genetic imprinting on complex diseases using a commonly used case-control design with family structure. For each subject sampled from a case and control population, we not only genotype its own single nucleotide polymorphisms (SNPs) but also collect its parents' genotypes. By tracing the transmission pattern of SNP alleles from parental to offspring generation, the model allows the characterization of genetic imprinting effects based on Pearson tests of a 2 × 2 contingency table. The model is expanded to test the interactions between imprinting effects and additive, dominant and epistatic effects in a complex web of genetic interactions. Statistical properties of the model are investigated, and its practical usefulness is validated by a real data analysis. The model will provide a useful tool for genome-wide association studies aimed to elucidate the picture of genetic control over complex human diseases.
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Powerful tests for association on quantitative trait loci incorporating imprinting effects. J Hum Genet 2013; 58:384-90. [PMID: 23552672 DOI: 10.1038/jhg.2013.22] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Abstract
Genomic imprinting is an important epigenetic factor in complex traits study, and there has recently been considerable interest in association study for quantitative traits by incorporating imprinting. However, these methods need the assumptions of Hardy-Weinberg equilibrium or only use information from families with one child. In this paper, by taking imprinting into account and making no assumption about the distribution of the quantitative traits, we propose two novel classes of Q-C-TDTI(c) and Q-C-MAX(c) family-based association tests for quantitative traits. The tests flexibly accommodate family data with missing parental genotype and with multiple siblings. Q-C-TDTI(c) is derived from a two-stage analysis, where in the first stage Q-C-PAT(c) is applied to test for imprinting effects and in the second stage we select the most appropriate statistic among three transmission disequilibrium tests for association according to the finding from Q-C-PAT(c). Another proposed Q-C-MAX(c) approach takes the maximum of the three statistics. Compared with the existing alternative methods, the simulation results demonstrate that the two proposed tests are robust to population stratification and have better performance for testing association under various scenarios. Further, the powerful and versatile Q-C-TDTI(c) test is applied to analyze Framingham Heart Study data.
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Robust Joint Analysis with Data Fusion in Two-Stage Quantitative Trait Genome-Wide Association Studies. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2013; 2013:843563. [PMID: 24288575 PMCID: PMC3832968 DOI: 10.1155/2013/843563] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/06/2013] [Accepted: 07/29/2013] [Indexed: 11/17/2022]
Abstract
Genome-wide association studies (GWASs) in identifying the disease-associated genetic variants
have been proved to be a great pioneering work. Two-stage design and analysis are often adopted in
GWASs. Considering the genetic model uncertainty, many robust procedures have been proposed and
applied in GWASs. However, the existing approaches mostly focused on binary traits, and few work
has been done on continuous (quantitative) traits, since the statistical significance of these robust tests
is difficult to calculate. In this paper, we develop a powerful F-statistic-based robust joint analysis
method for quantitative traits using the combined raw data from both stages in the framework of
two-staged GWASs. Explicit expressions are obtained to calculate the statistical significance and
power. We show using simulations that the proposed method is substantially more robust than the
F-test based on the additive model when the underlying genetic model is unknown. An example for
rheumatic arthritis (RA) is used for illustration.
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A powerful parent-of-origin effects test for qualitative traits incorporating control children in nuclear families. J Hum Genet 2012; 57:500-7. [PMID: 22648181 DOI: 10.1038/jhg.2012.58] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Abstract
Genomic imprinting is an important epigenetic phenomenon in studying complex traits and has generally been examined by detecting parent-of-origin effects of alleles. The parental-asymmetry test (PAT) based on nuclear families with both parents and its extensions to deal with missing parental genotypes is simple and powerful for such a task. However, these methods only use case (affected) children in nuclear families and thus do not make full use of information on control (unaffected) children, if available, in these families. In this article, we propose a novel parent-of-origin effects test C-PATu (the combined test of PATu and 1-PATu) by using both the control and case children in nuclear families with one or both parents. C-PATu is essentially a weighted framework, in which the test based on all the control children and their parents and that based on all the case children and their parents are weighted according to the population disease prevalence. Simulation results demonstrate that the proposed tests control the size well under no parent-of-origin effects and using additional information from control children improves the power of the tests under the imprinting alternative. Application of C-PATu to a Framingham Heart Study data set further shows the feasibility in practical application of the test.
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