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Lau PY, Yeung KF, Zhou JY, Fung WK. Two Powerful Tests for Parent-of-Origin Effects at Quantitative Trait Loci on the X Chromosome. Hum Hered 2019; 83:250-273. [PMID: 30959502 DOI: 10.1159/000496987] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2018] [Accepted: 01/14/2019] [Indexed: 11/19/2022] Open
Abstract
Parent-of-origin effects, which describe an occurrence where the expression of a gene depends on its parental origin, are an important phenomenon in epigenetics. Statistical methods for detecting parent-of-origin effects on autosomes have been investigated for 20 years, but the development of statistical methods for detecting parent-of-origin effects on the X chromosome is relatively new. In the literature, a class of Q-XPAT-type tests are the only tests for the parent-of-origin effects for quantitative traits on the X chromosome. In this paper, we propose two simple and powerful classes of tests to detect parent-of-origin effects for quantitative trait values on the X chromosome. The proposed tests can accommodate complete and incomplete nuclear families with any number of daughters. The simulation study shows that our proposed tests produce empirical type I error rates that are close to their respective nominal levels, as well as powers that are larger than those of the Q-XPAT-type tests. The proposed tests are applied to a real data set on Turner's syndrome, and the proposed tests give a more significant finding than the Q-C-XPAT test.
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Affiliation(s)
- Pui Yin Lau
- Department of Statistics and Actuarial Science, The University of Hong Kong, Hong Kong SAR, China
| | - Kar Fu Yeung
- Department of Statistics and Actuarial Science, The University of Hong Kong, Hong Kong SAR, China
| | - Ji-Yuan Zhou
- State Key Laboratory of Organ Failure Research, Ministry of Education, Guangzhou, China.,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 SAR, China,
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Wang Q, Gan J, Wei K, Berceli SA, Gragnoli C, Wu R. A unified mapping framework of multifaceted pharmacodynamic responses to hypertension interventions. Drug Discov Today 2019; 24:883-889. [PMID: 30690194 PMCID: PMC6492935 DOI: 10.1016/j.drudis.2019.01.009] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2018] [Revised: 01/03/2019] [Accepted: 01/17/2019] [Indexed: 02/04/2023]
Abstract
The personalized therapy for hypertension needs comprehensive knowledge about how blood pressures (BPs; systolic and diastolic) and their pulsatile and steady components are controlled by genetic factors. Here, we propose a unified pharmacodynamic (PD) functional mapping framework for identifying specific quantitative trait loci (QTLs) that mediate multivariate response-dose curves of BP. This framework can characterize how QTLs govern pulsatile and steady components through jointly regulating systolic and diastolic pressures. The model can quantify the genetic effects of individual QTLs on maximal drug effect, the maximal rate of drug response, and the dose window of maximal drug response. This unified mapping framework provides a tool for identifying pharmacological genes potentially useful to design the right medication and right dose for patients.
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Affiliation(s)
- Qian Wang
- Center for Computational Biology, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China
| | - Jingwen Gan
- Center for Computational Biology, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China
| | - Kun Wei
- Center for Computational Biology, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China
| | - Scott A Berceli
- Malcom Randall VA Medical Center, Gainesville, FL 32610, USA; Department of Surgery, University of Florida, Box 100128, Gainesville, FL 32610, USA; Department of Biomedical Engineering, University of Florida, Gainesville, FL 32610, USA
| | - Claudia Gragnoli
- Division of Endocrinology, Diabetes, and Metabolic Disease, Translational Medicine, Department of Medicine, Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, PA 19107, USA; Department of Public Health Sciences, Penn State College of Medicine, Hershey, PA 17033, USA; Molecular Biology Laboratory, Bios Biotech Multi Diagnostic Health Center, Rome 00197, Italy
| | - Rongling Wu
- Center for Computational Biology, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China; Department of Public Health Sciences, Penn State College of Medicine, Hershey, PA 17033, USA; Center for Statistical Genetics, Departments of Public Health Sciences and Statistics, Pennsylvania State University, Hershey, PA 17033, USA.
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Detecting Rare Mutations with Heterogeneous Effects Using a Family-Based Genetic Random Field Method. Genetics 2018; 210:463-476. [PMID: 30104420 DOI: 10.1534/genetics.118.301266] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2018] [Accepted: 07/29/2018] [Indexed: 01/19/2023] Open
Abstract
The genetic etiology of many complex diseases is highly heterogeneous. A complex disease can be caused by multiple mutations within the same gene or mutations in multiple genes at various genomic loci. Although these disease-susceptibility mutations can be collectively common in the population, they are often individually rare or even private to certain families. Family-based studies are powerful for detecting rare variants enriched in families, which is an important feature for sequencing studies due to the heterogeneous nature of rare variants. In addition, family designs can provide robust protection against population stratification. Nevertheless, statistical methods for analyzing family-based sequencing data are underdeveloped, especially those accounting for heterogeneous etiology of complex diseases. In this article, we introduce a random field framework for detecting gene-phenotype associations in family-based sequencing studies, referred to as family-based genetic random field (FGRF). Similar to existing family-based association tests, FGRF could utilize within-family and between-family information separately or jointly to test an association. We demonstrate that FGRF has comparable statistical power with existing methods when there is no genetic heterogeneity, but can improve statistical power when there is genetic heterogeneity across families. The proposed method also shares the same advantages with the conventional family-based association tests (e.g., being robust to population stratification). Finally, we applied the proposed method to a sequencing data from the Minnesota Twin Family Study, and revealed several genes, including SAMD14, potentially associated with alcohol dependence.
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Lin Z, Wang Z, Hegarty JP, Lin TR, Wang Y, Deiling S, Wu R, Thomas NJ, Floros J. Genetic association and epistatic interaction of the interleukin-10 signaling pathway in pediatric inflammatory bowel disease. World J Gastroenterol 2017; 23:4897-4909. [PMID: 28785144 PMCID: PMC5526760 DOI: 10.3748/wjg.v23.i27.4897] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/27/2016] [Revised: 02/18/2017] [Accepted: 06/01/2017] [Indexed: 02/06/2023] Open
Abstract
AIM To study the genetic association and epistatic interaction of the interleukin (IL)-10 and IL-10/STAT3 pathways in pediatric inflammatory bowel disease (IBD).
METHODS A total of 159 pediatric inflammatory IBD patients (Crohn’s disease, n = 136; ulcerative colitis, n = 23) and 129 matched controls were studied for genetic association of selected single nucleotide polymorphisms (SNPs) of the IL-10 gene and the genes IL10RA, IL10RB, STAT3, and HO1, from the IL-10/STAT3 signaling pathway. As interactions between SNPs from different loci may significantly affect the associated risk for disease, additive (a) and dominant (d) modeling of SNP interactions was also performed to examine high-order epistasis between combinations of the individual SNPs.
RESULTS The results showed that IL-10 rs304496 was associated with pediatric IBD (P = 0.022), but no association was found for two other IL-10 SNPs, rs1800872 and rs2034498, or for SNPs in genes IL10RA, IL10RB, STAT3, and HO1. However, analysis of epistatic interaction among these genes showed significant interactions: (1) between two IL-10 SNPs rs1800872 and rs3024496 (additive-additive P = 0.00015, Bonferroni P value (Bp) = 0.003); (2) between IL-10RB rs2834167 and HO1 rs2071746 (dominant-additive, P = 0.0018, Bp = 0.039); and (3) among IL-10 rs1800872, IL10RB rs2834167, and HO1 rs2071746 (additive-dominant-additive, P = 0.00015, Bp = 0.005), as well as weak interactions among IL-10 rs1800872, IL-10 rs3024496, and IL-10RA (additive-additive-additive, P = 0.003; Bp = 0.099), and among IL10RA, IL10RB, and HO1 genes (additive-dominant-additive, P = 0.008, Bp = 0.287).
CONCLUSION These results indicate that both the IL-10 gene itself, and through epistatic interaction with genes within the IL-10/STAT3 signaling pathway, contribute to the risk of pediatric IBD.
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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: 0.4] [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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Zhang F, Khalili A, Lin S. Optimum study design for detecting imprinting and maternal effects based on partial likelihood. Biometrics 2015; 72:95-105. [PMID: 26288102 DOI: 10.1111/biom.12380] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2014] [Revised: 06/01/2015] [Accepted: 07/01/2015] [Indexed: 11/28/2022]
Abstract
Despite spectacular advances in molecular genomic technologies in the past two decades, resources available for genomic studies are still finite and limited, especially for family-based studies. Hence, it is important to consider an optimum study design to maximally utilize limited resources to increase statistical power in family-based studies. A particular question of interest is whether it is more profitable to genotype siblings of probands or to recruit more independent families. Numerous studies have attempted to address this study design issue for simultaneous detection of imprinting and maternal effects, two important epigenetic factors for studying complex diseases. The question is far from settled, however, mainly due to the fact that results and recommendations in the literature are based on anecdotal evidence from limited simulation studies rather than based on rigorous statistical analysis. In this article, we propose a systematic approach to study various designs based on a partial likelihood formulation. We derive the asymptotic properties and obtain formulas for computing the information contents of study designs being considered. Our results show that, for a common disease, recruiting additional siblings is beneficial because both affected and unaffected individuals will be included. However, if a disease is rare, then any additional siblings recruited are most likely to be unaffected, thus contributing little additional information; in such cases, additional families will be a better choice with a fixed amount of resources. Our work thus offers a practical strategy for investigators to select the optimum study design within a case-control family scheme before data collection.
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Affiliation(s)
- Fangyuan Zhang
- Department of Statistics, The Ohio State University, 1958 Neil Avenue, Columbus, Ohio 43210, U.S.A
| | - Abbas Khalili
- Department of Mathematics and Statistics, McGill University, 805 Sherbrooke Street West, Montreal, Quebec H3A 0B9, Canada
| | - Shili Lin
- Department of Statistics, The Ohio State University, 1958 Neil Avenue, Columbus, Ohio 43210, U.S.A
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Abstract
Despite increasing emphasis on the genetic study of quantitative traits, we are still far from being able to chart a clear picture of their genetic architecture, given an inherent complexity involved in trait formation. A competing theory for studying such complex traits has emerged by viewing their phenotypic formation as a "system" in which a high-dimensional group of interconnected components act and interact across different levels of biological organization from molecules through cells to whole organisms. This system is initiated by a machinery of DNA sequences that regulate a cascade of biochemical pathways to synthesize endophenotypes and further assemble these endophenotypes toward the end-point phenotype in virtue of various developmental changes. This review focuses on a conceptual framework for genetic mapping of complex traits by which to delineate the underlying components, interactions and mechanisms that govern the system according to biological principles and understand how these components function synergistically under the control of quantitative trait loci (QTLs) to comprise a unified whole. This framework is built by a system of differential equations that quantifies how alterations of different components lead to the global change of trait development and function, and provides a quantitative and testable platform for assessing the multiscale interplay between QTLs and development. The method will enable geneticists to shed light on the genetic complexity of any biological system and predict, alter or engineer its physiological and pathological states.
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Affiliation(s)
- Lidan Sun
- National Engineering Research Center for Floriculture, College of Landscape Architecture, Beijing Forestry University, Beijing 100083, China; Center for Statistical Genetics, Departments of Public Health Sciences and Statistics, The Pennsylvania State University, Hershey, PA 17033, USA
| | - Rongling Wu
- Center for Computational Biology, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China; Center for Statistical Genetics, Departments of Public Health Sciences and Statistics, The Pennsylvania State University, Hershey, PA 17033, USA.
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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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Zhang F, Lin S. Nonparametric method for detecting imprinting effect using all members of general pedigrees with missing data. J Hum Genet 2014; 59:541-8. [PMID: 25119724 DOI: 10.1038/jhg.2014.67] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2014] [Revised: 06/05/2014] [Accepted: 06/26/2014] [Indexed: 11/09/2022]
Abstract
Imprinting effects can lead to parent-of-origin patterns in complex human diseases. For a diallelic marker locus, Pedigree Parental-Asymmetry Test (PPAT) and its extension MCPPAT using pedigrees allowing for missing genotypes are simple and powerful for detecting imprinting effects. However, these approaches only take affected offspring into consideration, thus not making full use of the data available. In this paper, we propose Monte Carlo Pedigree Parental-Asymmetry Test using both affected and unaffected (MCPPATu) offsprings, which allows for missing genotypes through Monte Carlo sampling. Simulation studies demonstrate that MCPPATu controls the empirical type I error rate well under the null hypotheses of no parent-of-origin effects. It is also demonstrated that the use of additional information from unaffected offspring and partially observed genotypes in the analysis can greatly improve the statistical power. Indeed, for common diseases, MCPPATu is much more powerful than MCPPAT when all genotypes are observed and the power improvement is even greater when there is missing data. For rarer diseases, there are still substantial power gains with the inclusion of unaffected offspring, although the gains are less impressive compared with those for more common diseases.
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Affiliation(s)
- Fangyuan Zhang
- Department of Statistics, The Ohio State University, Columbus, OH, USA
| | - Shili Lin
- Department of Statistics, The Ohio State University, Columbus, OH, USA
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