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Coombes B, Basu S, Guha S, Schork N. Weighted Score Tests Implementing Model-Averaging Schemes in Detection of Rare Variants in Case-Control Studies. PLoS One 2015; 10:e0139355. [PMID: 26436424 PMCID: PMC4593572 DOI: 10.1371/journal.pone.0139355] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2015] [Accepted: 09/11/2015] [Indexed: 12/04/2022] Open
Abstract
Multi-locus effect modeling is a powerful approach for detection of genes influencing a complex disease. Especially for rare variants, we need to analyze multiple variants together to achieve adequate power for detection. In this paper, we propose several parsimonious branching model techniques to assess the joint effect of a group of rare variants in a case-control study. These models implement a data reduction strategy within a likelihood framework and use a weighted score test to assess the statistical significance of the effect of the group of variants on the disease. The primary advantage of the proposed approach is that it performs model-averaging over a substantially smaller set of models supported by the data and thus gains power to detect multi-locus effects. We illustrate these proposed approaches on simulated and real data and study their performance compared to several existing rare variant detection approaches. The primary goal of this paper is to assess if there is any gain in power to detect association by averaging over a number of models instead of selecting the best model. Extensive simulations and real data application demonstrate the advantage the proposed approach in presence of causal variants with opposite directional effects along with a moderate number of null variants in linkage disequilibrium.
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Affiliation(s)
- Brandon Coombes
- Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN, United States of America
| | - Saonli Basu
- Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN, United States of America
| | - Sharmistha Guha
- Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN, United States of America
| | - Nicholas Schork
- J. Craig Venter Institute, La Jolla, CA, United States of America
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The power of gene-based rare variant methods to detect disease-associated variation and test hypotheses about complex disease. PLoS Genet 2015; 11:e1005165. [PMID: 25906071 PMCID: PMC4407972 DOI: 10.1371/journal.pgen.1005165] [Citation(s) in RCA: 114] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2014] [Accepted: 03/20/2015] [Indexed: 01/09/2023] Open
Abstract
Genome and exome sequencing in large cohorts enables characterization of the role of rare variation in complex diseases. Success in this endeavor, however, requires investigators to test a diverse array of genetic hypotheses which differ in the number, frequency and effect sizes of underlying causal variants. In this study, we evaluated the power of gene-based association methods to interrogate such hypotheses, and examined the implications for study design. We developed a flexible simulation approach, using 1000 Genomes data, to (a) generate sequence variation at human genes in up to 10K case-control samples, and (b) quantify the statistical power of a panel of widely used gene-based association tests under a variety of allelic architectures, locus effect sizes, and significance thresholds. For loci explaining ~1% of phenotypic variance underlying a common dichotomous trait, we find that all methods have low absolute power to achieve exome-wide significance (~5-20% power at α=2.5×10-6) in 3K individuals; even in 10K samples, power is modest (~60%). The combined application of multiple methods increases sensitivity, but does so at the expense of a higher false positive rate. MiST, SKAT-O, and KBAC have the highest individual mean power across simulated datasets, but we observe wide architecture-dependent variability in the individual loci detected by each test, suggesting that inferences about disease architecture from analysis of sequencing studies can differ depending on which methods are used. Our results imply that tens of thousands of individuals, extensive functional annotation, or highly targeted hypothesis testing will be required to confidently detect or exclude rare variant signals at complex disease loci. Re-sequencing technologies allow for a more complete interrogation of the role of human variation in complex disease. The inadequate power of single variant methods to assess the role of less common variation has led to the development of numerous statistical methods for testing aggregate groups of variants for association with disease. Such endeavors pose substantial analytical challenges, however, due to the diverse array of genetic hypotheses that need to be considered. In this work, we systematically quantify and compare the performance of a panel of commonly used gene-based association methods under a range of allelic architectures, significance thresholds, locus effect sizes, sample sizes, and filters for neutral variation. We find that MiST, SKAT-O, and KBAC have the highest mean power across simulated datasets. Across all methods, however, the power to detect even loci of relatively large effect is very low at exome-wide significance thresholds for sample sizes comparable with those of ongoing sequencing studies; as such, the absence of signal in studies of a few thousand individuals does not exclude a role for rare variation in complex traits. Finally, we directly compare the results reported by different gene-based methods in order to identify their comparative advantages and disadvantages under distinct locus architectures. Our findings have implications for meaningful interpretation of both positive and negative findings in ongoing and future sequencing studies.
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Norden-Krichmar TM, Gizer IR, Wilhelmsen KC, Schork NJ, Ehlers CL. Protective variant associated with alcohol dependence in a Mexican American cohort. BMC MEDICAL GENETICS 2014; 15:136. [PMID: 25527893 PMCID: PMC4337107 DOI: 10.1186/s12881-014-0136-z] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/18/2014] [Accepted: 12/08/2014] [Indexed: 01/11/2023]
Abstract
Background Mexican Americans, particularly those born in the United States, are at greater risk for alcohol associated morbidity and mortality. The present study sought to investigate whether specific genetic variants may be associated with alcohol use disorder phenotypes in a select population of Mexican American young adults. Methods The study evaluated a cohort of 427 (age 18 – 30 years) Mexican American men (n = 171) and women (n = 256). Information on alcohol dependence was obtained through interview using the Semi-Structured Assessment for the Genetics of Alcoholism (SSAGA). For all subjects, DNA was extracted from blood samples, followed by genotyping using an Affymetrix Axiom Exome1A chip. Results A protective variant (rs991316) located downstream from the ADH7 (alcohol dehydrogenase 7) gene showed suggestive significance in association with alcohol dependence symptom counts derived from DSM-III-R and DSM-IV criteria, as well as to clustered alcohol dependence symptoms. Additional linkage analysis suggested that nearby variants in linkage disequilibrium with rs991316 were not responsible for the observed association with the alcohol dependence phenotypes in this study. Conclusions ADH7 has been shown to have a protective role against alcohol dependence in previous studies involving other ethnicities, but has not been reported for Mexican Americans. These results suggest that variants near ADH7 may play a role in protection from alcohol dependence in this Mexican American cohort.
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Kim JH, Song P, Lim H, Lee JH, Lee JH, Park SA. Gene-based rare allele analysis identified a risk gene of Alzheimer's disease. PLoS One 2014; 9:e107983. [PMID: 25329708 PMCID: PMC4203677 DOI: 10.1371/journal.pone.0107983] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2014] [Accepted: 08/25/2014] [Indexed: 12/17/2022] Open
Abstract
Alzheimer’s disease (AD) has a strong propensity to run in families. However, the known risk genes excluding APOE are not clinically useful. In various complex diseases, gene studies have targeted rare alleles for unsolved heritability. Our study aims to elucidate previously unknown risk genes for AD by targeting rare alleles. We used data from five publicly available genetic studies from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and the database of Genotypes and Phenotypes (dbGaP). A total of 4,171 cases and 9,358 controls were included. The genotype information of rare alleles was imputed using 1,000 genomes. We performed gene-based analysis of rare alleles (minor allele frequency≤3%). The genome-wide significance level was defined as meta P<1.8×10–6 (0.05/number of genes in human genome = 0.05/28,517). ZNF628, which is located at chromosome 19q13.42, showed a genome-wide significant association with AD. The association of ZNF628 with AD was not dependent on APOE ε4. APOE and TREM2 were also significantly associated with AD, although not at genome-wide significance levels. Other genes identified by targeting common alleles could not be replicated in our gene-based rare allele analysis. We identified that rare variants in ZNF628 are associated with AD. The protein encoded by ZNF628 is known as a transcription factor. Furthermore, the associations of APOE and TREM2 with AD were highly significant, even in gene-based rare allele analysis, which implies that further deep sequencing of these genes is required in AD heritability studies.
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Affiliation(s)
- Jong Hun Kim
- Department of Neurology, Dementia Center, Stroke Center, Ilsan hospital, National Health Insurance Service, Goyang-shi, South Korea
| | - Pamela Song
- Department of Neurology, Inje University Ilsan Paik Hospital, Goyang-shi, South Korea
| | - Hyunsun Lim
- Clinical Research Management Team, Ilsan hospital, National Health Insurance Service, Goyang-shi, South Korea
| | - Jae-Hyung Lee
- Department of Life and Nanopharmaceutical Sciences and Department of Maxillofacial Biomedical Engineering, School of Dentistry, Kyung Hee University, Seoul, South Korea
| | - Jun Hong Lee
- Department of Neurology, Dementia Center, Stroke Center, Ilsan hospital, National Health Insurance Service, Goyang-shi, South Korea
| | - Sun Ah Park
- Department of Neurology, Soonchunhyang University Bucheon Hospital, Bucheon-shi, South Korea
- * E-mail:
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King CR, Nicolae DL. GWAS to Sequencing: Divergence in Study Design and Analysis. Genes (Basel) 2014; 5:460-76. [PMID: 24879455 PMCID: PMC4094943 DOI: 10.3390/genes5020460] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2013] [Revised: 05/13/2014] [Accepted: 05/15/2014] [Indexed: 12/03/2022] Open
Abstract
The success of genome-wide association studies (GWAS) in uncovering genetic risk factors for complex traits has generated great promise for the complete data generated by sequencing. The bumpy transition from GWAS to whole-exome or whole-genome association studies (WGAS) based on sequencing investigations has highlighted important differences in analysis and interpretation. We show how the loss in power due to the allele frequency spectrum targeted by sequencing is difficult to compensate for with realistic effect sizes and point to study designs that may help. We discuss several issues in interpreting the results, including a special case of the winner's curse. Extrapolation and prediction using rare SNPs is complex, because of the selective ascertainment of SNPs in case-control studies and the low amount of information at each SNP, and naive procedures are biased under the alternative. We also discuss the challenges in tuning gene-based tests and accounting for multiple testing when genes have very different sets of SNPs. The examples we emphasize in this paper highlight the difficult road we must travel for a two-letter switch.
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Affiliation(s)
| | - Dan L Nicolae
- Departments of Medicine, Statistics, and Human Genetics, University of Chicago, Chicago,IL 60637, USA.
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Xu C, Ciampi A, Greenwood CMT. Exploring the potential benefits of stratified false discovery rates for region-based testing of association with rare genetic variation. Front Genet 2014; 5:11. [PMID: 24523729 PMCID: PMC3905218 DOI: 10.3389/fgene.2014.00011] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2013] [Accepted: 01/13/2014] [Indexed: 01/13/2023] Open
Abstract
When analyzing the data that arises from exome or whole-genome sequencing studies, window-based tests, (i.e., tests that jointly analyze all genetic data in a small genomic region), are very popular. However, power is known to be quite low for finding associations with phenotypes using these tests, and therefore a variety of analytic strategies may be employed to potentially improve power. Using sequencing data of all of chromosome 3 from an interim release of data on 2432 individuals from the UK10K project, we simulated phenotypes associated with rare genetic variation, and used the results to explore the window-based test power. We asked two specific questions: firstly, whether there could be substantial benefits associated with incorporating information from external annotation on the genetic variants, and secondly whether the false discovery rate (FDRs) would be a useful metric for assessing significance. Although, as expected, there are benefits to using additional information (such as annotation) when it is associated with causality, we confirmed the general pattern of low sensitivity and power for window-based tests. For our chosen example, even when power is high to detect some of the associations, many of the regions containing causal variants are not detectable, despite using lax significance thresholds and optimal analytic methods. Furthermore, our estimated FDR values tended to be much smaller than the true FDRs. Long-range correlations between variants—due to linkage disequilibrium—likely explain some of this bias. A more sophisticated approach to using the annotation information may improve power, however, many causal variants of realistic effect sizes may simply be undetectable, at least with this sample size. Perhaps annotation information could assist in distinguishing windows containing causal variants from windows that are merely correlated with causal variants.
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Affiliation(s)
- Changjiang Xu
- Lady Davis Institute for Medical Research, Jewish General Hospital Montreal, QC, Canada ; Department of Epidemiology, Biostatistics and Occupational Health, McGill University Montreal, QC, Canada
| | - Antonio Ciampi
- Department of Epidemiology, Biostatistics and Occupational Health, McGill University Montreal, QC, Canada
| | - Celia M T Greenwood
- Lady Davis Institute for Medical Research, Jewish General Hospital Montreal, QC, Canada ; Department of Epidemiology, Biostatistics and Occupational Health, McGill University Montreal, QC, Canada ; Departments of Oncology and Human Genetics, McGill University Montreal, QC, Canada
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Chen YC, Carter H, Parla J, Kramer M, Goes FS, Pirooznia M, Zandi PP, McCombie WR, Potash JB, Karchin R. A hybrid likelihood model for sequence-based disease association studies. PLoS Genet 2013; 9:e1003224. [PMID: 23358228 PMCID: PMC3554549 DOI: 10.1371/journal.pgen.1003224] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2012] [Accepted: 11/21/2012] [Indexed: 11/18/2022] Open
Abstract
In the past few years, case-control studies of common diseases have shifted their focus from single genes to whole exomes. New sequencing technologies now routinely detect hundreds of thousands of sequence variants in a single study, many of which are rare or even novel. The limitation of classical single-marker association analysis for rare variants has been a challenge in such studies. A new generation of statistical methods for case-control association studies has been developed to meet this challenge. A common approach to association analysis of rare variants is the burden-style collapsing methods to combine rare variant data within individuals across or within genes. Here, we propose a new hybrid likelihood model that combines a burden test with a test of the position distribution of variants. In extensive simulations and on empirical data from the Dallas Heart Study, the new model demonstrates consistently good power, in particular when applied to a gene set (e.g., multiple candidate genes with shared biological function or pathway), when rare variants cluster in key functional regions of a gene, and when protective variants are present. When applied to data from an ongoing sequencing study of bipolar disorder (191 cases, 107 controls), the model identifies seven gene sets with nominal p-values0.05, of which one MAPK signaling pathway (KEGG) reaches trend-level significance after correcting for multiple testing. Inexpensive, high-throughput sequencing has transformed the field of case-control association studies. For the first time, it may be possible to identify the genetic underpinnings of complex diseases, by sequencing the DNA of hundreds (even thousands) of cases and controls and comparing patterns of DNA sequence variation. However, complex diseases are likely to be caused by many variants, some of which are very rare. Taken one at a time, the association between variant and disease phenotype may not be detectable by current statistical methods. One strategy is to identify regions where important variants occur by “collapsing” variants into groups. Here, we present a new collapsing approach, capable of detecting subtle genetic differences between cases and controls. We show, in extensive simulations and using a benchmark set of genes involved in human triglyceride levels, that the approach is potentially more powerful than existing methods. We apply the new method to an ongoing sequencing study of bipolar cases and controls and identify a set of genes found in neuronal synapses, which may be implicated in bipolar disorder.
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Affiliation(s)
- Yun-Ching Chen
- Department of Biomedical Engineering and Institute for Computational Medicine, Johns Hopkins University, Baltimore, Maryland, United States of America
| | - Hannah Carter
- Department of Biomedical Engineering and Institute for Computational Medicine, Johns Hopkins University, Baltimore, Maryland, United States of America
| | - Jennifer Parla
- Stanley Institute for Cognitive Genomics, Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, United States of America
| | - Melissa Kramer
- Stanley Institute for Cognitive Genomics, Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, United States of America
| | - Fernando S. Goes
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, Maryland, United States of America
| | - Mehdi Pirooznia
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, Maryland, United States of America
| | - Peter P. Zandi
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, Maryland, United States of America
| | - W. Richard McCombie
- Stanley Institute for Cognitive Genomics, Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, United States of America
| | - James B. Potash
- Department of Psychiatry, University of Iowa, Iowa City, Iowa, United States of America
| | - Rachel Karchin
- Department of Biomedical Engineering and Institute for Computational Medicine, Johns Hopkins University, Baltimore, Maryland, United States of America
- * E-mail:
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Chung D, Zhang Q, Kraja AT, Borecki IB, Province MA. Distance-based phenotypic association analysis of DNA sequence data. BMC Proc 2011; 5 Suppl 9:S54. [PMID: 22373107 PMCID: PMC3287892 DOI: 10.1186/1753-6561-5-s9-s54] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
As the cost of sequencing decreases, the demand for association tests that use exhaustive DNA sequence information increases. One such association test is multivariate distance matrix regression (MDMR). We explore some of the features of MDMR using Genetic Analysis Workshop 17 simulated data in search of potential improvements in distance measures. We used genotype data from 697 unrelated individuals, in 200 replications, to test the power of MDMR to detect 13 trait Q2 causative genes based on the Euclidean distance metric. We also estimated the false-positive rate of MDMR using 508 control genes. In addition, we compared MDMR with Mantel's test and collapsing analysis for rare variants. MDMR performed comparably well even with the Euclidean distance measure.
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Affiliation(s)
- Doyoung Chung
- Division of Statistical Genomics, Department of Genetics, Washington University School of Medicine, St, Louis, MO 63110, USA.
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Stitziel NO, Kiezun A, Sunyaev S. Computational and statistical approaches to analyzing variants identified by exome sequencing. Genome Biol 2011; 12:227. [PMID: 21920052 PMCID: PMC3308043 DOI: 10.1186/gb-2011-12-9-227] [Citation(s) in RCA: 99] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/30/2023] Open
Abstract
New sequencing technology has enabled the identification of thousands of single nucleotide polymorphisms in the exome, and many computational and statistical approaches to identify disease-association signals have emerged.
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Affiliation(s)
- Nathan O Stitziel
- Division of Cardiovascular Medicine, Brigham and Women’s Hospital, Harvard Medical School, 75 Francis Street, Boston, MA 02115, USA
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Bansal V, Libiger O, Torkamani A, Schork NJ. Statistical analysis strategies for association studies involving rare variants. Nat Rev Genet 2010; 11:773-85. [PMID: 20940738 PMCID: PMC3743540 DOI: 10.1038/nrg2867] [Citation(s) in RCA: 381] [Impact Index Per Article: 27.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
The limitations of genome-wide association (GWA) studies that focus on the phenotypic influence of common genetic variants have motivated human geneticists to consider the contribution of rare variants to phenotypic expression. The increasing availability of high-throughput sequencing technologies has enabled studies of rare variants but these methods will not be sufficient for their success as appropriate analytical methods are also needed. We consider data analysis approaches to testing associations between a phenotype and collections of rare variants in a defined genomic region or set of regions. Ultimately, although a wide variety of analytical approaches exist, more work is needed to refine them and determine their properties and power in different contexts.
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Affiliation(s)
- Vikas Bansal
- The Scripps Translational Science Institute, 3344 North Torrey Pines Court, Suite 300, La Jolla, California 92037, USA
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