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He J, Ma W, Zhou Y. Gene association detection via local linear regression method. J Hum Genet 2019; 65:115-123. [PMID: 31602004 DOI: 10.1038/s10038-019-0676-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2018] [Revised: 09/10/2019] [Accepted: 09/18/2019] [Indexed: 11/09/2022]
Abstract
The development of next-generation sequencing technology has provided us with great convenience in genetic association studies and many effective analysis methods were proposed continuously. However, population stratification is still a major issue in current genetic association studies. Many existing methods have been developed to remove the bias due to population stratification for common variant association studies, but such methods may be not effective for rare variant, which will lead to power reduction. Therefore, in this paper, we develop a principal component analysis strategy (called PC-LLR) based on local linear regression method to eliminate population stratification effect in both rare variant and common variant association studies. Simulation results indicate that the new PC-LLR method can eliminate population stratification effect well. It has correct type I error rates in all cases and higher powers in most cases, while most existing methods have inflated type I error rates at least in some cases. We also demonstrate that the PC-LLR is more effective to eliminate population stratification effect through applying the PC-LLR to the whole-exome sequencing data set from genetic analysis workshop 19 (GAW19).
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Affiliation(s)
- Jinli He
- Department of Statistics, School of Mathematical Sciences, Heilongjiang University and Heilongjiang Provincial Key Laboratory of the Theory and Computation of Complex Systems, Harbin, 150080, China
| | - Weijun Ma
- Department of Statistics, School of Mathematical Sciences, Heilongjiang University and Heilongjiang Provincial Key Laboratory of the Theory and Computation of Complex Systems, Harbin, 150080, China
| | - Ying Zhou
- Department of Statistics, School of Mathematical Sciences, Heilongjiang University and Heilongjiang Provincial Key Laboratory of the Theory and Computation of Complex Systems, Harbin, 150080, China.
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2
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Cologne J, Loo L, Shvetsov YB, Misumi M, Lin P, Haiman CA, Wilkens LR, Le Marchand L. Stepwise approach to SNP-set analysis illustrated with the Metabochip and colorectal cancer in Japanese Americans of the Multiethnic Cohort. BMC Genomics 2018; 19:524. [PMID: 29986644 PMCID: PMC6038257 DOI: 10.1186/s12864-018-4910-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2017] [Accepted: 06/29/2018] [Indexed: 12/19/2022] Open
Abstract
BACKGROUND Common variants have explained less than the amount of heritability expected for complex diseases, which has led to interest in less-common variants and more powerful approaches to the analysis of whole-genome scans. Because of low frequency (low statistical power), less-common variants are best analyzed using SNP-set methods such as gene-set or pathway-based analyses. However, there is as yet no clear consensus regarding how to focus in on potential risk variants following set-based analyses. We used a stepwise, telescoping approach to analyze common- and rare-variant data from the Illumina Metabochip array to assess genomic association with colorectal cancer (CRC) in the Japanese sub-population of the Multiethnic Cohort (676 cases, 7180 controls). We started with pathway analysis of SNPs that are in genes and pathways having known mechanistic roles in colorectal cancer, then focused on genes within the pathways that evidenced association with CRC, and finally assessed individual SNPs within the genes that evidenced association. Pathway SNPs downloaded from the dbSNP database were cross-matched with Metabochip SNPs and analyzed using the logistic kernel machine regression approach (logistic SNP-set kernel-machine association test, or sequence kernel association test; SKAT) and related methods. RESULTS The TGF-β and WNT pathways were associated with all CRC, and the WNT pathway was associated with colon cancer. Individual genes demonstrating the strongest associations were TGFBR2 in the TGF-β pathway and SMAD7 (which is involved in both the TGF-β and WNT pathways). As partial validation of our approach, a known CRC risk variant in SMAD7 (in both the TGF-β and WNT pathways: rs11874392) was associated with CRC risk in our data. We also detected two novel candidate CRC risk variants (rs13075948 and rs17025857) in TGFBR2, a gene known to be associated with CRC risk. CONCLUSIONS A stepwise, telescoping approach identified some potentially novel risk variants associated with colorectal cancer, so it may be a useful method for following up on results of set-based SNP analyses. Further work is required to assess the statistical characteristics of the approach, and additional applications should aid in better clarifying its utility.
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Affiliation(s)
- John Cologne
- Department of Statistics, Radiation Effects Research Foundation, Hiroshima, 732-0815, Japan.
| | - Lenora Loo
- Epidemiology Program, University of Hawaii Cancer Center, Honolulu, HI, 96813, USA
| | - Yurii B Shvetsov
- Epidemiology Program, University of Hawaii Cancer Center, Honolulu, HI, 96813, USA
| | - Munechika Misumi
- Department of Statistics, Radiation Effects Research Foundation, Hiroshima, 732-0815, Japan
| | - Philip Lin
- Epidemiology Program, University of Hawaii Cancer Center, Honolulu, HI, 96813, USA
| | - Christopher A Haiman
- Department of Preventive Medicine and Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, CA, 90033, USA
| | - Lynne R Wilkens
- Biostatistics and Informatics Shared Resource, University of Hawaii Cancer Center, Honolulu, HI, 96813, USA
| | - Loïc Le Marchand
- Epidemiology Program, University of Hawaii Cancer Center, Honolulu, HI, 96813, USA
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Duan Q, Xu Z, Raffield L, Chang S, Wu D, Lange EM, Reiner AP, Li Y. A robust and powerful two-step testing procedure for local ancestry adjusted allelic association analysis in admixed populations. Genet Epidemiol 2018; 42:288-302. [PMID: 29226381 PMCID: PMC5851818 DOI: 10.1002/gepi.22104] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2017] [Revised: 09/07/2017] [Accepted: 10/20/2017] [Indexed: 12/23/2022]
Abstract
Genetic association studies in admixed populations allow us to gain deeper understanding of the genetic architecture of human diseases and traits. However, population stratification, complicated linkage disequilibrium (LD) patterns, and the complex interplay of allelic and ancestry effects on phenotypic traits pose challenges in such analyses. These issues may lead to detecting spurious associations and/or result in reduced statistical power. Fortunately, if handled appropriately, these same challenges provide unique opportunities for gene mapping. To address these challenges and to take these opportunities, we propose a robust and powerful two-step testing procedure Local Ancestry Adjusted Allelic (LAAA) association. In the first step, LAAA robustly captures associations due to allelic effect, ancestry effect, and interaction effect, allowing detection of effect heterogeneity across ancestral populations. In the second step, LAAA identifies the source of association, namely allelic, ancestry, or the combination. By jointly modeling allele, local ancestry, and ancestry-specific allelic effects, LAAA is highly powerful in capturing the presence of interaction between ancestry and allele effect. We evaluated the validity and statistical power of LAAA through simulations over a broad spectrum of scenarios. We further illustrated its usefulness by application to the Candidate Gene Association Resource (CARe) African American participants for association with hemoglobin levels. We were able to replicate independent groups' previously identified loci that would have been missed in CARe without joint testing. Moreover, the loci, for which LAAA detected potential effect heterogeneity, were replicated among African Americans from the Women's Health Initiative study. LAAA is freely available at https://yunliweb.its.unc.edu/LAAA.
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Affiliation(s)
- Qing Duan
- Department of Genetics, University of North Carolina, Chapel Hill, NC, USA
- Curriculum in Bioinformatics and Computational Biology, University of North Carolina, Chapel Hill, NC, USA
- Department of Statistics, University of North Carolina, Chapel Hill, NC, USA
| | - Zheng Xu
- Department of Genetics, University of North Carolina, Chapel Hill, NC, USA
- Department of Biostatistics, University of North Carolina, Chapel Hill, NC, USA
- Department of Computer Science, University of North Carolina, Chapel Hill, NC, USA
- Department of Statistics, University of Nebraska-Lincoln, Lincoln, NE
- Initiative of Quantitative Life Sciences, University of Nebraska-Lincoln, Lincoln, NE
| | - Laura Raffield
- Department of Genetics, University of North Carolina, Chapel Hill, NC, USA
| | - Suhua Chang
- Institute of Psychology, Chinese Academy of Science, Beijing, China
| | - Di Wu
- Department of Biostatistics, University of North Carolina, Chapel Hill, NC, USA
- Department of Periodontology, University of North Carolina, Chapel Hill, NC, USA
| | - Ethan M. Lange
- Department of Medicine, University of Colorado at Denver, Anschutz Medical Campus, Aurora, CO, USA
- Department of Biostatistics and Informatics, University of Colorado at Denver, Anschutz Medical Campus, Aurora, CO, USA
| | - Alex P. Reiner
- Fred Hutchinson Cancer Research Center, Seattle, WA, USA
- Department of Epidemiology, University of Washington, Seattle, WA, USA
| | - Yun Li
- Department of Genetics, University of North Carolina, Chapel Hill, NC, USA
- Curriculum in Bioinformatics and Computational Biology, University of North Carolina, Chapel Hill, NC, USA
- Department of Biostatistics, University of North Carolina, Chapel Hill, NC, USA
- Department of Computer Science, University of North Carolina, Chapel Hill, NC, USA
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Mensah-Ablorh A, Lindstrom S, Haiman CA, Henderson BE, Marchand LL, Lee S, Stram DO, Eliassen AH, Price A, Kraft P. Meta-Analysis of Rare Variant Association Tests in Multiethnic Populations. Genet Epidemiol 2015; 40:57-65. [PMID: 26639010 DOI: 10.1002/gepi.21939] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2015] [Revised: 09/15/2015] [Accepted: 09/19/2015] [Indexed: 12/30/2022]
Abstract
Several methods have been proposed to increase power in rare variant association testing by aggregating information from individual rare variants (MAF < 0.005). However, how to best combine rare variants across multiple ethnicities and the relative performance of designs using different ethnic sampling fractions remains unknown. In this study, we compare the performance of several statistical approaches for assessing rare variant associations across multiple ethnicities. We also explore how different ethnic sampling fractions perform, including single-ethnicity studies and studies that sample up to four ethnicities. We conducted simulations based on targeted sequencing data from 4,611 women in four ethnicities (African, European, Japanese American, and Latina). As with single-ethnicity studies, burden tests had greater power when all causal rare variants were deleterious, and variance component-based tests had greater power when some causal rare variants were deleterious and some were protective. Multiethnic studies had greater power than single-ethnicity studies at many loci, with inclusion of African Americans providing the largest impact. On average, studies including African Americans had as much as 20% greater power than equivalently sized studies without African Americans. This suggests that association studies between rare variants and complex disease should consider including subjects from multiple ethnicities, with preference given to genetically diverse groups.
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Affiliation(s)
- Akweley Mensah-Ablorh
- Department of Epidemiology, Harvard School of Public Health, Boston, Massachusetts, United States of America.,Program in Genetic Epidemiology and Statistical Genetics, Harvard School of Public Health, Boston, Massachusetts, United States of America
| | - Sara Lindstrom
- Department of Epidemiology, Harvard School of Public Health, Boston, Massachusetts, United States of America.,Program in Genetic Epidemiology and Statistical Genetics, Harvard School of Public Health, Boston, Massachusetts, United States of America
| | - Christopher A Haiman
- Department of Preventive Medicine, Keck School of Medicine and Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, California, United States of America
| | - Brian E Henderson
- Department of Preventive Medicine, Keck School of Medicine and Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, California, United States of America
| | - Loic Le Marchand
- Epidemiology Program, University of Hawaii Cancer Research Center, Honolulu, Hawaii, United States of America
| | - Seunngeun Lee
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, Michigan, United States of America
| | - Daniel O Stram
- Department of Preventive Medicine, Keck School of Medicine and Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, California, United States of America
| | - A Heather Eliassen
- Department of Epidemiology, Harvard School of Public Health, Boston, Massachusetts, United States of America.,Channing Division of Network Medicine, Brigham & Women's Hospital, Boston, Massachusetts, United States of America
| | - Alkes Price
- Department of Epidemiology, Harvard School of Public Health, Boston, Massachusetts, United States of America.,Program in Genetic Epidemiology and Statistical Genetics, Harvard School of Public Health, Boston, Massachusetts, United States of America.,Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts, United States of America
| | - Peter Kraft
- Department of Epidemiology, Harvard School of Public Health, Boston, Massachusetts, United States of America.,Program in Genetic Epidemiology and Statistical Genetics, Harvard School of Public Health, Boston, Massachusetts, United States of America.,Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts, United States of America
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Wang X, Zhang S, Li Y, Li M, Sha Q. A powerful approach to test an optimally weighted combination of rare variants in admixed populations. Genet Epidemiol 2015; 39:294-305. [PMID: 25758547 DOI: 10.1002/gepi.21894] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2014] [Revised: 01/09/2015] [Accepted: 01/26/2015] [Indexed: 11/09/2022]
Abstract
Population stratification has long been recognized as an issue in genetic association studies because unrecognized population stratification can lead to both false-positive and false-negative findings and can obscure true association signals if not appropriately corrected. This issue can be even worse in rare variant association analyses because rare variants often demonstrate stronger and potentially different patterns of stratification than common variants. To correct for population stratification in genetic association studies, we proposed a novel method to Test the effect of an Optimally Weighted combination of variants in Admixed populations (TOWA) in which the analytically derived optimal weights can be calculated from existing phenotype and genotype data. TOWA up weights rare variants and those variants that have strong associations with the phenotype. Additionally, it can adjust for the direction of the association, and allows for local ancestry difference among study subjects. Extensive simulations show that the type I error rate of TOWA is under control in the presence of population stratification and it is more powerful than existing methods. We have also applied TOWA to a real sequencing data. Our simulation studies as well as real data analysis results indicate that TOWA is a useful tool for rare variant association analyses in admixed populations.
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Affiliation(s)
- Xuexia Wang
- Joseph J. Zilber School of Public Health, University of Wisconsin-Milwaukee, Milwaukee, Wisconsin, United States of America
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Byrnes AE, Wu MC, Wright FA, Li M, Li Y. The value of statistical or bioinformatics annotation for rare variant association with quantitative trait. Genet Epidemiol 2013; 37:666-74. [PMID: 23836599 PMCID: PMC4083762 DOI: 10.1002/gepi.21747] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2013] [Revised: 05/20/2013] [Accepted: 06/03/2013] [Indexed: 11/06/2022]
Abstract
In the past few years, a plethora of methods for rare variant association with phenotype have been proposed. These methods aggregate information from multiple rare variants across genomic region(s), but there is little consensus as to which method is most effective. The weighting scheme adopted when aggregating information across variants is one of the primary determinants of effectiveness. Here we present a systematic evaluation of multiple weighting schemes through a series of simulations intended to mimic large sequencing studies of a quantitative trait. We evaluate existing phenotype-independent and phenotype-dependent methods, as well as weights estimated by penalized regression approaches including Lasso, Elastic Net, and SCAD. We find that the difference in power between phenotype-dependent schemes is negligible when high-quality functional annotations are available. When functional annotations are unavailable or incomplete, all methods suffer from power loss; however, the variable selection methods outperform the others at the cost of increased computational time. Therefore, in the absence of good annotation, we recommend variable selection methods (which can be viewed as "statistical annotation") on top of regions implicated by a phenotype-independent weighting scheme. Further, once a region is implicated, variable selection can help to identify potential causal single nucleotide polymorphisms for biological validation. These findings are supported by an analysis of a high coverage targeted sequencing study of 1,898 individuals.
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Affiliation(s)
- Andrea E. Byrnes
- Department of Biostatistics, University of North Carolina, Chapel Hill, North Carolina 27599
| | - Michael C. Wu
- Department of Biostatistics, University of North Carolina, Chapel Hill, North Carolina 27599
| | - Fred A. Wright
- Department of Biostatistics, University of North Carolina, Chapel Hill, North Carolina 27599
| | - Mingyao Li
- Department of Biostatistics and Epidemiology, University of Pennsylvania School of Medicine, Philadelphia, PA 19104
| | - Yun Li
- Department of Biostatistics, University of North Carolina, Chapel Hill, North Carolina 27599
- Department of Genetics, University of North Carolina, Chapel Hill, North Carolina 27599
- Department of Computer Science, University of North Carolina, Chapel Hill, North Carolina 27599
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