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Shin T, Song JHT, Kosicki M, Kenny C, Beck SG, Kelley L, Antony I, Qian X, Bonacina J, Papandile F, Gonzalez D, Scotellaro J, Bushinsky EM, Andersen RE, Maury E, Pennacchio LA, Doan RN, Walsh CA. Rare variation in non-coding regions with evolutionary signatures contributes to autism spectrum disorder risk. CELL GENOMICS 2024; 4:100609. [PMID: 39019033 DOI: 10.1016/j.xgen.2024.100609] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Revised: 03/11/2024] [Accepted: 06/24/2024] [Indexed: 07/19/2024]
Abstract
Little is known about the role of non-coding regions in the etiology of autism spectrum disorder (ASD). We examined three classes of non-coding regions: human accelerated regions (HARs), which show signatures of positive selection in humans; experimentally validated neural VISTA enhancers (VEs); and conserved regions predicted to act as neural enhancers (CNEs). Targeted and whole-genome analysis of >16,600 samples and >4,900 ASD probands revealed that likely recessive, rare, inherited variants in HARs, VEs, and CNEs substantially contribute to ASD risk in probands whose parents share ancestry, which enriches for recessive contributions, but modestly contribute, if at all, in simplex family structures. We identified multiple patient variants in HARs near IL1RAPL1 and in VEs near OTX1 and SIM1 and showed that they change enhancer activity. Our results implicate both human-evolved and evolutionarily conserved non-coding regions in ASD risk and suggest potential mechanisms of how regulatory changes can modulate social behavior.
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Affiliation(s)
- Taehwan Shin
- Division of Genetics and Genomics, Boston Children's Hospital, Boston, MA 02115, USA; Department of Pediatrics, Harvard Medical School, Boston, MA 02115, USA; Allen Discovery Center for Human Brain Evolution, Boston, MA 02115, USA; Department of Neurology, Harvard Medical School, Boston, MA 02115, USA; Howard Hughes Medical Institute, Boston Children's Hospital, Boston, MA 02115, USA
| | - Janet H T Song
- Division of Genetics and Genomics, Boston Children's Hospital, Boston, MA 02115, USA; Department of Pediatrics, Harvard Medical School, Boston, MA 02115, USA; Allen Discovery Center for Human Brain Evolution, Boston, MA 02115, USA; Department of Neurology, Harvard Medical School, Boston, MA 02115, USA; Howard Hughes Medical Institute, Boston Children's Hospital, Boston, MA 02115, USA
| | - Michael Kosicki
- Environmental Genomics & System Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Connor Kenny
- Division of Genetics and Genomics, Boston Children's Hospital, Boston, MA 02115, USA; Department of Pediatrics, Harvard Medical School, Boston, MA 02115, USA; Allen Discovery Center for Human Brain Evolution, Boston, MA 02115, USA; Department of Neurology, Harvard Medical School, Boston, MA 02115, USA; Howard Hughes Medical Institute, Boston Children's Hospital, Boston, MA 02115, USA
| | - Samantha G Beck
- Division of Genetics and Genomics, Boston Children's Hospital, Boston, MA 02115, USA; Department of Pediatrics, Harvard Medical School, Boston, MA 02115, USA; Allen Discovery Center for Human Brain Evolution, Boston, MA 02115, USA; Department of Neurology, Harvard Medical School, Boston, MA 02115, USA; Howard Hughes Medical Institute, Boston Children's Hospital, Boston, MA 02115, USA
| | - Lily Kelley
- Division of Genetics and Genomics, Boston Children's Hospital, Boston, MA 02115, USA; Department of Pediatrics, Harvard Medical School, Boston, MA 02115, USA; Allen Discovery Center for Human Brain Evolution, Boston, MA 02115, USA
| | - Irene Antony
- Division of Genetics and Genomics, Boston Children's Hospital, Boston, MA 02115, USA; Department of Pediatrics, Harvard Medical School, Boston, MA 02115, USA; Allen Discovery Center for Human Brain Evolution, Boston, MA 02115, USA; Department of Neurology, Harvard Medical School, Boston, MA 02115, USA; Howard Hughes Medical Institute, Boston Children's Hospital, Boston, MA 02115, USA
| | - Xuyu Qian
- Division of Genetics and Genomics, Boston Children's Hospital, Boston, MA 02115, USA; Department of Pediatrics, Harvard Medical School, Boston, MA 02115, USA; Allen Discovery Center for Human Brain Evolution, Boston, MA 02115, USA; Department of Neurology, Harvard Medical School, Boston, MA 02115, USA; Howard Hughes Medical Institute, Boston Children's Hospital, Boston, MA 02115, USA
| | - Julieta Bonacina
- Division of Genetics and Genomics, Boston Children's Hospital, Boston, MA 02115, USA; Department of Pediatrics, Harvard Medical School, Boston, MA 02115, USA; Allen Discovery Center for Human Brain Evolution, Boston, MA 02115, USA
| | - Frances Papandile
- Division of Genetics and Genomics, Boston Children's Hospital, Boston, MA 02115, USA; Department of Pediatrics, Harvard Medical School, Boston, MA 02115, USA; Allen Discovery Center for Human Brain Evolution, Boston, MA 02115, USA; Department of Neurology, Harvard Medical School, Boston, MA 02115, USA; Howard Hughes Medical Institute, Boston Children's Hospital, Boston, MA 02115, USA
| | - Dilenny Gonzalez
- Division of Genetics and Genomics, Boston Children's Hospital, Boston, MA 02115, USA; Department of Pediatrics, Harvard Medical School, Boston, MA 02115, USA; Allen Discovery Center for Human Brain Evolution, Boston, MA 02115, USA; Department of Neurology, Harvard Medical School, Boston, MA 02115, USA; Howard Hughes Medical Institute, Boston Children's Hospital, Boston, MA 02115, USA
| | - Julia Scotellaro
- Division of Genetics and Genomics, Boston Children's Hospital, Boston, MA 02115, USA; Department of Pediatrics, Harvard Medical School, Boston, MA 02115, USA
| | - Evan M Bushinsky
- Division of Genetics and Genomics, Boston Children's Hospital, Boston, MA 02115, USA; Department of Pediatrics, Harvard Medical School, Boston, MA 02115, USA; Allen Discovery Center for Human Brain Evolution, Boston, MA 02115, USA; Department of Neurology, Harvard Medical School, Boston, MA 02115, USA; Howard Hughes Medical Institute, Boston Children's Hospital, Boston, MA 02115, USA
| | - Rebecca E Andersen
- Division of Genetics and Genomics, Boston Children's Hospital, Boston, MA 02115, USA; Department of Pediatrics, Harvard Medical School, Boston, MA 02115, USA; Allen Discovery Center for Human Brain Evolution, Boston, MA 02115, USA; Department of Neurology, Harvard Medical School, Boston, MA 02115, USA; Howard Hughes Medical Institute, Boston Children's Hospital, Boston, MA 02115, USA
| | - Eduardo Maury
- Division of Genetics and Genomics, Boston Children's Hospital, Boston, MA 02115, USA; Department of Pediatrics, Harvard Medical School, Boston, MA 02115, USA; Allen Discovery Center for Human Brain Evolution, Boston, MA 02115, USA; Department of Neurology, Harvard Medical School, Boston, MA 02115, USA; Howard Hughes Medical Institute, Boston Children's Hospital, Boston, MA 02115, USA
| | - Len A Pennacchio
- Environmental Genomics & System Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Ryan N Doan
- Division of Genetics and Genomics, Boston Children's Hospital, Boston, MA 02115, USA; Department of Pediatrics, Harvard Medical School, Boston, MA 02115, USA; Allen Discovery Center for Human Brain Evolution, Boston, MA 02115, USA.
| | - Christopher A Walsh
- Division of Genetics and Genomics, Boston Children's Hospital, Boston, MA 02115, USA; Department of Pediatrics, Harvard Medical School, Boston, MA 02115, USA; Allen Discovery Center for Human Brain Evolution, Boston, MA 02115, USA; Department of Neurology, Harvard Medical School, Boston, MA 02115, USA; Howard Hughes Medical Institute, Boston Children's Hospital, Boston, MA 02115, USA.
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Mera-Charria A, Nieto-Lopez F, Francès MP, Arbex PM, Vila-Vecilla L, Russo V, Silva CCV, De Souza GT. Genetic variant panel allows predicting both obesity risk, and efficacy of procedures and diet in weight loss. Front Nutr 2023; 10:1274662. [PMID: 38035352 PMCID: PMC10687570 DOI: 10.3389/fnut.2023.1274662] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2023] [Accepted: 10/24/2023] [Indexed: 12/02/2023] Open
Abstract
Purpose Obesity is a multifactorial condition with a relevant genetic correlation. Recent advances in genomic research have identified several single nucleotide polymorphisms (SNPs) in genes such as FTO, MCM6, HLA, and MC4R, associated with obesity. This study aimed to evaluate the association of 102 SNPs with BMI and weight loss treatment response in a multi-ethnic population. Methods The study analyzed 9,372 patients for the correlation between SNPs and BMI (dataset A). The correlation between SNP and weight loss was accessed in 474 patients undergoing different treatments (dataset B). Patients in dataset B were further divided into 3 categories based on the type of intervention: dietary therapy, intragastric balloon procedures, or surgeries. SNP association analysis and multiple models of inheritance were performed. Results In dataset A, ten SNPs, including rs9939609 (FTO), rs4988235 (MCM6), and rs2395182 (HLA), were significantly associated with increased BMI. Additionally, other four SNPs, rs7903146 (TCF7L2), (rs6511720), rs5400 (SLC2A2), and rs7498665 (SH2B1), showed sex-specific correlation. For dataset B, SNPs rs2016520 (PPAR-Delta) and rs2419621 (ACSL5) demonstrated significant correlation with weight loss for all treatment types. In patients who adhered to dietary therapy, SNPs rs6544713 (ABCG8) and rs762551 (CYP1A2) were strongly correlated with weight loss. Patients undergoing surgical or endoscopic procedures exhibited differential correlations with several SNPs, including rs1801725 (CASR) and rs12970134 (MC4R), and weight loss. Conclusion This study provides valuable insights into the genetic factors influencing BMI and weight loss response to different treatments. The findings highlight the potential for personalized weight management approaches based on individual genetic profiles.
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Affiliation(s)
| | - Francisco Nieto-Lopez
- Dorsia Clinics, Madrid, Spain
- Catedra UCAM Dorsia, Catholic University San Antonio of Murcia, Guadalupe, Spain
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3
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Boutry S, Helaers R, Lenaerts T, Vikkula M. Rare variant association on unrelated individuals in case-control studies using aggregation tests: existing methods and current limitations. Brief Bioinform 2023; 24:bbad412. [PMID: 37974506 DOI: 10.1093/bib/bbad412] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Revised: 10/14/2023] [Accepted: 10/28/2023] [Indexed: 11/19/2023] Open
Abstract
Over the past years, progress made in next-generation sequencing technologies and bioinformatics have sparked a surge in association studies. Especially, genome-wide association studies (GWASs) have demonstrated their effectiveness in identifying disease associations with common genetic variants. Yet, rare variants can contribute to additional disease risk or trait heterogeneity. Because GWASs are underpowered for detecting association with such variants, numerous statistical methods have been recently proposed. Aggregation tests collapse multiple rare variants within a genetic region (e.g. gene, gene set, genomic loci) to test for association. An increasing number of studies using such methods successfully identified trait-associated rare variants and led to a better understanding of the underlying disease mechanism. In this review, we compare existing aggregation tests, their statistical features and scope of application, splitting them into the five classical classes: burden, adaptive burden, variance-component, omnibus and other. Finally, we describe some limitations of current aggregation tests, highlighting potential direction for further investigations.
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Affiliation(s)
- Simon Boutry
- Human Molecular Genetics, de Duve Institute, University of Louvain, Avenue Hippocrate 74 (+5) bte B1.74.06, 1200 Brussels, Belgium
- Interuniversity Institute of Bioinformatics in Brussels, Université Libre de Bruxelles-Vrije Universiteit Brussels, 1050 Brussels, Belgium
| | - Raphaël Helaers
- Human Molecular Genetics, de Duve Institute, University of Louvain, Avenue Hippocrate 74 (+5) bte B1.74.06, 1200 Brussels, Belgium
| | - Tom Lenaerts
- Interuniversity Institute of Bioinformatics in Brussels, Université Libre de Bruxelles-Vrije Universiteit Brussels, 1050 Brussels, Belgium
- Machine Learning Group, Université Libre de Bruxelles, 1050 Brussels, Belgium
- Artificial Intelligence laboratory, Vrije Universiteit Brussel, 1050 Brussels, Belgium
| | - Miikka Vikkula
- Human Molecular Genetics, de Duve Institute, University of Louvain, Avenue Hippocrate 74 (+5) bte B1.74.06, 1200 Brussels, Belgium
- WELBIO department, WEL Research Institute, avenue Pasteur, 6, 1300 Wavre, Belgium
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4
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Shin T, Song JH, Kosicki M, Kenny C, Beck SG, Kelley L, Qian X, Bonacina J, Papandile F, Antony I, Gonzalez D, Scotellaro J, Bushinsky EM, Andersen RE, Maury E, Pennacchio LA, Doan RN, Walsh CA. Rare variation in noncoding regions with evolutionary signatures contributes to autism spectrum disorder risk. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.09.19.23295780. [PMID: 37790480 PMCID: PMC10543033 DOI: 10.1101/2023.09.19.23295780] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/05/2023]
Abstract
Little is known about the role of noncoding regions in the etiology of autism spectrum disorder (ASD). We examined three classes of noncoding regions: Human Accelerated Regions (HARs), which show signatures of positive selection in humans; experimentally validated neural Vista Enhancers (VEs); and conserved regions predicted to act as neural enhancers (CNEs). Targeted and whole genome analysis of >16,600 samples and >4900 ASD probands revealed that likely recessive, rare, inherited variants in HARs, VEs, and CNEs substantially contribute to ASD risk in probands whose parents share ancestry, which enriches for recessive contributions, but modestly, if at all, in simplex family structures. We identified multiple patient variants in HARs near IL1RAPL1 and in a VE near SIM1 and showed that they change enhancer activity. Our results implicate both human-evolved and evolutionarily conserved noncoding regions in ASD risk and suggest potential mechanisms of how changes in regulatory regions can modulate social behavior.
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Affiliation(s)
- Taehwan Shin
- Division of Genetics and Genomics, Boston Children’s Hospital; Departments of Pediatrics and Neurology, Harvard Medical School; Allen Discovery Center for Human Brain Evolution; Howard Hughes Medical Institute, Boston Children’s Hospital, Boston, MA, 02115, USA
| | - Janet H.T. Song
- Division of Genetics and Genomics, Boston Children’s Hospital; Departments of Pediatrics and Neurology, Harvard Medical School; Allen Discovery Center for Human Brain Evolution; Howard Hughes Medical Institute, Boston Children’s Hospital, Boston, MA, 02115, USA
| | - Michael Kosicki
- Environmental Genomics & Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Connor Kenny
- Division of Genetics and Genomics, Boston Children’s Hospital; Departments of Pediatrics and Neurology, Harvard Medical School; Allen Discovery Center for Human Brain Evolution; Howard Hughes Medical Institute, Boston Children’s Hospital, Boston, MA, 02115, USA
| | - Samantha G. Beck
- Division of Genetics and Genomics, Boston Children’s Hospital; Departments of Pediatrics and Neurology, Harvard Medical School; Allen Discovery Center for Human Brain Evolution; Howard Hughes Medical Institute, Boston Children’s Hospital, Boston, MA, 02115, USA
| | - Lily Kelley
- Division of Genetics and Genomics, Boston Children’s Hospital; Department of Pediatrics, Harvard Medical School; Allen Discovery Center for Human Brain Evolution, Boston, MA, 02115, USA
| | - Xuyu Qian
- Division of Genetics and Genomics, Boston Children’s Hospital; Departments of Pediatrics and Neurology, Harvard Medical School; Allen Discovery Center for Human Brain Evolution; Howard Hughes Medical Institute, Boston Children’s Hospital, Boston, MA, 02115, USA
| | - Julieta Bonacina
- Division of Genetics and Genomics, Boston Children’s Hospital; Department of Pediatrics, Harvard Medical School; Allen Discovery Center for Human Brain Evolution, Boston, MA, 02115, USA
| | - Frances Papandile
- Division of Genetics and Genomics, Boston Children’s Hospital; Departments of Pediatrics and Neurology, Harvard Medical School; Allen Discovery Center for Human Brain Evolution; Howard Hughes Medical Institute, Boston Children’s Hospital, Boston, MA, 02115, USA
| | - Irene Antony
- Division of Genetics and Genomics, Boston Children’s Hospital; Departments of Pediatrics and Neurology, Harvard Medical School; Allen Discovery Center for Human Brain Evolution; Howard Hughes Medical Institute, Boston Children’s Hospital, Boston, MA, 02115, USA
| | - Dilenny Gonzalez
- Division of Genetics and Genomics, Boston Children’s Hospital; Departments of Pediatrics and Neurology, Harvard Medical School; Allen Discovery Center for Human Brain Evolution; Howard Hughes Medical Institute, Boston Children’s Hospital, Boston, MA, 02115, USA
| | - Julia Scotellaro
- Division of Genetics and Genomics, Boston Children’s Hospital; Department of Pediatrics, Harvard Medical School; Allen Discovery Center for Human Brain Evolution, Boston, MA, 02115, USA
| | - Evan M. Bushinsky
- Division of Genetics and Genomics, Boston Children’s Hospital; Departments of Pediatrics and Neurology, Harvard Medical School; Allen Discovery Center for Human Brain Evolution; Howard Hughes Medical Institute, Boston Children’s Hospital, Boston, MA, 02115, USA
| | - Rebecca E. Andersen
- Division of Genetics and Genomics, Boston Children’s Hospital; Departments of Pediatrics and Neurology, Harvard Medical School; Allen Discovery Center for Human Brain Evolution; Howard Hughes Medical Institute, Boston Children’s Hospital, Boston, MA, 02115, USA
| | - Eduardo Maury
- Division of Genetics and Genomics, Boston Children’s Hospital; Departments of Pediatrics and Neurology, Harvard Medical School; Allen Discovery Center for Human Brain Evolution; Howard Hughes Medical Institute, Boston Children’s Hospital, Boston, MA, 02115, USA
| | - Len A. Pennacchio
- Environmental Genomics & Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Ryan N. Doan
- Division of Genetics and Genomics, Boston Children’s Hospital; Department of Pediatrics, Harvard Medical School; Allen Discovery Center for Human Brain Evolution, Boston, MA, 02115, USA
| | - Christopher A. Walsh
- Division of Genetics and Genomics, Boston Children’s Hospital; Departments of Pediatrics and Neurology, Harvard Medical School; Allen Discovery Center for Human Brain Evolution; Howard Hughes Medical Institute, Boston Children’s Hospital, Boston, MA, 02115, USA
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5
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Novelli G, Cassadonte C, Sbraccia P, Biancolella M. Genetics: A Starting Point for the Prevention and the Treatment of Obesity. Nutrients 2023; 15:2782. [PMID: 37375686 DOI: 10.3390/nu15122782] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Revised: 06/15/2023] [Accepted: 06/16/2023] [Indexed: 06/29/2023] Open
Abstract
Obesity is a common, serious, and costly disease. More than 1 billion people worldwide are obese-650 million adults, 340 million adolescents, and 39 million children. The WHO estimates that, by 2025, approximately 167 million people-adults and children-will become less healthy because they are overweight or obese. Obesity-related conditions include heart disease, stroke, type 2 diabetes, and certain types of cancer. These are among the leading causes of preventable, premature death. The estimated annual medical cost of obesity in the United States was nearly $173 billion in 2019 dollars. Obesity is considered the result of a complex interaction between genes and the environment. Both genes and the environment change in different populations. In fact, the prevalence changes as the result of eating habits, lifestyle, and expression of genes coding for factors involved in the regulation of body weight, food intake, and satiety. Expression of these genes involves different epigenetic processes, such as DNA methylation, histone modification, or non-coding micro-RNA synthesis, as well as variations in the gene sequence, which results in functional alterations. Evolutionary and non-evolutionary (i.e., genetic drift, migration, and founder's effect) factors have shaped the genetic predisposition or protection from obesity in modern human populations. Understanding and knowing the pathogenesis of obesity will lead to prevention and treatment strategies not only for obesity, but also for other related diseases.
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Affiliation(s)
- Giuseppe Novelli
- Department of Biomedicine and Prevention, Medical School, University of Rome Tor Vergata, Via Montpellier 1, 00133 Rome, Italy
- Italian Barometer Diabetes Observatory Foundation, IBDO, 00186 Rome, Italy
- Department of Pharmacology, School of Medicine, University of Nevada, Reno, NV 89557, USA
| | - Carmen Cassadonte
- Department of Biomedicine and Prevention, Medical School, University of Rome Tor Vergata, Via Montpellier 1, 00133 Rome, Italy
| | - Paolo Sbraccia
- Italian Barometer Diabetes Observatory Foundation, IBDO, 00186 Rome, Italy
- Department of Systems Medicine, Medical School, University of Rome Tor Vergata, Via Montpellier 1, 00133 Rome, Italy
| | - Michela Biancolella
- Department of Biology, Tor Vergata University of Rome, Via della Ricerca Scientifica 1, 00133 Rome, Italy
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6
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Church JA, Grigorenko EL, Fletcher JM. The Role of Neural and Genetic Processes in Learning to Read and Specific Reading Disabilities: Implications for Instruction. READING RESEARCH QUARTERLY 2023; 58:203-219. [PMID: 37456924 PMCID: PMC10348696 DOI: 10.1002/rrq.439] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/20/2020] [Accepted: 06/29/2021] [Indexed: 07/18/2023]
Abstract
To learn to read, the brain must repurpose neural systems for oral language and visual processing to mediate written language. We begin with a description of computational models for how alphabetic written language is processed. Next, we explain the roles of a dorsal sublexical system in the brain that relates print and speech, a ventral lexical system that develops the visual expertise for rapid orthographic processing at the word level, and the role of cognitive control networks that regulate attentional processes as children read. We then use studies of children, adult illiterates learning to read, and studies of poor readers involved in intervention, to demonstrate the plasticity of these neural networks in development and in relation to instruction. We provide a brief overview of the rapid increase in the field's understanding and technology for assessing genetic influence on reading. Family studies of twins have shown that reading skills are heritable, and molecular genetic studies have identified numerous regions of the genome that may harbor candidate genes for the heritability of reading. In selected families, reading impairment has been associated with major genetic effects, despite individual gene contributions across the broader population that appear to be small. Neural and genetic studies do not prescribe how children should be taught to read, but these studies have underscored the critical role of early intervention and ongoing support. These studies also have highlighted how structured instruction that facilitates access to the sublexical components of words is a critical part of training the brain to read.
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Affiliation(s)
| | - Elena L Grigorenko
- University of Houston, Texas, USA; Baylor College of Medicine, Houston, Texas, USA; and St. Petersburg State University, Russia
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7
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Functionally Significant Variants in Genes Associated with Abdominal Obesity: A Review. J Pers Med 2023; 13:jpm13030460. [PMID: 36983642 PMCID: PMC10056771 DOI: 10.3390/jpm13030460] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Revised: 02/23/2023] [Accepted: 02/26/2023] [Indexed: 03/05/2023] Open
Abstract
The high prevalence of obesity and of its associated diseases is a major problem worldwide. Genetic predisposition and the influence of environmental factors contribute to the development of obesity. Changes in the structure and functional activity of genes encoding adipocytokines are involved in the predisposition to weight gain and obesity. In this review, variants in genes associated with adipocyte function are examined, as are variants in genes associated with metabolic aberrations and the accompanying disorders in visceral obesity.
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8
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In Silico Examination of Single Nucleotide Missense Mutations in NHLH2, a Gene Linked to Infertility and Obesity. Int J Mol Sci 2023; 24:ijms24043193. [PMID: 36834605 PMCID: PMC9968165 DOI: 10.3390/ijms24043193] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2022] [Revised: 01/25/2023] [Accepted: 01/30/2023] [Indexed: 02/09/2023] Open
Abstract
Continual advances in our understanding of the human genome have led to exponential increases in known single nucleotide variants. The characterization of each of the variants lags behind. For researchers needing to study a single gene, or multiple genes in a pathway, there must be ways to narrow down pathogenic variants from those that are silent or pose less pathogenicity. In this study, we use the NHLH2 gene which encodes the nescient helix-loop-helix 2 (Nhlh2) transcription factor in a systematic analysis of all missense mutations to date in the gene. The NHLH2 gene was first described in 1992. Knockout mice created in 1997 indicated a role for this protein in body weight control, puberty, and fertility, as well as the motivation for sex and exercise. Only recently have human carriers of NHLH2 missense variants been characterized. Over 300 missense variants for the NHLH2 gene are listed in the NCBI single nucleotide polymorphism database (dbSNP). Using in silico tools, predicted pathogenicity of the variants narrowed the missense variants to 37 which were predicted to affect NHLH2 function. These 37 variants cluster around the basic-helix-loop-helix and DNA binding domains of the transcription factor, and further analysis using in silico tools provided 21 SNV resulting in 22 amino acid changes for future wet lab analysis. The tools used, findings, and predictions for the variants are discussed considering the known function of the NHLH2 transcription factor. Overall use of these in silico tools and analysis of these data contribute to our knowledge of a protein which is both involved in the human genetic syndrome, Prader-Willi syndrome, and in controlling genes involved in body weight control, fertility, puberty, and behavior in the general population, and may provide a systematic methodology for others to characterize variants for their gene of interest.
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9
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Topaloglu AK, Simsek E, Kocher MA, Mammadova J, Bober E, Kotan LD, Turan I, Celiloglu C, Gurbuz F, Yuksel B, Good DJ. Inactivating NHLH2 variants cause idiopathic hypogonadotropic hypogonadism and obesity in humans. Hum Genet 2022; 141:295-304. [PMID: 35066646 DOI: 10.1007/s00439-021-02422-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2021] [Accepted: 12/15/2021] [Indexed: 11/25/2022]
Abstract
Metabolism has a role in determining the time of pubertal development and fertility. Nonetheless, molecular/cellular pathways linking metabolism/body weight to puberty/reproduction are unknown. The KNDy (Kisspeptin/Neurokinin B/Dynorphin) neurons in the arcuate nucleus of the hypothalamus constitute the GnRH (gonadotropin-releasing hormone) pulse generator. We previously created a mouse model with a whole-body targeted deletion of nescient helix-loop-helix 2 (Nhlh2; N2KO), a class II member of the basic helix-loop-helix family of transcription factors. As this mouse model features pubertal failure and late-onset obesity, we wanted to study whether NHLH2 represents a candidate molecule to link metabolism and puberty in the hypothalamus. Exome sequencing of a large Idiopathic Hypogonadotropic Hypogonadism cohort revealed obese patients with rare sequence variants in NHLH2, which were characterized by in-silico protein analysis, chromatin immunoprecipitation, and luciferase reporter assays. In vitro heterologous expression studies demonstrated that the variant p.R79C impairs Nhlh2 binding to the Mc4r promoter. Furthermore, p.R79C and other variants show impaired transactivation of the human KISS1 promoter. These are the first inactivating human variants that support NHLH2's critical role in human puberty and body weight control. Failure to carry out this function results in the absence of pubertal development and late-onset obesity in humans.
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Affiliation(s)
- A Kemal Topaloglu
- Department of Pediatrics, Division of Pediatric Endocrinology, University of Mississippi Medical Center, Jackson, MS, USA.
- Department of Neurobiology and Anatomical Sciences, University of Mississippi Medical Center, Jackson, MS, USA.
| | - Enver Simsek
- Division of Pediatric Endocrinology, Faculty of Medicine, Eskisehir Osman Gazi University, Eskisehir, Turkey
| | - Matthew A Kocher
- Translational Biology, Medicine and Health Graduate Program, Virginia Tech, Roanoke, VA, USA
| | - Jamala Mammadova
- Division of Pediatric Endocrinology, Faculty of Medicine, Ondokuz Mayis University, Samsun, Turkey
| | - Ece Bober
- Division of Pediatric Endocrinology, Faculty of Medicine, Dokuz Eylul University, Izmir, Turkey
| | - Leman Damla Kotan
- Division of Pediatric Endocrinology, Faculty of Medicine, Cukurova University, Adana, Turkey
| | - Ihsan Turan
- Division of Pediatric Endocrinology, Faculty of Medicine, Cukurova University, Adana, Turkey
| | - Can Celiloglu
- Division of Pediatric Endocrinology, Faculty of Medicine, Cukurova University, Adana, Turkey
| | - Fatih Gurbuz
- Division of Pediatric Endocrinology, Faculty of Medicine, Cukurova University, Adana, Turkey
| | - Bilgin Yuksel
- Division of Pediatric Endocrinology, Faculty of Medicine, Cukurova University, Adana, Turkey
| | - Deborah J Good
- Translational Biology, Medicine and Health Graduate Program, Virginia Tech, Roanoke, VA, USA
- Department of Human Nutrition, Foods, and Exercise, Virginia Tech, Blacksburg, VA, USA
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10
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Whole-exome imputation within UK Biobank powers rare coding variant association and fine-mapping analyses. Nat Genet 2021; 53:1260-1269. [PMID: 34226706 PMCID: PMC8349845 DOI: 10.1038/s41588-021-00892-1] [Citation(s) in RCA: 99] [Impact Index Per Article: 33.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2020] [Accepted: 05/28/2021] [Indexed: 02/06/2023]
Abstract
Exome association studies to date have generally been underpowered to systematically evaluate the phenotypic impact of very rare coding variants. We leveraged extensive haplotype sharing between 49,960 exome-sequenced UK Biobank participants and the remainder of the cohort (total N~500K) to impute exome-wide variants with accuracy (R2>0.5) down to minor allele frequency (MAF) ~0.00005. Association and fine-mapping analyses of 54 quantitative traits identified 1,189 significant associations (P<5 x 10−8) involving 675 distinct rare protein-altering variants (MAF<0.01) that passed stringent filters for likely causality. Across all traits, 49% of associations (578/1,189) occurred in genes with two or more hits; follow-up analyses of these genes identified allelic series containing up to 45 distinct likely-causal variants. Our results demonstrate the utility of within-cohort imputation in population-scale GWAS cohorts, provide a catalog of likely-causal, large-effect coding variant associations, and foreshadow the insights that will be revealed as genetic biobank studies continue to grow.
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Sharma S, Thibodeau S, Lytton J. Signal pathway analysis of selected obesity-associated melanocortin-4 receptor class V mutants. Biochim Biophys Acta Mol Basis Dis 2020; 1866:165835. [PMID: 32423884 DOI: 10.1016/j.bbadis.2020.165835] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2019] [Revised: 04/18/2020] [Accepted: 05/06/2020] [Indexed: 01/30/2023]
Abstract
Mutations in the melanocortin-4 receptor (MC4R) in humans are the single most common cause of rare monogenic 1severe obesity, and polymorphisms in this gene are also associated with obesity in the general population. The MC4R is a G-protein coupled receptor, and in vitro analysis suggests that MC4R can signal through several different G-protein subtypes. In vivo studies show complex outcomes, with different G-proteins in different cells responsible for different physiological responses linked to obesity. There is an emerging consensus that Gαq-linked signals in the paraventricular nucleus of the hypothalamus are essential for normal satiety and the control of feeding behavior. Many MC4R mutations have been analyzed for the molecular defect underlying their association with obesity, which has revealed a group - referred to as class V mutants - with no measurable change in receptor function. However, Gαq-linked signaling leading to Ca2+ release has only been examined for a few MC4R mutations. In this study, we have examined seven MC4R class V mutants, as well as two other well-characterized signal-defective mutants as controls, with respect to G-protein signaling coupled to cAMP production, mitogen-activated protein kinase (MAPK) activation, and Ca2+ release. These data confirm, with one exception (E308K), the expected pattern of cAMP and MAPK signaling for wild type and mutant MC4R. Our results also demonstrate normal MSH-induced Ca2+ signals for wild type as well as all the class V mutants, but not the signal-defective controls. Thus, the means by which class V MC4R mutations lead to obesity remains an open question.
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Affiliation(s)
- Sunita Sharma
- Department of Biochemistry & Molecular Biology, Libin Cardiovascular Institute and Hotchkiss Brain Institute, Cumming School of Medicine, University of Calgary, Canada
| | - Stephanie Thibodeau
- Department of Biochemistry & Molecular Biology, Libin Cardiovascular Institute and Hotchkiss Brain Institute, Cumming School of Medicine, University of Calgary, Canada
| | - Jonathan Lytton
- Department of Biochemistry & Molecular Biology, Libin Cardiovascular Institute and Hotchkiss Brain Institute, Cumming School of Medicine, University of Calgary, Canada.
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Differential Signaling Profiles of MC4R Mutations with Three Different Ligands. Int J Mol Sci 2020; 21:ijms21041224. [PMID: 32059383 PMCID: PMC7072973 DOI: 10.3390/ijms21041224] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2019] [Revised: 02/06/2020] [Accepted: 02/06/2020] [Indexed: 12/21/2022] Open
Abstract
The melanocortin 4 receptor (MC4R) is a key player in hypothalamic weight regulation and energy expenditure as part of the leptin–melanocortin pathway. Mutations in this G protein coupled receptor (GPCR) are the most common cause for monogenetic obesity, which appears to be mediated by changes in the anorectic action of MC4R via GS-dependent cyclic adenosine-monophosphate (cAMP) signaling as well as other signaling pathways. To study potential bias in the effects of MC4R mutations between the different signaling pathways, we investigated three major MC4R mutations: a GS loss-of-function (S127L) and a GS gain-of-function mutant (H158R), as well as the most common European single nucleotide polymorphism (V103I). We tested signaling of all four major G protein families plus extracellular regulated kinase (ERK) phosphorylation and β-arrestin2 recruitment, using the two endogenous agonists, α- and β-melanocyte stimulating hormone (MSH), along with a synthetic peptide agonist (NDP-α-MSH). The S127L mutation led to a full loss-of-function in all investigated pathways, whereas V103I and H158R were clearly biased towards the Gq/11 pathway when challenged with the endogenous ligands. These results show that MC4R mutations can cause vastly different changes in the various MC4R signaling pathways and highlight the importance of a comprehensive characterization of receptor mutations.
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Good DJ, Zhang H, Grange RW, Braun T. Pro-opiomelanocortin Neurons and the Transcriptional Regulation of Motivated Exercise. Exerc Sport Sci Rev 2020; 48:74-82. [DOI: 10.1249/jes.0000000000000219] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
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Lee S, Kim S, Kim Y, Oh B, Hwang H, Park T. Pathway analysis of rare variants for the clustered phenotypes by using hierarchical structured components analysis. BMC Med Genomics 2019; 12:100. [PMID: 31296220 PMCID: PMC6624181 DOI: 10.1186/s12920-019-0517-4] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023] Open
Abstract
BACKGROUNDS Recent large-scale genetic studies often involve clustered phenotypes such as repeated measurements. Compared to a series of univariate analyses of single phenotypes, an analysis of clustered phenotypes can be useful for substantially increasing statistical power to detect more genetic associations. Moreover, for the analysis of rare variants, incorporation of biological information can boost weak effects of the rare variants. RESULTS Through simulation studies, we showed that the proposed method outperforms other method currently available for pathway-level analysis of clustered phenotypes. Moreover, a real data analysis using a large-scale whole exome sequencing dataset of 995 samples with metabolic syndrome-related phenotypes successfully identified the glyoxylate and dicarboxylate metabolism pathway that could not be identified by the univariate analyses of single phenotypes and other existing method. CONCLUSION In this paper, we introduced a novel pathway-level association test by combining hierarchical structured components analysis and penalized generalized estimating equations. The proposed method analyzes all pathways in a single unified model while considering their correlations. C/C++ implementation of PHARAOH-GEE is publicly available at http://statgen.snu.ac.kr/software/pharaoh-gee/ .
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Affiliation(s)
- Sungyoung Lee
- Center for Precision Medicine, Seoul National University Hospital, Seoul, Korea
| | - Sunmee Kim
- Department of Psychology, McGill University, Montreal, Canada
| | - Yongkang Kim
- Department of Statistics, Seoul National University, Seoul, Korea
| | - Bermseok Oh
- Department of Biochemistry and Molecular Biology, School of Medicine, Kyung Hee University, Seoul, Korea
| | - Heungsun Hwang
- Department of Psychology, McGill University, Montreal, Canada
| | - Taesung Park
- Department of Statistics, Seoul National University, Seoul, Korea.
- Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, Korea.
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Qin H, Zhao J, Zhu X. Identifying Rare Variant Associations in Admixed Populations. Sci Rep 2019; 9:5458. [PMID: 30931973 PMCID: PMC6443736 DOI: 10.1038/s41598-019-41845-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2018] [Accepted: 03/12/2019] [Indexed: 12/27/2022] Open
Abstract
An admixed population and its ancestral populations bear different burdens of a complex disease. The ancestral populations may have different haplotypes of deleterious alleles and thus ancestry-gene interaction can influence disease risk in the admixed population. Among admixed individuals, deleterious haplotypes and their ancestries are dependent and can provide non-redundant association information. Herein we propose a local ancestry boosted sum test (LABST) for identifying chromosomal blocks that harbor rare variants but have no ancestry switches. For such a stable ancestral block, our LABST exploits ancestry-gene interaction and the number of rare alleles therein. Under the null of no genetic association, the test statistic asymptotically follows a chi-square distribution with one degree of freedom (1-df). Our LABST properly controlled type I error rates under extensive simulations, suggesting that the asymptotic approximation was accurate for the null distribution of the test statistic. In terms of power for identifying rare variant associations, our LABST uniformly outperformed several famed methods under four important modes of disease genetics over a large range of relative risks. In conclusion, exploiting ancestry-gene interaction can boost statistical power for rare variant association mapping in admixed populations.
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Affiliation(s)
- Huaizhen Qin
- Department of Epidemiology, College of Public Health and Health Professions and College of Medicine, University of Florida, Gainesville, FL, 32611, USA
- Department of Global Biostatistics and Data Science, Tulane University School of Public Health and Tropical Medicine, 1440 Canal Street, New Orleans, LA, 70112, USA
| | - Jinying Zhao
- Department of Epidemiology, College of Public Health and Health Professions and College of Medicine, University of Florida, Gainesville, FL, 32611, USA
| | - Xiaofeng Zhu
- Department of Population and Quantitative Health Sciences, Case Western Reserve University School of Medicine, 10900 Euclid Avenue, Cleveland, Ohio, 44106, USA.
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16
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Qin H, Niu T, Zhao J. Identifying Multi-Omics Causers and Causal Pathways for Complex Traits. Front Genet 2019; 10:110. [PMID: 30847004 PMCID: PMC6393387 DOI: 10.3389/fgene.2019.00110] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2018] [Accepted: 01/30/2019] [Indexed: 12/23/2022] Open
Abstract
The central dogma of molecular biology delineates a unidirectional causal flow, i.e., DNA → RNA → protein → trait. Genome-wide association studies, next-generation sequencing association studies, and their meta-analyses have successfully identified ~12,000 susceptibility genetic variants that are associated with a broad array of human physiological traits. However, such conventional association studies ignore the mediate causers (i.e., RNA, protein) and the unidirectional causal pathway. Such studies may not be ideally powerful; and the genetic variants identified may not necessarily be genuine causal variants. In this article, we model the central dogma by a mediate causal model and analytically prove that the more remote an omics level is from a physiological trait, the smaller the magnitude of their correlation is. Under both random and extreme sampling schemes, we numerically demonstrate that the proteome-trait correlation test is more powerful than the transcriptome-trait correlation test, which in turn is more powerful than the genotype-trait association test. In conclusion, integrating RNA and protein expressions with DNA data and causal inference are necessary to gain a full understanding of how genetic causal variants contribute to phenotype variations.
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Affiliation(s)
- Huaizhen Qin
- Department of Epidemiology, College of Public Health and Health Professions and College of Medicine, University of Florida, Gainesville, FL, United States
- Department of Global Biostatistics and Data Science, Tulane University, New Orleans, LA, United States
| | - Tianhua Niu
- Department of Global Biostatistics and Data Science, Tulane University, New Orleans, LA, United States
- Department of Biochemistry and Molecular Biology, Tulane University School Medicine, New Orleans, LA, United States
| | - Jinying Zhao
- Department of Epidemiology, College of Public Health and Health Professions and College of Medicine, University of Florida, Gainesville, FL, United States
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Matharu N, Rattanasopha S, Tamura S, Maliskova L, Wang Y, Bernard A, Hardin A, Eckalbar WL, Vaisse C, Ahituv N. CRISPR-mediated activation of a promoter or enhancer rescues obesity caused by haploinsufficiency. Science 2018; 363:science.aau0629. [PMID: 30545847 DOI: 10.1126/science.aau0629] [Citation(s) in RCA: 202] [Impact Index Per Article: 33.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2018] [Accepted: 12/06/2018] [Indexed: 12/30/2022]
Abstract
A wide range of human diseases result from haploinsufficiency, where the function of one of the two gene copies is lost. Here, we targeted the remaining functional copy of a haploinsufficient gene using CRISPR-mediated activation (CRISPRa) in Sim1 and Mc4r heterozygous mouse models to rescue their obesity phenotype. Transgenic-based CRISPRa targeting of the Sim1 promoter or its distant hypothalamic enhancer up-regulated its expression from the endogenous functional allele in a tissue-specific manner, rescuing the obesity phenotype in Sim1 heterozygous mice. To evaluate the therapeutic potential of CRISPRa, we injected CRISPRa-recombinant adeno-associated virus into the hypothalamus, which led to reversal of the obesity phenotype in Sim1 and Mc4r haploinsufficient mice. Our results suggest that endogenous gene up-regulation could be a potential strategy to treat altered gene dosage diseases.
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Affiliation(s)
- Navneet Matharu
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA 94158, USA.,Institute for Human Genetics, University of California San Francisco, San Francisco, CA 94158, USA
| | - Sawitree Rattanasopha
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA 94158, USA.,Institute for Human Genetics, University of California San Francisco, San Francisco, CA 94158, USA.,Doctor of Philosophy Program in Medical Sciences, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
| | - Serena Tamura
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA 94158, USA.,Institute for Human Genetics, University of California San Francisco, San Francisco, CA 94158, USA
| | - Lenka Maliskova
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA 94158, USA.,Institute for Human Genetics, University of California San Francisco, San Francisco, CA 94158, USA
| | - Yi Wang
- Diabetes Center, University of California San Francisco, San Francisco, CA 94143, USA
| | - Adelaide Bernard
- Diabetes Center, University of California San Francisco, San Francisco, CA 94143, USA
| | - Aaron Hardin
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA 94158, USA.,Institute for Human Genetics, University of California San Francisco, San Francisco, CA 94158, USA
| | - Walter L Eckalbar
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA 94158, USA.,Institute for Human Genetics, University of California San Francisco, San Francisco, CA 94158, USA
| | - Christian Vaisse
- Diabetes Center, University of California San Francisco, San Francisco, CA 94143, USA
| | - Nadav Ahituv
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA 94158, USA. .,Institute for Human Genetics, University of California San Francisco, San Francisco, CA 94158, USA
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18
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Abstract
Erectile dysfunction is a common condition of men in middle and older ages. Twin studies suggest that about one-third of the risk is due to genetic factors, independent of other known erectile dysfunction risk factors. However, studies that have searched for specific genetic contributors have been limited due to small sample sizes, candidate gene approaches, and weak phenotyping. As a result, there are no confirmed genetic risk factors for erectile dysfunction. This study finds a specific genetic cause for erectile dysfunction. Erectile dysfunction affects millions of men worldwide. Twin studies support the role of genetic risk factors underlying erectile dysfunction, but no specific genetic variants have been identified. We conducted a large-scale genome-wide association study of erectile dysfunction in 36,649 men in the multiethnic Kaiser Permanente Northern California Genetic Epidemiology Research in Adult Health and Aging cohort. We also undertook replication analyses in 222,358 men from the UK Biobank. In the discovery cohort, we identified a single locus (rs17185536-T) on chromosome 6 near the single-minded family basic helix-loop-helix transcription factor 1 (SIM1) gene that was significantly associated with the risk of erectile dysfunction (odds ratio = 1.26, P = 3.4 × 10−25). The association replicated in the UK Biobank sample (odds ratio = 1.25, P = 6.8 × 10−14), and the effect is independent of known erectile dysfunction risk factors, including body mass index (BMI). The risk locus resides on the same topologically associating domain as SIM1 and interacts with the SIM1 promoter, and the rs17185536-T risk allele showed differential enhancer activity. SIM1 is part of the leptin–melanocortin system, which has an established role in body weight homeostasis and sexual function. Because the variants associated with erectile dysfunction are not associated with differences in BMI, our findings suggest a mechanism that is specific to sexual function.
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19
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Kono TJY, Lei L, Shih CH, Hoffman PJ, Morrell PL, Fay JC. Comparative Genomics Approaches Accurately Predict Deleterious Variants in Plants. G3 (BETHESDA, MD.) 2018; 8:3321-3329. [PMID: 30139765 PMCID: PMC6169392 DOI: 10.1534/g3.118.200563] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/08/2018] [Accepted: 08/10/2018] [Indexed: 12/11/2022]
Abstract
Recent advances in genome resequencing have led to increased interest in prediction of the functional consequences of genetic variants. Variants at phylogenetically conserved sites are of particular interest, because they are more likely than variants at phylogenetically variable sites to have deleterious effects on fitness and contribute to phenotypic variation. Numerous comparative genomic approaches have been developed to predict deleterious variants, but the approaches are nearly always assessed based on their ability to identify known disease-causing mutations in humans. Determining the accuracy of deleterious variant predictions in nonhuman species is important to understanding evolution, domestication, and potentially to improving crop quality and yield. To examine our ability to predict deleterious variants in plants we generated a curated database of 2,910 Arabidopsis thaliana mutants with known phenotypes. We evaluated seven approaches and found that while all performed well, their relative ranking differed from prior benchmarks in humans. We conclude that deleterious mutations can be reliably predicted in A. thaliana and likely other plant species, but that the relative performance of various approaches does not necessarily translate from one species to another.
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Affiliation(s)
- Thomas J Y Kono
- Department of Agronomy & Plant Genetics, University of Minnesota, St. Paul, MN 551085
| | - Li Lei
- Department of Agronomy & Plant Genetics, University of Minnesota, St. Paul, MN 551085
| | - Ching-Hua Shih
- Department of Genetics, Washington University, St. Louis, MO 63110
| | - Paul J Hoffman
- Department of Agronomy & Plant Genetics, University of Minnesota, St. Paul, MN 551085
| | - Peter L Morrell
- Department of Agronomy & Plant Genetics, University of Minnesota, St. Paul, MN 551085
| | - Justin C Fay
- Department of Genetics, Washington University, St. Louis, MO 63110
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20
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Labos C, Thanassoulis G. Genetic Risk Prediction for Primary and Secondary Prevention of Atherosclerotic Cardiovascular Disease: an Update. Curr Cardiol Rep 2018; 20:36. [PMID: 29574623 DOI: 10.1007/s11886-018-0980-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
PURPOSE OF REVIEW This review aims to summarize the research on genetic risk scores and their ability to improve risk prediction in both a primary and a secondary prevention population. RECENT FINDINGS Several groups have examined the role of genetic scores in different patient populations. Recent studies have capitalized on the growing number of identified genetic variants to construct polygenic risk scores that include hundreds and sometimes thousands of SNPs. Also, recent studies have demonstrated that individuals with high genetic risk scores can attenuate their risk with lifestyle modifications and with statins, for which the benefit of treatment may be greater in those at highest genetic risk. Genetic risk scores when added to existing clinical models appear to improve risk prediction, particularly in the setting of incident cardiovascular disease and may provide actionable information to optimize prevention early in life. Future research will need to establish how to best use this genetic risk information either as a means to further individualize treatment decisions or to better identify high-risk populations.
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Affiliation(s)
- Christopher Labos
- Division of Cardiology, Preventive and Genomic Cardiology, McGill University Health Center, 1001 Boulevard Decarie, Montreal, QC, H4A 3J1, Canada
| | - George Thanassoulis
- Division of Cardiology, Preventive and Genomic Cardiology, McGill University Health Center, 1001 Boulevard Decarie, Montreal, QC, H4A 3J1, Canada.
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21
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Zhang B, Chen MY, Shen YJ, Zhuo XB, Gao P, Zhou FS, Liang B, Zu J, Zhang Q, Suleman S, Xu YH, Xu MG, Xu JK, Liu CC, Giannareas N, Xia JH, Zhao Y, Huang ZL, Yang Z, Cheng HD, Li N, Hong YY, Li W, Zhang MJ, Yu KD, Li G, Sun MH, Chen ZD, Wei GH, Shao ZM. A Large-Scale, Exome-Wide Association Study of Han Chinese Women Identifies Three Novel Loci Predisposing to Breast Cancer. Cancer Res 2018; 78:3087-3097. [PMID: 29572226 DOI: 10.1158/0008-5472.can-17-1721] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2017] [Revised: 10/25/2017] [Accepted: 03/20/2018] [Indexed: 11/16/2022]
Affiliation(s)
- Bo Zhang
- Department of Oncology, No. 2 Hospital, Anhui Medical University, Hefei, Anhui, China.
- School of Life Sciences, Anhui Medical University, Hefei, Anhui, China
| | - Men-Yun Chen
- School of Life Sciences, Anhui Medical University, Hefei, Anhui, China
| | - Yu-Jun Shen
- State Key Laboratory Incubation Base of Dermatology, Ministry of National Science and Technology, Hefei, Anhui, China
| | - Xian-Bo Zhuo
- State Key Laboratory Incubation Base of Dermatology, Ministry of National Science and Technology, Hefei, Anhui, China
| | - Ping Gao
- Biocenter Oulu, Faculty of Biochemistry and Molecular Medicine, University of Oulu, Oulu, Finland
| | - Fu-Sheng Zhou
- State Key Laboratory Incubation Base of Dermatology, Ministry of National Science and Technology, Hefei, Anhui, China
| | - Bo Liang
- State Key Laboratory Incubation Base of Dermatology, Ministry of National Science and Technology, Hefei, Anhui, China
| | - Jun Zu
- State Key Laboratory Incubation Base of Dermatology, Ministry of National Science and Technology, Hefei, Anhui, China
| | - Qin Zhang
- Biocenter Oulu, Faculty of Biochemistry and Molecular Medicine, University of Oulu, Oulu, Finland
| | - Sufyan Suleman
- Biocenter Oulu, Faculty of Biochemistry and Molecular Medicine, University of Oulu, Oulu, Finland
| | - Yi-Hui Xu
- School of Life Sciences, Anhui Medical University, Hefei, Anhui, China
| | - Min-Gui Xu
- School of Life Sciences, Anhui Medical University, Hefei, Anhui, China
| | - Jin-Kai Xu
- School of Life Sciences, Anhui Medical University, Hefei, Anhui, China
| | - Chen-Cheng Liu
- School of Life Sciences, Anhui Medical University, Hefei, Anhui, China
| | - Nikolaos Giannareas
- Biocenter Oulu, Faculty of Biochemistry and Molecular Medicine, University of Oulu, Oulu, Finland
| | - Ji-Han Xia
- Biocenter Oulu, Faculty of Biochemistry and Molecular Medicine, University of Oulu, Oulu, Finland
| | - Yuan Zhao
- State Key Laboratory Incubation Base of Dermatology, Ministry of National Science and Technology, Hefei, Anhui, China
| | - Zhong-Lian Huang
- Department of Oncology, No. 2 Hospital, Anhui Medical University, Hefei, Anhui, China
| | - Zhen Yang
- Department of Oncology, No. 2 Hospital, Anhui Medical University, Hefei, Anhui, China
| | - Huai-Dong Cheng
- Department of Oncology, No. 2 Hospital, Anhui Medical University, Hefei, Anhui, China
| | - Na Li
- Department of Oncology, No. 2 Hospital, Anhui Medical University, Hefei, Anhui, China
| | - Yan-Yan Hong
- Department of Oncology, No. 2 Hospital, Anhui Medical University, Hefei, Anhui, China
| | - Wei Li
- Department of Oncology, No. 2 Hospital, Anhui Medical University, Hefei, Anhui, China
| | - Min-Jun Zhang
- Department of Oncology, No. 2 Hospital, Anhui Medical University, Hefei, Anhui, China
| | - Ke-Da Yu
- Department of Breast Surgery, Fudan University Shanghai Cancer Center/Cancer Institute, Shanghai, China
| | - Guoliang Li
- Bio-Medical Center, College of Informatics, Huazhong Agricultural University, Wuhan, China
| | - Meng-Hong Sun
- Department of Breast Surgery, Fudan University Shanghai Cancer Center/Cancer Institute, Shanghai, China
| | - Zhen-Dong Chen
- Department of Oncology, No. 2 Hospital, Anhui Medical University, Hefei, Anhui, China
| | - Gong-Hong Wei
- Biocenter Oulu, Faculty of Biochemistry and Molecular Medicine, University of Oulu, Oulu, Finland.
| | - Zhi-Min Shao
- Department of Breast Surgery, Fudan University Shanghai Cancer Center/Cancer Institute, Shanghai, China.
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22
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Lin X, Chen Z, Gao P, Gao Z, Chen H, Qi J, Liu F, Ye D, Jiang H, Na R, Yu H, Shi R, Lu D, Zheng SL, Mo Z, Sun Y, Ding Q, Xu J. TEX15: A DNA repair gene associated with prostate cancer risk in Han Chinese. Prostate 2017; 77:1271-1278. [PMID: 28730685 DOI: 10.1002/pros.23387] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/13/2017] [Accepted: 06/22/2017] [Indexed: 11/12/2022]
Abstract
BACKGROUND Both common and rare genetic variants may contribute to risk of developing prostate cancer. Genome-wide association studies (GWASs) have identified ∼100 independent, common variants associated with prostate cancer risk. However, little is known about the association of rare variants (minor allele frequency [MAF] <1%) in the genome with prostate cancer risk. METHODS A two-stage study was used to test the association of rare, deleterious coding variants, annotated using predictive algorithms, with prostate cancer risk in Chinese men. Predicted rare, deleterious coding variants in the Illumina HumanExome-12 v1.1 beadchip were first evaluated in 1343 prostate cancer patients and 1008 controls. Significant variants were then validated in an additional 1816 prostate cancer patients and 1549 controls. RESULTS In the discovery stage, 14 predicted rare, deleterious coding variants were significantly associated with prostate cancer risk (P < 0.01). In the confirmation stage, Q1631H in TEX15 (rs142485241), a DNA repair gene, was significantly associated with prostate cancer risk (P = 0.0069). The estimated odds ratio (OR) of the variant in the combined analysis was 3.24 (95% Confidence Interval 1.85-6.06), P = 8.81 × 10-5 . Additionally, rs28756990 (V741F) at MLH3 (P = 0.06) and rs2961144 (I126V) at OR2A5 (P = 0.065) were marginally associated with prostate cancer risk in the replication stage. CONCLUSIONS Our study provided preliminary evidence that the rare variant Q1631H in DNA repair gene TEX15 is associated with prostate cancer risk. This finding complements known common prostate cancer risk-associated variants and suggests the possible role of DNA repair genes in prostate cancer development.
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Affiliation(s)
- Xiaoling Lin
- Fudan Institute of Urology, Huashan Hospital, Fudan University, Shanghai, China
- Department of Urology, Huashan Hospital, Fudan University, Shanghai, China
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Fudan University, Shanghai, China
| | - Zhongzhong Chen
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Fudan University, Shanghai, China
| | - Peng Gao
- Fudan Institute of Urology, Huashan Hospital, Fudan University, Shanghai, China
- Department of Urology, Huashan Hospital, Fudan University, Shanghai, China
| | - Zhimei Gao
- Central Laboratory, Shanghai Children's Hospital, Shanghai Jiaotong University, Shanghai, China
| | - Haitao Chen
- Center for Genomic Translational Medicine and Prevention, School of Public Health, Fudan University, Shanghai, China
| | - Jun Qi
- Department of Urology, Xinhua Hospital, School of Medicine, Shanghai Jiaotong University, Shanghai, China
| | - Fang Liu
- Fudan Institute of Urology, Huashan Hospital, Fudan University, Shanghai, China
- Department of Urology, Huashan Hospital, Fudan University, Shanghai, China
| | - Dingwei Ye
- Department of Urology, Fudan University Shanghai Cancer Center and Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Haowen Jiang
- Fudan Institute of Urology, Huashan Hospital, Fudan University, Shanghai, China
- Department of Urology, Huashan Hospital, Fudan University, Shanghai, China
| | - Rong Na
- Fudan Institute of Urology, Huashan Hospital, Fudan University, Shanghai, China
- Department of Urology, Huashan Hospital, Fudan University, Shanghai, China
| | - Hongjie Yu
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Fudan University, Shanghai, China
| | - Rong Shi
- School of Public Health, Shanghai Jiaotong University, Shanghai, China
| | - Daru Lu
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Fudan University, Shanghai, China
| | - Siqun Lilly Zheng
- Program for Personalized Cancer Care, NorthShore University HealthSystem, Evanston, Illinois
| | - Zengnan Mo
- Center for Genomic and Personalized Medicine, Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region, China
- Department of Urology, First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region, China
| | - Yinghao Sun
- Department of Urology, Shanghai Changhai Hospital, The Second Military Medical University, Shanghai, China
| | - Qiang Ding
- Fudan Institute of Urology, Huashan Hospital, Fudan University, Shanghai, China
- Department of Urology, Huashan Hospital, Fudan University, Shanghai, China
| | - Jianfeng Xu
- Fudan Institute of Urology, Huashan Hospital, Fudan University, Shanghai, China
- Department of Urology, Huashan Hospital, Fudan University, Shanghai, China
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23
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Ning T, Zou Y, Yang M, Lu Q, Chen M, Liu W, Zhao S, Sun Y, Shi J, Ma Q, Hong J, Liu R, Wang J, Ning G. Genetic interaction of DGAT2 and FAAH in the development of human obesity. Endocrine 2017; 56:366-378. [PMID: 28243972 DOI: 10.1007/s12020-017-1261-1] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/05/2016] [Accepted: 02/06/2017] [Indexed: 01/22/2023]
Abstract
PURPOSE DGAT2 is the critical catalyzing enzyme for triglyceride biosynthesis, and excess triglyceride accumulation in fat tissues is a fundamental process for obesity. Mutations in DGAT2 or other genes interacting with DGAT2 associated with adiposity have not been reported in human to date. METHODS DGAT2 mutation was identified based on our in-home database-exome sequencing 227 young obese subjects (body-mass index (BMI), 35.1-61.7 kg/m2) and 219 lean controls (BMI, 17.5-23.0 kg/m2), further validated in 1190 lean subjects and the pedigree of the proband. The trios of the proband were further subjected to whole-exome sequencing to explore the candidate genes for obesity. The mutations in DGAT2 and FAAH were functionally evaluated in vitro. RESULTS We detected two rare variants in DGAT2 with no significant difference between obese and lean individuals. One novel heterozygous nonsense variant c.382C > T (p.R128*) was identified in one obese subject but not in 219 lean subjects and another 1190 lean subjects. Notably, in vitro study showed that R128* mutation severely damaged the TG-biosynthesis ability of DGAT2, and all other R128* carriers in the pedigree were lean. Thus, we further identified a loss-of-function variant c. 944G > T (p.R315I) in FAAH in the proband inheriting from his obese father. Importantly, FAAH overexpression inhibited DGAT2 expression and TG synthesis, while R315I mutant largely eliminated this inhibitory effect. We first report loss-of-function mutations in DGAT2 and FAAH in one obese subject, which may interact with each other to affect the adiposity penetrance, providing a model of genetic interaction associated with human obesity.
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Affiliation(s)
- Tinglu Ning
- Institute of Health Sciences, Shanghai Institutes for Biological Sciences (SIBS), Chinese Academy of Sciences (CAS) & Shanghai Jiao Tong University School of Medicine (SJTUSM), Shanghai, 200025, China
| | - Yaoyu Zou
- Institute of Health Sciences, Shanghai Institutes for Biological Sciences (SIBS), Chinese Academy of Sciences (CAS) & Shanghai Jiao Tong University School of Medicine (SJTUSM), Shanghai, 200025, China
| | - Minglan Yang
- Shanghai Clinical Center for Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Department of Endocrinology and Metabolism, National Key Laboratory for Medical Genomes, China National Research Center for Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine (SJTUSM), Shanghai, 200025, China
| | - Qianqian Lu
- Institute of Health Sciences, Shanghai Institutes for Biological Sciences (SIBS), Chinese Academy of Sciences (CAS) & Shanghai Jiao Tong University School of Medicine (SJTUSM), Shanghai, 200025, China
| | - Maopei Chen
- Shanghai Clinical Center for Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Department of Endocrinology and Metabolism, National Key Laboratory for Medical Genomes, China National Research Center for Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine (SJTUSM), Shanghai, 200025, China
| | - Wen Liu
- Shanghai Clinical Center for Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Department of Endocrinology and Metabolism, National Key Laboratory for Medical Genomes, China National Research Center for Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine (SJTUSM), Shanghai, 200025, China
| | - Shaoqian Zhao
- Shanghai Clinical Center for Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Department of Endocrinology and Metabolism, National Key Laboratory for Medical Genomes, China National Research Center for Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine (SJTUSM), Shanghai, 200025, China
| | - Yingkai Sun
- Shanghai Clinical Center for Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Department of Endocrinology and Metabolism, National Key Laboratory for Medical Genomes, China National Research Center for Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine (SJTUSM), Shanghai, 200025, China
| | - Juan Shi
- Shanghai Clinical Center for Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Department of Endocrinology and Metabolism, National Key Laboratory for Medical Genomes, China National Research Center for Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine (SJTUSM), Shanghai, 200025, China
| | - Qinyun Ma
- Shanghai Clinical Center for Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Department of Endocrinology and Metabolism, National Key Laboratory for Medical Genomes, China National Research Center for Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine (SJTUSM), Shanghai, 200025, China
| | - Jie Hong
- Shanghai Clinical Center for Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Department of Endocrinology and Metabolism, National Key Laboratory for Medical Genomes, China National Research Center for Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine (SJTUSM), Shanghai, 200025, China
| | - Ruixin Liu
- Shanghai Clinical Center for Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Department of Endocrinology and Metabolism, National Key Laboratory for Medical Genomes, China National Research Center for Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine (SJTUSM), Shanghai, 200025, China
| | - Jiqiu Wang
- Shanghai Clinical Center for Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Department of Endocrinology and Metabolism, National Key Laboratory for Medical Genomes, China National Research Center for Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine (SJTUSM), Shanghai, 200025, China.
| | - Guang Ning
- Institute of Health Sciences, Shanghai Institutes for Biological Sciences (SIBS), Chinese Academy of Sciences (CAS) & Shanghai Jiao Tong University School of Medicine (SJTUSM), Shanghai, 200025, China.
- Shanghai Clinical Center for Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Department of Endocrinology and Metabolism, National Key Laboratory for Medical Genomes, China National Research Center for Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine (SJTUSM), Shanghai, 200025, China.
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Nguyen AL, Marin D, Zhou A, Gentilello AS, Smoak EM, Cao Z, Fedick A, Wang Y, Taylor D, Scott RT, Xing J, Treff N, Schindler K. Identification and characterization of Aurora kinase B and C variants associated with maternal aneuploidy. Mol Hum Reprod 2017; 23:406-416. [PMID: 28369513 PMCID: PMC9915067 DOI: 10.1093/molehr/gax018] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2016] [Accepted: 03/20/2017] [Indexed: 12/16/2022] Open
Abstract
STUDY QUESTION Are single nucleotide variants (SNVs) in Aurora kinases B and C (AURKB, AURKC) associated with risk of aneuploid conception? SUMMARY ANSWER Two SNVs were found in patients with extreme aneuploid concepti rates with respect to their age; one variant, AURKC p.I79V, is benign, while another, AURKB p.L39P, is a potential gain-of-function mutant with increased efficiency in promoting chromosome alignment. WHAT IS KNOWN ALREADY Maternal age does not always predict aneuploidy risk, and rare gene variants can be drivers of disease. The AURKB and AURKC regulate chromosome segregation, and are associated with reproductive impairments in mouse and human. STUDY DESIGN, SIZE, DURATION An extreme phenotype sample selection scheme was performed for variant discovery. Ninety-six DNA samples were from young patients with higher than average embryonic aneuploidy rates and an additional 96 DNA samples were from older patients with lower than average aneuploidy rates. PARTICIPANTS/MATERIALS, SETTING, METHODS Using the192 DNA samples, the coding regions of AURKB and AURKC were sequenced using next generation sequencing. To assess biological significance, we expressed complementary RNA encoding the human variants in mouse oocytes. Assays such as determining subcellular localization and assessing catalytic activity were performed to determine alterations in protein function during meiosis. MAIN RESULTS AND THE ROLE OF CHANCE Ten SNVs were identified using three independent variant-calling methods. Two of the SNVs (AURKB p.L39P and AURKC p.I79V) were non-synonymous and identified by at least two variant-identification methods. The variant encoding AURKC p.I79V, identified in a young woman with a higher than average rate of aneuploid embryos, showed wild-type localization pattern and catalytic activity. On the other hand, the variant encoding AURKB p.L39P, identified in an older woman with lower than average rates of aneuploid embryos, increased the protein's ability to regulate alignment of chromosomes at the metaphase plate. These experiments were repeated three independent times using 2-3 mice for each trial. LARGE SCALE DATA N/A. LIMITATIONS, REASONS FOR CAUTION Biological significance of the human variants was assessed in an in vitro mouse oocyte model where the variants are over-expressed. Therefore, the human protein may not function identically to the mouse homolog, or the same in mouse oocytes as in human oocytes. Furthermore, supraphysiological expression levels may not accurately reflect endogenous activity. Moreover, the evaluated variants were identified in one patient each, and no trial linking the SNV to pregnancy outcomes was conducted. Finally, the patient aneuploidy rates were established by performing comprehensive chromosome screening in blastocysts, and because of the link between female gamete aneuploidy giving rise to aneuploid embryos, we evaluate the role of the variants in Meiosis I. However, it is possible that the chromosome segregation mistake arose during Meiosis II or in mitosis in the preimplantation embryo. Their implications in human female meiosis and aneuploidy risk remain to be determined. WIDER IMPLICATIONS OF THE FINDINGS The data provide evidence that gene variants exist in reproductively younger or advanced aged women that are predictive of the risk of producing aneuploid concepti in humans. Furthermore, a single amino acid in the N-terminus of AURKB is a gain-of-function mutant that could be protective of euploidy. STUDY FUNDING/COMPETING INTERESTS This work was supported by a Research Grant from the American Society of Reproductive Medicine and support from the Charles and Johanna Busch Memorial Fund at Rutgers, the State University of NJ to K.S. and the Foundation for Embryonic Competence, Inc to N.T. The authors declare no conflicts of interest.
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Affiliation(s)
| | | | - Anbo Zhou
- Department of Genetics, Rutgers, The State University of New Jersey, 145 Bevier Rd. Piscataway, NJ 08854, USA
| | - Amanda S. Gentilello
- Department of Genetics, Rutgers, The State University of New Jersey, 145 Bevier Rd. Piscataway, NJ 08854, USA
| | - Evan M. Smoak
- Department of Genetics, Rutgers, The State University of New Jersey, 145 Bevier Rd. Piscataway, NJ 08854, USA
| | - Zubing Cao
- Department of Genetics, Rutgers, The State University of New Jersey, 145 Bevier Rd. Piscataway, NJ 08854, USA
| | - Anastasia Fedick
- Department of Genetics, Rutgers, The State University of New Jersey, 145 Bevier Rd. Piscataway, NJ 08854, USA,Reproductive Medicine Associates of New Jersey, 140 Allen Rd, Basking Ridge, NJ 07920, USA
| | - Yujue Wang
- Department of Genetics, Rutgers, The State University of New Jersey, 145 Bevier Rd. Piscataway, NJ 08854, USA,Reproductive Medicine Associates of New Jersey, 140 Allen Rd, Basking Ridge, NJ 07920, USA
| | - Deanne Taylor
- Reproductive Medicine Associates of New Jersey, 140 Allen Rd, Basking Ridge, NJ 07920, USA,
Present address: Department of Biomedical and Health Informatics, The Children's Hospital of Philadelphia; Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, 3501 Civic Center Blvd, Philadelphia, PA 19104, USA
| | - Richard T. Scott
- Reproductive Medicine Associates of New Jersey, 140 Allen Rd, Basking Ridge, NJ 07920, USA
| | - Jinchuan Xing
- Department of Genetics, Rutgers, The State University of New Jersey, 145 Bevier Rd. Piscataway, NJ 08854, USA
| | - Nathan Treff
- Reproductive Medicine Associates of New Jersey, 140 Allen Rd, Basking Ridge, NJ 07920, USA
| | - Karen Schindler
- Correspondence address. Department of Genetics, Rutgers, The State University of New Jersey, NJ, USA. E-mail:
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Longitudinal data analysis for rare variants detection with penalized quadratic inference function. Sci Rep 2017; 7:650. [PMID: 28381821 PMCID: PMC5429681 DOI: 10.1038/s41598-017-00712-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2016] [Accepted: 03/08/2017] [Indexed: 11/08/2022] Open
Abstract
Longitudinal genetic data provide more information regarding genetic effects over time compared with cross-sectional data. Coupled with next-generation sequencing technologies, it becomes reality to identify important genes containing both rare and common variants in a longitudinal design. In this work, we adopted a weighted sum statistic (WSS) to collapse multiple variants in a gene region to form a gene score. When multiple genes in a pathway were considered together, a penalized longitudinal model under the quadratic inference function (QIF) framework was applied for efficient gene selection. We evaluated the estimation accuracy and model selection performance under different model settings, then applied the method to a real dataset from the Genetic Analysis Workshop 18 (GAW18). Compared with the unpenalized QIF method, the penalized QIF (pQIF) method achieved better estimation accuracy and higher selection efficiency. The pQIF remained optimal even when the working correlation structure was mis-specified. The real data analysis identified one important gene, angiotensin II receptor type 1 (AGTR1), in the Ca2+/AT-IIR/α-AR signaling pathway. The estimated effect implied that AGTR1 may have a protective effect for hypertension. Our pQIF method provides a general tool for longitudinal sequencing studies involving large numbers of genetic variants.
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26
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Achermann JC, Schwabe J, Fairall L, Chatterjee K. Genetic disorders of nuclear receptors. J Clin Invest 2017; 127:1181-1192. [PMID: 28368288 DOI: 10.1172/jci88892] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023] Open
Abstract
Following the first isolation of nuclear receptor (NR) genes, genetic disorders caused by NR gene mutations were initially discovered by a candidate gene approach based on their known roles in endocrine pathways and physiologic processes. Subsequently, the identification of disorders has been informed by phenotypes associated with gene disruption in animal models or by genetic linkage studies. More recently, whole exome sequencing has associated pathogenic genetic variants with unexpected, often multisystem, human phenotypes. To date, defects in 20 of 48 human NR genes have been associated with human disorders, with different mutations mediating phenotypes of varying severity or several distinct conditions being associated with different changes in the same gene. Studies of individuals with deleterious genetic variants can elucidate novel roles of human NRs, validating them as targets for drug development or providing new insights into structure-function relationships. Importantly, human genetic discoveries enable definitive disease diagnosis and can provide opportunities to therapeutically manage affected individuals. Here we review germline changes in human NR genes associated with "monogenic" conditions, including a discussion of the structural basis of mutations that cause distinctive changes in NR function and the molecular mechanisms mediating pathogenesis.
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27
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Exploring the relationship between α-actinin-3 deficiency and obesity in mice and humans. Int J Obes (Lond) 2017; 41:1154-1157. [PMID: 28293018 PMCID: PMC5504447 DOI: 10.1038/ijo.2017.72] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/26/2016] [Revised: 02/06/2017] [Accepted: 02/26/2017] [Indexed: 11/08/2022]
Abstract
Obesity is a worldwide health crisis, and the identification of genetic modifiers of weight gain is crucial in understanding this complex disorder. A common null polymorphism in the fast fiber-specific gene ACTN3 (R577X) is known to influence skeletal muscle function and metabolism. α-Actinin-3 deficiency occurs in an estimated 1.5 billion people worldwide, and results in reduced muscle strength and a shift towards a more efficient oxidative metabolism. The X-allele has undergone strong positive selection during recent human evolution, and in this study, we sought to determine whether ACTN3 genotype influences weight gain and obesity in mice and humans. An Actn3 KO mouse has been generated on two genetic backgrounds (129X1/SvJ and C57BL/6J) and fed a high-fat diet (HFD, 45% calories from fat). Anthropomorphic features (including body weight) were examined and show that Actn3 KO 129X1/SvJ mice gained less weight compared to WT. In addition, six independent human cohorts were genotyped for ACTN3 R577X (Rs1815739) and body mass index (BMI), waist-to-hip ratio-adjusted BMI (WHRadjBMI) and obesity-related traits were assessed. In humans, ACTN3 genotype alone does not contribute to alterations in BMI or obesity.
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28
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Yang X, Wang S, Zhang S, Sha Q. Detecting association of rare and common variants based on cross-validation prediction error. Genet Epidemiol 2017; 41:233-243. [PMID: 28176359 DOI: 10.1002/gepi.22034] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2016] [Revised: 11/22/2016] [Accepted: 11/26/2016] [Indexed: 12/13/2022]
Abstract
Despite the extensive discovery of disease-associated common variants, much of the genetic contribution to complex traits remains unexplained. Rare variants may explain additional disease risk or trait variability. Although sequencing technology provides a supreme opportunity to investigate the roles of rare variants in complex diseases, detection of these variants in sequencing-based association studies presents substantial challenges. In this article, we propose novel statistical tests to test the association between rare and common variants in a genomic region and a complex trait of interest based on cross-validation prediction error (PE). We first propose a PE method based on Ridge regression. Based on PE, we also propose another two tests PE-WS and PE-TOW by testing a weighted combination of variants with two different weighting schemes. PE-WS is the PE version of the test based on the weighted sum statistic (WS) and PE-TOW is the PE version of the test based on the optimally weighted combination of variants (TOW). Using extensive simulation studies, we are able to show that (1) PE-TOW and PE-WS are consistently more powerful than TOW and WS, respectively, and (2) PE is the most powerful test when causal variants contain both common and rare variants.
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Affiliation(s)
- Xinlan Yang
- Department of Mathematical Sciences, Michigan Technological University, Houghton, MI, USA
| | | | - Shuanglin Zhang
- Department of Mathematical Sciences, Michigan Technological University, Houghton, MI, USA
| | - Qiuying Sha
- Department of Mathematical Sciences, Michigan Technological University, Houghton, MI, USA
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29
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Detecting multiple variants associated with disease based on sequencing data of case-parent trios. J Hum Genet 2016; 61:851-860. [PMID: 27278787 DOI: 10.1038/jhg.2016.63] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2016] [Revised: 05/02/2016] [Accepted: 05/03/2016] [Indexed: 01/13/2023]
Abstract
With the advance of next-generation sequencing technology, the rare variants join the common ones in explaining more proportions of heritability. The coexistence of variants of common with rare, causal with neutral and deleterious with protective is a norm and should be appropriately addressed. Some existing methods suffer from low power when one or more forms of coexistence present, impeding their applications in practice. In this paper, for case-parent trios, pseudocontrols are constructed using the nontransmitted alleles of the parents. The Kullback-Leibler divergence is utilized to measure the difference between the distributions of variants in a genetic region for the affected children and pseudocontrols, and two nonparametric test statistics KLTT and cKLTT are proposed. Extensive simulations show that they are robust to the opposite directions of the causal variants and the amount of neutral variants, and have superiority over the existing methods when both rare and common variants are involved. Furthermore, their efficiency is demonstrated in the application to the data from Framingham Heart Study.
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30
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Lee S, Choi S, Kim YJ, Kim BJ, Hwang H, Park T. Pathway-based approach using hierarchical components of collapsed rare variants. Bioinformatics 2016; 32:i586-i594. [PMID: 27587678 PMCID: PMC5013912 DOI: 10.1093/bioinformatics/btw425] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
Abstract
MOTIVATION To address 'missing heritability' issue, many statistical methods for pathway-based analyses using rare variants have been proposed to analyze pathways individually. However, neglecting correlations between multiple pathways can result in misleading solutions, and pathway-based analyses of large-scale genetic datasets require massive computational burden. We propose a Pathway-based approach using HierArchical components of collapsed RAre variants Of High-throughput sequencing data (PHARAOH) for the analysis of rare variants by constructing a single hierarchical model that consists of collapsed gene-level summaries and pathways and analyzes entire pathways simultaneously by imposing ridge-type penalties on both gene and pathway coefficient estimates; hence our method considers the correlation of pathways without constraint by a multiple testing problem. RESULTS Through simulation studies, the proposed method was shown to have higher statistical power than the existing pathway-based methods. In addition, our method was applied to the large-scale whole-exome sequencing data with levels of a liver enzyme using two well-known pathway databases Biocarta and KEGG. This application demonstrated that our method not only identified associated pathways but also successfully detected biologically plausible pathways for a phenotype of interest. These findings were successfully replicated by an independent large-scale exome chip study. AVAILABILITY AND IMPLEMENTATION An implementation of PHARAOH is available at http://statgen.snu.ac.kr/software/pharaoh/ CONTACT tspark@stats.snu.ac.kr SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Sungyoung Lee
- Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul 151-747, Korea
| | - Sungkyoung Choi
- Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul 151-747, Korea
| | - Young Jin Kim
- Center for Genome Science, National Institute of Health, Osong Health Technology Administration Complex, Chungcheongbuk-Do 363-951, Korea
| | - Bong-Jo Kim
- Center for Genome Science, National Institute of Health, Osong Health Technology Administration Complex, Chungcheongbuk-Do 363-951, Korea
| | - Heungsun Hwang
- Department of Psychology, McGill University, Montreal, QC H3A 1B1, Canada
| | - Taesung Park
- Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul 151-747, Korea Department of Statistics, Seoul National University, Seoul 151-747, Korea
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Wingo T, Nesil T, Choi JS, Li MD. Novelty Seeking and Drug Addiction in Humans and Animals: From Behavior to Molecules. J Neuroimmune Pharmacol 2016; 11:456-70. [PMID: 26481371 PMCID: PMC4837094 DOI: 10.1007/s11481-015-9636-7] [Citation(s) in RCA: 94] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2015] [Accepted: 10/13/2015] [Indexed: 12/21/2022]
Abstract
Global treatment of drug addiction costs society billions of dollars annually, but current psychopharmacological therapies have not been successful at desired rates. The increasing number of individuals suffering from substance abuse has turned attention to what makes some people more vulnerable to drug addiction than others. One personality trait that stands out as a contributing factor is novelty seeking. Novelty seeking, affected by both genetic and environmental factors, is defined as the tendency to desire novel stimuli and environments. It can be measured in humans through questionnaires and in rodents using behavioral tasks. On the behavioral level, both human and rodent studies demonstrate that high novelty seeking can predict the initiation of drug use and a transition to compulsive drug use and create a propensity to relapse. These predictions are valid for several drugs of abuse, such as alcohol, nicotine, cocaine, amphetamine, and opiates. On the molecular level, both novelty seeking and addiction are modulated by the central reward system in the brain. Dopamine is the primary neurotransmitter involved in the overlapping neural substrates of both parameters. In sum, the novelty-seeking trait can be valuable for predicting individual vulnerability to drug addiction and for generating successful treatment for patients with substance abuse disorders.
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Affiliation(s)
- Taylor Wingo
- Department of Psychiatry and Neurobehavioral Sciences, University of Virginia, 450 Ray C Hunt Drive, Suite G-170, Charlottesville, VA, 22903, USA
| | - Tanseli Nesil
- Department of Psychiatry and Neurobehavioral Sciences, University of Virginia, 450 Ray C Hunt Drive, Suite G-170, Charlottesville, VA, 22903, USA
| | - Jung-Seok Choi
- Department of Psychiatry and Neurobehavioral Sciences, University of Virginia, 450 Ray C Hunt Drive, Suite G-170, Charlottesville, VA, 22903, USA
- Department of Psychiatry, SMG-SNU Boramae Medical Center and Department of Psychiatry and Behavioral Science, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Ming D Li
- Department of Psychiatry and Neurobehavioral Sciences, University of Virginia, 450 Ray C Hunt Drive, Suite G-170, Charlottesville, VA, 22903, USA.
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32
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Jeng XJ, Daye ZJ, Lu W, Tzeng JY. Rare Variants Association Analysis in Large-Scale Sequencing Studies at the Single Locus Level. PLoS Comput Biol 2016; 12:e1004993. [PMID: 27355347 PMCID: PMC4927097 DOI: 10.1371/journal.pcbi.1004993] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2015] [Accepted: 05/21/2016] [Indexed: 11/24/2022] Open
Abstract
Genetic association analyses of rare variants in next-generation sequencing (NGS) studies are fundamentally challenging due to the presence of a very large number of candidate variants at extremely low minor allele frequencies. Recent developments often focus on pooling multiple variants to provide association analysis at the gene instead of the locus level. Nonetheless, pinpointing individual variants is a critical goal for genomic researches as such information can facilitate the precise delineation of molecular mechanisms and functions of genetic factors on diseases. Due to the extreme rarity of mutations and high-dimensionality, significances of causal variants cannot easily stand out from those of noncausal ones. Consequently, standard false-positive control procedures, such as the Bonferroni and false discovery rate (FDR), are often impractical to apply, as a majority of the causal variants can only be identified along with a few but unknown number of noncausal variants. To provide informative analysis of individual variants in large-scale sequencing studies, we propose the Adaptive False-Negative Control (AFNC) procedure that can include a large proportion of causal variants with high confidence by introducing a novel statistical inquiry to determine those variants that can be confidently dispatched as noncausal. The AFNC provides a general framework that can accommodate for a variety of models and significance tests. The procedure is computationally efficient and can adapt to the underlying proportion of causal variants and quality of significance rankings. Extensive simulation studies across a plethora of scenarios demonstrate that the AFNC is advantageous for identifying individual rare variants, whereas the Bonferroni and FDR are exceedingly over-conservative for rare variants association studies. In the analyses of the CoLaus dataset, AFNC has identified individual variants most responsible for gene-level significances. Moreover, single-variant results using the AFNC have been successfully applied to infer related genes with annotation information. Next-generation sequencing technologies have allowed genetic association studies of complex traits at the single base-pair resolution, where most genetic variants have extremely low mutation frequencies. These rare variants have been the focus of modern statistical-computational genomics due to their potential to explain missing disease heritability. The identification of individual rare variants associated with diseases can provide new biological insights and enable the precise delineation of disease mechanisms. However, due to the extreme rarity of mutations and large numbers of variants, significances of causative variants tend to be mixed inseparably with a few noncausative ones, and standard multiple testing procedures controlling for false positives fail to provide a meaningful way to include a large proportion of the causative variants. To address the challenge of detecting weak biological signals, we propose a novel statistical procedure, based on false-negative control, to provide a practical approach for variant inclusion in large-scale sequencing studies. By determining those variants that can be confidently dispatched as noncausative, the proposed procedure offers an objective selection of a modest number of potentially causative variants at the single-locus level. Results can be further prioritized or used to infer disease-associated genes with annotation information.
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Affiliation(s)
- Xinge Jessie Jeng
- Department of Statistics, North Carolina State University, Raleigh, North Carolina, United States of America
| | - Zhongyin John Daye
- Epidemiology and Biostatistics, University of Arizona, Tucson, Arizona, United States of America
| | - Wenbin Lu
- Department of Statistics, North Carolina State University, Raleigh, North Carolina, United States of America
| | - Jung-Ying Tzeng
- Department of Statistics, North Carolina State University, Raleigh, North Carolina, United States of America
- Bioinformatics Research Center, North Carolina State University, Raleigh, North Carolina, United States of America
- Department of Statistics, National Cheng-Kung University, Tainan, Taiwan
- * E-mail:
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Fang H, Zhang H, Yang Y. Poisson Approximation-Based Score Test for Detecting Association of Rare Variants. Ann Hum Genet 2016; 80:221-34. [PMID: 27346734 DOI: 10.1111/ahg.12154] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2015] [Accepted: 02/26/2016] [Indexed: 11/30/2022]
Abstract
Genome-wide association study (GWAS) has achieved great success in identifying genetic variants, but the nature of GWAS has determined its inherent limitations. Under the common disease rare variants (CDRV) hypothesis, the traditional association analysis methods commonly used in GWAS for common variants do not have enough power for detecting rare variants with a limited sample size. As a solution to this problem, pooling rare variants by their functions provides an efficient way for identifying susceptible genes. Rare variant typically have low frequencies of minor alleles, and the distribution of the total number of minor alleles of the rare variants can be approximated by a Poisson distribution. Based on this fact, we propose a new test method, the Poisson Approximation-based Score Test (PAST), for association analysis of rare variants. Two testing methods, namely, ePAST and mPAST, are proposed based on different strategies of pooling rare variants. Simulation results and application to the CRESCENDO cohort data show that our methods are more powerful than the existing methods.
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Affiliation(s)
- Hongyan Fang
- Department of Statistics and Finance, University of Science and Technology of China, Hefei, Anhui, 230026, China
| | - Hong Zhang
- Institute of Biostatistics, Fudan School of Life Sciences, Fudan, Shanghai, 200433, China
| | - Yaning Yang
- Department of Statistics and Finance, University of Science and Technology of China, Hefei, Anhui, 230026, China
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34
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Choi S, Lee S, Qiao D, Hardin M, Cho MH, Silverman EK, Park T, Won S. FARVATX: Family-Based Rare Variant Association Test for X-Linked Genes. Genet Epidemiol 2016; 40:475-85. [PMID: 27325607 DOI: 10.1002/gepi.21979] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2015] [Revised: 03/05/2016] [Accepted: 04/04/2016] [Indexed: 11/06/2022]
Abstract
Although the X chromosome has many genes that are functionally related to human diseases, the complicated biological properties of the X chromosome have prevented efficient genetic association analyses, and only a few significantly associated X-linked variants have been reported for complex traits. For instance, dosage compensation of X-linked genes is often achieved via the inactivation of one allele in each X-linked variant in females; however, some X-linked variants can escape this X chromosome inactivation. Efficient genetic analyses cannot be conducted without prior knowledge about the gene expression process of X-linked variants, and misspecified information can lead to power loss. In this report, we propose new statistical methods for rare X-linked variant genetic association analysis of dichotomous phenotypes with family-based samples. The proposed methods are computationally efficient and can complete X-linked analyses within a few hours. Simulation studies demonstrate the statistical efficiency of the proposed methods, which were then applied to rare-variant association analysis of the X chromosome in chronic obstructive pulmonary disease. Some promising significant X-linked genes were identified, illustrating the practical importance of the proposed methods.
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Affiliation(s)
- Sungkyoung Choi
- Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, Korea
| | - Sungyoung Lee
- Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, Korea
| | - Dandi Qiao
- Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Megan Hardin
- Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, United States of America.,Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Boston, Massachusetts, United States of America
| | - Michael H Cho
- Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, United States of America.,Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Boston, Massachusetts, United States of America
| | - Edwin K Silverman
- Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, United States of America.,Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Boston, Massachusetts, United States of America
| | - Taesung Park
- Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, Korea.,Department of Statistics, Seoul National University, Seoul, Korea
| | - Sungho Won
- Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, Korea.,Department of Public Health Science, Seoul National University, Seoul, Korea.,Institute of Health and Environment, Seoul National University, Seoul, Korea
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35
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Kim S, Lee K, Sun H. Statistical selection strategy for risk and protective rare variants associated with complex traits. J Comput Biol 2015; 22:1034-43. [PMID: 26469994 DOI: 10.1089/cmb.2015.0091] [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] [Indexed: 01/05/2023] Open
Abstract
In genetic association studies with deep sequencing data, it is a challenging statistical problem to precisely locate rare variants associated with complex diseases or traits due to the limited number of observed genetic mutations. In particular, both risk and protective rare variants can be present in the same gene or genetic region. There currently exist very few statistical methods to separate casual rare variants from noncausal variants within a disease/trait-related gene or a genetic region, while there are relatively many statistical tests to detect a phenotypic association of a group of rare variants such as a gene or a genetic region. In this article, we propose a new statistical selection strategy that is able to locate causal rare variants within the disease/trait-related gene or a genetic region. The proposed procedure is to linearly combine potential risk and protective variants in order to find the optimal combination of rare variants that can have the strongest association signal. It is also computationally very efficient since the procedure is based on forward selection. In simulation studies we demonstrate that the selection performance of the proposed procedure is more powerful than other existing methods when both risk and protective variants are present. We also applied it to the real sequencing data on the ANGPTL gene family from the Dallas Heart Study.
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Affiliation(s)
- Sera Kim
- Department of Statistics, Pusan National University , Busan, Korea
| | - Kyeongjun Lee
- Department of Statistics, Pusan National University , Busan, Korea
| | - Hokeun Sun
- Department of Statistics, Pusan National University , Busan, Korea
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36
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37
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Individualized iterative phenotyping for genome-wide analysis of loss-of-function mutations. Am J Hum Genet 2015; 96:913-25. [PMID: 26046366 DOI: 10.1016/j.ajhg.2015.04.013] [Citation(s) in RCA: 57] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2014] [Accepted: 04/21/2015] [Indexed: 12/21/2022] Open
Abstract
Next-generation sequencing provides the opportunity to practice predictive medicine based on identified variants. Putative loss-of-function (pLOF) variants are common in genomes and understanding their contribution to disease is critical for predictive medicine. To this end, we characterized the consequences of pLOF variants in an exome cohort by iterative phenotyping. Exome data were generated on 951 participants from the ClinSeq cohort and filtered for pLOF variants in genes likely to cause a phenotype in heterozygotes. 103 of 951 exomes had such a pLOF variant and 79 participants were evaluated. Of those 79, 34 had findings or family histories that could be attributed to the variant (28 variants in 18 genes), 2 had indeterminate findings (2 variants in 2 genes), and 43 had no findings or a negative family history for the trait (34 variants in 28 genes). The presence of a phenotype was correlated with two mutation attributes: prior report of pathogenicity for the variant (p = 0.0001) and prior report of other mutations in the same exon (p = 0.0001). We conclude that 1/30 unselected individuals harbor a pLOF mutation associated with a phenotype either in themselves or their family. This is more common than has been assumed and has implications for the setting of prior probabilities of affection status for predictive medicine.
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38
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Kao CF, Liu JR, Hung H, Kuo PH. A robust GWSS method to simultaneously detect rare and common variants for complex disease. PLoS One 2015; 10:e0120873. [PMID: 25880329 PMCID: PMC4399906 DOI: 10.1371/journal.pone.0120873] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2014] [Accepted: 01/26/2015] [Indexed: 11/19/2022] Open
Abstract
The rapid advances in sequencing technologies and the resulting next-generation sequencing data provide the opportunity to detect disease-associated variants with a better solution, in particular for low-frequency variants. Although both common and rare variants might exert their independent effects on the risk for the trait of interest, previous methods to detect the association effects rarely consider them simultaneously. We proposed a class of test statistics, the generalized weighted-sum statistic (GWSS), to detect disease associations in the presence of common and rare variants with a case-control study design. Information of rare variants was aggregated using a weighted sum method, while signal directions and strength of the variants were considered at the same time. Permutations were performed to obtain the empirical p-values of the test statistics. Our simulation showed that, compared to the existing methods, the GWSS method had better performance in most of the scenarios. The GWSS (in particular VDWSS-t) method is particularly robust for opposite association directions, association strength, and varying distributions of minor-allele frequencies. It is therefore promising for detecting disease-associated loci. For empirical data application, we also applied our GWSS method to the Genetic Analysis Workshop 17 data, and the results were consistent with the simulation, suggesting good performance of our method. As re-sequencing studies become more popular to identify putative disease loci, we recommend the use of this newly developed GWSS to detect associations with both common and rare variants.
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Affiliation(s)
- Chung-Feng Kao
- Department of Public Health, Institute of Epidemiology and Preventive Medicine, National Taiwan University, Taipei, Taiwan
| | - Jia-Rou Liu
- Department of Public Health, Institute of Epidemiology and Preventive Medicine, National Taiwan University, Taipei, Taiwan
- Department of Public Health, Chang Gung University, Taoyuan,Taiwan
| | - Hung Hung
- Department of Public Health, Institute of Epidemiology and Preventive Medicine, National Taiwan University, Taipei, Taiwan
- Research Center for Genes, Environment and Human Health, National Taiwan University, Taipei, Taiwan
- * E-mail: (PHK); (HH)
| | - Po-Hsiu Kuo
- Department of Public Health, Institute of Epidemiology and Preventive Medicine, National Taiwan University, Taipei, Taiwan
- Research Center for Genes, Environment and Human Health, National Taiwan University, Taipei, Taiwan
- * E-mail: (PHK); (HH)
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39
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Zuo X, Sun L, Yin X, Gao J, Sheng Y, Xu J, Zhang J, He C, Qiu Y, Wen G, Tian H, Zheng X, Liu S, Wang W, Li W, Cheng Y, Liu L, Chang Y, Wang Z, Li Z, Li L, Wu J, Fang L, Shen C, Zhou F, Liang B, Chen G, Li H, Cui Y, Xu A, Yang X, Hao F, Xu L, Fan X, Li Y, Wu R, Wang X, Liu X, Zheng M, Song S, Ji B, Fang H, Yu J, Sun Y, Hui Y, Zhang F, Yang R, Yang S, Zhang X. Whole-exome SNP array identifies 15 new susceptibility loci for psoriasis. Nat Commun 2015; 6:6793. [PMID: 25854761 PMCID: PMC4403312 DOI: 10.1038/ncomms7793] [Citation(s) in RCA: 103] [Impact Index Per Article: 11.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2014] [Accepted: 02/28/2015] [Indexed: 12/30/2022] Open
Abstract
Genome-wide association studies (GWASs) have reproducibly associated ∼40 susceptibility loci with psoriasis. However, the missing heritability is evident and the contributions of coding variants have not yet been systematically evaluated. Here, we present a large-scale whole-exome array analysis for psoriasis consisting of 42,760 individuals. We discover 16 SNPs within 15 new genes/loci associated with psoriasis, including C1orf141, ZNF683, TMC6, AIM2, IL1RL1, CASR, SON, ZFYVE16, MTHFR, CCDC129, ZNF143, AP5B1, SYNE2, IFNGR2 and 3q26.2-q27 (P<5.00 × 10(-08)). In addition, we also replicate four known susceptibility loci TNIP1, NFKBIA, IL12B and LCE3D-LCE3E. These susceptibility variants identified in the current study collectively account for 1.9% of the psoriasis heritability. The variant within AIM2 is predicted to impact protein structure. Our findings increase the number of genetic risk factors for psoriasis and highlight new and plausible biological pathways in psoriasis.
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Affiliation(s)
- Xianbo Zuo
- Institute of Dermatology and Department of Dermatology, No. 1 Hospital, Anhui Medical University, Hefei, Anhui 230022, China
- Department of Dermatology, Huashan Hospital, Fudan University, Shanghai 200040, China
- Department of Dermatology, No.2 Hospital, Anhui Medical University, Hefei, Anhui 230022, China
- Collaborative Innovation Center of Complex and Severe Skin Disease, Anhui Medical University, Hefei, Anhui 230032, China
- State Key Lab Incubation of Dermatology, Ministry of Science and Technology, Hefei, Anhui 230032, China
- Key Lab of Dermatology, Ministry of Education, Hefei, Anhui 230032, China
- Key Lab of Gene Resources Utilization for Severe Inherited Disorders, Anhui 230032, China
| | - Liangdan Sun
- Institute of Dermatology and Department of Dermatology, No. 1 Hospital, Anhui Medical University, Hefei, Anhui 230022, China
- Department of Dermatology, Huashan Hospital, Fudan University, Shanghai 200040, China
- Department of Dermatology, No.2 Hospital, Anhui Medical University, Hefei, Anhui 230022, China
- Collaborative Innovation Center of Complex and Severe Skin Disease, Anhui Medical University, Hefei, Anhui 230032, China
- State Key Lab Incubation of Dermatology, Ministry of Science and Technology, Hefei, Anhui 230032, China
- Key Lab of Dermatology, Ministry of Education, Hefei, Anhui 230032, China
- Key Lab of Gene Resources Utilization for Severe Inherited Disorders, Anhui 230032, China
| | - Xianyong Yin
- Institute of Dermatology and Department of Dermatology, No. 1 Hospital, Anhui Medical University, Hefei, Anhui 230022, China
- Department of Dermatology, Huashan Hospital, Fudan University, Shanghai 200040, China
- Department of Dermatology, No.2 Hospital, Anhui Medical University, Hefei, Anhui 230022, China
- Collaborative Innovation Center of Complex and Severe Skin Disease, Anhui Medical University, Hefei, Anhui 230032, China
- State Key Lab Incubation of Dermatology, Ministry of Science and Technology, Hefei, Anhui 230032, China
- Key Lab of Dermatology, Ministry of Education, Hefei, Anhui 230032, China
- Key Lab of Gene Resources Utilization for Severe Inherited Disorders, Anhui 230032, China
| | - Jinping Gao
- Institute of Dermatology and Department of Dermatology, No. 1 Hospital, Anhui Medical University, Hefei, Anhui 230022, China
- Collaborative Innovation Center of Complex and Severe Skin Disease, Anhui Medical University, Hefei, Anhui 230032, China
- State Key Lab Incubation of Dermatology, Ministry of Science and Technology, Hefei, Anhui 230032, China
- Key Lab of Dermatology, Ministry of Education, Hefei, Anhui 230032, China
- Key Lab of Gene Resources Utilization for Severe Inherited Disorders, Anhui 230032, China
| | - Yujun Sheng
- Institute of Dermatology and Department of Dermatology, No. 1 Hospital, Anhui Medical University, Hefei, Anhui 230022, China
- Collaborative Innovation Center of Complex and Severe Skin Disease, Anhui Medical University, Hefei, Anhui 230032, China
- State Key Lab Incubation of Dermatology, Ministry of Science and Technology, Hefei, Anhui 230032, China
- Key Lab of Dermatology, Ministry of Education, Hefei, Anhui 230032, China
- Key Lab of Gene Resources Utilization for Severe Inherited Disorders, Anhui 230032, China
| | - Jinhua Xu
- Department of Dermatology, Huashan Hospital, Fudan University, Shanghai 200040, China
- Collaborative Innovation Center of Complex and Severe Skin Disease, Anhui Medical University, Hefei, Anhui 230032, China
| | - Jianzhong Zhang
- Department of Dermatology, Peking University People’s Hospital, Beijing 100044, China
| | - Chundi He
- Department of Dermatology, No.1 Hospital of China Medical University, Shenyang, Liaoning 110001, China
| | - Ying Qiu
- Department of Dermatology, Jining No. 1 People’s Hospital, Jining, Shandong 272011, China
| | - Guangdong Wen
- Department of Dermatology, Peking University People’s Hospital, Beijing 100044, China
| | - Hongqing Tian
- Shandong Provincial Institute of Dermatology and Venereology, Jinan, Shandong 250022, China
| | - Xiaodong Zheng
- Institute of Dermatology and Department of Dermatology, No. 1 Hospital, Anhui Medical University, Hefei, Anhui 230022, China
- Collaborative Innovation Center of Complex and Severe Skin Disease, Anhui Medical University, Hefei, Anhui 230032, China
- State Key Lab Incubation of Dermatology, Ministry of Science and Technology, Hefei, Anhui 230032, China
- Key Lab of Dermatology, Ministry of Education, Hefei, Anhui 230032, China
- Key Lab of Gene Resources Utilization for Severe Inherited Disorders, Anhui 230032, China
| | - Shengxiu Liu
- Institute of Dermatology and Department of Dermatology, No. 1 Hospital, Anhui Medical University, Hefei, Anhui 230022, China
- Collaborative Innovation Center of Complex and Severe Skin Disease, Anhui Medical University, Hefei, Anhui 230032, China
- State Key Lab Incubation of Dermatology, Ministry of Science and Technology, Hefei, Anhui 230032, China
- Key Lab of Dermatology, Ministry of Education, Hefei, Anhui 230032, China
- Key Lab of Gene Resources Utilization for Severe Inherited Disorders, Anhui 230032, China
| | - Wenjun Wang
- Institute of Dermatology and Department of Dermatology, No. 1 Hospital, Anhui Medical University, Hefei, Anhui 230022, China
- Collaborative Innovation Center of Complex and Severe Skin Disease, Anhui Medical University, Hefei, Anhui 230032, China
- State Key Lab Incubation of Dermatology, Ministry of Science and Technology, Hefei, Anhui 230032, China
- Key Lab of Dermatology, Ministry of Education, Hefei, Anhui 230032, China
- Key Lab of Gene Resources Utilization for Severe Inherited Disorders, Anhui 230032, China
| | - Weiran Li
- Institute of Dermatology and Department of Dermatology, No. 1 Hospital, Anhui Medical University, Hefei, Anhui 230022, China
- Collaborative Innovation Center of Complex and Severe Skin Disease, Anhui Medical University, Hefei, Anhui 230032, China
- State Key Lab Incubation of Dermatology, Ministry of Science and Technology, Hefei, Anhui 230032, China
- Key Lab of Dermatology, Ministry of Education, Hefei, Anhui 230032, China
- Key Lab of Gene Resources Utilization for Severe Inherited Disorders, Anhui 230032, China
| | - Yuyan Cheng
- Institute of Dermatology and Department of Dermatology, No. 1 Hospital, Anhui Medical University, Hefei, Anhui 230022, China
- Collaborative Innovation Center of Complex and Severe Skin Disease, Anhui Medical University, Hefei, Anhui 230032, China
- State Key Lab Incubation of Dermatology, Ministry of Science and Technology, Hefei, Anhui 230032, China
- Key Lab of Dermatology, Ministry of Education, Hefei, Anhui 230032, China
- Key Lab of Gene Resources Utilization for Severe Inherited Disorders, Anhui 230032, China
| | - Longdan Liu
- Institute of Dermatology and Department of Dermatology, No. 1 Hospital, Anhui Medical University, Hefei, Anhui 230022, China
- Collaborative Innovation Center of Complex and Severe Skin Disease, Anhui Medical University, Hefei, Anhui 230032, China
- State Key Lab Incubation of Dermatology, Ministry of Science and Technology, Hefei, Anhui 230032, China
- Key Lab of Dermatology, Ministry of Education, Hefei, Anhui 230032, China
- Key Lab of Gene Resources Utilization for Severe Inherited Disorders, Anhui 230032, China
| | - Yan Chang
- Institute of Dermatology and Department of Dermatology, No. 1 Hospital, Anhui Medical University, Hefei, Anhui 230022, China
- Collaborative Innovation Center of Complex and Severe Skin Disease, Anhui Medical University, Hefei, Anhui 230032, China
- State Key Lab Incubation of Dermatology, Ministry of Science and Technology, Hefei, Anhui 230032, China
- Key Lab of Dermatology, Ministry of Education, Hefei, Anhui 230032, China
- Key Lab of Gene Resources Utilization for Severe Inherited Disorders, Anhui 230032, China
| | - Zaixing Wang
- Institute of Dermatology and Department of Dermatology, No. 1 Hospital, Anhui Medical University, Hefei, Anhui 230022, China
- Collaborative Innovation Center of Complex and Severe Skin Disease, Anhui Medical University, Hefei, Anhui 230032, China
- State Key Lab Incubation of Dermatology, Ministry of Science and Technology, Hefei, Anhui 230032, China
- Key Lab of Dermatology, Ministry of Education, Hefei, Anhui 230032, China
- Key Lab of Gene Resources Utilization for Severe Inherited Disorders, Anhui 230032, China
| | - Zenggang Li
- Institute of Dermatology and Department of Dermatology, No. 1 Hospital, Anhui Medical University, Hefei, Anhui 230022, China
- Collaborative Innovation Center of Complex and Severe Skin Disease, Anhui Medical University, Hefei, Anhui 230032, China
- State Key Lab Incubation of Dermatology, Ministry of Science and Technology, Hefei, Anhui 230032, China
- Key Lab of Dermatology, Ministry of Education, Hefei, Anhui 230032, China
- Key Lab of Gene Resources Utilization for Severe Inherited Disorders, Anhui 230032, China
| | - Longnian Li
- Institute of Dermatology and Department of Dermatology, No. 1 Hospital, Anhui Medical University, Hefei, Anhui 230022, China
- Collaborative Innovation Center of Complex and Severe Skin Disease, Anhui Medical University, Hefei, Anhui 230032, China
- State Key Lab Incubation of Dermatology, Ministry of Science and Technology, Hefei, Anhui 230032, China
- Key Lab of Dermatology, Ministry of Education, Hefei, Anhui 230032, China
- Key Lab of Gene Resources Utilization for Severe Inherited Disorders, Anhui 230032, China
| | - Jianping Wu
- Institute of Dermatology and Department of Dermatology, No. 1 Hospital, Anhui Medical University, Hefei, Anhui 230022, China
- Collaborative Innovation Center of Complex and Severe Skin Disease, Anhui Medical University, Hefei, Anhui 230032, China
- State Key Lab Incubation of Dermatology, Ministry of Science and Technology, Hefei, Anhui 230032, China
- Key Lab of Dermatology, Ministry of Education, Hefei, Anhui 230032, China
- Key Lab of Gene Resources Utilization for Severe Inherited Disorders, Anhui 230032, China
| | - Ling Fang
- Institute of Dermatology and Department of Dermatology, No. 1 Hospital, Anhui Medical University, Hefei, Anhui 230022, China
- Collaborative Innovation Center of Complex and Severe Skin Disease, Anhui Medical University, Hefei, Anhui 230032, China
- State Key Lab Incubation of Dermatology, Ministry of Science and Technology, Hefei, Anhui 230032, China
- Key Lab of Dermatology, Ministry of Education, Hefei, Anhui 230032, China
- Key Lab of Gene Resources Utilization for Severe Inherited Disorders, Anhui 230032, China
| | - Changbing Shen
- Institute of Dermatology and Department of Dermatology, No. 1 Hospital, Anhui Medical University, Hefei, Anhui 230022, China
- Collaborative Innovation Center of Complex and Severe Skin Disease, Anhui Medical University, Hefei, Anhui 230032, China
- State Key Lab Incubation of Dermatology, Ministry of Science and Technology, Hefei, Anhui 230032, China
- Key Lab of Dermatology, Ministry of Education, Hefei, Anhui 230032, China
- Key Lab of Gene Resources Utilization for Severe Inherited Disorders, Anhui 230032, China
| | - Fusheng Zhou
- Institute of Dermatology and Department of Dermatology, No. 1 Hospital, Anhui Medical University, Hefei, Anhui 230022, China
- Collaborative Innovation Center of Complex and Severe Skin Disease, Anhui Medical University, Hefei, Anhui 230032, China
- State Key Lab Incubation of Dermatology, Ministry of Science and Technology, Hefei, Anhui 230032, China
- Key Lab of Dermatology, Ministry of Education, Hefei, Anhui 230032, China
- Key Lab of Gene Resources Utilization for Severe Inherited Disorders, Anhui 230032, China
| | - Bo Liang
- Institute of Dermatology and Department of Dermatology, No. 1 Hospital, Anhui Medical University, Hefei, Anhui 230022, China
- Collaborative Innovation Center of Complex and Severe Skin Disease, Anhui Medical University, Hefei, Anhui 230032, China
- State Key Lab Incubation of Dermatology, Ministry of Science and Technology, Hefei, Anhui 230032, China
- Key Lab of Dermatology, Ministry of Education, Hefei, Anhui 230032, China
- Key Lab of Gene Resources Utilization for Severe Inherited Disorders, Anhui 230032, China
| | - Gang Chen
- Institute of Dermatology and Department of Dermatology, No. 1 Hospital, Anhui Medical University, Hefei, Anhui 230022, China
- Collaborative Innovation Center of Complex and Severe Skin Disease, Anhui Medical University, Hefei, Anhui 230032, China
- State Key Lab Incubation of Dermatology, Ministry of Science and Technology, Hefei, Anhui 230032, China
- Key Lab of Dermatology, Ministry of Education, Hefei, Anhui 230032, China
- Key Lab of Gene Resources Utilization for Severe Inherited Disorders, Anhui 230032, China
| | - Hui Li
- Institute of Dermatology and Department of Dermatology, No. 1 Hospital, Anhui Medical University, Hefei, Anhui 230022, China
- Collaborative Innovation Center of Complex and Severe Skin Disease, Anhui Medical University, Hefei, Anhui 230032, China
- State Key Lab Incubation of Dermatology, Ministry of Science and Technology, Hefei, Anhui 230032, China
- Key Lab of Dermatology, Ministry of Education, Hefei, Anhui 230032, China
- Key Lab of Gene Resources Utilization for Severe Inherited Disorders, Anhui 230032, China
| | - Yong Cui
- Collaborative Innovation Center of Complex and Severe Skin Disease, Anhui Medical University, Hefei, Anhui 230032, China
- State Key Lab Incubation of Dermatology, Ministry of Science and Technology, Hefei, Anhui 230032, China
- Key Lab of Dermatology, Ministry of Education, Hefei, Anhui 230032, China
- Key Lab of Gene Resources Utilization for Severe Inherited Disorders, Anhui 230032, China
| | - Aie Xu
- The Third People's Hospital of Hangzhou, Hangzhou, Zhejiang 310009, China
| | - Xueqin Yang
- Department of Dermatology, General Hospital of PLA Air Force, Beijing 100036, China
| | - Fei Hao
- Department of Dermatology, Southwest Hospital, Third Military Medical University, Chongqing 400038, China
| | - Limin Xu
- Department of Dermatology, Tianjin Changzheng Hospital, Tianjin 300106, China
| | - Xing Fan
- Institute of Dermatology and Department of Dermatology, No. 1 Hospital, Anhui Medical University, Hefei, Anhui 230022, China
- Collaborative Innovation Center of Complex and Severe Skin Disease, Anhui Medical University, Hefei, Anhui 230032, China
- State Key Lab Incubation of Dermatology, Ministry of Science and Technology, Hefei, Anhui 230032, China
- Key Lab of Dermatology, Ministry of Education, Hefei, Anhui 230032, China
- Key Lab of Gene Resources Utilization for Severe Inherited Disorders, Anhui 230032, China
| | - Yuzhen Li
- Department of Dermatology, Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang 150000, China
| | - Rina Wu
- Department of Dermatology, The Affiliated Hospital of Inner Mongolia Medical College, Huhehot, Inner Mongolia 010050, China
| | - Xiuli Wang
- Shanghai Skin Diseases and STD Hospital, Shanghai 200050, China
| | - Xiaoming Liu
- Department of Dermatology, The First Affiliated Hospital of Dalian Medical University, Dalian, Liaoning 116011, China
| | - Min Zheng
- Department of Dermatology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhenjiang 310009, China
| | - Shunpeng Song
- Department of Dermatology, Dalian Dermatosis Hosptial, Liaoning 116011, China
| | - Bihua Ji
- Department of Dermatology, Yijishan Hospital of Wannan Medical College, Wuhu, Anhui 241000, China
| | - Hong Fang
- Department of Dermatology, The First Affiliated Hospital of Zhejiang University School of Medicine, Zhenjiang 310006, China
| | - Jianbin Yu
- Department of Dermatology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan 450052, China
| | - Yongxin Sun
- Department of Dermatology, Anshan Tanggangzi hosptial, Liaoning 210300, China
| | - Yan Hui
- Department of Dermatology, First Affiliated Hospital of Xinjiang Medical University, Xinjiang 830054, China
| | - Furen Zhang
- Shandong Provincial Institute of Dermatology and Venereology, Jinan, Shandong 250022, China
| | - Rongya Yang
- Department of Dermatology, General Hospital of Beijing Military Command, Beijing 100010, China
| | - Sen Yang
- Institute of Dermatology and Department of Dermatology, No. 1 Hospital, Anhui Medical University, Hefei, Anhui 230022, China
- Collaborative Innovation Center of Complex and Severe Skin Disease, Anhui Medical University, Hefei, Anhui 230032, China
- State Key Lab Incubation of Dermatology, Ministry of Science and Technology, Hefei, Anhui 230032, China
- Key Lab of Dermatology, Ministry of Education, Hefei, Anhui 230032, China
- Key Lab of Gene Resources Utilization for Severe Inherited Disorders, Anhui 230032, China
| | - Xuejun Zhang
- Institute of Dermatology and Department of Dermatology, No. 1 Hospital, Anhui Medical University, Hefei, Anhui 230022, China
- Department of Dermatology, Huashan Hospital, Fudan University, Shanghai 200040, China
- Department of Dermatology, No.2 Hospital, Anhui Medical University, Hefei, Anhui 230022, China
- Collaborative Innovation Center of Complex and Severe Skin Disease, Anhui Medical University, Hefei, Anhui 230032, China
- State Key Lab Incubation of Dermatology, Ministry of Science and Technology, Hefei, Anhui 230032, China
- Key Lab of Dermatology, Ministry of Education, Hefei, Anhui 230032, China
- Key Lab of Gene Resources Utilization for Severe Inherited Disorders, Anhui 230032, China
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Apalasamy YD, Mohamed Z. Obesity and genomics: role of technology in unraveling the complex genetic architecture of obesity. Hum Genet 2015; 134:361-74. [PMID: 25687726 DOI: 10.1007/s00439-015-1533-x] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2014] [Accepted: 02/02/2015] [Indexed: 01/15/2023]
Abstract
Obesity is a complex and multifactorial disease that occurs as a result of the interaction between "obesogenic" environmental factors and genetic components. Although the genetic component of obesity is clear from the heritability studies, the genetic basis remains largely elusive. Successes have been achieved in identifying the causal genes for monogenic obesity using animal models and linkage studies, but these approaches are not fruitful for polygenic obesity. The developments of genome-wide association approach have brought breakthrough discovery of genetic variants for polygenic obesity where tens of new susceptibility loci were identified. However, the common SNPs only accounted for a proportion of heritability. The arrival of NGS technologies and completion of 1000 Genomes Project have brought other new methods to dissect the genetic architecture of obesity, for example, the use of exome genotyping arrays and deep sequencing of candidate loci identified from GWAS to study rare variants. In this review, we summarize and discuss the developments of these genetic approaches in human obesity.
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Affiliation(s)
- Yamunah Devi Apalasamy
- Department of Pharmacology, Pharmacogenomics Laboratory, Faculty of Medicine, University of Malaya, 50603, Kuala Lumpur, Malaysia,
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Guey LT, Kravic J, Melander O, Burtt NP, Laramie JM, Lyssenko V, Jonsson A, Lindholm E, Tuomi T, Isomaa B, Nilsson P, Almgren P, Kathiresan S, Groop L, Seymour AB, Altshuler D, Voight BF. Power in the phenotypic extremes: a simulation study of power in discovery and replication of rare variants. Genet Epidemiol 2015; 35:236-46. [PMID: 21308769 DOI: 10.1002/gepi.20572] [Citation(s) in RCA: 67] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2010] [Revised: 11/17/2010] [Accepted: 01/10/2011] [Indexed: 12/19/2022]
Abstract
Next-generation sequencing technologies are making it possible to study the role of rare variants in human disease. Many studies balance statistical power with cost-effectiveness by (a) sampling from phenotypic extremes and (b) utilizing a two-stage design. Two-stage designs include a broad-based discovery phase and selection of a subset of potential causal genes/variants to be further examined in independent samples. We evaluate three parameters: first, the gain in statistical power due to extreme sampling to discover causal variants; second, the informativeness of initial (Phase I) association statistics to select genes/variants for follow-up; third, the impact of extreme and random sampling in (Phase 2) replication. We present a quantitative method to select individuals from the phenotypic extremes of a binary trait, and simulate disease association studies under a variety of sample sizes and sampling schemes. First, we find that while studies sampling from extremes have excellent power to discover rare variants, they have limited power to associate them to phenotype—suggesting high false-negative rates for upcoming studies. Second, consistent with previous studies, we find that the effect sizes estimated in these studies are expected to be systematically larger compared with the overall population effect size; in a well-cited lipids study, we estimate the reported effect to be twofold larger. Third, replication studies require large samples from the general population to have sufficient power; extreme sampling could reduce the required sample size as much as fourfold. Our observations offer practical guidance for the design and interpretation of studies that utilize extreme sampling.
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Affiliation(s)
- Lin T Guey
- Applied Quantitative Genotherapeutics, Pfizer Biotherapeutics, Cambridge, MA 02144, USA
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Bacanu SA, Nelson MR, Whittaker JC. Comparison of methods and sampling designs to test for association between rare variants and quantitative traits. Genet Epidemiol 2015; 35:226-35. [PMID: 21370253 DOI: 10.1002/gepi.20570] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2010] [Revised: 12/14/2010] [Accepted: 01/10/2011] [Indexed: 11/07/2022]
Abstract
Genome-wide association studies succeeded in finding genetic variants associated with various phenotypes, but a large portion of the predicted genetic contribution to many traits remains unknown. One plausible explanation is that some missing variation is due to rare variants. Latest sequencing technology facilitates the investigation of such rare variants, but their statistical analysis remains challenging. For quantitative traits, a commonly used approach is to contrast the frequency of putatively functional rare variants between subjects in the two tails of the trait distribution. The contrast is usually performed by Fisher's exact or similar test. These tests are conservative as they discard trait rank information and are most useful under the unrealistic homogeneity assumption (i.e., variants have similar effects). We propose, and investigate via simulations, various designs for resequencing studies and statistical methods that incorporate information about rank, predicted function and allow for heterogeneity of effects. We propose designs which accommodate heterogeneity by sequencing both tails and the middle of the trait and novel statistical tests for trend, for heterogeneity and for a combination of the two. The conclusions of the simulations are four fold: (1) sequencing both tails and the middle of the trait distributions is desirable when heterogeneity is suspected, (2) trend and heterogeneity statistics should be used alongside other methods, (3) using rank information improves power over Fisher's exact test when the number of rare variants is not very large and (4) due to high misclassification rates, incorporating current predictions of a variant's function does not improve power.
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Wei C, Li M, He Z, Vsevolozhskaya O, Schaid DJ, Lu Q. A weighted U-statistic for genetic association analyses of sequencing data. Genet Epidemiol 2014; 38:699-708. [PMID: 25331574 DOI: 10.1002/gepi.21864] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2014] [Revised: 08/15/2014] [Accepted: 09/05/2014] [Indexed: 12/13/2022]
Abstract
With advancements in next-generation sequencing technology, a massive amount of sequencing data is generated, which offers a great opportunity to comprehensively investigate the role of rare variants in the genetic etiology of complex diseases. Nevertheless, the high-dimensional sequencing data poses a great challenge for statistical analysis. The association analyses based on traditional statistical methods suffer substantial power loss because of the low frequency of genetic variants and the extremely high dimensionality of the data. We developed a Weighted U Sequencing test, referred to as WU-SEQ, for the high-dimensional association analysis of sequencing data. Based on a nonparametric U-statistic, WU-SEQ makes no assumption of the underlying disease model and phenotype distribution, and can be applied to a variety of phenotypes. Through simulation studies and an empirical study, we showed that WU-SEQ outperformed a commonly used sequence kernel association test (SKAT) method when the underlying assumptions were violated (e.g., the phenotype followed a heavy-tailed distribution). Even when the assumptions were satisfied, WU-SEQ still attained comparable performance to SKAT. Finally, we applied WU-SEQ to sequencing data from the Dallas Heart Study (DHS), and detected an association between ANGPTL 4 and very low density lipoprotein cholesterol.
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Affiliation(s)
- Changshuai Wei
- Department of Epidemiology and Biostatistics, Michigan State University, East Lansing, Michigan, United States of America; Department of Biostatistics and Epidemiology, University of North Texas Health Science Center, Fort Worth, Texas, United States of America
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44
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Xing C, Dupuis J, Cupples LA. Performance of statistical methods on CHARGE targeted sequencing data. BMC Genet 2014; 15:104. [PMID: 25277365 PMCID: PMC4197341 DOI: 10.1186/s12863-014-0104-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2014] [Accepted: 09/22/2014] [Indexed: 11/10/2022] Open
Abstract
Background The CHARGE (Cohorts for Heart and Aging Research in Genomic Epidemiology) Sequencing Project is a national, collaborative effort from 3 studies: Framingham Heart Study (FHS), Cardiovascular Health Study (CHS), and Atherosclerosis Risk in Communities (ARIC). It uses a case-cohort design, whereby a random sample of study participants is enriched with participants in extremes of traits. Although statistical methods are available to investigate the role of rare variants, few have evaluated their performance in a case-cohort design. Results We evaluate several methods, including the sequence kernel association test (SKAT), Score-Seq, and weighted (Madsen and Browning) and unweighted burden tests. Using genotypes from the CHARGE targeted-sequencing project for FHS (n = 1096), we simulate phenotypes in a large population for 11 correlated traits and then sample individuals to mimic the CHARGE Sequencing study design. We evaluate type I error and power for 77 targeted regions. Conclusions We provide some guidelines on the performance of these aggregate-based tests to detect associations with rare variants when applied to case-cohort study designs, using CHARGE targeted sequencing data. Type I error is conservative when we consider variants with minor allele frequency (MAF) < 1%. Power is generally low, although it is relatively larger for Score-Seq. Greater numbers of causal variants and a greater proportion of variance improve the power, but it tends to be lower in the presence of bi-directionality of effects of causal genotypes, especially for Score-Seq. Electronic supplementary material The online version of this article (doi:10.1186/s12863-014-0104-9) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Chuanhua Xing
- Department of Biostatistics, Boston University, Boston, MA, USA.
| | - Josée Dupuis
- Department of Biostatistics, Boston University, Boston, MA, USA. .,Framingham Heart Study, Framingham, MA, USA.
| | - L Adrienne Cupples
- Department of Biostatistics, Boston University, Boston, MA, USA. .,Framingham Heart Study, Framingham, MA, USA.
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Abstract
A major impetus to initiating the Human Genome Project was the belief that information encoded in the human genome would "accelerate progress in understanding disease pathogenesis and in developing new approaches to diagnosis, treatment, and prevention in many areas of medicine". Alopecia areata (AA) is a notable example of how understanding the genetic basis of a disease can have an impact on the care of patients in a relatively short time. Our first genome-wide association study in AA identified an initial set of common variants that increase risk of AA, some of which are shared with other autoimmune diseases. Thus, there has already been rapid progress in the translation of this information into new therapeutic strategies for patients, as drugs are already on the market for some of these disorders that can now be tested in AA. Informed by the progress achieved with genetic studies for mechanistically aligned autoimmune diseases, we are poised to carry this work forward and interrogate the underlying disease mechanisms in AA. Importantly, future genetic studies aimed at identifying additional susceptibility genes will further establish the foundation for the application of precision medicine in the care of AA patients.
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Cole CB, Nikpay M, Lau P, Stewart AFR, Davies RW, Wells GA, Dent R, McPherson R. Adiposity significantly modifies genetic risk for dyslipidemia. J Lipid Res 2014; 55:2416-22. [PMID: 25225679 PMCID: PMC4617143 DOI: 10.1194/jlr.p052522] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/05/2022] Open
Abstract
Recent genome-wide association studies have identified multiple loci robustly associated with plasma lipids, which also contribute to extreme lipid phenotypes. However, these common genetic variants explain <12% of variation in lipid traits. Adiposity is also an important determinant of plasma lipoproteins, particularly plasma TGs and HDL cholesterol (HDLc) concentrations. Thus, interactions between genes and clinical phenotypes may contribute to this unexplained heritability. We have applied a weighted genetic risk score (GRS) for both plasma TGs and HDLc in two large cohorts at the extremes of BMI. Both BMI and GRS were strongly associated with these lipid traits. A significant interaction between obese/lean status and GRS was noted for each of TG (PInteraction = 2.87 × 10−4) and HDLc (PInteraction = 1.05 × 10−3). These interactions were largely driven by SNPs tagging APOA5, glucokinase receptor (GCKR), and LPL for TG, and cholesteryl ester transfer protein (CETP), GalNAc-transferase (GALNT2), endothelial lipase (LIPG), and phospholipid transfer protein (PLTP) for HDLc. In contrast, the GRSLDL cholesterol × adiposity interaction was not significant. Sexual dimorphism was evident for the GRSHDL on HDLc in obese (PInteraction = 0.016) but not lean subjects. SNP by BMI interactions may provide biological insight into specific genetic associations and missing heritability.
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Affiliation(s)
- Christopher B Cole
- Atherogenomics Laboratory, University of Ottawa Heart Institute, Ottawa, Canada Ruddy Cardiovascular Genetics Centre, University of Ottawa Heart Institute, Ottawa, Canada
| | - Majid Nikpay
- Atherogenomics Laboratory, University of Ottawa Heart Institute, Ottawa, Canada Ruddy Cardiovascular Genetics Centre, University of Ottawa Heart Institute, Ottawa, Canada
| | - Paulina Lau
- Atherogenomics Laboratory, University of Ottawa Heart Institute, Ottawa, Canada Ruddy Cardiovascular Genetics Centre, University of Ottawa Heart Institute, Ottawa, Canada
| | - Alexandre F R Stewart
- Ruddy Cardiovascular Genetics Centre, University of Ottawa Heart Institute, Ottawa, Canada
| | - Robert W Davies
- Cardiovascular Research Methods Centre, University of Ottawa Heart Institute, Ottawa, Canada
| | - George A Wells
- Cardiovascular Research Methods Centre, University of Ottawa Heart Institute, Ottawa, Canada
| | - Robert Dent
- Bariatric Centre of Excellence, Ottawa Hospital, Ottawa, Canada
| | - Ruth McPherson
- Atherogenomics Laboratory, University of Ottawa Heart Institute, Ottawa, Canada Ruddy Cardiovascular Genetics Centre, University of Ottawa Heart Institute, Ottawa, Canada
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Zhang Q, Wang L, Koboldt D, Boreki IB, Province MA. Adjusting family relatedness in data-driven burden test of rare variants. Genet Epidemiol 2014; 38:722-7. [PMID: 25169066 DOI: 10.1002/gepi.21848] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2014] [Revised: 07/01/2014] [Accepted: 07/16/2014] [Indexed: 11/08/2022]
Abstract
Family data represent a rich resource for detecting association between rare variants (RVs) and human traits. However, most RV association analysis methods developed in recent years are data-driven burden tests which can adaptively learn weights from data but require permutation to evaluate significance, thus are not readily applicable to family data, because random permutation will destroy family structure. Direct application of these methods to family data may result in a significant inflation of false positives. To overcome this issue, we have developed a generalized, weighted sum mixed model (WSMM), and corresponding computational techniques that can incorporate family information into data-driven burden tests, and allow adaptive and efficient permutation test in family data. Using simulated and real datasets, we demonstrate that the WSMM method can be used to appropriately adjust for genetic relatedness among family members and has a good control for the inflation of false positives. We compare WSMM with a nondata-driven, family-based Sequence Kernel Association Test (famSKAT), showing that WSMM has significantly higher power in some cases. WSMM provides a generalized, flexible framework for adapting different data-driven burden tests to analyze data with any family structures, and it can be extended to binary and time-to-onset traits, with or without covariates.
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Affiliation(s)
- Qunyuan Zhang
- Division of Statistical Genomics, Washington University School of Medicine, St. Louis, Missouri, United States of America
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48
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Moutsianas L, Morris AP. Methodology for the analysis of rare genetic variation in genome-wide association and re-sequencing studies of complex human traits. Brief Funct Genomics 2014; 13:362-70. [PMID: 24916163 PMCID: PMC4168660 DOI: 10.1093/bfgp/elu012] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
Genome-wide association studies have been successful in identifying common variants that impact complex human traits and diseases. However, despite this success, the joint effects of these variants explain only a small proportion of the genetic variance in these phenotypes, leading to speculation that rare genetic variation might account for much of the ‘missing heritability’. Consequently, there has been an exciting period of research and development into the methodology for the analysis of rare genetic variants, typically by considering their joint effects on complex traits within the same functional unit or genomic region. In this review, we describe a general framework for modelling the joint effects of rare genetic variants on complex traits in association studies of unrelated individuals. We summarise a range of widely used association tests that have been developed from this model and provide an overview of the relative performance of these approaches from published simulation studies.
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49
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Peterson RE, Maes HH, Lin P, Kramer JR, Hesselbrock VM, Bauer LO, Nurnberger JI, Edenberg HJ, Dick DM, Webb BT. On the association of common and rare genetic variation influencing body mass index: a combined SNP and CNV analysis. BMC Genomics 2014; 15:368. [PMID: 24884913 PMCID: PMC4035084 DOI: 10.1186/1471-2164-15-368] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2013] [Accepted: 04/27/2014] [Indexed: 12/18/2022] Open
Abstract
Background As the architecture of complex traits incorporates a widening spectrum of genetic variation, analyses integrating common and rare variation are needed. Body mass index (BMI) represents a model trait, since common variation shows robust association but accounts for a fraction of the heritability. A combined analysis of single nucleotide polymorphisms (SNP) and copy number variation (CNV) was performed using 1850 European and 498 African-Americans from the Study of Addiction: Genetics and Environment. Genetic risk sum scores (GRSS) were constructed using 32 BMI-validated SNPs and aggregate-risk methods were compared: count versus weighted and proxy versus imputation. Results The weighted SNP-GRSS constructed from imputed probabilities of risk alleles performed best and was highly associated with BMI (p = 4.3×10−16) accounting for 3% of the phenotypic variance. In addition to BMI-validated SNPs, common and rare BMI/obesity-associated CNVs were identified from the literature. Of the 84 CNVs previously reported, only 21-kilobase deletions on 16p12.3 showed evidence for association with BMI (p = 0.003, frequency = 16.9%), with two CNVs nominally associated with class II obesity, 1p36.1 duplications (OR = 3.1, p = 0.009, frequency 1.2%) and 5q13.2 deletions (OR = 1.5, p = 0.048, frequency 7.7%). All other CNVs, individually and in aggregate, were not associated with BMI or obesity. The combined model, including covariates, SNP-GRSS, and 16p12.3 deletion accounted for 11.5% of phenotypic variance in BMI (3.2% from genetic effects). Models significantly predicted obesity classification with maximum discriminative ability for morbid-obesity (p = 3.15×10−18). Conclusion Results show that incorporating validated effect sizes and allelic probabilities improve prediction algorithms. Although rare-CNVs did not account for significant phenotypic variation, results provide a framework for integrated analyses. Electronic supplementary material The online version of this article (doi:10.1186/1471-2164-15-368) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Roseann E Peterson
- Virginia Institute for Psychiatric and Behavioral Genetics, Department of Human and Molecular Genetics, School of Medicine, Virginia Commonwealth University, Biotech I, 800 E, Leigh Street, Richmond, VA 23298-0126, USA.
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50
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Simulation of Finnish population history, guided by empirical genetic data, to assess power of rare-variant tests in Finland. Am J Hum Genet 2014; 94:710-20. [PMID: 24768551 DOI: 10.1016/j.ajhg.2014.03.019] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2013] [Accepted: 03/27/2014] [Indexed: 12/18/2022] Open
Abstract
Finnish samples have been extensively utilized in studying single-gene disorders, where the founder effect has clearly aided in discovery, and more recently in genome-wide association studies of complex traits, where the founder effect has had less obvious impacts. As the field starts to explore rare variants' contribution to polygenic traits, it is of great importance to characterize and confirm the Finnish founder effect in sequencing data and to assess its implications for rare-variant association studies. Here, we employ forward simulation, guided by empirical deep resequencing data, to model the genetic architecture of quantitative polygenic traits in both the general European and the Finnish populations simultaneously. We demonstrate that power of rare-variant association tests is higher in the Finnish population, especially when variants' phenotypic effects are tightly coupled with fitness effects and therefore reflect a greater contribution of rarer variants. SKAT-O, variable-threshold tests, and single-variant tests are more powerful than other rare-variant methods in the Finnish population across a range of genetic models. We also compare the relative power and efficiency of exome array genotyping to those of high-coverage exome sequencing. At a fixed cost, less expensive genotyping strategies have far greater power than sequencing; in a fixed number of samples, however, genotyping arrays miss a substantial portion of genetic signals detected in sequencing, even in the Finnish founder population. As genetic studies probe sequence variation at greater depth in more diverse populations, our simulation approach provides a framework for evaluating various study designs for gene discovery.
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