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Boutry S, Helaers R, Lenaerts T, Vikkula M. Rare variant association on unrelated individuals in case-control studies using aggregation tests: existing methods and current limitations. Brief Bioinform 2023; 24:bbad412. [PMID: 37974506 DOI: 10.1093/bib/bbad412] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Revised: 10/14/2023] [Accepted: 10/28/2023] [Indexed: 11/19/2023] Open
Abstract
Over the past years, progress made in next-generation sequencing technologies and bioinformatics have sparked a surge in association studies. Especially, genome-wide association studies (GWASs) have demonstrated their effectiveness in identifying disease associations with common genetic variants. Yet, rare variants can contribute to additional disease risk or trait heterogeneity. Because GWASs are underpowered for detecting association with such variants, numerous statistical methods have been recently proposed. Aggregation tests collapse multiple rare variants within a genetic region (e.g. gene, gene set, genomic loci) to test for association. An increasing number of studies using such methods successfully identified trait-associated rare variants and led to a better understanding of the underlying disease mechanism. In this review, we compare existing aggregation tests, their statistical features and scope of application, splitting them into the five classical classes: burden, adaptive burden, variance-component, omnibus and other. Finally, we describe some limitations of current aggregation tests, highlighting potential direction for further investigations.
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Affiliation(s)
- Simon Boutry
- Human Molecular Genetics, de Duve Institute, University of Louvain, Avenue Hippocrate 74 (+5) bte B1.74.06, 1200 Brussels, Belgium
- Interuniversity Institute of Bioinformatics in Brussels, Université Libre de Bruxelles-Vrije Universiteit Brussels, 1050 Brussels, Belgium
| | - Raphaël Helaers
- Human Molecular Genetics, de Duve Institute, University of Louvain, Avenue Hippocrate 74 (+5) bte B1.74.06, 1200 Brussels, Belgium
| | - Tom Lenaerts
- Interuniversity Institute of Bioinformatics in Brussels, Université Libre de Bruxelles-Vrije Universiteit Brussels, 1050 Brussels, Belgium
- Machine Learning Group, Université Libre de Bruxelles, 1050 Brussels, Belgium
- Artificial Intelligence laboratory, Vrije Universiteit Brussel, 1050 Brussels, Belgium
| | - Miikka Vikkula
- Human Molecular Genetics, de Duve Institute, University of Louvain, Avenue Hippocrate 74 (+5) bte B1.74.06, 1200 Brussels, Belgium
- WELBIO department, WEL Research Institute, avenue Pasteur, 6, 1300 Wavre, Belgium
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2
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Jung Y, Zhang H, Hu J. Transformed low-rank ANOVA models for high-dimensional variable selection. Stat Methods Med Res 2018; 28:1230-1246. [DOI: 10.1177/0962280217753726] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
High-dimensional data are often encountered in biomedical, environmental, and other studies. For example, in biomedical studies that involve high-throughput omic data, an important problem is to search for genetic variables that are predictive of a particular phenotype. A conventional solution is to characterize such relationships through regression models in which a phenotype is treated as the response variable and the variables are treated as covariates; this approach becomes particularly challenging when the number of variables exceeds the number of samples. We propose a general framework for expressing the transformed mean of high-dimensional variables in an exponential distribution family via ANOVA models in which a low-rank interaction space captures the association between the phenotype and the variables. This alternative method transforms the variable selection problem into a well-posed problem with the number of observations larger than the number of variables. In addition, we propose a model selection criterion for the new model framework with a diverging number of parameters, and establish the consistency of the selection criterion. We demonstrate the appealing performance of the proposed method in terms of prediction and detection accuracy through simulations and real data analyses.
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Affiliation(s)
- Yoonsuh Jung
- Department of Statistics, Korea University, Seoul, South Korea
| | - Hong Zhang
- Institute of Biostatistics, Fudan University, Shanghai, People’s Republic of China
| | - Jianhua Hu
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
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3
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Druley TE, Wang L, Lin SJ, Lee JH, Zhang Q, Daw EW, Abel HJ, Chasnoff SE, Ramos EI, Levinson BT, Thyagarajan B, Newman AB, Christensen K, Mayeux R, Province MA. Candidate gene resequencing to identify rare, pedigree-specific variants influencing healthy aging phenotypes in the long life family study. BMC Geriatr 2016; 16:80. [PMID: 27060904 PMCID: PMC4826550 DOI: 10.1186/s12877-016-0253-y] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2015] [Accepted: 04/04/2016] [Indexed: 11/22/2022] Open
Abstract
Background The Long Life Family Study (LLFS) is an international study to identify the genetic components of various healthy aging phenotypes. We hypothesized that pedigree-specific rare variants at longevity-associated genes could have a similar functional impact on healthy phenotypes. Methods We performed custom hybridization capture sequencing to identify the functional variants in 464 candidate genes for longevity or the major diseases of aging in 615 pedigrees (4,953 individuals) from the LLFS, using a multiplexed, custom hybridization capture. Variants were analyzed individually or as a group across an entire gene for association to aging phenotypes using family based tests. Results We found significant associations to three genes and nine single variants. Most notably, we found a novel variant significantly associated with exceptional survival in the 3’ UTR OBFC1 in 13 individuals from six pedigrees. OBFC1 (chromosome 10) is involved in telomere maintenance, and falls within a linkage peak recently reported from an analysis of telomere length in LLFS families. Two different algorithms for single gene associations identified three genes with an enrichment of variation that was significantly associated with three phenotypes (GSK3B with the Healthy Aging Index, NOTCH1 with diastolic blood pressure and TP53 with serum HDL). Conclusions Sequencing analysis of family-based associations for age-related phenotypes can identify rare or novel variants. Electronic supplementary material The online version of this article (doi:10.1186/s12877-016-0253-y) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Todd E Druley
- Center for Genome Sciences and Systems Biology, Washington University School of Medicine, 660 South Euclid Avenue, Campus Box 8116, St. Louis, MO, 63108, USA.,Department of Pediatrics, Washington University School of Medicine, 660 South Euclid Avenue, Campus Box 8116, St. Louis, MO, 63108, USA
| | - Lihua Wang
- Center for Genome Sciences and Systems Biology, Washington University School of Medicine, 660 South Euclid Avenue, Campus Box 8116, St. Louis, MO, 63108, USA.,Division of Statistical Genomics, Department of Genetics, Washington University School of Medicine, St. Louis, MO, USA
| | - Shiow J Lin
- Center for Genome Sciences and Systems Biology, Washington University School of Medicine, 660 South Euclid Avenue, Campus Box 8116, St. Louis, MO, 63108, USA.,Division of Statistical Genomics, Department of Genetics, Washington University School of Medicine, St. Louis, MO, USA
| | - Joseph H Lee
- Sergievsky Center, College of Physicians and Surgeons, Columbia University New York, New York, NY, USA.,Taub Institute, College of Physicians and Surgeons, Columbia University New York, New York, NY, USA.,Department of Epidemiology, School of Public Health, Columbia University New York, New York, NY, USA
| | - Qunyuan Zhang
- Center for Genome Sciences and Systems Biology, Washington University School of Medicine, 660 South Euclid Avenue, Campus Box 8116, St. Louis, MO, 63108, USA.,Division of Statistical Genomics, Department of Genetics, Washington University School of Medicine, St. Louis, MO, USA
| | - E Warwick Daw
- Center for Genome Sciences and Systems Biology, Washington University School of Medicine, 660 South Euclid Avenue, Campus Box 8116, St. Louis, MO, 63108, USA.,Division of Statistical Genomics, Department of Genetics, Washington University School of Medicine, St. Louis, MO, USA
| | - Haley J Abel
- Center for Genome Sciences and Systems Biology, Washington University School of Medicine, 660 South Euclid Avenue, Campus Box 8116, St. Louis, MO, 63108, USA.,Division of Statistical Genomics, Department of Genetics, Washington University School of Medicine, St. Louis, MO, USA
| | - Sara E Chasnoff
- Center for Genome Sciences and Systems Biology, Washington University School of Medicine, 660 South Euclid Avenue, Campus Box 8116, St. Louis, MO, 63108, USA.,Department of Pediatrics, Washington University School of Medicine, 660 South Euclid Avenue, Campus Box 8116, St. Louis, MO, 63108, USA
| | - Enrique I Ramos
- Center for Genome Sciences and Systems Biology, Washington University School of Medicine, 660 South Euclid Avenue, Campus Box 8116, St. Louis, MO, 63108, USA.,Department of Pediatrics, Washington University School of Medicine, 660 South Euclid Avenue, Campus Box 8116, St. Louis, MO, 63108, USA
| | - Benjamin T Levinson
- Center for Genome Sciences and Systems Biology, Washington University School of Medicine, 660 South Euclid Avenue, Campus Box 8116, St. Louis, MO, 63108, USA.,Department of Pediatrics, Washington University School of Medicine, 660 South Euclid Avenue, Campus Box 8116, St. Louis, MO, 63108, USA
| | - Bharat Thyagarajan
- Department of Laboratory Medicine and Pathology, University of Minnesota, Minneapolis, MN, USA
| | - Anne B Newman
- Department of Epidemiology, University of Pittsburgh Graduate School of Public Health, Pittsburgh, PA, USA
| | - Kaare Christensen
- The Danish Aging Research Center, Epidemiology, University of Southern Denmark, Odense, Denmark
| | - Richard Mayeux
- Gertrude H. Sergievsky Center and the Taub Institute for Research on Alzheimer's Disease and the Aging Brain, Columbia University, New York City, NY, USA
| | - Michael A Province
- Center for Genome Sciences and Systems Biology, Washington University School of Medicine, 660 South Euclid Avenue, Campus Box 8116, St. Louis, MO, 63108, USA. .,Division of Statistical Genomics, Department of Genetics, Washington University School of Medicine, St. Louis, MO, USA.
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4
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Pan W, Kwak IY, Wei P. A Powerful Pathway-Based Adaptive Test for Genetic Association with Common or Rare Variants. Am J Hum Genet 2015; 97:86-98. [PMID: 26119817 DOI: 10.1016/j.ajhg.2015.05.018] [Citation(s) in RCA: 54] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2015] [Accepted: 05/21/2015] [Indexed: 12/11/2022] Open
Abstract
In spite of the success of genome-wide association studies (GWASs), only a small proportion of heritability for each complex trait has been explained by identified genetic variants, mainly SNPs. Likely reasons include genetic heterogeneity (i.e., multiple causal genetic variants) and small effect sizes of causal variants, for which pathway analysis has been proposed as a promising alternative to the standard single-SNP-based analysis. A pathway contains a set of functionally related genes, each of which includes multiple SNPs. Here we propose a pathway-based test that is adaptive at both the gene and SNP levels, thus maintaining high power across a wide range of situations with varying numbers of the genes and SNPs associated with a trait. The proposed method is applicable to both common variants and rare variants and can incorporate biological knowledge on SNPs and genes to boost statistical power. We use extensively simulated data and a WTCCC GWAS dataset to compare our proposal with several existing pathway-based and SNP-set-based tests, demonstrating its promising performance and its potential use in practice.
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5
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Zhang Q. Associating rare genetic variants with human diseases. Front Genet 2015; 6:133. [PMID: 25904936 PMCID: PMC4389536 DOI: 10.3389/fgene.2015.00133] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2015] [Accepted: 03/19/2015] [Indexed: 11/20/2022] Open
Affiliation(s)
- Qunyuan Zhang
- Division of Statistical Genomics, Washington University School of Medicine St. Louis, MO, USA
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6
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Dering C, König IR, Ramsey LB, Relling MV, Yang W, Ziegler A. A comprehensive evaluation of collapsing methods using simulated and real data: excellent annotation of functionality and large sample sizes required. Front Genet 2014; 5:323. [PMID: 25309579 PMCID: PMC4164031 DOI: 10.3389/fgene.2014.00323] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2014] [Accepted: 08/28/2014] [Indexed: 01/23/2023] Open
Abstract
The advent of next generation sequencing (NGS) technologies enabled the investigation of the rare variant-common disease hypothesis in unrelated individuals, even on the genome-wide level. Analysis of this hypothesis requires tailored statistical methods as single marker tests fail on rare variants. An entire class of statistical methods collapses rare variants from a genomic region of interest (ROI), thereby aggregating rare variants. In an extensive simulation study using data from the Genetic Analysis Workshop 17 we compared the performance of 15 collapsing methods by means of a variety of pre-defined ROIs regarding minor allele frequency thresholds and functionality. Findings of the simulation study were additionally confirmed by a real data set investigating the association between methotrexate clearance and the SLCO1B1 gene in patients with acute lymphoblastic leukemia. Our analyses showed substantially inflated type I error levels for many of the proposed collapsing methods. Only four approaches yielded valid type I errors in all considered scenarios. None of the statistical tests was able to detect true associations over a substantial proportion of replicates in the simulated data. Detailed annotation of functionality of variants is crucial to detect true associations. These findings were confirmed in the analysis of the real data. Recent theoretical work showed that large power is achieved in gene-based analyses only if large sample sizes are available and a substantial proportion of causing rare variants is present in the gene-based analysis. Many of the investigated statistical approaches use permutation requiring high computational cost. There is a clear need for valid, powerful and fast to calculate test statistics for studies investigating rare variants.
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Affiliation(s)
- Carmen Dering
- Institut für Medizinische Biometrie und Statistik, Universität zu Lübeck, Universitätsklinikum Schleswig-Holstein Lübeck, Germany
| | - Inke R König
- Institut für Medizinische Biometrie und Statistik, Universität zu Lübeck, Universitätsklinikum Schleswig-Holstein Lübeck, Germany
| | - Laura B Ramsey
- Pharmaceutical Department, St. Jude Children's Research Hospital Memphis, TN, USA
| | - Mary V Relling
- Pharmaceutical Department, St. Jude Children's Research Hospital Memphis, TN, USA
| | - Wenjian Yang
- Pharmaceutical Department, St. Jude Children's Research Hospital Memphis, TN, USA
| | - Andreas Ziegler
- Institut für Medizinische Biometrie und Statistik, Universität zu Lübeck, Universitätsklinikum Schleswig-Holstein Lübeck, Germany ; Zentrum für Klinische Studien, Universität zu Lübeck Lübeck, Germany ; School of Mathematics, Statistics and Computer Science, University of KwaZulu-Natal Durban, South Africa
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7
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Zhang Q, Wang L, Koboldt D, Boreki IB, Province MA. Adjusting family relatedness in data-driven burden test of rare variants. Genet Epidemiol 2014; 38:722-7. [PMID: 25169066 DOI: 10.1002/gepi.21848] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2014] [Revised: 07/01/2014] [Accepted: 07/16/2014] [Indexed: 11/08/2022]
Abstract
Family data represent a rich resource for detecting association between rare variants (RVs) and human traits. However, most RV association analysis methods developed in recent years are data-driven burden tests which can adaptively learn weights from data but require permutation to evaluate significance, thus are not readily applicable to family data, because random permutation will destroy family structure. Direct application of these methods to family data may result in a significant inflation of false positives. To overcome this issue, we have developed a generalized, weighted sum mixed model (WSMM), and corresponding computational techniques that can incorporate family information into data-driven burden tests, and allow adaptive and efficient permutation test in family data. Using simulated and real datasets, we demonstrate that the WSMM method can be used to appropriately adjust for genetic relatedness among family members and has a good control for the inflation of false positives. We compare WSMM with a nondata-driven, family-based Sequence Kernel Association Test (famSKAT), showing that WSMM has significantly higher power in some cases. WSMM provides a generalized, flexible framework for adapting different data-driven burden tests to analyze data with any family structures, and it can be extended to binary and time-to-onset traits, with or without covariates.
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Affiliation(s)
- Qunyuan Zhang
- Division of Statistical Genomics, Washington University School of Medicine, St. Louis, Missouri, United States of America
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8
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Association between SNPs in defined functional pathways and risk of early or late toxicity as well as individual radiosensitivity. Strahlenther Onkol 2014; 191:59-66. [DOI: 10.1007/s00066-014-0741-y] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2014] [Accepted: 07/16/2014] [Indexed: 12/13/2022]
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9
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Abstract
This article focuses on conducting global testing for association between a binary trait and a set of rare variants (RVs), although its application can be much broader to other types of traits, common variants (CVs), and gene set or pathway analysis. We show that many of the existing tests have deteriorating performance in the presence of many nonassociated RVs: their power can dramatically drop as the proportion of nonassociated RVs in the group to be tested increases. We propose a class of so-called sum of powered score (SPU) tests, each of which is based on the score vector from a general regression model and hence can deal with different types of traits and adjust for covariates, e.g., principal components accounting for population stratification. The SPU tests generalize the sum test, a representative burden test based on pooling or collapsing genotypes of RVs, and a sum of squared score (SSU) test that is closely related to several other powerful variance component tests; a previous study (Basu and Pan 2011) has demonstrated good performance of one, but not both, of the Sum and SSU tests in many situations. The SPU tests are versatile in the sense that one of them is often powerful, although its identity varies with the unknown true association parameters. We propose an adaptive SPU (aSPU) test to approximate the most powerful SPU test for a given scenario, consequently maintaining high power and being highly adaptive across various scenarios. We conducted extensive simulations to show superior performance of the aSPU test over several state-of-the-art association tests in the presence of many nonassociated RVs. Finally we applied the SPU and aSPU tests to the GAW17 mini-exome sequence data to compare its practical performance with some existing tests, demonstrating their potential usefulness.
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10
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Cook K, Benitez A, Fu C, Tintle N. Evaluating the impact of genotype errors on rare variant tests of association. Front Genet 2014; 5:62. [PMID: 24744770 PMCID: PMC3978329 DOI: 10.3389/fgene.2014.00062] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2013] [Accepted: 03/11/2014] [Indexed: 01/23/2023] Open
Abstract
The new class of rare variant tests has usually been evaluated assuming perfect genotype information. In reality, rare variant genotypes may be incorrect, and so rare variant tests should be robust to imperfect data. Errors and uncertainty in SNP genotyping are already known to dramatically impact statistical power for single marker tests on common variants and, in some cases, inflate the type I error rate. Recent results show that uncertainty in genotype calls derived from sequencing reads are dependent on several factors, including read depth, calling algorithm, number of alleles present in the sample, and the frequency at which an allele segregates in the population. We have recently proposed a general framework for the evaluation and investigation of rare variant tests of association, classifying most rare variant tests into one of two broad categories (length or joint tests). We use this framework to relate factors affecting genotype uncertainty to the power and type I error rate of rare variant tests. We find that non-differential genotype errors (an error process that occurs independent of phenotype) decrease power, with larger decreases for extremely rare variants, and for the common homozygote to heterozygote error. Differential genotype errors (an error process that is associated with phenotype status), lead to inflated type I error rates which are more likely to occur at sites with more common homozygote to heterozygote errors than vice versa. Finally, our work suggests that certain rare variant tests and study designs may be more robust to the inclusion of genotype errors. Further work is needed to directly integrate genotype calling algorithm decisions, study costs and test statistic choices to provide comprehensive design and analysis advice which appropriately accounts for the impact of genotype errors.
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Affiliation(s)
- Kaitlyn Cook
- Department of Mathematics, Carleton College Northfield, MN, USA
| | - Alejandra Benitez
- Department of Applied Mathematics, Brown University Providence, RI, USA
| | - Casey Fu
- Department of Mathematics, Massachusetts Institute of Technology Boston, MA, USA
| | - Nathan Tintle
- Department of Mathematics, Statistics and Computer Science, Dordt College Sioux Center, IA, USA
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Koboldt DC, Steinberg KM, Larson DE, Wilson RK, Mardis ER. The next-generation sequencing revolution and its impact on genomics. Cell 2013; 155:27-38. [PMID: 24074859 DOI: 10.1016/j.cell.2013.09.006] [Citation(s) in RCA: 613] [Impact Index Per Article: 55.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2013] [Indexed: 02/07/2023]
Abstract
Genomics is a relatively new scientific discipline, having DNA sequencing as its core technology. As technology has improved the cost and scale of genome characterization over sequencing's 40-year history, the scope of inquiry has commensurately broadened. Massively parallel sequencing has proven revolutionary, shifting the paradigm of genomics to address biological questions at a genome-wide scale. Sequencing now empowers clinical diagnostics and other aspects of medical care, including disease risk, therapeutic identification, and prenatal testing. This Review explores the current state of genomics in the massively parallel sequencing era.
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Affiliation(s)
- Daniel C Koboldt
- The Genome Institute, School of Medicine, Washington University, St. Louis, MO 63108, USA
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12
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13
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Liu K, Fast S, Zawistowski M, Tintle NL. A geometric framework for evaluating rare variant tests of association. Genet Epidemiol 2013; 37:345-57. [PMID: 23526307 DOI: 10.1002/gepi.21722] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2012] [Revised: 02/12/2013] [Accepted: 02/13/2013] [Indexed: 11/08/2022]
Abstract
The wave of next-generation sequencing data has arrived. However, many questions still remain about how to best analyze sequence data, particularly the contribution of rare genetic variants to human disease. Numerous statistical methods have been proposed to aggregate association signals across multiple rare variant sites in an effort to increase statistical power; however, the precise relation between the tests is often not well understood. We present a geometric representation for rare variant data in which rare allele counts in case and control samples are treated as vectors in Euclidean space. The geometric framework facilitates a rigorous classification of existing rare variant tests into two broad categories: tests for a difference in the lengths of the case and control vectors, and joint tests for a difference in either the lengths or angles of the two vectors. We demonstrate that genetic architecture of a trait, including the number and frequency of risk alleles, directly relates to the behavior of the length and joint tests. Hence, the geometric framework allows prediction of which tests will perform best under different disease models. Furthermore, the structure of the geometric framework immediately suggests additional classes and types of rare variant tests. We consider two general classes of tests which show robustness to noncausal and protective variants. The geometric framework introduces a novel and unique method to assess current rare variant methodology and provides guidelines for both applied and theoretical researchers.
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Affiliation(s)
- Keli Liu
- Department of Statistics, Harvard University, Cambridge, MA, USA
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14
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Irvin MR, Zhang Q, Kabagambe EK, Perry RT, Straka RJ, Tiwari HK, Borecki IB, Shimmin LC, Stuart C, Zhong Y, Hixson JE, Arnett DK. Rare PPARA variants and extreme response to fenofibrate in the Genetics of Lipid-Lowering Drugs and Diet Network Study. Pharmacogenet Genomics 2012; 22:367-72. [PMID: 22336959 PMCID: PMC3325369 DOI: 10.1097/fpc.0b013e328351a486] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
Abstract
OBJECTIVE Fenofibrate, a peroxisome proliferator-activated receptor-α (PPARα) agonist, reduces triglyceride (TG) concentrations by 25-60%. Given significant interindividual variations in the TG response, we investigated the association of PPARA rare variants with treatment response in the Genetics of Lipid-Lowering Drugs and Diet Network study. METHODS We calculated the change in the TG concentration (ΔTG) among 861 GOLDN participants treated with fenofibrate (160 mg/day) for 3 weeks. From the distribution of ΔTG adjusted for age and sex, the 150 highest and 150 lowest fenofibrate responders were selected from the tails of the distribution for PPARA resequencing. The resequencing strategy was based on VariantSEQr technology for the amplification of exons and regulatory regions. RESULTS We identified 73 variants with an average minor allele frequency of 4.8% (range: 0.2-16%). We tested the association of rare variants located in a coding or a regulatory region (minor allele frequency<1%, 13 variants) with treatment response group by an indicator variable (presence/absence of ≥1 rare variant) using general linear mixed models to allow for adjustment for family relationship. After adjusting for baseline, fasting TG concentration carrying at least one rare variant was associated with a low fenofibrate response (odds ratio=6.46; 95% confidence interval: 1.4-30.8). Carrier status was also associated with a relative change in the total cholesterol concentration (P=0.02), but not high-density lipoprotein or low-density lipoprotein concentration. CONCLUSION Rare, potentially functional variants in PPARA may play a role in the TG response to fenofibrate, but future experimental studies will be necessary to replicate the findings and confirm functional effects.
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Affiliation(s)
- Marguerite R Irvin
- Department of Epidemiology, School of Public Health, University of Alabama at Birmingham, Birmingham, Alabama 35294-0022, USA.
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