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Christoffersen B, Mahjani B, Clements M, Kjellström H, Humphreys K. Quasi-Monte Carlo Methods for Binary Event Models with Complex Family Data. J Comput Graph Stat 2022. [DOI: 10.1080/10618600.2022.2151454] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/03/2022]
Affiliation(s)
- Benjamin Christoffersen
- Division of Robotics, Perception and Learning, KTH Royal Institute of Technology
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet
- Swedish e-Science Research Center (SeRC)
| | - Behrang Mahjani
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet
- Seaver Autism Center for Research and Treatment, Icahn School of Medicine at Mount Sinai
| | - Mark Clements
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet
- Swedish e-Science Research Center (SeRC)
| | - Hedvig Kjellström
- Division of Robotics, Perception and Learning, KTH Royal Institute of Technology
- Swedish e-Science Research Center (SeRC)
| | - Keith Humphreys
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet
- Swedish e-Science Research Center (SeRC)
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2
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Datta AS, Lin S, Biswas S. A Family-Based Rare Haplotype Association Method for Quantitative Traits. Hum Hered 2019; 83:175-195. [PMID: 30799419 DOI: 10.1159/000493543] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2018] [Accepted: 09/07/2018] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND The variants identified in genome-wide association studies account for only a small fraction of disease heritability. A key to this "missing heritability" is believed to be rare variants. Specifically, we focus on rare haplotype variant (rHTV). The existing methods for detecting rHTV are mostly population-based, and as such, are susceptible to population stratification and admixture, leading to an inflated false-positive rate. Family-based methods are more robust in this respect. METHODS We propose a method for detecting rHTVs associated with quantitative traits called family-based quantitative Bayesian LASSO (famQBL). FamQBL can analyze any type of pedigree and is based on a mixed model framework. We regularize the haplotype effects using Bayesian LASSO and estimate the posterior distributions using Markov chain Monte Carlo methods. RESULTS We conduct simulation studies, including analyses of Genetic Analysis Workshop 18 simulated data, to study the properties of famQBL and compare with a standard family-based haplotype association test implemented in FBAT (family-based association test) software. We find famQBL to be more powerful than FBAT with well-controlled false-positive rates. We also apply famQBL to the Framingham Heart Study data and detect an rHTV associated with diastolic blood pressure. CONCLUSION FamQBL can help uncover rHTVs associated with quantitative traits.
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Affiliation(s)
- Ananda S Datta
- Department of Mathematical Sciences, University of Texas at Dallas, Richardson, Texas, USA
| | - Shili Lin
- Department of Statistics, The Ohio State University, Columbus, Ohio, USA
| | - Swati Biswas
- Department of Mathematical Sciences, University of Texas at Dallas, Richardson, Texas, USA,
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3
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Fan BJ, Chen X, Sondhi N, Sharmila PF, Soumittra N, Sripriya S, Sacikala S, Asokan R, Friedman DS, Pasquale LR, Gao XR, Vijaya L, Cooke Bailey J, Vitart V, MacGregor S, Hammond CJ, Khor CC, Haines JL, George R, Wiggs JL. Family-Based Genome-Wide Association Study of South Indian Pedigrees Supports WNT7B as a Central Corneal Thickness Locus. Invest Ophthalmol Vis Sci 2019; 59:2495-2502. [PMID: 29847655 PMCID: PMC5961220 DOI: 10.1167/iovs.17-23536] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023] Open
Abstract
Purpose To identify genetic risk factors contributing to central corneal thickness (CCT) in individuals from South India, a population with a high prevalence of ocular disorders. Methods One hundred ninety-five individuals from 15 large South Indian pedigrees were genotyped using the Omni2.5 bead array. Family-based association for CCT was conducted using the score test in MERLIN. Results Genome-wide association study (GWAS) identified strongest association for single nucleotide polymorphisms (SNPs) in the first intron of WNT7B and CCT (top SNP rs9330813; β = −0.57, 95% confidence interval [CI]: −0.78 to −0.36; P = 1.7 × 10−7). We further investigated rs9330813 in a Latino cohort and four independent European cohorts. A meta-analysis of these data sets demonstrated statistically significant association between rs9330813 and CCT (β = −3.94, 95% CI: −5.23 to −2.66; P = 1.7 × 10−9). WNT7B SNPs located in the same genomic region that includes rs9330813 have previously been associated with CCT in Latinos but with other ocular quantitative traits related to myopia (corneal curvature and axial length) in a Japanese population (rs10453441 and rs200329677). To evaluate the specificity of the observed WNT7B association with CCT in the South Indian families, we completed an ocular phenome-wide association study (PheWAS) for the top WNT7B SNPs using 45 ocular traits measured in these same families including corneal curvature and axial length. The ocular PheWAS results indicate that in the South Indian families WNT7B SNPs are primarily associated with CCT. Conclusions The results indicate robust evidence for association between WNT7B SNPs and CCT in South Indian pedigrees, and suggest that WNT7B SNPs can have population-specific effects on ocular quantitative traits.
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Affiliation(s)
- Bao Jian Fan
- Department of Ophthalmology, Harvard Medical School, Massachusetts Eye and Ear Infirmary, Boston, Massachusetts, United States
| | - Xueli Chen
- Department of Ophthalmology & Visual Science, Eye & Ear Nose Throat Hospital, Shanghai Medical College, Fudan University, Shanghai, China
| | - Nisha Sondhi
- Department of Ophthalmology, Harvard Medical School, Massachusetts Eye and Ear Infirmary, Boston, Massachusetts, United States
| | - P Ferdinamarie Sharmila
- SNONGC Department of Genetics and Molecular Biology, Vision Research Foundation, Sankara Nethralaya, Chennai, India
| | - Nagasamy Soumittra
- SNONGC Department of Genetics and Molecular Biology, Vision Research Foundation, Sankara Nethralaya, Chennai, India
| | - Sarangapani Sripriya
- SNONGC Department of Genetics and Molecular Biology, Vision Research Foundation, Sankara Nethralaya, Chennai, India
| | - Srinivasan Sacikala
- SNONGC Department of Genetics and Molecular Biology, Vision Research Foundation, Sankara Nethralaya, Chennai, India
| | - Rashima Asokan
- Medical Research Foundation, Sankara Nethralaya, Chennai, India
| | - David S Friedman
- The Dana Center for Preventive Ophthalmology, Johns Hopkins Medical School, Wilmer Eye Institute, Baltimore, Maryland, United States
| | - Louis R Pasquale
- Department of Ophthalmology, Harvard Medical School, Massachusetts Eye and Ear Infirmary, Boston, Massachusetts, United States.,Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, United States
| | - X Raymond Gao
- Department of Ophthalmology and Visual Sciences, University of Illinois at Chicago, Chicago, Illinois, United States
| | - Lingam Vijaya
- Medical Research Foundation, Sankara Nethralaya, Chennai, India
| | - Jessica Cooke Bailey
- Department of Epidemiology and Biostatistics, Institute for Computational Biology, Case Western Reserve University School of Medicine, Cleveland, Ohio, United States
| | - Veronique Vitart
- MRC Human Genetics Unit, Institute for Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, United Kingdom
| | - Stuart MacGregor
- QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Christopher J Hammond
- Department of Twin Research and Genetic Epidemiology, King's College London, London, United Kingdom
| | - Chiea Chuen Khor
- Division of Human Genetics, Genome Institute of Singapore, Singapore
| | - Jonathan L Haines
- Department of Epidemiology and Biostatistics, Institute for Computational Biology, Case Western Reserve University School of Medicine, Cleveland, Ohio, United States
| | - Ronnie George
- Medical Research Foundation, Sankara Nethralaya, Chennai, India
| | - Janey L Wiggs
- Department of Ophthalmology, Harvard Medical School, Massachusetts Eye and Ear Infirmary, Boston, Massachusetts, United States
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4
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Ullah E, Mall R, Abbas MM, Kunji K, Nato AQ, Bensmail H, Wijsman EM, Saad M. Comparison and assessment of family- and population-based genotype imputation methods in large pedigrees. Genome Res 2018; 29:125-134. [PMID: 30514702 PMCID: PMC6314157 DOI: 10.1101/gr.236315.118] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2018] [Accepted: 11/30/2018] [Indexed: 01/19/2023]
Abstract
Genotype imputation is widely used in genome-wide association studies to boost variant density, allowing increased power in association testing. Many studies currently include pedigree data due to increasing interest in rare variants coupled with the availability of appropriate analysis tools. The performance of population-based (subjects are unrelated) imputation methods is well established. However, the performance of family- and population-based imputation methods on family data has been subject to much less scrutiny. Here, we extensively compare several family- and population-based imputation methods on family data of large pedigrees with both European and African ancestry. Our comparison includes many widely used family- and population-based tools and another method, Ped_Pop, which combines family- and population-based imputation results. We also compare four subject selection strategies for full sequencing to serve as the reference panel for imputation: GIGI-Pick, ExomePicks, PRIMUS, and random selection. Moreover, we compare two imputation accuracy metrics: the Imputation Quality Score and Pearson's correlation R 2 for predicting power of association analysis using imputation results. Our results show that (1) GIGI outperforms Merlin; (2) family-based imputation outperforms population-based imputation for rare variants but not for common ones; (3) combining family- and population-based imputation outperforms all imputation approaches for all minor allele frequencies; (4) GIGI-Pick gives the best selection strategy based on the R 2 criterion; and (5) R 2 is the best measure of imputation accuracy. Our study is the first to extensively evaluate the imputation performance of many available family- and population-based tools on the same family data and provides guidelines for future studies.
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Affiliation(s)
- Ehsan Ullah
- Qatar Computing Research Institute, Hamad Bin Khalifa University, Doha, Qatar
| | - Raghvendra Mall
- Qatar Computing Research Institute, Hamad Bin Khalifa University, Doha, Qatar
| | - Mostafa M Abbas
- Qatar Computing Research Institute, Hamad Bin Khalifa University, Doha, Qatar
| | - Khalid Kunji
- Qatar Computing Research Institute, Hamad Bin Khalifa University, Doha, Qatar
| | - Alejandro Q Nato
- Division of Medical Genetics, Department of Medicine, University of Washington, Seattle, Washington 98195-9460, USA.,Department of Biomedical Sciences, Joan C. Edwards School of Medicine, Marshall University, Huntington, West Virginia 25755, USA
| | - Halima Bensmail
- Qatar Computing Research Institute, Hamad Bin Khalifa University, Doha, Qatar
| | - Ellen M Wijsman
- Division of Medical Genetics, Department of Medicine, University of Washington, Seattle, Washington 98195-9460, USA.,Department of Biostatistics, University of Washington, Seattle, Washington 98195-9460, USA
| | - Mohamad Saad
- Qatar Computing Research Institute, Hamad Bin Khalifa University, Doha, Qatar
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Silva CT, Zorkoltseva IV, Niemeijer MN, van den Berg ME, Amin N, Demirkan A, van Leeuwen E, Iglesias AI, Piñeros-Hernández LB, Restrepo CM, Kors JA, Kirichenko AV, Willemsen R, Oostra BA, Stricker BH, Uitterlinden AG, Axenovich TI, van Duijn CM, Isaacs A. A combined linkage, microarray and exome analysis suggests MAP3K11 as a candidate gene for left ventricular hypertrophy. BMC Med Genomics 2018; 11:22. [PMID: 29506515 PMCID: PMC5838853 DOI: 10.1186/s12920-018-0339-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2017] [Accepted: 02/21/2018] [Indexed: 01/19/2023] Open
Abstract
BACKGROUND Electrocardiographic measures of left ventricular hypertrophy (LVH) are used as predictors of cardiovascular risk. We combined linkage and association analyses to discover novel rare genetic variants involved in three such measures and two principal components derived from them. METHODS The study was conducted among participants from the Erasmus Rucphen Family Study (ERF), a Dutch family-based sample from the southwestern Netherlands. Variance components linkage analyses were performed using Merlin. Regions of interest (LOD > 1.9) were fine-mapped using microarray and exome sequence data. RESULTS We observed one significant LOD score for the second principal component on chromosome 15 (LOD score = 3.01) and 12 suggestive LOD scores. Several loci contained variants identified in GWAS for these traits; however, these did not explain the linkage peaks, nor did other common variants. Exome sequence data identified two associated variants after multiple testing corrections were applied. CONCLUSIONS We did not find common SNPs explaining these linkage signals. Exome sequencing uncovered a relatively rare variant in MAPK3K11 on chromosome 11 (MAF = 0.01) that helped account for the suggestive linkage peak observed for the first principal component. Conditional analysis revealed a drop in LOD from 2.01 to 0.88 for MAP3K11, suggesting that this variant may partially explain the linkage signal at this chromosomal location. MAP3K11 is related to the JNK pathway and is a pro-apoptotic kinase that plays an important role in the induction of cardiomyocyte apoptosis in various pathologies, including LVH.
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Affiliation(s)
- Claudia Tamar Silva
- Genetic Epidemiology Unit, Department of Epidemiology, Erasmus University Medical Center, Rotterdam, the Netherlands
- Center for Research in Genetics and Genomics (CIGGUR), Institute of Translational Medicine (IMT), GENIUROS Research group, School of Medicine and Health Science, Universidad del Rosario, Bogotá, Colombia
- Doctoral Program in Biomedical Sciences, Universidad del Rosario, Bogotá, Colombia
| | | | - Maartje N. Niemeijer
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Marten E. van den Berg
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Najaf Amin
- Genetic Epidemiology Unit, Department of Epidemiology, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Ayşe Demirkan
- Genetic Epidemiology Unit, Department of Epidemiology, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Elisa van Leeuwen
- Genetic Epidemiology Unit, Department of Epidemiology, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Adriana I. Iglesias
- Genetic Epidemiology Unit, Department of Epidemiology, Erasmus University Medical Center, Rotterdam, the Netherlands
- Department of Clinical Genetics, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Laura B. Piñeros-Hernández
- Center for Research in Genetics and Genomics (CIGGUR), Institute of Translational Medicine (IMT), GENIUROS Research group, School of Medicine and Health Science, Universidad del Rosario, Bogotá, Colombia
| | - Carlos M. Restrepo
- Center for Research in Genetics and Genomics (CIGGUR), Institute of Translational Medicine (IMT), GENIUROS Research group, School of Medicine and Health Science, Universidad del Rosario, Bogotá, Colombia
| | - Jan A. Kors
- Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, the Netherlands
| | | | - Rob Willemsen
- Department of Clinical Genetics, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Ben A. Oostra
- Genetic Epidemiology Unit, Department of Epidemiology, Erasmus University Medical Center, Rotterdam, the Netherlands
- Center for Medical Systems Biology, Leiden, the Netherlands
| | - Bruno H. Stricker
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, the Netherlands
- Department of Internal Medicine, Erasmus University Medical Center, Rotterdam, the Netherlands
- Inspectorate of Health care, The Hague, the Netherlands
| | - André G. Uitterlinden
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, the Netherlands
- Department of Internal Medicine, Erasmus University Medical Center, Rotterdam, the Netherlands
| | | | - Cornelia M. van Duijn
- Genetic Epidemiology Unit, Department of Epidemiology, Erasmus University Medical Center, Rotterdam, the Netherlands
- Center for Medical Systems Biology, Leiden, the Netherlands
| | - Aaron Isaacs
- Genetic Epidemiology Unit, Department of Epidemiology, Erasmus University Medical Center, Rotterdam, the Netherlands
- CARIM School for Cardiovascular Diseases, Maastricht Centre for Systems Biology (MaCSBio), and Department of Biochemistry, Maastricht University, Maastricht, the Netherlands
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Silva CT, Zorkoltseva IV, Amin N, Demirkan A, van Leeuwen EM, Kors JA, van den Berg M, Stricker BH, Uitterlinden AG, Kirichenko AV, Witteman JCM, Willemsen R, Oostra BA, Axenovich TI, van Duijn CM, Isaacs A. A Combined Linkage and Exome Sequencing Analysis for Electrocardiogram Parameters in the Erasmus Rucphen Family Study. Front Genet 2016; 7:190. [PMID: 27877193 PMCID: PMC5099142 DOI: 10.3389/fgene.2016.00190] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2016] [Accepted: 10/11/2016] [Indexed: 12/30/2022] Open
Abstract
Electrocardiogram (ECG) measurements play a key role in the diagnosis and prediction of cardiac arrhythmias and sudden cardiac death. ECG parameters, such as the PR, QRS, and QT intervals, are known to be heritable and genome-wide association studies of these phenotypes have been successful in identifying common variants; however, a large proportion of the genetic variability of these traits remains to be elucidated. The aim of this study was to discover loci potentially harboring rare variants utilizing variance component linkage analysis in 1547 individuals from a large family-based study, the Erasmus Rucphen Family Study (ERF). Linked regions were further explored using exome sequencing. Five suggestive linkage peaks were identified: two for QT interval (1q24, LOD = 2.63; 2q34, LOD = 2.05), one for QRS interval (1p35, LOD = 2.52) and two for PR interval (9p22, LOD = 2.20; 14q11, LOD = 2.29). Fine-mapping using exome sequence data identified a C > G missense variant (c.713C > G, p.Ser238Cys) in the FCRL2 gene associated with QT (rs74608430; P = 2.8 × 10-4, minor allele frequency = 0.019). Heritability analysis demonstrated that the SNP explained 2.42% of the trait’s genetic variability in ERF (P = 0.02). Pathway analysis suggested that the gene is involved in cytosolic Ca2+ levels (P = 3.3 × 10-3) and AMPK stimulated fatty acid oxidation in muscle (P = 4.1 × 10-3). Look-ups in bioinformatics resources showed that expression of FCRL2 is associated with ARHGAP24 and SETBP1 expression. This finding was not replicated in the Rotterdam study. Combining the bioinformatics information with the association and linkage analyses, FCRL2 emerges as a strong candidate gene for QT interval.
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Affiliation(s)
- Claudia T Silva
- Genetic Epidemiology Unit, Department of Epidemiology, Erasmus University Medical CenterRotterdam, Netherlands; Doctoral Program in Biomedical Sciences, Universidad del RosarioBogotá, Colombia; GENIUROS Group, Genetics and Genomics Research Center CIGGUR, School of Medicine and Health Sciences, Universidad del RosarioBogotá, Colombia
| | - Irina V Zorkoltseva
- Institute of Cytology and Genetics, Siberian Branch of Russian Academy of Sciences Novosibirsk, Russia
| | - Najaf Amin
- Genetic Epidemiology Unit, Department of Epidemiology, Erasmus University Medical Center Rotterdam, Netherlands
| | - Ayşe Demirkan
- Genetic Epidemiology Unit, Department of Epidemiology, Erasmus University Medical CenterRotterdam, Netherlands; Department of Human Genetics, Leiden University Medical CenterLeiden, Netherlands
| | - Elisabeth M van Leeuwen
- Genetic Epidemiology Unit, Department of Epidemiology, Erasmus University Medical Center Rotterdam, Netherlands
| | - Jan A Kors
- Department of Medical Informatics, Erasmus University Medical Center Rotterdam, Netherlands
| | - Marten van den Berg
- Department of Medical Informatics, Erasmus University Medical Center Rotterdam, Netherlands
| | - Bruno H Stricker
- Department of Epidemiology, Erasmus University Medical CenterRotterdam, Netherlands; Department of Internal Medicine, Erasmus University Medical CenterRotterdam, Netherlands; Inspectorate of Health CareThe Hague, Netherlands
| | - André G Uitterlinden
- Department of Internal Medicine, Erasmus University Medical Center Rotterdam, Netherlands
| | - Anatoly V Kirichenko
- Institute of Cytology and Genetics, Siberian Branch of Russian Academy of Sciences Novosibirsk, Russia
| | | | - Rob Willemsen
- Department of Clinical Genetics, Erasmus University Medical Center Rotterdam, Netherlands
| | - Ben A Oostra
- Genetic Epidemiology Unit, Department of Epidemiology, Erasmus University Medical CenterRotterdam, Netherlands; Center for Medical Systems BiologyLeiden, Netherlands
| | - Tatiana I Axenovich
- Institute of Cytology and Genetics, Siberian Branch of Russian Academy of Sciences Novosibirsk, Russia
| | - Cornelia M van Duijn
- Genetic Epidemiology Unit, Department of Epidemiology, Erasmus University Medical CenterRotterdam, Netherlands; Center for Medical Systems BiologyLeiden, Netherlands
| | - Aaron Isaacs
- Genetic Epidemiology Unit, Department of Epidemiology, Erasmus University Medical CenterRotterdam, Netherlands; Center for Medical Systems BiologyLeiden, Netherlands
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Zhuang WV, Murabito JM, Lunetta KL. Phenotypically Enriched Genotypic Imputation in Genetic Association Tests. Hum Hered 2016; 81:35-45. [PMID: 27576319 DOI: 10.1159/000446986] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2014] [Accepted: 05/20/2016] [Indexed: 11/19/2022] Open
Abstract
BACKGROUND In longitudinal epidemiological studies there may be individuals with rich phenotype data who die or are lost to follow-up before providing DNA for genetic studies. Often, the genotypic and phenotypic data of the relatives are available. Two strategies for analyzing the incomplete data are to exclude ungenotyped subjects from analysis (the complete-case method, CC) and to include phenotyped but ungenotyped individuals in analysis by using relatives' genotypes for genotype imputation (GI). In both strategies, the information in the phenotypic data was not used to handle the missing-genotype problem. METHODS We propose a phenotypically enriched genotypic imputation (PEGI) method that uses the EM (expectation-maximization)-based maximum likelihood method to incorporate observed phenotypes into genotype imputation. RESULTS Our simulations with genotypes missing completely at random show that, for a single-nucleotide polymorphism (SNP) with moderate to strong effect on a phenotype, PEGI improves power more than GI without excess type I errors. Using the Framingham Heart Study data set, we compare the ability of the PEGI, GI, and CC to detect the associations between 5 SNPs and age at natural menopause. CONCLUSION The PEGI method may improve power to detect an association over both CC and GI under many circumstances.
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Affiliation(s)
- Wei Vivian Zhuang
- Department of Biostatistics, Boston University School of Public Health, Boston, Mass., USA
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Levine AP, Connor TMF, Oygar DD, Neild GH, Segal AW, Maxwell PH, Gale DP. Combinatorial Conflicting Homozygosity (CCH) analysis enables the rapid identification of shared genomic regions in the presence of multiple phenocopies. BMC Genomics 2015; 16:163. [PMID: 25888400 PMCID: PMC4364077 DOI: 10.1186/s12864-015-1360-4] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2014] [Accepted: 02/19/2015] [Indexed: 11/19/2022] Open
Abstract
Background The ability to identify regions of the genome inherited with a dominant trait in one or more families has become increasingly valuable with the wide availability of high throughput sequencing technology. While a number of methods exist for mapping of homozygous variants segregating with recessive traits in consanguineous families, dominant conditions are conventionally analysed by linkage analysis, which requires computationally demanding haplotype reconstruction from marker genotypes and, even using advanced parallel approximation implementations, can take substantial time, particularly for large pedigrees. In addition, linkage analysis lacks sensitivity in the presence of phenocopies (individuals sharing the trait but not the genetic variant responsible). Combinatorial Conflicting Homozygosity (CCH) analysis uses high density biallelic single nucleotide polymorphism (SNP) marker genotypes to identify genetic loci within which consecutive markers are not homozygous for different alleles. This allows inference of identical by descent (IBD) inheritance of a haplotype among a set or subsets of related or unrelated individuals. Results A single genome-wide conflicting homozygosity analysis takes <3 seconds and parallelisation permits multiple combinations of subsets of individuals to be analysed quickly. Analysis of unrelated individuals demonstrated that in the absence of IBD inheritance, runs of no CH exceeding 4 cM are not observed. At this threshold, CCH is >97% sensitive and specific for IBD regions within a pedigree exceeding this length and was able to identify the locus responsible for a dominantly inherited kidney disease in a Turkish Cypriot family in which six out 17 affected individuals were phenocopies. It also revealed shared ancestry at the disease-linked locus among affected individuals from two different Cypriot populations. Conclusions CCH does not require computationally demanding haplotype reconstruction and can detect regions of shared inheritance of a haplotype among subsets of related or unrelated individuals directly from SNP genotype data. In contrast to parametric linkage allowing for phenocopies, CCH directly provides the exact number and identity of individuals sharing each locus. CCH can also identify regions of shared ancestry among ostensibly unrelated individuals who share a trait. CCH is implemented in Python and is freely available (as source code) from http://sourceforge.net/projects/cchsnp/. Electronic supplementary material The online version of this article (doi:10.1186/s12864-015-1360-4) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Adam P Levine
- Division of Medicine, University College London, London, UK.
| | | | - D Deren Oygar
- Nicosia State Hospital, Burhan Nalbantoğlu State Hospital, Nicosia, North Cyprus.
| | - Guy H Neild
- Division of Medicine, University College London, London, UK.
| | - Anthony W Segal
- Division of Medicine, University College London, London, UK.
| | | | - Daniel P Gale
- Division of Medicine, University College London, London, UK. .,UCL Centre for Nephrology Rowland, Hill Street, Royal Free Hospital, Rowland Hill Street, London, NW3 2PF, UK.
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9
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Aslibekyan S, Vaughan LK, Wiener HW, Lemas DJ, Klimentidis YC, Havel PJ, Stanhope KL, O'Brien DM, Hopkins SE, Boyer BB, Tiwari HK. Evidence for novel genetic loci associated with metabolic traits in Yup'ik people. Am J Hum Biol 2013; 25:673-80. [PMID: 23907821 PMCID: PMC3785243 DOI: 10.1002/ajhb.22429] [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] [Received: 03/11/2013] [Revised: 06/24/2013] [Accepted: 06/29/2013] [Indexed: 12/14/2022] Open
Abstract
OBJECTIVES To identify genomic regions associated with fasting plasma lipid profiles, insulin, glucose, and glycosylated hemoglobin in a Yup'ik study population, and to evaluate whether the observed associations between genetic factors and metabolic traits were modified by dietary intake of marine derived omega-3 polyunsaturated acids (n-3 PUFA). METHODS A genome-wide linkage scan was conducted among 982 participants of the Center for Alaska Native Health Research study. n-3 PUFA intake was estimated using the nitrogen stable isotope ratio (δ(15) N) of erythrocytes. All genotyped SNPs located within genomic regions with LOD scores > 2 were subsequently tested for individual SNP associations with metabolic traits using linear models that account for familial correlation as well as age, sex, community group, and n-3 PUFA intake. Separate linear models were fit to evaluate interactions between the genotype of interest and n-3 PUFA intake. RESULTS We identified several chromosomal regions linked to serum apolipoprotein A2, high density lipoprotein-, low density lipoprotein-, and total cholesterol, insulin, and glycosylated hemoglobin. Genetic variants found to be associated with total cholesterol mapped to a region containing previously validated lipid loci on chromosome 19, and additional novel peaks of biological interest were identified at 11q12.2-11q13.2. We did not observe any significant interactions between n-3 PUFA intake, genotypes, and metabolic traits. CONCLUSIONS We have completed a whole genome linkage scan for metabolic traits in Native Alaskans, confirming previously identified loci, and offering preliminary evidence of novel loci implicated in chronic disease pathogenesis in this population.
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Affiliation(s)
- Stella Aslibekyan
- Department of Epidemiology, University of Alabama at Birmingham, Birmingham, AL 35294
| | - Laura Kelly Vaughan
- Section on Statistical Genetics, Department of Biostatistics, University of Alabama at Birmingham, Birmingham, AL 35294
| | - Howard W. Wiener
- Department of Epidemiology, University of Alabama at Birmingham, Birmingham, AL 35294
| | - Dominick J. Lemas
- Department of Pediatrics, University of Colorado Anschutz Medical Campus, Aurora, CO 80045
| | - Yann C. Klimentidis
- Epidemiology and Biostatistics Division, Mel and Enid Zuckerman College of Public Health, University of Arizona, Tucson, AZ 85724
| | - Peter J. Havel
- Departments of Nutrition and Molecular Biosciences, University of California at Davis, Davis, CA 95616
| | - Kimber L. Stanhope
- Departments of Nutrition and Molecular Biosciences, University of California at Davis, Davis, CA 95616
| | - Diane M. O'Brien
- Center for Alaska Native Health Research, Institute of Arctic Biology, University of Alaska at Fairbanks, Fairbanks, AK 99775
| | - Scarlett E. Hopkins
- Center for Alaska Native Health Research, Institute of Arctic Biology, University of Alaska at Fairbanks, Fairbanks, AK 99775
| | - Bert B. Boyer
- Center for Alaska Native Health Research, Institute of Arctic Biology, University of Alaska at Fairbanks, Fairbanks, AK 99775
| | - Hemant K. Tiwari
- Section on Statistical Genetics, Department of Biostatistics, University of Alabama at Birmingham, Birmingham, AL 35294
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Silberstein M, Weissbrod O, Otten L, Tzemach A, Anisenia A, Shtark O, Tuberg D, Galfrin E, Gannon I, Shalata A, Borochowitz ZU, Dechter R, Thompson E, Geiger D. A system for exact and approximate genetic linkage analysis of SNP data in large pedigrees. Bioinformatics 2012; 29:197-205. [PMID: 23162081 DOI: 10.1093/bioinformatics/bts658] [Citation(s) in RCA: 34] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
MOTIVATION The use of dense single nucleotide polymorphism (SNP) data in genetic linkage analysis of large pedigrees is impeded by significant technical, methodological and computational challenges. Here we describe Superlink-Online SNP, a new powerful online system that streamlines the linkage analysis of SNP data. It features a fully integrated flexible processing workflow comprising both well-known and novel data analysis tools, including SNP clustering, erroneous data filtering, exact and approximate LOD calculations and maximum-likelihood haplotyping. The system draws its power from thousands of CPUs, performing data analysis tasks orders of magnitude faster than a single computer. By providing an intuitive interface to sophisticated state-of-the-art analysis tools coupled with high computing capacity, Superlink-Online SNP helps geneticists unleash the potential of SNP data for detecting disease genes. RESULTS Computations performed by Superlink-Online SNP are automatically parallelized using novel paradigms, and executed on unlimited number of private or public CPUs. One novel service is large-scale approximate Markov Chain-Monte Carlo (MCMC) analysis. The accuracy of the results is reliably estimated by running the same computation on multiple CPUs and evaluating the Gelman-Rubin Score to set aside unreliable results. Another service within the workflow is a novel parallelized exact algorithm for inferring maximum-likelihood haplotyping. The reported system enables genetic analyses that were previously infeasible. We demonstrate the system capabilities through a study of a large complex pedigree affected with metabolic syndrome. AVAILABILITY Superlink-Online SNP is freely available for researchers at http://cbl-hap.cs.technion.ac.il/superlink-snp. The system source code can also be downloaded from the system website. CONTACT omerw@cs.technion.ac.il SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Mark Silberstein
- Department of Computer Science, Technion-Israel Institute of Technology, Haifa, Israel
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Axenovich TI, Aulchenko YS. MQScore_SNP software for multipoint parametric linkage analysis of quantitative traits in large pedigrees. Ann Hum Genet 2010; 74:286-9. [PMID: 20529018 DOI: 10.1111/j.1469-1809.2010.00576.x] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
We describe software for multipoint parametric linkage analysis of quantitative traits using information about SNP genotypes. A mixed model of major gene and polygene inheritance is implemented in this software. Implementation of several algorithms to avoid computational underflow and decrease running time permits application of our software to the analysis of very large pedigrees collected in human genetically isolated populations. We tested our software by performing linkage analysis of adult height in a large pedigree from a Dutch isolated population. Three significant and four suggestive loci were identified with the help of our programs, whereas variance-component-based linkage analysis, which requires the pedigree fragmentation, demonstrated only three suggestive peaks. The software package MQScore_SNP is available at http://mga.bionet.nsc.ru/soft/index.html.
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Affiliation(s)
- Tatiana I Axenovich
- Institute of Cytology & Genetics, Siberian Division, Russian Academy of Sciences, Novosibirsk, 630090, Russia.
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