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Blechter B, Wong JYY, Agnes Hsiung C, Hosgood HD, Yin Z, Shu XO, Zhang H, Shi J, Song L, Song M, Zheng W, Wang Z, Caporaso N, Burdette L, Yeager M, Berndt SI, Teresa Landi M, Chen CJ, Chang GC, Hsiao CF, Tsai YH, Chen KY, Huang MS, Su WC, Chen YM, Chien LH, Chen CH, Yang TY, Wang CL, Hung JY, Lin CC, Perng RP, Chen CY, Chen KC, Li YJ, Yu CJ, Chen YS, Chen YH, Tsai FY, Jie Seow W, Bassig BA, Hu W, Ji BT, Wu W, Guan P, He Q, Gao YT, Cai Q, Chow WH, Xiang YB, Lin D, Wu C, Wu YL, Shin MH, Hong YC, Matsuo K, Chen K, Pik Wong M, Lu D, Jin L, Wang JC, Seow A, Wu T, Shen H, Fraumeni JF, Yang PC, Chang IS, Zhou B, Chanock SJ, Rothman N, Chatterjee N, Lan Q. Sub-multiplicative interaction between polygenic risk score and household coal use in relation to lung adenocarcinoma among never-smoking women in Asia. ENVIRONMENT INTERNATIONAL 2021; 147:105975. [PMID: 33385923 PMCID: PMC8378844 DOI: 10.1016/j.envint.2020.105975] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/25/2020] [Revised: 07/07/2020] [Accepted: 07/10/2020] [Indexed: 06/01/2023]
Abstract
We previously identified 10 lung adenocarcinoma susceptibility loci in a genome-wide association study (GWAS) conducted in the Female Lung Cancer Consortium in Asia (FLCCA), the largest genomic study of lung cancer among never-smoking women to date. Furthermore, household coal use for cooking and heating has been linked to lung cancer in Asia, especially in Xuanwei, China. We investigated the potential interaction between genetic susceptibility and coal use in FLCCA. We analyzed GWAS-data from Taiwan, Shanghai, and Shenyang (1472 cases; 1497 controls), as well as a separate study conducted in Xuanwei (152 cases; 522 controls) for additional analyses. We summarized genetic susceptibility using a polygenic risk score (PRS), which was the weighted sum of the risk-alleles from the 10 previously identified loci. We estimated associations between a PRS, coal use (ever/never), and lung adenocarcinoma with multivariable logistic regression models, and evaluated potential gene-environment interactions using likelihood ratio tests. There was a strong association between continuous PRS and lung adenocarcinoma among never coal users (Odds Ratio (OR) = 1.69 (95% Confidence Interval (CI) = 1.53, 1.87), p=1 × 10-26). This effect was attenuated among ever coal users (OR = 1.24 (95% CI: 1.03, 1.50), p = 0.02, p-interaction = 6 × 10-3). We observed similar attenuation among coal users from Xuanwei. Our study provides evidence that genetic susceptibility to lung adenocarcinoma among never-smoking Asian women is weaker among coal users. These results suggest that lung cancer pathogenesis may differ, at least partially, depending on exposure to coal combustion products. Notably, these novel findings are among the few instances of sub-multiplicative gene-environment interactions in the cancer literature.
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Affiliation(s)
- Batel Blechter
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
| | - Jason Y Y Wong
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA
| | - Chao Agnes Hsiung
- Institute of Population Health Sciences, National Health Research Institutes, Zhunan, Taiwan
| | - H Dean Hosgood
- Department of Epidemiology & Population Health, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Zhihua Yin
- Department of Epidemiology, School of Public Health, China Medical University, Shenyang, People's Republic of China
| | - Xiao-Ou Shu
- Division of Epidemiology, Department of Medicine, Vanderbilt University Medical Center and Vanderbilt-Ingram Cancer Center, Nashville, TN, USA
| | - Han Zhang
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA
| | - Jianxin Shi
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA
| | - Lei Song
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA
| | - Minsun Song
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA; Department of Statistics, Sookmyung Women's University, Seoul, Republic of Korea
| | - Wei Zheng
- Division of Epidemiology, Department of Medicine, Vanderbilt University Medical Center and Vanderbilt-Ingram Cancer Center, Nashville, TN, USA
| | - Zhaoming Wang
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA; Department of Computational Biology, St. Jude Children's Research Hospital, Memphis, TN, USA
| | - Neil Caporaso
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA
| | - Laurie Burdette
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA; Cancer Genomics Research Laboratory, Division of Cancer Epidemiology and Genetics, Frederick National Laboratory for Cancer Research, Frederick, MD, USA; Leidos Biomedical Research, Inc., Frederick, MD, USA
| | - Meredith Yeager
- Cancer Genomics Research Laboratory, Division of Cancer Epidemiology and Genetics, Frederick National Laboratory for Cancer Research, Frederick, MD, USA
| | - Sonja I Berndt
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA
| | - Maria Teresa Landi
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA
| | - Chien-Jen Chen
- Genomic Research Center, Academia Sinica, Taipei, Taiwan
| | - Gee-Chen Chang
- Faculty of Medicine, School of Medicine, National Yang-Ming University, Taipei, Taiwan; Division of Chest Medicine, Department of Internal Medicine, Taichung Veterans General Hospital, Taichung, Taiwan
| | - Chin-Fu Hsiao
- Institute of Population Health Sciences, National Health Research Institutes, Zhunan, Taiwan
| | - Ying-Huang Tsai
- Division of Pulmonary and Critical Care Medicine, Chiayi Chang Gung Memorial Hospital, Chiayi, Taiwan
| | - Kuan-Yu Chen
- Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan
| | - Ming-Shyan Huang
- Department of Internal Medicine, E-Da Cancer Hospital, School of Medicine, I-Shou University, Kaohsiung, Taiwan
| | - Wu-Chou Su
- Department of Internal Medicine, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan, Taiwan
| | - Yuh-Min Chen
- Department of Chest Medicine, Taipei Veterans General Hospital, Taipei, Taiwan; College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan
| | - Li-Hsin Chien
- Institute of Population Health Sciences, National Health Research Institutes, Zhunan, Taiwan
| | - Chung-Hsing Chen
- National Institute of Cancer Research, National Health Research Institutes, Zhunan, Taiwan
| | - Tsung-Ying Yang
- Division of Chest Medicine, Department of Internal Medicine, Taichung Veterans General Hospital, Taichung, Taiwan
| | - Chih-Liang Wang
- Department of Pulmonary and Critical Care, Chang Gung Memorial Hospital, Taoyuan, Taiwan
| | - Jen-Yu Hung
- Department of Internal Medicine, Kaohsiung Medical University Hospital, School of Medicine, Kaohsiung Medical University, Kaohsiung, Taiwan
| | - Chien-Chung Lin
- Department of Internal Medicine, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan, Taiwan
| | - Reury-Perng Perng
- Department of Chest Medicine, Taipei Veterans General Hospital, Taipei, Taiwan
| | - Chih-Yi Chen
- Institute of Medicine, Chung Shan Medical University, Taichung, Taiwan; Division of Thoracic Surgery, Department of Surgery, Chung Shan Medical University Hospital, Taichung, Taiwan
| | - Kun-Chieh Chen
- Division of Chest Medicine, Department of Internal Medicine, Taichung Veterans General Hospital, Taichung, Taiwan
| | - Yao-Jen Li
- Genomic Research Center, Academia Sinica, Taipei, Taiwan
| | - Chong-Jen Yu
- Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan
| | - Yi-Song Chen
- Institute of Population Health Sciences, National Health Research Institutes, Zhunan, Taiwan
| | - Ying-Hsiang Chen
- Institute of Population Health Sciences, National Health Research Institutes, Zhunan, Taiwan
| | - Fang-Yu Tsai
- National Institute of Cancer Research, National Health Research Institutes, Zhunan, Taiwan
| | - Wei Jie Seow
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore; Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Bryan A Bassig
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA
| | - Wei Hu
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA
| | - Bu-Tian Ji
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA
| | - Wei Wu
- Department of Epidemiology, School of Public Health, China Medical University, Shenyang, People's Republic of China
| | - Peng Guan
- Department of Epidemiology, School of Public Health, China Medical University, Shenyang, People's Republic of China
| | - Qincheng He
- Department of Epidemiology, School of Public Health, China Medical University, Shenyang, People's Republic of China
| | - Yu-Tang Gao
- Department of Epidemiology, Shanghai Cancer Institute, Shanghai, People's Republic of China
| | - Qiuyin Cai
- Division of Epidemiology, Department of Medicine, Vanderbilt University Medical Center and Vanderbilt-Ingram Cancer Center, Nashville, TN, USA
| | - Wong-Ho Chow
- Department of Epidemiology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Yong-Bing Xiang
- Department of Epidemiology, Shanghai Cancer Institute, Shanghai, People's Republic of China
| | - Dongxin Lin
- Department of Etiology & Carcinogenesis and State Key Laboratory of Molecular Oncology, Cancer Institute and Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, People's Republic of China
| | - Chen Wu
- Department of Etiology & Carcinogenesis and State Key Laboratory of Molecular Oncology, Cancer Institute and Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, People's Republic of China
| | - Yi-Long Wu
- Guangdong Lung Cancer Institute, Medical Research Center and Cancer Center of Guangdong General Hospital, Guangdong Academy of Medical Sciences, Guangzhou, People's Republic of China
| | - Min-Ho Shin
- Department of Preventive Medicine, Chonnam National University Medical School, Gwangju, Republic of Korea
| | - Yun-Chul Hong
- Department of Preventive Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Keitaro Matsuo
- Division of Molecular Medicine, Aichi Cancer Center Research Institute, Nagoya, Japan
| | - Kexin Chen
- Department of Epidemiology and Biostatistics, Tianjin Medical University Cancer Institute and Hospital, Tianjin, People's Republic of China
| | - Maria Pik Wong
- Department of Pathology, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong
| | - Daru Lu
- Ministry of Education Key Laboratory of Contemporary Anthropology, School of Life Sciences, Fudan University, Shanghai, People's Republic of China; State Key Laboratory of Genetic Engineering, School of Life Sciences, Fudan University, Shanghai, People's Republic of China
| | - Li Jin
- Ministry of Education Key Laboratory of Contemporary Anthropology, School of Life Sciences, Fudan University, Shanghai, People's Republic of China; State Key Laboratory of Genetic Engineering, School of Life Sciences, Fudan University, Shanghai, People's Republic of China
| | - Jiu-Cun Wang
- Ministry of Education Key Laboratory of Contemporary Anthropology, School of Life Sciences, Fudan University, Shanghai, People's Republic of China; State Key Laboratory of Genetic Engineering, School of Life Sciences, Fudan University, Shanghai, People's Republic of China
| | - Adeline Seow
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore
| | - Tangchun Wu
- Department of Occupational and Environmental Health and Ministry of Education Key Lab for Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, People's Republic of China
| | - Hongbing Shen
- Department of Epidemiology and Biostatistics, Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, School of Public Health, Nanjing Medical University, Nanjing, People's Republic of China; Department of Epidemiology, School of Public Health, Nanjing Medical University, Nanjing, People's Republic of China
| | - Joseph F Fraumeni
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA
| | - Pan-Chyr Yang
- Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan
| | - I-Shou Chang
- National Institute of Cancer Research, National Health Research Institutes, Zhunan, Taiwan
| | - Baosen Zhou
- Department of Epidemiology, School of Public Health, China Medical University, Shenyang, People's Republic of China
| | - Stephen J Chanock
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA
| | - Nathaniel Rothman
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA
| | - Nilanjan Chatterjee
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Qing Lan
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA
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Kroetsch SA, Kidd KA, Monk WA, Culp JM, Compson ZG, Pavey SA. The effects of taxonomy, diet, and ecology on the microbiota of riverine macroinvertebrates. Ecol Evol 2020; 10:14000-14019. [PMID: 33391698 PMCID: PMC7771166 DOI: 10.1002/ece3.6993] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2020] [Revised: 10/10/2020] [Accepted: 10/11/2020] [Indexed: 12/18/2022] Open
Abstract
Freshwater macroinvertebrates play key ecological roles in riverine food webs, such as the transfer of nutrients to consumers and decomposition of organic matter. Although local habitat quality drives macroinvertebrate diversity and abundance, little is known about their microbiota. In most animals, the microbiota provides benefits, such as increasing the rate at which nutrients are metabolized, facilitating immune system development, and defending against pathogenic attack. Our objectives were to identify the bacteria within aquatic invertebrates and determine whether their composition varied with taxonomy, habitat, diet, and time of sample collection. In 2016 and 2017, we collected 264 aquatic invertebrates from the mainstem Saint John (Wolastoq) River in New Brunswick, Canada, representing 15 orders. We then amplified the V3-V4 hypervariable region of the 16S rRNA gene within each individual, which revealed nearly 20,000 bacterial operational taxonomic units (OTUs). The microbiota across all aquatic invertebrates were dominated by Proteobacteria (69.25% of the total sequence reads), but they differed significantly in beta diversity, both among host invertebrate taxa (genus-, family-, and order-levels) and temporally. In contrast to previous work, we observed no microbiota differences among functional feeding groups or traditional feeding habits, and neither water velocity nor microhabitat type structured microbiota variability. Our findings suggest that host invertebrate taxonomy was the most important factor in modulating the composition of the microbiota, likely through a combination of vertical and horizontal bacterial transmission, and evolutionary processes. This is one of the most comprehensive studies of freshwater invertebrate microbiota to date, and it underscores the need for future studies of invertebrate microbiota evolution and linkages to environmental bacteria and physico-chemical conditions.
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Affiliation(s)
- Shawn A. Kroetsch
- Department of Biological SciencesUniversity of New BrunswickSaint JohnNew BrunswickCanada
- Canadian Rivers InstituteUniversity of New BrunswickSaint JohnNew BrunswickCanada
| | - Karen A. Kidd
- Department of Biological SciencesUniversity of New BrunswickSaint JohnNew BrunswickCanada
- Canadian Rivers InstituteUniversity of New BrunswickSaint JohnNew BrunswickCanada
- Department of Biology and School of Geography and Earth SciencesMcMaster UniversityHamiltonOntarioCanada
| | - Wendy A. Monk
- Environment and Climate Change Canada @ Canadian Rivers InstituteFaculty of Forestry and Environmental ManagementUniversity of New BrunswickFrederictonNew BrunswickCanada
| | - Joseph M. Culp
- Environment and Climate Change CanadaDepartment of Biology and Geography and Environmental StudiesWilfrid Laurier UniversityWaterlooOntarioCanada
| | - Zacchaeus G. Compson
- Environment and Climate Change Canada @ Canadian Rivers InstituteUniversity of New BrunswickFrederictonNew BrunswickCanada
- Centre for Environmental Genomics Applications (CEGA)St. John’sNewfoundland and LabradorCanada
| | - Scott A. Pavey
- Department of Biological SciencesUniversity of New BrunswickSaint JohnNew BrunswickCanada
- Canadian Rivers InstituteUniversity of New BrunswickSaint JohnNew BrunswickCanada
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Lee R, Lee NE, Choi SH, Nam SM, Kim HC, Rhim H, Cho IH, Hwang SH, Nah SY. Effects of gintonin-enriched fraction on hippocampal gene expressions. Integr Med Res 2020; 10:100475. [PMID: 33134079 PMCID: PMC7588706 DOI: 10.1016/j.imr.2020.100475] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2020] [Revised: 07/02/2020] [Accepted: 07/06/2020] [Indexed: 01/30/2023] Open
Abstract
Background Recently, gintonin and gintonin-enriched fraction (GEF) have been isolated from ginseng, a herbal medicine. Gintonin induces [Ca2+]i transition in cultured hippocampal neurons and stimulates acetylcholine release through LPA receptor activation. Oral administration of GEF is linked to hippocampus-dependent cognitive enhancement and other neuroprotective effects; however, effects of its long-term administration on hippocampal gene expression remains unknown. Here, we used next-generation sequence (NGS) analysis to examine changes in hippocampal gene expressions after long-term oral administration of GEF. Methods C57BL/6 mice were divided into three groups: control group, GEF50 (GEF 50 mg/kg, p.o.), and GEF100 (GEF 100 mg/kg, p.o.). After 22 days, total RNA was extracted from mouse hippocampal tissues. NGS was used for gene expression profiling; quantitative-real-time PCR and western blot were performed to quantify the changes in specific genes and to confirm the protein expression levels in treatment groups. Results NGS analysis screened a total of 23,282 genes, analyzing 11-related categories. We focused on the neurogenesis category, which includes four genes for candidate markers: choline acetyltransferase (ChAT) gene, β3-adrenergic receptor (Adrb3) gene, and corticotrophin-releasing hormone (Crh) gene, and tryptophan 2,3-dioxygenase (Tdo2) gene. Real-time PCR showed a marked overexpression of ChAT, Adrb3, and Crh genes, while reduced expression of Tdo2. Western blot analysis also confirmed increased ChAT and decreased Tdo2 protein levels. Conclusion We found that GEF affects mouse hippocampal gene expressions, associated with memory, cognitive, anti-stress and anti-anxiety functions, and neurodegeneration at differential degree, that might explain the genetic bases of GEF-mediated neuroprotective effects.
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Affiliation(s)
- Rami Lee
- Ginsentology Research Laboratory and Department of Physiology, College of Veterinary Medicine, Konkuk University, Seoul, Republic of Korea
| | - Na-Eun Lee
- Ginsentology Research Laboratory and Department of Physiology, College of Veterinary Medicine, Konkuk University, Seoul, Republic of Korea
| | - Sun-Hye Choi
- Ginsentology Research Laboratory and Department of Physiology, College of Veterinary Medicine, Konkuk University, Seoul, Republic of Korea
| | - Sung Min Nam
- Ginsentology Research Laboratory and Department of Physiology, College of Veterinary Medicine, Konkuk University, Seoul, Republic of Korea
| | - Hyoung-Chun Kim
- Neuro Psychopharmacology and Toxicology Program, College of Pharmacy, Kangwon National University, Chunchon, Republic of Korea
| | - Hyewhon Rhim
- Center for Neuroscience, Korea Institute of Science and Technology, Seoul, Republic of Korea
| | - Ik-Hyun Cho
- Department of Convergence Medical Science, Department of Science in Korean Medicine and Brain Korea 21 Plus Program, Graduate School, Kyung Hee University, Seoul, Republic of Korea
| | - Sung-Hee Hwang
- Department of Pharmaceutical Engineering, College of Health Sciences, Sangji University, Wonju, Republic of Korea
| | - Seung-Yeol Nah
- Ginsentology Research Laboratory and Department of Physiology, College of Veterinary Medicine, Konkuk University, Seoul, Republic of Korea
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Coyote-Maestas W, Nedrud D, Okorafor S, He Y, Schmidt D. Targeted insertional mutagenesis libraries for deep domain insertion profiling. Nucleic Acids Res 2020; 48:e11. [PMID: 31745561 PMCID: PMC6954442 DOI: 10.1093/nar/gkz1110] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2019] [Revised: 09/22/2019] [Accepted: 11/08/2019] [Indexed: 11/21/2022] Open
Abstract
Domain recombination is a key principle in protein evolution and protein engineering, but inserting a donor domain into every position of a target protein is not easily experimentally accessible. Most contemporary domain insertion profiling approaches rely on DNA transposons, which are constrained by sequence bias. Here, we establish Saturated Programmable Insertion Engineering (SPINE), an unbiased, comprehensive, and targeted domain insertion library generation technique using oligo library synthesis and multi-step Golden Gate cloning. Through benchmarking to MuA transposon-mediated library generation on four ion channel genes, we demonstrate that SPINE-generated libraries are enriched for in-frame insertions, have drastically reduced sequence bias as well as near-complete and highly-redundant coverage. Unlike transposon-mediated domain insertion that was severely biased and sparse for some genes, SPINE generated high-quality libraries for all genes tested. Using the Inward Rectifier K+ channel Kir2.1, we validate the practical utility of SPINE by constructing and comparing domain insertion permissibility maps. SPINE is the first technology to enable saturated domain insertion profiling. SPINE could help explore the relationship between domain insertions and protein function, and how this relationship is shaped by evolutionary forces and can be engineered for biomedical applications.
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Affiliation(s)
- Willow Coyote-Maestas
- Dept. of Biochemistry, Molecular Biology & Biophysics, University of Minnesota, Minneapolis, MN 55455, USA
| | - David Nedrud
- Dept. of Biochemistry, Molecular Biology & Biophysics, University of Minnesota, Minneapolis, MN 55455, USA
| | - Steffan Okorafor
- Dept. of Neuroscience, University of Minnesota, Minneapolis, MN 55455, USA
| | - Yungui He
- Dept. of Genetics, Cell Biology & Development, University of Minnesota, Minneapolis, MN 55455, USA
| | - Daniel Schmidt
- Dept. of Genetics, Cell Biology & Development, University of Minnesota, Minneapolis, MN 55455, USA
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Klasberg S, Surendranath V, Lange V, Schöfl G. Bioinformatics Strategies, Challenges, and Opportunities for Next Generation Sequencing-Based HLA Genotyping. Transfus Med Hemother 2019; 46:312-325. [PMID: 31832057 PMCID: PMC6876610 DOI: 10.1159/000502487] [Citation(s) in RCA: 38] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2019] [Accepted: 07/30/2019] [Indexed: 12/16/2022] Open
Abstract
The advent of next generation sequencing (NGS) has altered the face of genotyping the human leukocyte antigen (HLA) system in clinical, stem cell donor registry, and research contexts. NGS has led to a dramatically increased sequencing throughput at high accuracy, while being more time and cost efficient than precursor technologies. This has led to a broader and deeper profiling of the key genes in the human immunogenetic make-up. The rapid evolution of sequencing technologies is evidenced by the development of varied short-read sequencing platforms with differing read lengths and sequencing capacities to long-read sequencing platforms capable of profiling full genes without fragmentation. Concomitantly, there has been development of a diverse set of computational analyses and software tools developed to deal with the various strengths and limitations of the sequencing data generated by the different sequencing platforms. This review surveys the different modalities involved in generating NGS HLA profiling sequence data. It systematically describes various computational approaches that have been developed to achieve HLA genotyping to different degrees of resolution. At each stage, this review enumerates the drawbacks and advantages of each of the platforms and analysis approaches, thus providing a comprehensive picture of the current state of HLA genotyping technologies.
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Banuelos M, Sindi S, Marcia R. Structural Variant Prediction in Extended Pedigrees Through Sparse Negative Binomial Genome Signal Recovery. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2018; 2018:1311-1314. [PMID: 30440632 DOI: 10.1109/embc.2018.8512519] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Structural variants (SVs) are rearrangements, such as deletions, insertions, duplications, inversions, and translocations, in an individual's genome relative to a reference. SV detection is often marred by high false positive rates due to errors in sequencing and mapping. In previous work, we proposed a maximum likelihood approach to SV prediction that incorporated low-coverage sequencing data and coverage distribution. In particular, we developed a negative binomial framework to reflect a more realistic representation DNA fragment distributions sampled from an individual's genome. In this paper, we leverage relationships between an off spring and both parents, in addition to the negative binomial framework, to improve SV identification accuracy. We present numerical results on both simulated genomes as well as two sequenced parent-child trios from the 1000 Genomes Project.
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Teitz LS, Pyntikova T, Skaletsky H, Page DC. Selection Has Countered High Mutability to Preserve the Ancestral Copy Number of Y Chromosome Amplicons in Diverse Human Lineages. Am J Hum Genet 2018; 103:261-275. [PMID: 30075113 DOI: 10.1016/j.ajhg.2018.07.007] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2018] [Accepted: 07/10/2018] [Indexed: 02/07/2023] Open
Abstract
Amplicons-large, highly identical segmental duplications-are a prominent feature of mammalian Y chromosomes. Although they encode genes essential for fertility, these amplicons differ vastly between species, and little is known about the selective constraints acting on them. Here, we develop computational tools to detect amplicon copy number with unprecedented accuracy from high-throughput sequencing data. We find that one-sixth (16.9%) of 1,216 males from the 1000 Genomes Project have at least one deleted or duplicated amplicon. However, each amplicon's reference copy number is scrupulously maintained among divergent branches of the Y chromosome phylogeny, including the ancient branch A00, indicating that the reference copy number is ancestral to all modern human Y chromosomes. Using phylogenetic analyses and simulations, we demonstrate that this pattern of variation is incompatible with neutral evolution and instead displays hallmarks of mutation-selection balance. We also observe cases of amplicon rescue, in which deleted amplicons are restored through subsequent duplications. These results indicate that, contrary to the lack of constraint suggested by the differences between species, natural selection has suppressed amplicon copy number variation in diverse human lineages.
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A method for allocating low-coverage sequencing resources by targeting haplotypes rather than individuals. Genet Sel Evol 2017; 49:78. [PMID: 29070022 PMCID: PMC5655873 DOI: 10.1186/s12711-017-0353-y] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2017] [Accepted: 10/18/2017] [Indexed: 11/25/2022] Open
Abstract
Background This paper describes a heuristic method for allocating low-coverage sequencing resources by targeting haplotypes rather than individuals. Low-coverage sequencing assembles high-coverage sequence information for every individual by accumulating data from the genome segments that they share with many other individuals into consensus haplotypes. Deriving the consensus haplotypes accurately is critical for achieving a high phasing and imputation accuracy. In order to enable accurate phasing and imputation of sequence information for the whole population, we allocate the available sequencing resources among individuals with existing phased genomic data by targeting the sequencing coverage of their haplotypes. Results Our method, called AlphaSeqOpt, prioritizes haplotypes using a score function that is based on the frequency of the haplotypes in the sequencing set relative to the target coverage. AlphaSeqOpt has two steps: (1) selection of an initial set of individuals by iteratively choosing the individuals that have the maximum score conditional on the current set, and (2) refinement of the set through several rounds of exchanges of individuals. AlphaSeqOpt is very effective for distributing a fixed amount of sequencing resources evenly across haplotypes, which results in a reduction of the proportion of haplotypes that are sequenced below the target coverage. AlphaSeqOpt can provide a greater proportion of haplotypes sequenced at the target coverage by sequencing less individuals, as compared with other methods that use a score function based on haplotype frequencies in the population. A refinement of the initially selected set can provide a larger more diverse set with more unique individuals, which is beneficial in the context of low-coverage sequencing. We extend the method with an approach for filtering rare haplotypes based on their flanking haplotypes, so that only those that are likely to derive from a recombination event are targeted. Conclusions We present a method for allocating sequencing resources so that a greater proportion of haplotypes are sequenced at a coverage that is sufficiently high for population-based imputation with low-coverage sequencing. The haplotype score function, the refinement step, and the new approach for filtering rare haplotypes make AlphaSeqOpt more effective for that purpose than previously reported methods for reducing sequencing redundancy. Electronic supplementary material The online version of this article (doi:10.1186/s12711-017-0353-y) contains supplementary material, which is available to authorized users.
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Zhao J, Liu J, Vemula SV, Lin C, Tan J, Ragupathy V, Wang X, Mbondji-wonje C, Ye Z, Landry ML, Hewlett I. Sensitive Detection and Simultaneous Discrimination of Influenza A and B Viruses in Nasopharyngeal Swabs in a Single Assay Using Next-Generation Sequencing-Based Diagnostics. PLoS One 2016; 11:e0163175. [PMID: 27658193 PMCID: PMC5033603 DOI: 10.1371/journal.pone.0163175] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2016] [Accepted: 09/02/2016] [Indexed: 11/18/2022] Open
Abstract
Reassortment of 2009 (H1N1) pandemic influenza virus (pdH1N1) with other strains may produce more virulent and pathogenic forms, detection and their rapid characterization is critical. In this study, we reported a “one-size-fits-all” approach using a next-generation sequencing (NGS) detection platform to extensively identify influenza viral genomes for diagnosis and determination of novel virulence and drug resistance markers. A de novo module and other bioinformatics tools were used to generate contiguous sequence and identify influenza types/subtypes. Of 162 archived influenza-positive patient specimens, 161(99.4%) were positive for either influenza A or B viruses determined using the NGS assay. Among these, 135(83.3%) were A(H3N2), 14(8.6%) were A(pdH1N1), 2(1.2%) were A(H3N2) and A(pdH1N1) virus co-infections and 10(6.2%) were influenza B viruses. Of the influenza A viruses, 66.7% of A(H3N2) viruses tested had a E627K mutation in the PB2 protein, and 87.8% of the influenza A viruses contained the S31N mutation in the M2 protein. Further studies demonstrated that the NGS assay could achieve a high level of sensitivity and reveal adequate genetic information for final laboratory confirmation. The current diagnostic platform allows for simultaneous identification of a broad range of influenza viruses, monitoring emerging influenza strains with pandemic potential that facilitating diagnostics and antiviral treatment in the clinical setting and protection of the public health.
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Affiliation(s)
- Jiangqin Zhao
- DETTD/OBRR/CBER, Food and Drug Administration, Silver Spring, MD, 20993, United States of America
- * E-mail: (JZ); (IH)
| | - Jikun Liu
- DETTD/OBRR/CBER, Food and Drug Administration, Silver Spring, MD, 20993, United States of America
| | - Sai Vikram Vemula
- DETTD/OBRR/CBER, Food and Drug Administration, Silver Spring, MD, 20993, United States of America
| | - Corinna Lin
- DETTD/OBRR/CBER, Food and Drug Administration, Silver Spring, MD, 20993, United States of America
| | - Jiying Tan
- DETTD/OBRR/CBER, Food and Drug Administration, Silver Spring, MD, 20993, United States of America
| | - Viswanath Ragupathy
- DETTD/OBRR/CBER, Food and Drug Administration, Silver Spring, MD, 20993, United States of America
| | - Xue Wang
- DETTD/OBRR/CBER, Food and Drug Administration, Silver Spring, MD, 20993, United States of America
| | - Christelle Mbondji-wonje
- DETTD/OBRR/CBER, Food and Drug Administration, Silver Spring, MD, 20993, United States of America
| | - Zhiping Ye
- DVP/OVRR/CBER, Food and Drug Administration, Silver Spring, MD, 20993, United States of America
| | - Marie L. Landry
- Department of Laboratory Medicine, Yale University School of Medicine, New Haven, CT, 06520, United States of America
| | - Indira Hewlett
- DETTD/OBRR/CBER, Food and Drug Administration, Silver Spring, MD, 20993, United States of America
- * E-mail: (JZ); (IH)
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10
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Gorjanc G, Jenko J, Hearne SJ, Hickey JM. Initiating maize pre-breeding programs using genomic selection to harness polygenic variation from landrace populations. BMC Genomics 2016; 17:30. [PMID: 26732811 PMCID: PMC4702314 DOI: 10.1186/s12864-015-2345-z] [Citation(s) in RCA: 58] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2015] [Accepted: 12/21/2015] [Indexed: 11/23/2022] Open
Abstract
Background The limited genetic diversity of elite maize germplasms raises concerns about the potential to breed for new challenges. Initiatives have been formed over the years to identify and utilize useful diversity from landraces to overcome this issue. The aim of this study was to evaluate the proposed designs to initiate a pre-breeding program within the Seeds of Discovery (SeeD) initiative with emphasis on harnessing polygenic variation from landraces using genomic selection. We evaluated these designs with stochastic simulation to provide decision support about the effect of several design factors on the quality of resulting (pre-bridging) germplasm. The evaluated design factors were: i) the approach to initiate a pre-breeding program from the selected landraces, doubled haploids of the selected landraces, or testcrosses of the elite hybrid and selected landraces, ii) the genetic parameters of landraces and phenotypes, and iii) logistical factors related to the size and management of a pre-breeding program. Results The results suggest a pre-breeding program should be initiated directly from landraces. Initiating from testcrosses leads to a rapid reconstruction of the elite donor genome during further improvement of the pre-bridging germplasm. The analysis of accuracy of genomic predictions across the various design factors indicate the power of genomic selection for pre-breeding programs with large genetic diversity and constrained resources for data recording. The joint effect of design factors was summarized with decision trees with easy to follow guidelines to optimize pre-breeding efforts of SeeD and similar initiatives. Conclusions Results of this study provide guidelines for SeeD and similar initiatives on how to initiate pre-breeding programs that aim to harness polygenic variation from landraces. Electronic supplementary material The online version of this article (doi:10.1186/s12864-015-2345-z) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Gregor Gorjanc
- Biotechnical Faculty, University of Ljubljana, 1000, Ljubljana, Slovenia. .,The Roslin Institute and Royal (Dick) School of Veterinary Studies, The University of Edinburgh, Easter Bush, Midlothian, Scotland, UK.
| | - Janez Jenko
- The Roslin Institute and Royal (Dick) School of Veterinary Studies, The University of Edinburgh, Easter Bush, Midlothian, Scotland, UK. .,Agricultural Institute of Slovenia, 1000, Ljubljana, Slovenia.
| | - Sarah J Hearne
- Genetic Resources Program, International Maize and Wheat Improvement Center (CIMMYT), Apdo, 06600, México, D.F., México.
| | - John M Hickey
- The Roslin Institute and Royal (Dick) School of Veterinary Studies, The University of Edinburgh, Easter Bush, Midlothian, Scotland, UK.
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11
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Gorjanc G, Cleveland MA, Houston RD, Hickey JM. Potential of genotyping-by-sequencing for genomic selection in livestock populations. Genet Sel Evol 2015; 47:12. [PMID: 25887531 PMCID: PMC4344748 DOI: 10.1186/s12711-015-0102-z] [Citation(s) in RCA: 67] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2014] [Accepted: 01/29/2015] [Indexed: 12/12/2022] Open
Abstract
Background Next-generation sequencing techniques, such as genotyping-by-sequencing (GBS), provide alternatives to single nucleotide polymorphism (SNP) arrays. The aim of this work was to evaluate the potential of GBS compared to SNP array genotyping for genomic selection in livestock populations. Methods The value of GBS was quantified by simulation analyses in which three parameters were varied: (i) genome-wide sequence read depth (x) per individual from 0.01x to 20x or using SNP array genotyping; (ii) number of genotyped markers from 3000 to 300 000; and (iii) size of training and prediction sets from 500 to 50 000 individuals. The latter was achieved by distributing the total available x of 1000x, 5000x, or 10 000x per genotyped locus among the varying number of individuals. With SNP arrays, genotypes were called from sequence data directly. With GBS, genotypes were called from sequence reads that varied between loci and individuals according to a Poisson distribution with mean equal to x. Simulated data were analyzed with ridge regression and the accuracy and bias of genomic predictions and response to selection were quantified under the different scenarios. Results Accuracies of genomic predictions using GBS data or SNP array data were comparable when large numbers of markers were used and x per individual was ~1x or higher. The bias of genomic predictions was very high at a very low x. When the total available x was distributed among the training individuals, the accuracy of prediction was maximized when a large number of individuals was used that had GBS data with low x for a large number of markers. Similarly, response to selection was maximized under the same conditions due to increasing both accuracy and selection intensity. Conclusions GBS offers great potential for developing genomic selection in livestock populations because it makes it possible to cover large fractions of the genome and to vary the sequence read depth per individual. Thus, the accuracy of predictions is improved by increasing the size of training populations and the intensity of selection is increased by genotyping a larger number of selection candidates. Electronic supplementary material The online version of this article (doi:10.1186/s12711-015-0102-z) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Gregor Gorjanc
- The Roslin Institute and Royal (Dick) School of Veterinary Studies, The University of Edinburgh, Easter Bush, Midlothian, Scotland, UK.
| | - Matthew A Cleveland
- Genus Plc, 100 Bluegrass Commons Blvd., Suite 2200, Hendersonville, TN, 37075, USA.
| | - Ross D Houston
- The Roslin Institute and Royal (Dick) School of Veterinary Studies, The University of Edinburgh, Easter Bush, Midlothian, Scotland, UK.
| | - John M Hickey
- The Roslin Institute and Royal (Dick) School of Veterinary Studies, The University of Edinburgh, Easter Bush, Midlothian, Scotland, UK.
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12
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Lange K, Papp JC, Sinsheimer JS, Sobel EM. Next Generation Statistical Genetics: Modeling, Penalization, and Optimization in High-Dimensional Data. ANNUAL REVIEW OF STATISTICS AND ITS APPLICATION 2014; 1:279-300. [PMID: 24955378 PMCID: PMC4062304 DOI: 10.1146/annurev-statistics-022513-115638] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/30/2023]
Abstract
Statistical genetics is undergoing the same transition to big data that all branches of applied statistics are experiencing. With the advent of inexpensive DNA sequencing, the transition is only accelerating. This brief review highlights some modern techniques with recent successes in statistical genetics. These include: (a) lasso penalized regression and association mapping, (b) ethnic admixture estimation, (c) matrix completion for genotype and sequence data, (d) the fused lasso and copy number variation, (e) haplotyping, (f) estimation of relatedness, (g) variance components models, and (h) rare variant testing. For more than a century, genetics has been both a driver and beneficiary of statistical theory and practice. This symbiotic relationship will persist for the foreseeable future.
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Affiliation(s)
- Kenneth Lange
- Depts of Biomathematics, Human Genetics, and Statistics, UCLA
| | | | - Janet S. Sinsheimer
- Depts of Biomathematics, Human Genetics, Statistics, and Biostatistics, UCLA
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13
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Cao CC, Li C, Huang Z, Ma X, Sun X. Identifying rare variants with optimal depth of coverage and cost-effective overlapping pool sequencing. Genet Epidemiol 2013; 37:820-30. [PMID: 24166758 DOI: 10.1002/gepi.21769] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2013] [Revised: 09/09/2013] [Accepted: 09/27/2013] [Indexed: 01/19/2023]
Abstract
Genome-wide association studies have identified hundreds of genetic variants associated with complex diseases although most variants identified so far explain only a small proportion of heritability, suggesting that rare variants are responsible for missing heritability. Identification of rare variants through large-scale resequencing becomes increasing important but still prohibitively expensive despite the rapid decline in the sequencing costs. Nevertheless, group testing based overlapping pool sequencing in which pooled rather than individual samples are sequenced will greatly reduces the efforts of sample preparation as well as the costs to screen for rare variants. Here, we proposed an overlapping pool sequencing to screen rare variants with optimal sequencing depth and a corresponding cost model. We formulated a model to compute the optimal depth for sufficient observations of variants in pooled sequencing. Utilizing shifted transversal design algorithm, appropriate parameters for overlapping pool sequencing could be selected to minimize cost and guarantee accuracy. Due to the mixing constraint and high depth for pooled sequencing, results showed that it was more cost-effective to divide a large population into smaller blocks which were tested using optimized strategies independently. Finally, we conducted an experiment to screen variant carriers with frequency equaled 1%. With simulated pools and publicly available human exome sequencing data, the experiment achieved 99.93% accuracy. Utilizing overlapping pool sequencing, the cost for screening variant carriers with frequency equaled 1% in 200 diploid individuals dropped to at least 66% at which target sequencing region was set to 30 Mb.
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Affiliation(s)
- Chang-Chang Cao
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, China
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14
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Faye LL, Machiela MJ, Kraft P, Bull SB, Sun L. Re-ranking sequencing variants in the post-GWAS era for accurate causal variant identification. PLoS Genet 2013; 9:e1003609. [PMID: 23950724 PMCID: PMC3738448 DOI: 10.1371/journal.pgen.1003609] [Citation(s) in RCA: 31] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2012] [Accepted: 05/20/2013] [Indexed: 11/30/2022] Open
Abstract
Next generation sequencing has dramatically increased our ability to localize disease-causing variants by providing base-pair level information at costs increasingly feasible for the large sample sizes required to detect complex-trait associations. Yet, identification of causal variants within an established region of association remains a challenge. Counter-intuitively, certain factors that increase power to detect an associated region can decrease power to localize the causal variant. First, combining GWAS with imputation or low coverage sequencing to achieve the large sample sizes required for high power can have the unintended effect of producing differential genotyping error among SNPs. This tends to bias the relative evidence for association toward better genotyped SNPs. Second, re-use of GWAS data for fine-mapping exploits previous findings to ensure genome-wide significance in GWAS-associated regions. However, using GWAS findings to inform fine-mapping analysis can bias evidence away from the causal SNP toward the tag SNP and SNPs in high LD with the tag. Together these factors can reduce power to localize the causal SNP by more than half. Other strategies commonly employed to increase power to detect association, namely increasing sample size and using higher density genotyping arrays, can, in certain common scenarios, actually exacerbate these effects and further decrease power to localize causal variants. We develop a re-ranking procedure that accounts for these adverse effects and substantially improves the accuracy of causal SNP identification, often doubling the probability that the causal SNP is top-ranked. Application to the NCI BPC3 aggressive prostate cancer GWAS with imputation meta-analysis identified a new top SNP at 2 of 3 associated loci and several additional possible causal SNPs at these loci that may have otherwise been overlooked. This method is simple to implement using R scripts provided on the author's website. As next-generation sequencing (NGS) costs continue to fall and genome-wide association study (GWAS) platform coverage improves, the human genetics community is positioned to identify potentially causal variants. However, current NGS or imputation-based studies of either the whole genome or regions previously identified by GWAS have not yet been very successful in identifying causal variants. A major hurdle is the development of methods to distinguish disease-causing variants from their highly-correlated proxies within an associated region. We show that various common factors, such as differential sequencing or imputation accuracy rates and linkage disequilibrium patterns, with or without GWAS-informed region selection, can substantially decrease the probability of identifying the correct causal SNP, often by more than half. We then describe a novel and easy-to-implement re-ranking procedure that can double the probability that the causal SNP is top-ranked in many settings. Application to the NCI Breast and Prostate Cancer (BPC3) Cohort Consortium aggressive prostate cancer data identified new top SNPs within two associated loci previously established via GWAS, as well as several additional possible causal SNPs that had been previously overlooked.
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Affiliation(s)
- Laura L. Faye
- Division of Biostatistics, Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
- Samuel Lunenfeld Research Institute, Prosserman Centre for Health Research, Mount Sinai Hospital, Toronto, Ontario, Canada
| | - Mitchell J. Machiela
- Program in Molecular and Genetic Epidemiology, Department of Epidemiology, Harvard School of Public Health, Boston, Massachusetts, United States of America
| | - Peter Kraft
- Program in Molecular and Genetic Epidemiology, Department of Epidemiology, Harvard School of Public Health, Boston, Massachusetts, United States of America
| | - Shelley B. Bull
- Division of Biostatistics, Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
- Samuel Lunenfeld Research Institute, Prosserman Centre for Health Research, Mount Sinai Hospital, Toronto, Ontario, Canada
| | - Lei Sun
- Division of Biostatistics, Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
- Department of Statistical Sciences, University of Toronto, Toronto, Ontario, Canada
- * E-mail:
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15
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Lin WY, Yi N, Lou XY, Zhi D, Zhang K, Gao G, Tiwari HK, Liu N. Haplotype kernel association test as a powerful method to identify chromosomal regions harboring uncommon causal variants. Genet Epidemiol 2013; 37:560-70. [PMID: 23740760 DOI: 10.1002/gepi.21740] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2012] [Revised: 05/01/2013] [Accepted: 05/06/2013] [Indexed: 01/09/2023]
Abstract
For most complex diseases, the fraction of heritability that can be explained by the variants discovered from genome-wide association studies is minor. Although the so-called "rare variants" (minor allele frequency [MAF] < 1%) have attracted increasing attention, they are unlikely to account for much of the "missing heritability" because very few people may carry these rare variants. The genetic variants that are likely to fill in the "missing heritability" include uncommon causal variants (MAF < 5%), which are generally untyped in association studies using tagging single-nucleotide polymorphisms (SNPs) or commercial SNP arrays. Developing powerful statistical methods can help to identify chromosomal regions harboring uncommon causal variants, while bypassing the genome-wide or exome-wide next-generation sequencing. In this work, we propose a haplotype kernel association test (HKAT) that is equivalent to testing the variance component of random effects for distinct haplotypes. With an appropriate weighting scheme given to haplotypes, we can further enhance the ability of HKAT to detect uncommon causal variants. With scenarios simulated according to the population genetics theory, HKAT is shown to be a powerful method for detecting chromosomal regions harboring uncommon causal variants.
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Affiliation(s)
- Wan-Yu Lin
- Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan
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16
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Machiela MJ, Chen C, Liang L, Diver WR, Stevens VL, Tsilidis KK, Haiman CA, Chanock SJ, Hunter DJ, Kraft P. One thousand genomes imputation in the National Cancer Institute Breast and Prostate Cancer Cohort Consortium aggressive prostate cancer genome-wide association study. Prostate 2013; 73:677-89. [PMID: 23255287 PMCID: PMC3962143 DOI: 10.1002/pros.22608] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/13/2012] [Accepted: 10/05/2012] [Indexed: 12/23/2022]
Abstract
BACKGROUND Genotype imputation substantially increases available markers for analysis in genome-wide association studies (GWAS) by leveraging linkage disequilibrium from a reference panel. We sought to (i) investigate the performance of imputation from the August 2010 release of the 1000 Genomes Project (1000GP) in an existing GWAS of prostate cancer, (ii) look for novel associations with prostate cancer risk, (iii) fine-map known prostate cancer susceptibility regions using an approximate Bayesian framework and stepwise regression, and (iv) compare power and efficiency of imputation and de novo sequencing. METHODS We used 2,782 aggressive prostate cancer cases and 4,458 controls from the NCI Breast and Prostate Cancer Cohort Consortium aggressive prostate cancer GWAS to infer 5.8 million well-imputed autosomal single nucleotide polymorphisms (SNPs). RESULTS Imputation quality, as measured by correlation between imputed and true allele counts, was higher among common variants than rare variants. We found no novel prostate cancer associations among a subset of 1.2 million well-imputed low-frequency variants. At a genome-wide sequencing cost of $2,500, imputation from SNP arrays is a more powerful strategy than sequencing for detecting disease associations of SNPs with minor allele frequencies (MAF) above 1%. CONCLUSIONS 1000GP imputation provided dense coverage of previously identified prostate cancer susceptibility regions, highlighting its potential as an inexpensive first-pass approach to fine mapping in regions such as 5p15 and 8q24. Our study shows 1000GP imputation can accurately identify low-frequency variants and stresses the importance of large sample size when studying these variants.
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Affiliation(s)
- Mitchell J. Machiela
- Program in Molecular and Genetic Epidemiology, Department of Epidemiology, Harvard School of Public Health, Boston, Massachusetts
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, Maryland
| | - Constance Chen
- Program in Molecular and Genetic Epidemiology, Department of Epidemiology, Harvard School of Public Health, Boston, Massachusetts
| | - Liming Liang
- Program in Molecular and Genetic Epidemiology, Department of Epidemiology, Harvard School of Public Health, Boston, Massachusetts
| | - W. Ryan Diver
- Epidemiology Research Program, American Cancer Society, Atlanta, Georgia
| | | | - Konstantinos K. Tsilidis
- Department of Hygiene and Epidemiology, University of Ioannina School of Medicine, Ioannina, Greece
- Cancer Epidemiology Unit, Nuffield Department of Clinical Medicine, University of Oxford, Oxford, United Kingdom
| | - Christopher A. Haiman
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, California
| | - Stephen J. Chanock
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, Maryland
| | - David J. Hunter
- Program in Molecular and Genetic Epidemiology, Department of Epidemiology, Harvard School of Public Health, Boston, Massachusetts
- Department of Nutrition, Harvard School of Public Health, Boston, Massachusetts
- Channing Laboratory, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts
| | - Peter Kraft
- Program in Molecular and Genetic Epidemiology, Department of Epidemiology, Harvard School of Public Health, Boston, Massachusetts
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17
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Kang J, Huang KC, Xu Z, Wang Y, Abecasis GR, Li Y. AbCD: arbitrary coverage design for sequencing-based genetic studies. Bioinformatics 2013; 29:799-801. [PMID: 23357921 DOI: 10.1093/bioinformatics/btt041] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023] Open
Abstract
Recent advances in sequencing technologies have revolutionized genetic studies. Although high-coverage sequencing can uncover most variants present in the sequenced sample, low-coverage sequencing is appealing for its cost effectiveness. Here, we present AbCD (arbitrary coverage design) to aid the design of sequencing-based studies. AbCD is a user-friendly interface providing pre-estimated effective sample sizes, specific to each minor allele frequency category, for designs with arbitrary coverage (0.5-30×) and sample size (20-10 000), and for four major ethnic groups (Europeans, Africans, Asians and African Americans). In addition, we also present two software tools: ShotGun and DesignPlanner, which were used to generate the estimates behind AbCD. ShotGun is a flexible short-read simulator for arbitrary user-specified read length and average depth, allowing cycle-specific sequencing error rates and realistic read depth distributions. DesignPlanner is a full pipeline that uses ShotGun to generate sequence data and performs initial SNP discovery, uses our previously presented linkage disequilibrium-aware method to call genotypes, and, finally, provides minor allele frequency-specific effective sample sizes. ShotGun plus DesignPlanner can accommodate effective sample size estimate for any combination of high-depth and low-depth data (for example, whole-genome low-depth plus exonic high-depth) or combination of sequence and genotype data [for example, whole-exome sequencing plus genotyping from existing Genomewide Association Study (GWAS)].
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Affiliation(s)
- Jian Kang
- Faculty of Kinesiology, University of Calgary, Calgary, AB T2N1N4, Canada
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18
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Abstract
Most current genotype imputation methods are model-based and computationally intensive, taking days to impute one chromosome pair on 1000 people. We describe an efficient genotype imputation method based on matrix completion. Our matrix completion method is implemented in MATLAB and tested on real data from HapMap 3, simulated pedigree data, and simulated low-coverage sequencing data derived from the 1000 Genomes Project. Compared with leading imputation programs, the matrix completion algorithm embodied in our program MENDEL-IMPUTE achieves comparable imputation accuracy while reducing run times significantly. Implementation in a lower-level language such as Fortran or C is apt to further improve computational efficiency.
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Affiliation(s)
- Eric C Chi
- Department of Human Genetics, University of California, Los Angeles, CA 90095, USA.
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19
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Saunders CJ, Miller NA, Soden SE, Dinwiddie DL, Noll A, Alnadi NA, Andraws N, Patterson ML, Krivohlavek LA, Fellis J, Humphray S, Saffrey P, Kingsbury Z, Weir JC, Betley J, Grocock RJ, Margulies EH, Farrow EG, Artman M, Safina NP, Petrikin JE, Hall KP, Kingsmore SF. Rapid whole-genome sequencing for genetic disease diagnosis in neonatal intensive care units. Sci Transl Med 2012; 4:154ra135. [PMID: 23035047 PMCID: PMC4283791 DOI: 10.1126/scitranslmed.3004041] [Citation(s) in RCA: 449] [Impact Index Per Article: 37.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Monogenic diseases are frequent causes of neonatal morbidity and mortality, and disease presentations are often undifferentiated at birth. More than 3500 monogenic diseases have been characterized, but clinical testing is available for only some of them and many feature clinical and genetic heterogeneity. Hence, an immense unmet need exists for improved molecular diagnosis in infants. Because disease progression is extremely rapid, albeit heterogeneous, in newborns, molecular diagnoses must occur quickly to be relevant for clinical decision-making. We describe 50-hour differential diagnosis of genetic disorders by whole-genome sequencing (WGS) that features automated bioinformatic analysis and is intended to be a prototype for use in neonatal intensive care units. Retrospective 50-hour WGS identified known molecular diagnoses in two children. Prospective WGS disclosed potential molecular diagnosis of a severe GJB2-related skin disease in one neonate; BRAT1-related lethal neonatal rigidity and multifocal seizure syndrome in another infant; identified BCL9L as a novel, recessive visceral heterotaxy gene (HTX6) in a pedigree; and ruled out known candidate genes in one infant. Sequencing of parents or affected siblings expedited the identification of disease genes in prospective cases. Thus, rapid WGS can potentially broaden and foreshorten differential diagnosis, resulting in fewer empirical treatments and faster progression to genetic and prognostic counseling.
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Affiliation(s)
- Carol Jean Saunders
- Center for Pediatric Genomic Medicine, Children’s Mercy Hospital, Kansas City, MO 64108, USA
- Department of Pediatrics, Children’s Mercy Hospital, Kansas City, MO 64108, USA
- Department of Pathology, Children’s Mercy Hospital, Kansas City, MO 64108, USA
- School of Medicine, University of Missouri-Kansas City, Kansas City, MO 64108, USA
- University of Kansas Medical Center, Kansas City, KS 66160, USA
| | - Neil Andrew Miller
- Center for Pediatric Genomic Medicine, Children’s Mercy Hospital, Kansas City, MO 64108, USA
- Department of Pediatrics, Children’s Mercy Hospital, Kansas City, MO 64108, USA
- School of Medicine, University of Missouri-Kansas City, Kansas City, MO 64108, USA
| | - Sarah Elizabeth Soden
- Center for Pediatric Genomic Medicine, Children’s Mercy Hospital, Kansas City, MO 64108, USA
- Department of Pediatrics, Children’s Mercy Hospital, Kansas City, MO 64108, USA
- School of Medicine, University of Missouri-Kansas City, Kansas City, MO 64108, USA
| | - Darrell Lee Dinwiddie
- Center for Pediatric Genomic Medicine, Children’s Mercy Hospital, Kansas City, MO 64108, USA
- Department of Pediatrics, Children’s Mercy Hospital, Kansas City, MO 64108, USA
- Department of Pathology, Children’s Mercy Hospital, Kansas City, MO 64108, USA
- School of Medicine, University of Missouri-Kansas City, Kansas City, MO 64108, USA
- University of Kansas Medical Center, Kansas City, KS 66160, USA
| | - Aaron Noll
- Center for Pediatric Genomic Medicine, Children’s Mercy Hospital, Kansas City, MO 64108, USA
| | - Noor Abu Alnadi
- School of Medicine, University of Missouri-Kansas City, Kansas City, MO 64108, USA
| | - Nevene Andraws
- Department of Pathology, Children’s Mercy Hospital, Kansas City, MO 64108, USA
| | - Melanie LeAnn Patterson
- Center for Pediatric Genomic Medicine, Children’s Mercy Hospital, Kansas City, MO 64108, USA
- Department of Pathology, Children’s Mercy Hospital, Kansas City, MO 64108, USA
| | - Lisa Ann Krivohlavek
- Center for Pediatric Genomic Medicine, Children’s Mercy Hospital, Kansas City, MO 64108, USA
- Department of Pathology, Children’s Mercy Hospital, Kansas City, MO 64108, USA
| | - Joel Fellis
- Illumina Inc., Chesterford Research Park, Little Chesterford, CB10 1XL Essex, UK
| | - Sean Humphray
- Illumina Inc., Chesterford Research Park, Little Chesterford, CB10 1XL Essex, UK
| | - Peter Saffrey
- Illumina Inc., Chesterford Research Park, Little Chesterford, CB10 1XL Essex, UK
| | - Zoya Kingsbury
- Illumina Inc., Chesterford Research Park, Little Chesterford, CB10 1XL Essex, UK
| | | | - Jason Betley
- Illumina Inc., Chesterford Research Park, Little Chesterford, CB10 1XL Essex, UK
| | | | | | - Emily Gwendolyn Farrow
- Center for Pediatric Genomic Medicine, Children’s Mercy Hospital, Kansas City, MO 64108, USA
| | - Michael Artman
- Department of Pediatrics, Children’s Mercy Hospital, Kansas City, MO 64108, USA
- School of Medicine, University of Missouri-Kansas City, Kansas City, MO 64108, USA
| | - Nicole Pauline Safina
- Center for Pediatric Genomic Medicine, Children’s Mercy Hospital, Kansas City, MO 64108, USA
- School of Medicine, University of Missouri-Kansas City, Kansas City, MO 64108, USA
| | - Joshua Erin Petrikin
- Department of Pediatrics, Children’s Mercy Hospital, Kansas City, MO 64108, USA
- Department of Pathology, Children’s Mercy Hospital, Kansas City, MO 64108, USA
| | - Kevin Peter Hall
- Illumina Inc., Chesterford Research Park, Little Chesterford, CB10 1XL Essex, UK
| | - Stephen Francis Kingsmore
- Center for Pediatric Genomic Medicine, Children’s Mercy Hospital, Kansas City, MO 64108, USA
- Department of Pediatrics, Children’s Mercy Hospital, Kansas City, MO 64108, USA
- Department of Pathology, Children’s Mercy Hospital, Kansas City, MO 64108, USA
- School of Medicine, University of Missouri-Kansas City, Kansas City, MO 64108, USA
- University of Kansas Medical Center, Kansas City, KS 66160, USA
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20
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Single Nucleotide Polymorphism (SNP) Detection and Genotype Calling from Massively Parallel Sequencing (MPS) Data. STATISTICS IN BIOSCIENCES 2012; 5:3-25. [PMID: 24489615 DOI: 10.1007/s12561-012-9067-4] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
Abstract
Massively parallel sequencing (MPS), since its debut in 2005, has transformed the field of genomic studies. These new sequencing technologies have resulted in the successful identification of causal variants for several rare Mendelian disorders. They have also begun to deliver on their promise to explain some of the missing heritability from genome-wide association studies (GWAS) of complex traits. We anticipate a rapidly growing number of MPS-based studies for a diverse range of applications in the near future. One crucial and nearly inevitable step is to detect SNPs and call genotypes at the detected polymorphic sites from the sequencing data. Here, we review statistical methods that have been proposed in the past five years for this purpose. In addition, we discuss emerging issues and future directions related to SNP detection and genotype calling from MPS data.
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21
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Polimanti R, Piacentini S, Lazzarin N, Re MA, Manfellotto D, Fuciarelli M. Lack of Association Between Essential Hypertension and GSTO1 Uncommon Genetic Variants in Italian Patients. Genet Test Mol Biomarkers 2012; 16:615-20. [DOI: 10.1089/gtmb.2011.0310] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023] Open
Affiliation(s)
- Renato Polimanti
- Department of Biology, University of Rome “Tor Vergata,” Rome, Italy
- Clinical Pathophysiology Center, AFaR, “San Giovanni Calibita” Fatebenefratelli Hospital, Isola Tiberina, Rome, Italy
| | - Sara Piacentini
- Department of Biology, University of Rome “Tor Vergata,” Rome, Italy
| | - Natalia Lazzarin
- Clinical Pathophysiology Center, AFaR, “San Giovanni Calibita” Fatebenefratelli Hospital, Isola Tiberina, Rome, Italy
| | - Maria Antonietta Re
- Clinical Pathophysiology Center, AFaR, “San Giovanni Calibita” Fatebenefratelli Hospital, Isola Tiberina, Rome, Italy
| | - Dario Manfellotto
- Clinical Pathophysiology Center, AFaR, “San Giovanni Calibita” Fatebenefratelli Hospital, Isola Tiberina, Rome, Italy
| | - Maria Fuciarelli
- Department of Biology, University of Rome “Tor Vergata,” Rome, Italy
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22
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Extremely low-coverage sequencing and imputation increases power for genome-wide association studies. Nat Genet 2012; 44:631-5. [PMID: 22610117 DOI: 10.1038/ng.2283] [Citation(s) in RCA: 177] [Impact Index Per Article: 14.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2011] [Accepted: 04/16/2012] [Indexed: 12/14/2022]
Abstract
Genome-wide association studies (GWAS) have proven to be a powerful method to identify common genetic variants contributing to susceptibility to common diseases. Here, we show that extremely low-coverage sequencing (0.1-0.5×) captures almost as much of the common (>5%) and low-frequency (1-5%) variation across the genome as SNP arrays. As an empirical demonstration, we show that genome-wide SNP genotypes can be inferred at a mean r(2) of 0.71 using off-target data (0.24× average coverage) in a whole-exome study of 909 samples. Using both simulated and real exome-sequencing data sets, we show that association statistics obtained using extremely low-coverage sequencing data attain similar P values at known associated variants as data from genotyping arrays, without an excess of false positives. Within the context of reductions in sample preparation and sequencing costs, funds invested in extremely low-coverage sequencing can yield several times the effective sample size of GWAS based on SNP array data and a commensurate increase in statistical power.
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23
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Piacentini S, Polimanti R, Squitti R, Mariani S, Migliore S, Vernieri F, Rossini PM, Manfellotto D, Fuciarelli M. GSTO1*E155del polymorphism associated with increased risk for late-onset Alzheimer's disease: association hypothesis for an uncommon genetic variant. Neurosci Lett 2011; 506:203-7. [PMID: 22100662 DOI: 10.1016/j.neulet.2011.11.005] [Citation(s) in RCA: 31] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2011] [Revised: 10/31/2011] [Accepted: 11/02/2011] [Indexed: 01/22/2023]
Abstract
Glutathione S-transferases are multifunctional enzymes involved in cellular detoxification. A genetic linkage was found between Alzheimer's Disease (AD) and the chromosome 10q, where the GSTO1 and GSTO2 genes are located, leading to the hypothesis that GST Omega class (GSTO) genes may be an AD risk factor. Since it is still controversial, we decided to explore GSTO polymorphisms in Italian cohorts. We analyzed 119 AD patients and 114 healthy controls for the GSTO gene polymorphisms. In particular we investigated two common polymorphisms (GSTO1*A140D, GSTO2*N142D) and two uncommon variants (GSTO1*E155del, GSTO1*E208K) to find loci associated with AD risk. Detection of GSTO1*A140D and GSTO2*N142D was performed by PCR-RFLP, while GSTO1*E155del and GSTO1*E208K were detected using confronting two-pair primer and allele specific PCR, respectively. While GSTO1*A140D, GSTO1*E208K and GSTO2*N142D polymorphisms did not show significant outcomes, the GSTO1*E155del polymorphism is associated with AD [P=0.003; adjusted OR=3.70 (1.57-8.75)]. Our results suggest that GSTO1-1 plays a role in AD since the GSTO1*del155 variant is involved in changes in GSTO1-1 activities decreasing in enzyme stability. Specifically, three hypotheses may explain the role of GSTO1-1 in the pathophysiology of AD: the antioxidant activity of GSTO1-1 may protect brain tissue against oxidative stress; GSTO1-1 activity regulate interleukin-1β activation and its genetic variation may act to modulate inflammation in AD; GSTO1-1 is involved in the arsenic biotransformation pathway and gene polymorphisms may be implicated in the modulation of arsenic neurotoxicity. In conclusion, we hypothesized that GSTO1*E155del is an uncommon genetic variant associated with AD risk.
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Affiliation(s)
- Sara Piacentini
- Department of Biology, University of Rome Tor Vergata, Via della Ricerca Scientifica 1, 00133, Rome, Italy
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24
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Ajay SS, Parker SCJ, Abaan HO, Fajardo KVF, Margulies EH. Accurate and comprehensive sequencing of personal genomes. Genome Res 2011; 21:1498-505. [PMID: 21771779 DOI: 10.1101/gr.123638.111] [Citation(s) in RCA: 139] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Abstract
As whole-genome sequencing becomes commoditized and we begin to sequence and analyze personal genomes for clinical and diagnostic purposes, it is necessary to understand what constitutes a complete sequencing experiment for determining genotypes and detecting single-nucleotide variants. Here, we show that the current recommendation of ∼30× coverage is not adequate to produce genotype calls across a large fraction of the genome with acceptably low error rates. Our results are based on analyses of a clinical sample sequenced on two related Illumina platforms, GAII(x) and HiSeq 2000, to a very high depth (126×). We used these data to establish genotype-calling filters that dramatically increase accuracy. We also empirically determined how the callable portion of the genome varies as a function of the amount of sequence data used. These results help provide a "sequencing guide" for future whole-genome sequencing decisions and metrics by which coverage statistics should be reported.
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Affiliation(s)
- Subramanian S Ajay
- Genome Informatics Section, Genome Technology Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA
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