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Liu S, Martin KE, Snelling WM, Long R, Leeds TD, Vallejo RL, Wiens GD, Palti Y. Accurate genotype imputation from low-coverage whole-genome sequencing data of rainbow trout. G3 (BETHESDA, MD.) 2024; 14:jkae168. [PMID: 39041837 PMCID: PMC11373650 DOI: 10.1093/g3journal/jkae168] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/19/2024] [Revised: 04/19/2024] [Accepted: 07/05/2024] [Indexed: 07/24/2024]
Abstract
With the rapid and significant cost reduction of next-generation sequencing, low-coverage whole-genome sequencing (lcWGS), followed by genotype imputation, is becoming a cost-effective alternative to single-nucleotide polymorphism (SNP)-array genotyping. The objectives of this study were 2-fold: (1) construct a haplotype reference panel for genotype imputation from lcWGS data in rainbow trout (Oncorhynchus mykiss); and (2) evaluate the concordance between imputed genotypes and SNP-array genotypes in 2 breeding populations. Medium-coverage (12×) whole-genome sequences were obtained from a total of 410 fish representing 5 breeding populations with various spawning dates. The short-read sequences were mapped to the rainbow trout reference genome, and genetic variants were identified using GATK. After data filtering, 20,434,612 biallelic SNPs were retained. The reference panel was phased with SHAPEIT5 and was used as a reference to impute genotypes from lcWGS data employing GLIMPSE2. A total of 90 fish from the Troutlodge November breeding population were sequenced with an average coverage of 1.3×, and these fish were also genotyped with the Axiom 57K rainbow trout SNP array. The concordance between array-based genotypes and imputed genotypes was 99.1%. After downsampling the coverage to 0.5×, 0.2×, and 0.1×, the concordance between array-based genotypes and imputed genotypes was 98.7, 97.8, and 96.7%, respectively. In the USDA odd-year breeding population, the concordance between array-based genotypes and imputed genotypes was 97.8% for 109 fish downsampled to 0.5× coverage. Therefore, the reference haplotype panel reported in this study can be used to accurately impute genotypes from lcWGS data in rainbow trout breeding populations.
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Affiliation(s)
- Sixin Liu
- United States Department of Agriculture, National Center for Cool and Cold Water Aquaculture, Agricultural Research Service, Kearneysville, WV 25430, USA
| | | | - Warren M Snelling
- United States Department of Agriculture, US Meat Animal Research Center, Agricultural Research Service, Clay Center, NE 68933, USA
| | - Roseanna Long
- United States Department of Agriculture, National Center for Cool and Cold Water Aquaculture, Agricultural Research Service, Kearneysville, WV 25430, USA
| | - Timothy D Leeds
- United States Department of Agriculture, National Center for Cool and Cold Water Aquaculture, Agricultural Research Service, Kearneysville, WV 25430, USA
| | - Roger L Vallejo
- United States Department of Agriculture, National Center for Cool and Cold Water Aquaculture, Agricultural Research Service, Kearneysville, WV 25430, USA
| | - Gregory D Wiens
- United States Department of Agriculture, National Center for Cool and Cold Water Aquaculture, Agricultural Research Service, Kearneysville, WV 25430, USA
| | - Yniv Palti
- United States Department of Agriculture, National Center for Cool and Cold Water Aquaculture, Agricultural Research Service, Kearneysville, WV 25430, USA
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2
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Doublet M, Degalez F, Lagarrigue S, Lagoutte L, Gueret E, Allais S, Lecerf F. Variant calling and genotyping accuracy of ddRAD-seq: Comparison with 20X WGS in layers. PLoS One 2024; 19:e0298565. [PMID: 39058708 PMCID: PMC11280156 DOI: 10.1371/journal.pone.0298565] [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: 01/26/2024] [Accepted: 05/23/2024] [Indexed: 07/28/2024] Open
Abstract
Whole Genome Sequencing (WGS) remains a costly or unsuitable method for routine genotyping of laying hens. Until now, breeding companies have been using or developing SNP chips. Nevertheless, alternatives methods based on sequencing have been developed. Among these, reduced representation sequencing approaches can offer sequencing quality and cost-effectiveness by reducing the genomic regions covered by sequencing. The aim of this study was to evaluate the ability of double digested Restriction site Associated DNA sequencing (ddRAD-seq) to identify and genotype SNPs in laying hens, by comparison with a presumed reliable WGS approach. Firstly, the sensitivity and precision of variant calling and the genotyping reliability of ddRADseq were determined. Next, the SNP Call Rate (CRSNP) and mean depth of sequencing per SNP (DPSNP) were compared between both methods. Finally, the effect of multiple combinations of thresholds for these parameters on genotyping reliability and amount of remaining SNPs in ddRAD-seq was studied. In raw form, the ddRAD-seq identified 349,497 SNPs evenly distributed on the genome with a CRSNP of 0.55, a DPSNP of 11X and a mean genotyping reliability rate per SNP of 80%. Considering genomic regions covered by expected enzymatic fragments (EFs), the sensitivity of the ddRAD-seq was estimated at 32.4% and its precision at 96.4%. The low CRSNP and DPSNP values were explained by the detection of SNPs outside the EFs theoretically generated by the ddRAD-seq protocol. Indeed, SNPs outside the EFs had significantly lower CRSNP (0.25) and DPSNP (1X) values than SNPs within the EFs (0.7 and 17X, resp.). The study demonstrated the relationship between CRSNP, DPSNP, genotyping reliability and the number of SNPs retained, to provide a decision-support tool for defining filtration thresholds. Severe quality control over ddRAD-seq data allowed to retain a minimum of 40% of the SNPs with a CcR of 98%. Then, ddRAD-seq was defined as a suitable method for variant calling and genotyping in layers.
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Affiliation(s)
| | | | | | | | - Elise Gueret
- MGX-Montpellier GenomiX, Univ. Montpellier, CNRS, INSERM, Montpellier, France
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3
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Trégouët DA, Morange PE. Next-generation sequencing strategies in venous thromboembolism: in whom and for what purpose? J Thromb Haemost 2024; 22:1826-1834. [PMID: 38641321 DOI: 10.1016/j.jtha.2024.04.004] [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: 02/13/2024] [Revised: 04/04/2024] [Accepted: 04/05/2024] [Indexed: 04/21/2024]
Abstract
This invited review follows the oral presentation "To Sequence or Not to Sequence, That Is Not the Question; But 'When, Who, Which and What For?' Is" given during the State of the Art session "Translational Genomics in Thrombosis: From OMICs to Clinics" of the International Society on Thrombosis and Haemostasis 2023 Congress. Emphasizing the power of next-generation sequencing technologies and the diverse strategies associated with DNA variant analysis, this review highlights the unresolved questions and challenges in their implementation both for the clinical diagnosis of venous thromboembolism and in translational research.
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Affiliation(s)
- David-Alexandre Trégouët
- University of Bordeaux, Institut National de la Santé et de la Recherche Médicale, Bordeaux Population Health Research Center, Unité Mixte de Recherche 1219, Bordeaux, France.
| | - Pierre-Emmanuel Morange
- Cardiovascular and Nutrition Research Center (Centre de Recherche en CardioVasculaire et Nutrition), Institut National de la Santé et de la Recherche Médicale, Institut National de Recherche pour l'agriculture, l' Alimentation et l'Environnement, Aix-Marseille University, Marseille, France
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4
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Santos R, Moreno-Torres V, Pintos I, Corral O, de Mendoza C, Soriano V, Corpas M. Low-coverage whole genome sequencing for a highly selective cohort of severe COVID-19 patients. GIGABYTE 2024; 2024:gigabyte127. [PMID: 38948510 PMCID: PMC11211761 DOI: 10.46471/gigabyte.127] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Accepted: 06/04/2024] [Indexed: 07/02/2024] Open
Abstract
Despite the advances in genetic marker identification associated with severe COVID-19, the full genetic characterisation of the disease remains elusive. This study explores imputation in low-coverage whole genome sequencing for a severe COVID-19 patient cohort. We generated a dataset of 79 imputed variant call format files using the GLIMPSE1 tool, each containing an average of 9.5 million single nucleotide variants. Validation revealed a high imputation accuracy (squared Pearson correlation ≍0.97) across sequencing platforms, showcasing GLIMPSE1's ability to confidently impute variants with minor allele frequencies as low as 2% in individuals with Spanish ancestry. We carried out a comprehensive analysis of the patient cohort, examining hospitalisation and intensive care utilisation, sex and age-based differences, and clinical phenotypes using a standardised set of medical terms developed to characterise severe COVID-19 symptoms. The methods and findings presented here can be leveraged for future genomic projects to gain vital insights into health challenges like COVID-19.
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Affiliation(s)
- Renato Santos
- National Heart & Lung Institute, Imperial College London, London, UK
| | - Víctor Moreno-Torres
- Puerta de Hierro University Hospital & Research Institute, Majadahonda, Madrid, Spain
| | - Ilduara Pintos
- Puerta de Hierro University Hospital & Research Institute, Majadahonda, Madrid, Spain
| | - Octavio Corral
- Health Sciences School & Medical Centre, Universidad Internacional La Rioja (UNIR), Madrid, Spain
| | - Carmen de Mendoza
- Puerta de Hierro University Hospital & Research Institute, Majadahonda, Madrid, Spain
| | - Vicente Soriano
- Health Sciences School & Medical Centre, Universidad Internacional La Rioja (UNIR), Madrid, Spain
| | - Manuel Corpas
- School of Life Sciences, University of Westminster, London, UK
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5
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Gómez-Palacio A, Morinaga G, Turner PE, Micieli MV, Elnour MAB, Salim B, Surendran SN, Ramasamy R, Powell JR, Soghigian J, Gloria-Soria A. Robustness in population-structure and demographic-inference results derived from the Aedes aegypti genotyping chip and whole-genome sequencing data. G3 (BETHESDA, MD.) 2024; 14:jkae082. [PMID: 38626295 PMCID: PMC11152066 DOI: 10.1093/g3journal/jkae082] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/04/2024] [Revised: 03/04/2024] [Accepted: 04/04/2024] [Indexed: 04/18/2024]
Abstract
The mosquito Aedes aegypti is the primary vector of many human arboviruses such as dengue, yellow fever, chikungunya, and Zika, which affect millions of people worldwide. Population genetic studies on this mosquito have been important in understanding its invasion pathways and success as a vector of human disease. The Axiom aegypti1 SNP chip was developed from a sample of geographically diverse A. aegypti populations to facilitate genomic studies on this species. We evaluate the utility of the Axiom aegypti1 SNP chip for population genetics and compare it with a low-depth shotgun sequencing approach using mosquitoes from the native (Africa) and invasive ranges (outside Africa). These analyses indicate that results from the SNP chip are highly reproducible and have a higher sensitivity to capture alternative alleles than a low-coverage whole-genome sequencing approach. Although the SNP chip suffers from ascertainment bias, results from population structure, ancestry, demographic, and phylogenetic analyses using the SNP chip were congruent with those derived from low-coverage whole-genome sequencing, and consistent with previous reports on Africa and outside Africa populations using microsatellites. More importantly, we identified a subset of SNPs that can be reliably used to generate merged databases, opening the door to combined analyses. We conclude that the Axiom aegypti1 SNP chip is a convenient, more accurate, low-cost alternative to low-depth whole-genome sequencing for population genetic studies of A. aegypti that do not rely on full allelic frequency spectra. Whole-genome sequencing and SNP chip data can be easily merged, extending the usefulness of both approaches.
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Affiliation(s)
- Andrés Gómez-Palacio
- Department of Entomology, Center for Vector Biology & Zoonotic Diseases, The Connecticut Agricultural Experiment Station, 123 Huntington St., New Haven, CT 06511, USA
- Laboratorio de Investigación en Genética Evolutiva, Universidad Pedagógica y Tecnológica de Colombia, Avenida Central del Norte 39-115, Boyacá 150003, Colombia
| | - Gen Morinaga
- Faculty of Veterinary Medicine, University of Calgary, 2500 University Drive NW., Calgary, AB 2TN 1N4, Canada
| | - Paul E Turner
- Department of Ecology and Evolutionary Biology, Yale University, 165 Prospect St., New Haven, CT 06511, USA
- Quantitative Biology Institute, Yale University, 260 Whitney Ave., New Haven, CT 06511, USA
| | - Maria Victoria Micieli
- Centro de Estudios Parasitológicos y de Vectores (CEPAVE), CONICET, Universidad Nacional de la Plata, Boulevard 120 s/n between Av. 60 and Calle 64, La Plata 1900, Argentina
| | - Mohammed-Ahmed B Elnour
- Department of Parasitology and Medical Entomology, Tropical Medicine Research Institute, National Center for Research, Khartoum 11111, Sudan
| | - Bashir Salim
- Faculty of Veterinary Medicine, Department of Parasitology, University of Khartoum, Khartoum North 11111, Sudan
- Camel Research Center, King Faisal University, P.O. Box. 400, Al-Ahsa 31982, Saudi Arabia
| | | | - Ranjan Ramasamy
- Department of Zoology, University of Jaffna, Jaffna 40000, Sri Lanka
| | - Jeffrey R Powell
- Department of Ecology and Evolutionary Biology, Yale University, 165 Prospect St., New Haven, CT 06511, USA
| | - John Soghigian
- Faculty of Veterinary Medicine, University of Calgary, 2500 University Drive NW., Calgary, AB 2TN 1N4, Canada
| | - Andrea Gloria-Soria
- Department of Entomology, Center for Vector Biology & Zoonotic Diseases, The Connecticut Agricultural Experiment Station, 123 Huntington St., New Haven, CT 06511, USA
- Department of Ecology and Evolutionary Biology, Yale University, 165 Prospect St., New Haven, CT 06511, USA
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6
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Bhérer C, Eveleigh R, Trajanoska K, St-Cyr J, Paccard A, Nadukkalam Ravindran P, Caron E, Bader Asbah N, McClelland P, Wei C, Baumgartner I, Schindewolf M, Döring Y, Perley D, Lefebvre F, Lepage P, Bourgey M, Bourque G, Ragoussis J, Mooser V, Taliun D. A cost-effective sequencing method for genetic studies combining high-depth whole exome and low-depth whole genome. NPJ Genom Med 2024; 9:8. [PMID: 38326393 PMCID: PMC10850497 DOI: 10.1038/s41525-024-00390-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2023] [Accepted: 12/07/2023] [Indexed: 02/09/2024] Open
Abstract
Whole genome sequencing (WGS) at high-depth (30X) allows the accurate discovery of variants in the coding and non-coding DNA regions and helps elucidate the genetic underpinnings of human health and diseases. Yet, due to the prohibitive cost of high-depth WGS, most large-scale genetic association studies use genotyping arrays or high-depth whole exome sequencing (WES). Here we propose a cost-effective method which we call "Whole Exome Genome Sequencing" (WEGS), that combines low-depth WGS and high-depth WES with up to 8 samples pooled and sequenced simultaneously (multiplexed). We experimentally assess the performance of WEGS with four different depth of coverage and sample multiplexing configurations. We show that the optimal WEGS configurations are 1.7-2.0 times cheaper than standard WES (no-plexing), 1.8-2.1 times cheaper than high-depth WGS, reach similar recall and precision rates in detecting coding variants as WES, and capture more population-specific variants in the rest of the genome that are difficult to recover when using genotype imputation methods. We apply WEGS to 862 patients with peripheral artery disease and show that it directly assesses more known disease-associated variants than a typical genotyping array and thousands of non-imputable variants per disease-associated locus.
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Affiliation(s)
- Claude Bhérer
- Department of Human Genetics, Faculty of Medicine and Health Sciences, McGill University, Montréal, Québec, Canada
- Victor Phillip Dahdaleh Institute of Genomic Medicine at McGill University, Montréal, Québec, Canada
- Canada Excellence Research Chair in Genomic Medicine, McGill University, Montréal, Québec, Canada
| | - Robert Eveleigh
- Victor Phillip Dahdaleh Institute of Genomic Medicine at McGill University, Montréal, Québec, Canada
- Canadian Centre for Computational Genomics, McGill University, Montréal, Québec, Canada
| | - Katerina Trajanoska
- Department of Human Genetics, Faculty of Medicine and Health Sciences, McGill University, Montréal, Québec, Canada
- Victor Phillip Dahdaleh Institute of Genomic Medicine at McGill University, Montréal, Québec, Canada
- Canada Excellence Research Chair in Genomic Medicine, McGill University, Montréal, Québec, Canada
| | - Janick St-Cyr
- Victor Phillip Dahdaleh Institute of Genomic Medicine at McGill University, Montréal, Québec, Canada
| | - Antoine Paccard
- Victor Phillip Dahdaleh Institute of Genomic Medicine at McGill University, Montréal, Québec, Canada
| | - Praveen Nadukkalam Ravindran
- Victor Phillip Dahdaleh Institute of Genomic Medicine at McGill University, Montréal, Québec, Canada
- Canada Excellence Research Chair in Genomic Medicine, McGill University, Montréal, Québec, Canada
| | - Elizabeth Caron
- Victor Phillip Dahdaleh Institute of Genomic Medicine at McGill University, Montréal, Québec, Canada
| | - Nimara Bader Asbah
- Department of Human Genetics, Faculty of Medicine and Health Sciences, McGill University, Montréal, Québec, Canada
- Victor Phillip Dahdaleh Institute of Genomic Medicine at McGill University, Montréal, Québec, Canada
| | - Peyton McClelland
- Department of Human Genetics, Faculty of Medicine and Health Sciences, McGill University, Montréal, Québec, Canada
- Victor Phillip Dahdaleh Institute of Genomic Medicine at McGill University, Montréal, Québec, Canada
- Canada Excellence Research Chair in Genomic Medicine, McGill University, Montréal, Québec, Canada
| | - Clare Wei
- Victor Phillip Dahdaleh Institute of Genomic Medicine at McGill University, Montréal, Québec, Canada
- Canada Excellence Research Chair in Genomic Medicine, McGill University, Montréal, Québec, Canada
| | - Iris Baumgartner
- Division of Angiology, Swiss Cardiovascular Center, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
- Department for BioMedical Research (DBMR), University of Bern, Bern, Switzerland
| | - Marc Schindewolf
- Division of Angiology, Swiss Cardiovascular Center, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
- Department for BioMedical Research (DBMR), University of Bern, Bern, Switzerland
| | - Yvonne Döring
- Division of Angiology, Swiss Cardiovascular Center, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
- Department for BioMedical Research (DBMR), University of Bern, Bern, Switzerland
- Institute for Cardiovascular Prevention (IPEK), Ludwig-Maximilians University Munich, Pettenkoferstr 9, 80336, Munich, Germany
| | - Danielle Perley
- Victor Phillip Dahdaleh Institute of Genomic Medicine at McGill University, Montréal, Québec, Canada
- Canadian Centre for Computational Genomics, McGill University, Montréal, Québec, Canada
| | - François Lefebvre
- Victor Phillip Dahdaleh Institute of Genomic Medicine at McGill University, Montréal, Québec, Canada
- Canadian Centre for Computational Genomics, McGill University, Montréal, Québec, Canada
| | - Pierre Lepage
- Victor Phillip Dahdaleh Institute of Genomic Medicine at McGill University, Montréal, Québec, Canada
| | | | - Guillaume Bourque
- Department of Human Genetics, Faculty of Medicine and Health Sciences, McGill University, Montréal, Québec, Canada
- Victor Phillip Dahdaleh Institute of Genomic Medicine at McGill University, Montréal, Québec, Canada
- Canadian Centre for Computational Genomics, McGill University, Montréal, Québec, Canada
| | - Jiannis Ragoussis
- Department of Human Genetics, Faculty of Medicine and Health Sciences, McGill University, Montréal, Québec, Canada
- Victor Phillip Dahdaleh Institute of Genomic Medicine at McGill University, Montréal, Québec, Canada
| | - Vincent Mooser
- Department of Human Genetics, Faculty of Medicine and Health Sciences, McGill University, Montréal, Québec, Canada
- Victor Phillip Dahdaleh Institute of Genomic Medicine at McGill University, Montréal, Québec, Canada
- Canada Excellence Research Chair in Genomic Medicine, McGill University, Montréal, Québec, Canada
| | - Daniel Taliun
- Department of Human Genetics, Faculty of Medicine and Health Sciences, McGill University, Montréal, Québec, Canada.
- Victor Phillip Dahdaleh Institute of Genomic Medicine at McGill University, Montréal, Québec, Canada.
- Canada Excellence Research Chair in Genomic Medicine, McGill University, Montréal, Québec, Canada.
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Qiu X, Weng J, Jiang Y, Cui L, Gu H, Jiang Y, Dai Y, Li H, Wang Y, Li Z. Association between clonal hematopoiesis-related gene mutations and unfavorable functional outcome in patients with large-artery atherosclerotic stroke. Eur J Med Res 2023; 28:599. [PMID: 38104193 PMCID: PMC10724961 DOI: 10.1186/s40001-023-01566-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Accepted: 12/04/2023] [Indexed: 12/19/2023] Open
Abstract
BACKGROUND Clonal hematopoiesis of indeterminate potential (CHIP) is a phenomenon that characterizes individuals with somatic mutations that are related to hematologic malignancy but without hematologic abnormalities. Presence of CHIP is associated with the atherosclerotic cardiovascular disease through the activation of the interleukin 6 (IL-6) pathway; however, its role on unfavorable functional outcomes in different etiologies of ischemic stroke remains unclear. We aimed to investigate the association between CHIP-related gene mutations and unfavorable functional outcomes of ischemic stroke with different etiologies. METHODS We prospectively studied a cohort of 3396 stroke patients with identified etiologies, and identified CHIP and the presence of the IL6R variant (IL6R p.Asp358Ala) by whole-genome sequencing. The IL6R p.Asp358Ala coding mutation was used as a genetic inhibition for IL-6 signaling. The primary outcome was unfavorable functional outcome [(Modified Rankin Scale), mRS 2-6] at 3 months. RESULTS Of the 3396 patients, 110 (3.2%) were CHIP carriers and the median age was 62 years (IQR, 54.0-69.0). The CHIP increased the risk of unfavorable functional outcome among patients with hyper-inflammation status of high-sensitivity C-reactive protein (hsCRP) > median levels in patients with large-artery atherosclerosis (LAA) (OR 2.45, 95% CI 1.00-5.98, p = 0.049, pinteraction = 0.01). Presence of IL6R variant (IL6R p.Asp358Ala) could attenuate the risk of unfavorable functional outcome only in patients with CHIP (OR 0.30, 95%CI 0.12-0.76, p = 0.01, pinteraction = 0.02), and especially in LAA patients with CHIP (OR 0.1, 95%CI 0.02-0.42, p = 0.002; pinteraction = 0.001). CONCLUSION CHIP is associated with unfavorable functional outcomes in patients with LAA stroke and hyper-inflammation. Genetic IL-6 signaling inhibition might attenuate the risk of unfavorable functional outcomes in CHIP carriers, especially in LAA stroke patients.
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Affiliation(s)
- Xin Qiu
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, No 119 South 4th Ring West Road, Fengtai District, Beijing, 100070, China
| | - Jiaxu Weng
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, No 119 South 4th Ring West Road, Fengtai District, Beijing, 100070, China
| | - Yingyu Jiang
- China National Clinical Research Center for Neurological Diseases, Beijing, China
| | - Lingyun Cui
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, No 119 South 4th Ring West Road, Fengtai District, Beijing, 100070, China
| | - Hongqiu Gu
- China National Clinical Research Center for Neurological Diseases, Beijing, China
| | - Yong Jiang
- China National Clinical Research Center for Neurological Diseases, Beijing, China
| | - Yalun Dai
- China National Clinical Research Center for Neurological Diseases, Beijing, China
| | - Hao Li
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, No 119 South 4th Ring West Road, Fengtai District, Beijing, 100070, China
- China National Clinical Research Center for Neurological Diseases, Beijing, China
| | - Yongjun Wang
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, No 119 South 4th Ring West Road, Fengtai District, Beijing, 100070, China
- China National Clinical Research Center for Neurological Diseases, Beijing, China
- Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
- Research Unit of Artificial Intelligence in Cerebrovascular Disease, Chinese Academy of Medical Sciences, Beijing, China
- Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China
| | - Zixiao Li
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, No 119 South 4th Ring West Road, Fengtai District, Beijing, 100070, China.
- China National Clinical Research Center for Neurological Diseases, Beijing, China.
- Research Unit of Artificial Intelligence in Cerebrovascular Disease, Chinese Academy of Medical Sciences, Beijing, China.
- Chinese Institute for Brain Research, Beijing (CIBR), Beijing, China.
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8
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Gilly A, Park YC, Tsafantakis E, Karaleftheri M, Dedoussis G, Zeggini E. Genome-wide meta-analysis of 92 cardiometabolic protein serum levels. Mol Metab 2023; 78:101810. [PMID: 37778719 PMCID: PMC10582065 DOI: 10.1016/j.molmet.2023.101810] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Revised: 09/11/2023] [Accepted: 09/19/2023] [Indexed: 10/03/2023] Open
Abstract
OBJECTIVES Global cardiometabolic disease prevalence has grown rapidly over the years, making it the leading cause of death worldwide. Proteins are crucial components in biological pathways dysregulated in disease states. Identifying genetic components that influence circulating protein levels may lead to the discovery of biomarkers for early stages of disease or offer opportunities as therapeutic targets. METHODS Here, we carry out a genome-wide association study (GWAS) utilising whole genome sequencing data in 3,005 individuals from the HELIC founder populations cohort, across 92 proteins of cardiometabolic relevance. RESULTS We report 322 protein quantitative trait loci (pQTL) signals across 92 proteins, of which 76 are located in or near the coding gene (cis-pQTL). We link those association signals with changes in protein expression and cardiometabolic disease risk using colocalisation and Mendelian randomisation (MR) analyses. CONCLUSIONS The majority of previously unknown signals we describe point to proteins or protein interactions involved in inflammation and immune response, providing genetic evidence for the contributing role of inflammation in cardiometabolic disease processes.
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Affiliation(s)
- Arthur Gilly
- Institute of Translational Genomics, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
| | - Young-Chan Park
- Institute of Translational Genomics, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
| | | | | | - George Dedoussis
- Department of Nutrition and Dietetics, School of Health Science and Education, Harokopio University of Athens, Athens, Greece
| | - Eleftheria Zeggini
- Institute of Translational Genomics, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany; Technical University of Munich (TUM) and Klinikum Rechts der Isar, TUM School of Medicine, Munich, Germany.
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9
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Sakaue S, Gurajala S, Curtis M, Luo Y, Choi W, Ishigaki K, Kang JB, Rumker L, Deutsch AJ, Schönherr S, Forer L, LeFaive J, Fuchsberger C, Han B, Lenz TL, de Bakker PIW, Okada Y, Smith AV, Raychaudhuri S. Tutorial: a statistical genetics guide to identifying HLA alleles driving complex disease. Nat Protoc 2023; 18:2625-2641. [PMID: 37495751 PMCID: PMC10786448 DOI: 10.1038/s41596-023-00853-4] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2022] [Accepted: 04/27/2023] [Indexed: 07/28/2023]
Abstract
The human leukocyte antigen (HLA) locus is associated with more complex diseases than any other locus in the human genome. In many diseases, HLA explains more heritability than all other known loci combined. In silico HLA imputation methods enable rapid and accurate estimation of HLA alleles in the millions of individuals that are already genotyped on microarrays. HLA imputation has been used to define causal variation in autoimmune diseases, such as type I diabetes, and in human immunodeficiency virus infection control. However, there are few guidelines on performing HLA imputation, association testing, and fine mapping. Here, we present a comprehensive tutorial to impute HLA alleles from genotype data. We provide detailed guidance on performing standard quality control measures for input genotyping data and describe options to impute HLA alleles and amino acids either locally or using the web-based Michigan Imputation Server, which hosts a multi-ancestry HLA imputation reference panel. We also offer best practice recommendations to conduct association tests to define the alleles, amino acids, and haplotypes that affect human traits. Along with the pipeline, we provide a step-by-step online guide with scripts and available software ( https://github.com/immunogenomics/HLA_analyses_tutorial ). This tutorial will be broadly applicable to large-scale genotyping data and will contribute to defining the role of HLA in human diseases across global populations.
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Affiliation(s)
- Saori Sakaue
- Center for Data Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Saisriram Gurajala
- Center for Data Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Michelle Curtis
- Center for Data Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Yang Luo
- Center for Data Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Kennedy Institute of Rheumatology, University of Oxford, Oxford, UK
| | - Wanson Choi
- Department of Biomedical Sciences, Seoul National University College of Medicine, Seoul, South Korea
| | - Kazuyoshi Ishigaki
- Center for Data Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Laboratory for Human Immunogenetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Joyce B Kang
- Center for Data Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Laurie Rumker
- Center for Data Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Aaron J Deutsch
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Diabetes Unit, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Program in Metabolism, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Sebastian Schönherr
- Institute of Genetic Epidemiology, Department of Genetics, Medical University of Innsbruck, Innsbruck, Austria
| | - Lukas Forer
- Institute of Genetic Epidemiology, Department of Genetics, Medical University of Innsbruck, Innsbruck, Austria
| | - Jonathon LeFaive
- Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI, USA
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, USA
| | - Christian Fuchsberger
- Institute of Genetic Epidemiology, Department of Genetics, Medical University of Innsbruck, Innsbruck, Austria
- Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI, USA
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, USA
- Institute for Biomedicine, Eurac Research, Bolzano, Italy
| | - Buhm Han
- Department of Biomedical Sciences, Seoul National University College of Medicine, Seoul, South Korea
- Interdisciplinary Program in Bioengineering, Seoul National University, Seoul, South Korea
| | - Tobias L Lenz
- Research Unit for Evolutionary Immunogenomics, Department of Biology, University of Hamburg, Hamburg, Germany
| | - Paul I W de Bakker
- Data and Computational Sciences, Vertex Pharmaceuticals, Boston, MA, USA
| | - Yukinori Okada
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
- Integrated Frontier Research for Medical Science Division, Institute for Open and Transdisciplinary Research Initiatives, Osaka University, Suita, Japan
- Laboratory of Statistical Immunology, Immunology Frontier Research Center (WPI-IFReC), Osaka University, Suita, Japan
- Laboratory for Systems Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
- Center for Infectious Disease Education and Research (CiDER), Osaka University, Suita, Japan
- Department of Genome Informatics, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Albert V Smith
- Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI, USA
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, USA
| | - Soumya Raychaudhuri
- Center for Data Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
- Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
- Centre for Genetics and Genomics Versus Arthritis, University of Manchester, Manchester, UK.
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10
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González-Recio O, López-Catalina A, Peiró-Pastor R, Nieto-Valle A, Castro M, Fernández A. Evaluating the potential of (epi)genotype-by-low pass nanopore sequencing in dairy cattle: a study on direct genomic value and methylation analysis. J Anim Sci Biotechnol 2023; 14:98. [PMID: 37434255 PMCID: PMC10337168 DOI: 10.1186/s40104-023-00896-3] [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: 02/09/2023] [Accepted: 05/17/2023] [Indexed: 07/13/2023] Open
Abstract
BACKGROUND Genotype-by-sequencing has been proposed as an alternative to SNP genotyping arrays in genomic selection to obtain a high density of markers along the genome. It requires a low sequencing depth to be cost effective, which may increase the error at the genotype assigment. Third generation nanopore sequencing technology offers low cost sequencing and the possibility to detect genome methylation, which provides added value to genotype-by-sequencing. The aim of this study was to evaluate the performance of genotype-by-low pass nanopore sequencing for estimating the direct genomic value in dairy cattle, and the possibility to obtain methylation marks simultaneously. RESULTS Latest nanopore chemistry (LSK14 and Q20) achieved a modal base calling accuracy of 99.55%, whereas previous kit (LSK109) achieved slightly lower accuracy (99.1%). The direct genomic value accuracy from genotype-by-low pass sequencing ranged between 0.79 and 0.99, depending on the trait (milk, fat or protein yield), with a sequencing depth as low as 2 × and using the latest chemistry (LSK114). Lower sequencing depth led to biased estimates, yet with high rank correlations. The LSK109 and Q20 achieved lower accuracies (0.57-0.93). More than one million high reliable methylated sites were obtained, even at low sequencing depth, located mainly in distal intergenic (87%) and promoter (5%) regions. CONCLUSIONS This study showed that the latest nanopore technology in useful in a LowPass sequencing framework to estimate direct genomic values with high reliability. It may provide advantages in populations with no available SNP chip, or when a large density of markers with a wide range of allele frequencies is needed. In addition, low pass sequencing provided nucleotide methylation status of > 1 million nucleotides at ≥ 10 × , which is an added value for epigenetic studies.
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Affiliation(s)
- Oscar González-Recio
- Dpt. Mejora Genética Animal, INIA-CSIC, Ctra La Coruña Km 7.5, 28040, Madrid, Spain.
| | | | - Ramón Peiró-Pastor
- Dpt. Mejora Genética Animal, INIA-CSIC, Ctra La Coruña Km 7.5, 28040, Madrid, Spain
| | - Alicia Nieto-Valle
- ETSIAAB, Universidad Politécnica de Madrid. Ciudad Universitaria S/N, 28040, Madrid, Spain
| | - Monica Castro
- Dpt. Mejora Genética Animal, INIA-CSIC, Ctra La Coruña Km 7.5, 28040, Madrid, Spain
| | - Almudena Fernández
- Dpt. Mejora Genética Animal, INIA-CSIC, Ctra La Coruña Km 7.5, 28040, Madrid, Spain
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11
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Bocher O, Willer CJ, Zeggini E. Unravelling the genetic architecture of human complex traits through whole genome sequencing. Nat Commun 2023; 14:3520. [PMID: 37316478 DOI: 10.1038/s41467-023-39259-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2022] [Accepted: 06/06/2023] [Indexed: 06/16/2023] Open
Affiliation(s)
- Ozvan Bocher
- Institute of Translational Genomics, Helmholtz Zentrum München, German Research Center for Environmental Health, 85764, Neuherberg, Germany
| | - Cristen J Willer
- Department of Internal Medicine, Division of Cardiology, University of Michigan, Ann Arbor, MI, 48109, USA
- Department of Human Genetics, University of Michigan, Ann Arbor, MI, 48109, USA
- Department of Computational Medicine and Biostatistics, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Eleftheria Zeggini
- Institute of Translational Genomics, Helmholtz Zentrum München, German Research Center for Environmental Health, 85764, Neuherberg, Germany.
- Technical University of Munich (TUM) and Klinikum Rechts der Isar, TUM School of Medicine, Ismaninger Str. 22, 81675, Munich, Germany.
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12
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Campos-Martin R, Schmickler S, Goel M, Schneeberger K, Tresch A. Reliable genotyping of recombinant genomes using a robust hidden Markov model. PLANT PHYSIOLOGY 2023; 192:821-836. [PMID: 36946207 PMCID: PMC10231367 DOI: 10.1093/plphys/kiad191] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Revised: 01/20/2023] [Accepted: 01/27/2023] [Indexed: 06/01/2023]
Abstract
Meiotic recombination is an essential mechanism during sexual reproduction and includes the exchange of chromosome segments between homologous chromosomes. New allelic combinations are transmitted to the new generation, introducing novel genetic variation in the offspring genomes. With the improvement of high-throughput whole-genome sequencing technologies, large numbers of recombinant individuals can now be sequenced with low sequencing depth at low costs, necessitating computational methods for reconstructing their haplotypes. The main challenge is the uncertainty in haplotype calling that arises from the low information content of a single genomic position. Straightforward sliding window-based approaches are difficult to tune and fail to place recombination breakpoints precisely. Hidden Markov model (HMM)-based approaches, on the other hand, tend to over-segment the genome. Here, we present RTIGER, an HMM-based model that exploits in a mathematically precise way the fact that true chromosome segments typically have a certain minimum length. We further separate the task of identifying the correct haplotype sequence from the accurate placement of haplotype borders, thereby maximizing the accuracy of border positions. By comparing segmentations based on simulated data with known underlying haplotypes, we highlight the reasons for RTIGER outperforming traditional segmentation approaches. We then analyze the meiotic recombination pattern of segregants of 2 Arabidopsis (Arabidopsis thaliana) accessions and a previously described hyper-recombining mutant. RTIGER is available as an R package with an efficient Julia implementation of the core algorithm.
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Affiliation(s)
- Rafael Campos-Martin
- Faculty of Medicine, University Hospital Cologne, Cologne 50937, Germany
- Department of Chromosome Biology, Max Planck Institute for Plant Breeding Research, Cologne 50829, Germany
- Division of Neurogenetics and Molecular Psychiatry, Department of Psychiatry and Psychotherapy, University of Cologne, Medical Faculty, Cologne 50937, Germany
| | - Sophia Schmickler
- Faculty of Medicine, University Hospital Cologne, Cologne 50937, Germany
| | - Manish Goel
- Department of Chromosome Biology, Max Planck Institute for Plant Breeding Research, Cologne 50829, Germany
- Faculty for Biology, LMU Munich, Planegg-Martinsried 82152, Germany
| | - Korbinian Schneeberger
- Department of Chromosome Biology, Max Planck Institute for Plant Breeding Research, Cologne 50829, Germany
- Faculty for Biology, LMU Munich, Planegg-Martinsried 82152, Germany
- Cluster of Excellence on Plant Sciences, Heinrich-Heine University, Düsseldorf 40225, Germany
| | - Achim Tresch
- Faculty of Medicine, University Hospital Cologne, Cologne 50937, Germany
- CECAD, University of Cologne, Cologne 50931, Germany
- Center for Data and Simulation Science, University of Cologne, Cologne 50931, Germany
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13
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Huynh S, Cloutier A, Sin SYW. Museomics and phylogenomics of lovebirds (Psittaciformes, Psittaculidae, Agapornis) using low-coverage whole-genome sequencing. Mol Phylogenet Evol 2023; 185:107822. [PMID: 37220800 DOI: 10.1016/j.ympev.2023.107822] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Revised: 05/07/2023] [Accepted: 05/19/2023] [Indexed: 05/25/2023]
Abstract
Natural history collections contain specimens that provide important insights into studies of ecology and evolution. With the advancement of high-throughput sequencing, historical DNA (hDNA) from museum specimens has become a valuable source of genomic data to study the evolutionary history of organisms. Low-coverage whole genome sequencing (WGS) has been increasingly applied to museum specimens for analyzing organelle genomes, but is still uncommon for genotyping the nuclear DNA fraction. In this study, we applied low-coverage WGS to phylogenomic analyses of parrots in the genus Agapornis by including both modern samples and historical specimens of ∼100-year-old. Agapornis are small-sized African and Malagasy parrots with diverse characters. Earlier phylogenetic studies failed to resolve the positions of some key lineages, prohibiting a robust interpretation of the biogeography and evolution of these African parrots. Here, we demonstrated the use of low-coverage WGS for generating both mitochondrial and nuclear genomic data, and evaluated data quality differences between modern and historical samples. Our resolved Agapornis phylogeny indicates the ancestor of Agapornis likely colonized Madagascar from Australasia by trans-oceanic dispersal events before dispersing to the African continent. Genome-wide SNPs also allowed us to identify the parental origins of hybrid Agapornis individuals. This study demonstrates the potential of applying low-coverage WGS to phylogenomics and population genomics analyses and illustrates how including historical museum specimens can address outstanding questions regarding the evolutionary history of contemporary lineages.
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Affiliation(s)
- Stella Huynh
- School of Biological Sciences, The University of Hong Kong, Pok Fu Lam Road, Hong Kong SAR, China
| | - Alison Cloutier
- Department of Organismic and Evolutionary Biology, Mueum of Comparative Zoology, Harvard University, 26 Oxford Street, Cambridge, MA 02138, USA
| | - Simon Yung Wa Sin
- School of Biological Sciences, The University of Hong Kong, Pok Fu Lam Road, Hong Kong SAR, China.
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14
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Li S, Yan B, Li TKT, Lu J, Gu Y, Tan Y, Gong F, Lam TW, Xie P, Wang Y, Lin G, Luo R. Ultra-low-coverage genome-wide association study-insights into gestational age using 17,844 embryo samples with preimplantation genetic testing. Genome Med 2023; 15:10. [PMID: 36788602 PMCID: PMC9926832 DOI: 10.1186/s13073-023-01158-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Accepted: 01/26/2023] [Indexed: 02/16/2023] Open
Abstract
BACKGROUND Very low-coverage (0.1 to 1×) whole genome sequencing (WGS) has become a promising and affordable approach to discover genomic variants of human populations for genome-wide association study (GWAS). To support genetic screening using preimplantation genetic testing (PGT) in a large population, the sequencing coverage goes below 0.1× to an ultra-low level. However, the feasibility and effectiveness of ultra-low-coverage WGS (ulcWGS) for GWAS remains undetermined. METHODS We built a pipeline to carry out analysis of ulcWGS data for GWAS. To examine its effectiveness, we benchmarked the accuracy of genotype imputation at the combination of different coverages below 0.1× and sample sizes from 2000 to 16,000, using 17,844 embryo PGT samples with approximately 0.04× average coverage and the standard Chinese sample HG005 with known genotypes. We then applied the imputed genotypes of 1744 transferred embryos who have gestational ages and complete follow-up records to GWAS. RESULTS The accuracy of genotype imputation under ultra-low coverage can be improved by increasing the sample size and applying a set of filters. From 1744 born embryos, we identified 11 genomic risk loci associated with gestational ages and 166 genes mapped to these loci according to positional, expression quantitative trait locus, and chromatin interaction strategies. Among these mapped genes, CRHBP, ICAM1, and OXTR were more frequently reported as preterm birth related. By joint analysis of gene expression data from previous studies, we constructed interrelationships of mainly CRHBP, ICAM1, PLAGL1, DNMT1, CNTLN, DKK1, and EGR2 with preterm birth, infant disease, and breast cancer. CONCLUSIONS This study not only demonstrates that ulcWGS could achieve relatively high accuracy of adequate genotype imputation and is capable of GWAS, but also provides insights into the associations between gestational age and genetic variations of the fetal embryos from Chinese population.
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Affiliation(s)
- Shumin Li
- grid.194645.b0000000121742757Department of Computer Science, The University of Hong Kong, Hong Kong, China
| | - Bin Yan
- grid.194645.b0000000121742757Department of Computer Science, The University of Hong Kong, Hong Kong, China
| | - Thomas K. T. Li
- grid.415550.00000 0004 1764 4144Department of Obstetrics & Gynecology, Queen Mary Hospital, The University of Hong Kong, Hong Kong, China
| | - Jianliang Lu
- grid.194645.b0000000121742757Department of Computer Science, The University of Hong Kong, Hong Kong, China
| | - Yifan Gu
- grid.216417.70000 0001 0379 7164NHC Key Laboratory of Human Stem Cell and Reproductive Engineering, School of Basic Medical Science, Institute of Reproductive and Stem Cell Engineering, Central South University, Changsha, 410008 Hunan China ,grid.477823.d0000 0004 1756 593XClinical Research Center for Reproduction and Genetics in Hunan Province, Reproductive and Genetic Hospital of CITIC-Xiangya, Changsha, 410013 Hunan China
| | - Yueqiu Tan
- grid.216417.70000 0001 0379 7164NHC Key Laboratory of Human Stem Cell and Reproductive Engineering, School of Basic Medical Science, Institute of Reproductive and Stem Cell Engineering, Central South University, Changsha, 410008 Hunan China ,grid.477823.d0000 0004 1756 593XClinical Research Center for Reproduction and Genetics in Hunan Province, Reproductive and Genetic Hospital of CITIC-Xiangya, Changsha, 410013 Hunan China
| | - Fei Gong
- grid.216417.70000 0001 0379 7164NHC Key Laboratory of Human Stem Cell and Reproductive Engineering, School of Basic Medical Science, Institute of Reproductive and Stem Cell Engineering, Central South University, Changsha, 410008 Hunan China ,grid.477823.d0000 0004 1756 593XClinical Research Center for Reproduction and Genetics in Hunan Province, Reproductive and Genetic Hospital of CITIC-Xiangya, Changsha, 410013 Hunan China
| | - Tak-Wah Lam
- grid.194645.b0000000121742757Department of Computer Science, The University of Hong Kong, Hong Kong, China
| | - Pingyuan Xie
- Hunan Normal University School of Medicine, Changsha, 410013, Hunan, China. .,National Engineering and Research Center of Human Stem Cell, Changsha, Hunan, China.
| | - Yuexuan Wang
- Department of Computer Science, The University of Hong Kong, Hong Kong, China. .,College of Computer Science and Technology, Zhejiang University, Hangzhou, China.
| | - Ge Lin
- NHC Key Laboratory of Human Stem Cell and Reproductive Engineering, School of Basic Medical Science, Institute of Reproductive and Stem Cell Engineering, Central South University, Changsha, 410008, Hunan, China. .,Clinical Research Center for Reproduction and Genetics in Hunan Province, Reproductive and Genetic Hospital of CITIC-Xiangya, Changsha, 410013, Hunan, China. .,National Engineering and Research Center of Human Stem Cell, Changsha, Hunan, China.
| | - Ruibang Luo
- Department of Computer Science, The University of Hong Kong, Hong Kong, China.
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15
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Zha C, Liu K, Wu J, Li P, Hou L, Liu H, Huang R, Wu W. Combining genome-wide association study based on low-coverage whole genome sequencing and transcriptome analysis to reveal the key candidate genes affecting meat color in pigs. Anim Genet 2023; 54:295-306. [PMID: 36727217 DOI: 10.1111/age.13300] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2022] [Revised: 01/04/2023] [Accepted: 01/16/2023] [Indexed: 02/03/2023]
Abstract
Meat color is an attractive trait that influences consumers' purchase decisions at the point of sale. To decipher the genetic basis of meat color traits, we performed a genome-wide association study based on low-coverage whole-genome sequencing. In total, 669 (Pietrain × Duroc) × (Landrace × Yorkshire) pigs were genotyped using low-coverage whole-genome sequencing. Single nucleotide polymorphism (SNP) calling and genotype imputation were performed using the BaseVar + STITCH channel. Six individuals with an average depth of 12.05× whole-genome resequencing were randomly selected to assess the accuracy of imputation. Heritability evaluation and genome-wide association study for meat color traits were conducted. Functional enrichment analysis of the candidate genes from genome-wide association study and integration analysis with our previous transcriptome data were conducted. The imputation accuracy parameters, allele frequency R2 , concordance rate, and dosage R2 were 0.959, 0.952, and 0.933, respectively. The heritability values of a*45 min , b*45 min , L*45 min , C*, and H0 were 0.19, 0.11, 0.06, 0.16, and 0.26, respectively. In total, 3884 significant SNPs and 15 QTL, corresponding to 382 genes, were associated with meat color traits. Functional enrichment analysis revealed that 10 genes were the potential candidates for regulating meat color. Moreover, integration analysis revealed that DMRT2, EFNA5, FGF10, and COL11A2 were the most promising candidates affecting meat color. In summary, this study provides new insights into the molecular basis of meat color traits, and provides a new theoretical basis for the molecular breeding of meat color traits in pigs.
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Affiliation(s)
- Chengwan Zha
- Department of Animal Genetics, Breeding and Reproduction, College of Animal Science and Technology, Nanjing Agricultural University, Nanjing, China
| | - Kaiyue Liu
- Department of Animal Genetics, Breeding and Reproduction, College of Animal Science and Technology, Nanjing Agricultural University, Nanjing, China
| | - Jian Wu
- Department of Animal Genetics, Breeding and Reproduction, College of Animal Science and Technology, Nanjing Agricultural University, Nanjing, China
| | - Pinghua Li
- Department of Animal Genetics, Breeding and Reproduction, College of Animal Science and Technology, Nanjing Agricultural University, Nanjing, China
| | - Liming Hou
- Department of Animal Genetics, Breeding and Reproduction, College of Animal Science and Technology, Nanjing Agricultural University, Nanjing, China
| | - Honglin Liu
- Department of Animal Genetics, Breeding and Reproduction, College of Animal Science and Technology, Nanjing Agricultural University, Nanjing, China
| | - Ruihua Huang
- Department of Animal Genetics, Breeding and Reproduction, College of Animal Science and Technology, Nanjing Agricultural University, Nanjing, China
| | - Wangjun Wu
- Department of Animal Genetics, Breeding and Reproduction, College of Animal Science and Technology, Nanjing Agricultural University, Nanjing, China
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16
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Wattacheril JJ, Raj S, Knowles DA, Greally JM. Using epigenomics to understand cellular responses to environmental influences in diseases. PLoS Genet 2023; 19:e1010567. [PMID: 36656803 PMCID: PMC9851565 DOI: 10.1371/journal.pgen.1010567] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023] Open
Abstract
It is a generally accepted model that environmental influences can exert their effects, at least in part, by changing the molecular regulators of transcription that are described as epigenetic. As there is biochemical evidence that some epigenetic regulators of transcription can maintain their states long term and through cell division, an epigenetic model encompasses the idea of maintenance of the effect of an exposure long after it is no longer present. The evidence supporting this model is mostly from the observation of alterations of molecular regulators of transcription following exposures. With the understanding that the interpretation of these associations is more complex than originally recognised, this model may be oversimplistic; therefore, adopting novel perspectives and experimental approaches when examining how environmental exposures are linked to phenotypes may prove worthwhile. In this review, we have chosen to use the example of nonalcoholic fatty liver disease (NAFLD), a common, complex human disease with strong environmental and genetic influences. We describe how epigenomic approaches combined with emerging functional genetic and single-cell genomic techniques are poised to generate new insights into the pathogenesis of environmentally influenced human disease phenotypes exemplified by NAFLD.
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Affiliation(s)
- Julia J. Wattacheril
- Department of Medicine, Center for Liver Disease and Transplantation, Columbia University Irving Medical Center, New York Presbyterian Hospital, New York, New York, United States of America
| | - Srilakshmi Raj
- Division of Genomics, Department of Genetics, Albert Einstein College of Medicine, Bronx, New York, United States of America
| | - David A. Knowles
- New York Genome Center, New York, New York, United States of America
- Department of Computer Science, Columbia University, New York, New York, United States of America
- Department of Systems Biology, Columbia University, New York, New York, United States of America
| | - John M. Greally
- Division of Genomics, Department of Genetics, Albert Einstein College of Medicine, Bronx, New York, United States of America
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17
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Lázaro-Guevara JM, Flores-Robles BJ, Garrido-Lopez KM, McKeown RJ, Flores-Morán AE, Labrador-Sánchez E, Pinillos-Aransay V, Trasahedo EA, López-Martín JA, Soberanis LSR, Melgar MY, Téllez-Arreola JL, Thébault SC. Identification of RP1 as the genetic cause of retinitis pigmentosa in a multi-generational pedigree using Extremely Low-Coverage Whole Genome Sequencing (XLC-WGS). Gene X 2023; 851:146956. [DOI: 10.1016/j.gene.2022.146956] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2022] [Revised: 09/25/2022] [Accepted: 10/03/2022] [Indexed: 11/04/2022] Open
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18
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Kalemkeridou M, Nanas I, Moutou K, Amiridis GS, Tsipourlianos A, Dovolou E, Mamuris Z, Giannoulis T. Genetic diversity and thermotolerance in Holstein cows: Pathway analysis and marker development using whole-genome sequencing. Reprod Domest Anim 2023; 58:146-157. [PMID: 36196498 DOI: 10.1111/rda.14274] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2022] [Accepted: 10/02/2022] [Indexed: 01/07/2023]
Abstract
Heat stress causes extensive losses in the dairy sector, due to negative effects on milk production and reproduction. Cows have evolved a series of protective mechanisms, (physiological, biochemical, behavioural) to cope with the thermostressing environments, which have allowed the preservation of productive and reproductive potential of specific animals during summer; these animals are considered thermotolerant and could be used to design programs of selective breeding. These programs, targeting the generations of a population of heat-resistant animals, would increase the frequency of the desired phenotypes, tackling the financial losses on one hand and reducing the carbon footprints of the dairy sector on the other. The development of genomics techniques has enabled genome wide variant calling, to detect SNPs associated with the desired phenotypes. In this study, we used a comparative genomics approach to detect genetic variation associated with thermotolerance and to design molecular markers for characterizing the animals as tolerant/sensitive. A total of 40 cows from each group were split in four sequencing pools and a whole-genome sequencing approach was used. Results and conclusion: Genome-wide genetic variation between groups was characterized and enrichment analysis revealed specific pathways which participate in the adaptive mechanisms of thermotolerance, implicated into systemic and cellular responses, including the immune system functionality, Heat Stress and Unfolded Protein Response. The markers made a promising set of results, as specific SNPs in five genes encoding for Heat Shock Proteins were significantly associated with thermotolerance.
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Affiliation(s)
- Maria Kalemkeridou
- Laboratory of Genetics, Comparative and Evolutionary Biology, Department of Biochemistry and Biotechnology, University of Thessaly, Larissa, Greece
| | - Ioannis Nanas
- Department of Obstetrics and Reproduction, Veterinary Faculty, University of Thessaly, Karditsa, Greece
| | - Katerina Moutou
- Laboratory of Genetics, Comparative and Evolutionary Biology, Department of Biochemistry and Biotechnology, University of Thessaly, Larissa, Greece
| | - Georgios S Amiridis
- Department of Obstetrics and Reproduction, Veterinary Faculty, University of Thessaly, Karditsa, Greece
| | - Andreas Tsipourlianos
- Laboratory of Genetics, Comparative and Evolutionary Biology, Department of Biochemistry and Biotechnology, University of Thessaly, Larissa, Greece
| | - Eleni Dovolou
- Department of Obstetrics and Reproduction, Veterinary Faculty, University of Thessaly, Karditsa, Greece.,Department of Animal Sciences, University of Thessaly, Larissa, Greece
| | - Zissis Mamuris
- Laboratory of Genetics, Comparative and Evolutionary Biology, Department of Biochemistry and Biotechnology, University of Thessaly, Larissa, Greece
| | - Themistoklis Giannoulis
- Department of Animal Sciences, University of Thessaly, Larissa, Greece.,Laboratory of Biology, Genetics and Bioinformatics, Department of Animal Science, University of Thessaly, Larissa, Greece
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19
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A Genome-Wide Association Study Reveals a BDNF-Centered Molecular Network Associated with Alcohol Dependence and Related Clinical Measures. Biomedicines 2022; 10:biomedicines10123007. [PMID: 36551763 PMCID: PMC9775455 DOI: 10.3390/biomedicines10123007] [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: 10/15/2022] [Revised: 11/15/2022] [Accepted: 11/18/2022] [Indexed: 11/24/2022] Open
Abstract
At least 50% of factors predisposing to alcohol dependence (AD) are genetic and women affected with this disorder present with more psychiatric comorbidities, probably indicating different genetic factors involved. We aimed to run a genome-wide association study (GWAS) followed by a bioinformatic functional annotation of associated genomic regions in patients with AD and eight related clinical measures. A genome-wide significant association of rs220677 with AD (p-value = 1.33 × 10-8 calculated with the Yates-corrected χ2 test under the assumption of dominant inheritance) was discovered in female patients. Associations of AD and related clinical measures with seven other single nucleotide polymorphisms listed in previous GWASs of psychiatric and addiction traits were differently replicated in male and female patients. The bioinformatic analysis showed that regulatory elements in the eight associated linkage disequilibrium blocks define the expression of 80 protein-coding genes. Nearly 68% of these and of 120 previously published coding genes associated with alcohol phenotypes directly interact in a single network, where BDNF is the most significant hub gene. This study indicates that several genes behind the pathogenesis of AD are different in male and female patients, but implicated molecular mechanisms are functionally connected. The study also reveals a central role of BDNF in the pathogenesis of AD.
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20
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Wang D, Xie K, Wang Y, Hu J, Li W, Yang A, Zhang Q, Ning C, Fan X. Cost-effectively dissecting the genetic architecture of complex wool traits in rabbits by low-coverage sequencing. Genet Sel Evol 2022; 54:75. [PMCID: PMC9673297 DOI: 10.1186/s12711-022-00766-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Accepted: 10/31/2022] [Indexed: 11/19/2022] Open
Abstract
Background Rabbit wool traits are important in fiber production and for model organism research on hair growth, but their genetic architecture remains obscure. In this study, we focused on wool characteristics in Angora rabbits, a breed well-known for the quality of its wool. Considering the cost to generate population-scale sequence data and the biased detection of variants using chip data, developing an effective genotyping strategy using low-coverage whole-genome sequencing (LCS) data is necessary to conduct genetic analyses. Results Different genotype imputation strategies (BaseVar + STITCH, Bcftools + Beagle4, and GATK + Beagle5), sequencing coverages (0.1X, 0.5X, 1.0X, 1.5X, and 2.0X), and sample sizes (100, 200, 300, 400, 500, and 600) were compared. Our results showed that using BaseVar + STITCH at a sequencing depth of 1.0X with a sample size larger than 300 resulted in the highest genotyping accuracy, with a genotype concordance higher than 98.8% and genotype accuracy higher than 0.97. We performed multivariate genome-wide association studies (GWAS), followed by conditional GWAS and estimation of the confidence intervals of quantitative trait loci (QTL) to investigate the genetic architecture of wool traits. Six QTL were detected, which explained 0.4 to 7.5% of the phenotypic variation. Gene-level mapping identified the fibroblast growth factor 10 (FGF10) gene as associated with fiber growth and diameter, which agrees with previous results from functional data analyses on the FGF gene family in other species, and is relevant for wool rabbit breeding. Conclusions We suggest that LCS followed by imputation can be a cost-effective alternative to array and high-depth sequencing for assessing common variants. GWAS combined with LCS can identify new QTL and candidate genes that are associated with quantitative traits. This study provides a cost-effective and powerful method for investigating the genetic architecture of complex traits, which will be useful for genomic breeding applications. Supplementary Information The online version contains supplementary material available at 10.1186/s12711-022-00766-y.
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Affiliation(s)
- Dan Wang
- grid.440622.60000 0000 9482 4676College of Animal Science and Veterinary Medicine, Shandong Agricultural University, Tai’an, China
| | - Kerui Xie
- grid.440622.60000 0000 9482 4676College of Animal Science and Veterinary Medicine, Shandong Agricultural University, Tai’an, China
| | - Yanyan Wang
- grid.440622.60000 0000 9482 4676College of Animal Science and Veterinary Medicine, Shandong Agricultural University, Tai’an, China
| | - Jiaqing Hu
- grid.440622.60000 0000 9482 4676College of Animal Science and Veterinary Medicine, Shandong Agricultural University, Tai’an, China
| | - Wenqiang Li
- grid.440622.60000 0000 9482 4676College of Animal Science and Veterinary Medicine, Shandong Agricultural University, Tai’an, China
| | - Aiguo Yang
- grid.440622.60000 0000 9482 4676College of Animal Science and Veterinary Medicine, Shandong Agricultural University, Tai’an, China
| | - Qin Zhang
- grid.440622.60000 0000 9482 4676College of Animal Science and Veterinary Medicine, Shandong Agricultural University, Tai’an, China
| | - Chao Ning
- grid.440622.60000 0000 9482 4676College of Animal Science and Veterinary Medicine, Shandong Agricultural University, Tai’an, China
| | - Xinzhong Fan
- grid.440622.60000 0000 9482 4676College of Animal Science and Veterinary Medicine, Shandong Agricultural University, Tai’an, China
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21
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Wragg D, Eynard SE, Basso B, Canale‐Tabet K, Labarthe E, Bouchez O, Bienefeld K, Bieńkowska M, Costa C, Gregorc A, Kryger P, Parejo M, Pinto MA, Bidanel J, Servin B, Le Conte Y, Vignal A. Complex population structure and haplotype patterns in the Western European honey bee from sequencing a large panel of haploid drones. Mol Ecol Resour 2022; 22:3068-3086. [PMID: 35689802 PMCID: PMC9796960 DOI: 10.1111/1755-0998.13665] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2021] [Revised: 05/26/2022] [Accepted: 06/01/2022] [Indexed: 01/07/2023]
Abstract
Honey bee subspecies originate from specific geographical areas in Africa, Europe and the Middle East, and beekeepers interested in specific phenotypes have imported genetic material to regions outside of the bees' original range for use either in pure lines or controlled crosses. Moreover, imported drones are present in the environment and mate naturally with queens from the local subspecies. The resulting admixture complicates population genetics analyses, and population stratification can be a major problem for association studies. To better understand Western European honey bee populations, we produced a whole genome sequence and single nucleotide polymorphism (SNP) genotype data set from 870 haploid drones and demonstrate its utility for the identification of nine genetic backgrounds and various degrees of admixture in a subset of 629 samples. Five backgrounds identified correspond to subspecies, two to isolated populations on islands and two to managed populations. We also highlight several large haplotype blocks, some of which coincide with the position of centromeres. The largest is 3.6 Mb long and represents 21% of chromosome 11, with two major haplotypes corresponding to the two dominant genetic backgrounds identified. This large naturally phased data set is available as a single vcf file that can now serve as a reference for subsequent populations genomics studies in the honey bee, such as (i) selecting individuals of verified homogeneous genetic backgrounds as references, (ii) imputing genotypes from a lower-density data set generated by an SNP-chip or by low-pass sequencing, or (iii) selecting SNPs compatible with the requirements of genotyping chips.
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Affiliation(s)
- David Wragg
- GenPhySEUniversité de Toulouse, INRAE, INPT, INP‐ENVTCastanet TolosanFrance,Roslin InstituteUniversity of EdinburghMidlothianUK
| | - Sonia E. Eynard
- GenPhySEUniversité de Toulouse, INRAE, INPT, INP‐ENVTCastanet TolosanFrance
| | - Benjamin Basso
- Institut de l'abeille (ITSAP), UMT PrADEAvignonFrance,INRAE, UR 406 Abeilles et Environment, UMT PrADEAvignonFrance
| | | | | | | | | | | | - Cecilia Costa
- CREA Research Centre for Agriculture and EnvironmentBolognaItaly
| | - Aleš Gregorc
- Faculty of Agriculture and Life SciencesUniversity of MariborPivolaSlovenia
| | - Per Kryger
- Department of Agroecology, Science and TechnologyAarhus UniversitySlagelseDenmark
| | - Melanie Parejo
- Agroscope, Swiss Bee Research CentreBernSwitzerland,Applied Genomics and Bioinformatics, Department of Genetics, Physical Anthropology and Animal PhysiologyUniversity of the Basque CountryLeioaSpain
| | - M. Alice Pinto
- Centro de Investigação de Montanha (CIMO)Instituto Politécnico de BragançaBragançaPortugal
| | | | - Bertrand Servin
- GenPhySEUniversité de Toulouse, INRAE, INPT, INP‐ENVTCastanet TolosanFrance
| | - Yves Le Conte
- INRAE, UR 406 Abeilles et Environment, UMT PrADEAvignonFrance
| | - Alain Vignal
- GenPhySEUniversité de Toulouse, INRAE, INPT, INP‐ENVTCastanet TolosanFrance
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22
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Lopes UV, Pires JL, Gramacho KP, Grattapaglia D. Genome-wide SNP genotyping as a simple and practical tool to accelerate the development of inbred lines in outbred tree species: An example in cacao (Theobroma cacao L.). PLoS One 2022; 17:e0270437. [PMID: 36288356 PMCID: PMC9604995 DOI: 10.1371/journal.pone.0270437] [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: 06/09/2022] [Accepted: 10/13/2022] [Indexed: 11/06/2022] Open
Abstract
Cacao is a globally important crop with a long history of domestication and selective breeding. Despite the increased use of elite clones by cacao farmers, worldwide plantations are established mainly using hybrid progeny material derived from heterozygous parents, therefore displaying high tree-to-tree variability. The deliberate development of hybrids from advanced inbred lines produced by successive generations of self-pollination has not yet been fully considered in cacao breeding. This is largely due to the self-incompatibility of the species, the long generation cycles (3–5 years) and the extensive trial areas needed to accomplish the endeavor. We propose a simple and accessible approach to develop inbred lines based on accelerating the buildup of homozygosity based on regular selfing assisted by genome-wide SNP genotyping. In this study we genotyped 90 clones from the Brazilian CEPEC´s germplasm collection and 49 inbred offspring of six S1 or S2 cacao families derived from self-pollinating clones CCN-51, PS-13.19, TSH-1188 and SIAL-169. A set of 3,380 SNPs distributed across the cacao genome were interrogated on the EMBRAPA multi-species 65k Infinium chip. The 90 cacao clones showed considerable variation in genome-wide SNP homozygosity (mean 0.727± 0.182) and 19 of them with homozygosity ≥90%. By assessing the increase in homozygosity across two generations of self-pollinations, SNP data revealed the wide variability in homozygosity within and between S1 and S2 families. Even in small families (<10 sibs), individuals were identified with up to ~1.5 standard deviations above the family mean homozygosity. From baseline homozygosities of 0.476 and 0.454, offspring with homozygosities of 0.862 and 0.879 were recovered for clones TSH-1188 and CCN-51 respectively, in only two generations of selfing (81–93% increase). SNP marker assisted monitoring and selection of inbred individuals can be a practical tool to optimize and accelerate the development of inbred lines of outbred tree species. This approach will allow a faster and more accurate exploitation of hybrid breeding strategies in cacao improvement programs and potentially in other perennial fruit and forest trees.
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Affiliation(s)
| | | | | | - Dario Grattapaglia
- Plant Genetics Laboratory, EMBRAPA Genetic Resources and Biotechnology, Brasilia, Brazil
- * E-mail:
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23
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Association of protein function-altering variants with cardiometabolic traits: the strong heart study. Sci Rep 2022; 12:9317. [PMID: 35665752 PMCID: PMC9167281 DOI: 10.1038/s41598-022-12866-2] [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: 01/15/2022] [Accepted: 05/05/2022] [Indexed: 11/08/2022] Open
Abstract
Clinical and biomarker phenotypic associations for carriers of protein function-altering variants may help to elucidate gene function and health effects in populations. We genotyped 1127 Strong Heart Family Study participants for protein function-altering single nucleotide variants (SNV) and indels selected from a low coverage whole exome sequencing of American Indians. We tested the association of each SNV/indel with 35 cardiometabolic traits. Among 1206 variants (average minor allele count = 20, range of 1 to 1064), ~ 43% were not present in publicly available repositories. We identified seven SNV-trait significant associations including a missense SNV at ABCA10 (rs779392624, p = 8 × 10-9) associated with fasting triglycerides, which gene product is involved in macrophage lipid homeostasis. Among non-diabetic individuals, missense SNVs at four genes were associated with fasting insulin adjusted for BMI (PHIL, chr6:79,650,711, p = 2.1 × 10-6; TRPM3, rs760461668, p = 5 × 10-8; SPTY2D1, rs756851199, p = 1.6 × 10-8; and TSPO, rs566547284, p = 2.4 × 10-6). PHIL encoded protein is involved in pancreatic β-cell proliferation and survival, and TRPM3 protein mediates calcium signaling in pancreatic β-cells in response to glucose. A genetic risk score combining increasing insulin risk alleles of these four genes was associated with 53% (95% confidence interval 1.09, 2.15) increased odds of incident diabetes and 83% (95% confidence interval 1.35, 2.48) increased odds of impaired fasting glucose at follow-up. Our study uncovered novel gene-trait associations through the study of protein-coding variants and demonstrates the advantages of association screenings targeting diverse and high-risk populations to study variants absent in publicly available repositories.
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24
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Auwerx C, Sadler MC, Reymond A, Kutalik Z. From pharmacogenetics to pharmaco-omics: Milestones and future directions. HGG ADVANCES 2022; 3:100100. [PMID: 35373152 PMCID: PMC8971318 DOI: 10.1016/j.xhgg.2022.100100] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022] Open
Abstract
The origins of pharmacogenetics date back to the 1950s, when it was established that inter-individual differences in drug response are partially determined by genetic factors. Since then, pharmacogenetics has grown into its own field, motivated by the translation of identified gene-drug interactions into therapeutic applications. Despite numerous challenges ahead, our understanding of the human pharmacogenetic landscape has greatly improved thanks to the integration of tools originating from disciplines as diverse as biochemistry, molecular biology, statistics, and computer sciences. In this review, we discuss past, present, and future developments of pharmacogenetics methodology, focusing on three milestones: how early research established the genetic basis of drug responses, how technological progress made it possible to assess the full extent of pharmacological variants, and how multi-dimensional omics datasets can improve the identification, functional validation, and mechanistic understanding of the interplay between genes and drugs. We outline novel strategies to repurpose and integrate molecular and clinical data originating from biobanks to gain insights analogous to those obtained from randomized controlled trials. Emphasizing the importance of increased diversity, we envision future directions for the field that should pave the way to the clinical implementation of pharmacogenetics.
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Affiliation(s)
- Chiara Auwerx
- Center for Integrative Genomics, University of Lausanne, Lausanne, Switzerland
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
- University Center for Primary Care and Public Health, Lausanne, Switzerland
| | - Marie C. Sadler
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
- University Center for Primary Care and Public Health, Lausanne, Switzerland
| | - Alexandre Reymond
- Center for Integrative Genomics, University of Lausanne, Lausanne, Switzerland
| | - Zoltán Kutalik
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
- University Center for Primary Care and Public Health, Lausanne, Switzerland
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25
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Chat V, Ferguson R, Morales L, Kirchhoff T. Ultra Low-Coverage Whole-Genome Sequencing as an Alternative to Genotyping Arrays in Genome-Wide Association Studies. Front Genet 2022; 12:790445. [PMID: 35251117 PMCID: PMC8889143 DOI: 10.3389/fgene.2021.790445] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2021] [Accepted: 12/09/2021] [Indexed: 11/25/2022] Open
Abstract
An array-based genotyping approach has been the standard practice for genome-wide association studies (GWASs); however, as sequencing costs plummet over the past years, ultra low-coverage whole-genome sequencing (ulcWGS <0.5× coverage) has emerged as a promising alternative that provides superior genomic coverage with substantial reduction of genotyping cost. To evaluate the potential utility of ulcWGS, we performed a whole-genome sequencing (WGS) of 72 European individuals to a target coverage of 0.4× and compared its performance with the widely used Infinium Global Screening Multi-Disease Array (GSA-MD). We showed that the number of variants captured by ulcWGS is comparable with imputed GSA-MD platform, particularly for low-frequency (95.5%) and common variants (99.9%), with high imputation R2 accuracy (mean 0.93 for SNPs and 0.86 for indels). Using deep-coverage 30× WGS as the “truth” genotypes, we found that ulcWGS has higher overall nonreference genotype concordance compared with imputed GSA-MD for both SNPs (0.90 vs. 0.88) and indels (0.86 vs. 0.83). In addition, ulcWGS proved to be as sensitive as the genotyping-based method in sex imputation and ancestry prediction producing similar principal component (PC) scores. Our findings provide important evidence that the cost efficient ulcWGS of <0.5× generates high genotype accuracy, outperforming the standard genotyping arrays, making it an attractive alternative to the array-based method in next-generation GWAS design.
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Affiliation(s)
- Vylyny Chat
- Perlmutter Cancer Center, New York University School of Medicine, New York, NY, United States
- Departments of Population Health and Environmental Medicine, New York University School of Medicine, New York, NY, United States
- The Interdisciplinary Melanoma Cooperative Group, New York University School of Medicine, New York, NY, United States
| | - Robert Ferguson
- Perlmutter Cancer Center, New York University School of Medicine, New York, NY, United States
- Departments of Population Health and Environmental Medicine, New York University School of Medicine, New York, NY, United States
- The Interdisciplinary Melanoma Cooperative Group, New York University School of Medicine, New York, NY, United States
| | - Leah Morales
- Perlmutter Cancer Center, New York University School of Medicine, New York, NY, United States
- Departments of Population Health and Environmental Medicine, New York University School of Medicine, New York, NY, United States
- The Interdisciplinary Melanoma Cooperative Group, New York University School of Medicine, New York, NY, United States
| | - Tomas Kirchhoff
- Perlmutter Cancer Center, New York University School of Medicine, New York, NY, United States
- Departments of Population Health and Environmental Medicine, New York University School of Medicine, New York, NY, United States
- The Interdisciplinary Melanoma Cooperative Group, New York University School of Medicine, New York, NY, United States
- *Correspondence: Tomas Kirchhoff,
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26
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Teng J, Zhao C, Wang D, Chen Z, Tang H, Li J, Mei C, Yang Z, Ning C, Zhang Q. Assessment of the performance of different imputation methods for low-coverage sequencing in Holstein cattle. J Dairy Sci 2022; 105:3355-3366. [DOI: 10.3168/jds.2021-21360] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2021] [Accepted: 12/13/2021] [Indexed: 12/27/2022]
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27
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Cavedon M, vonHoldt B, Hebblewhite M, Hegel T, Heppenheimer E, Hervieux D, Mariani S, Schwantje H, Steenweg R, Theoret J, Watters M, Musiani M. Genomic legacy of migration in endangered caribou. PLoS Genet 2022; 18:e1009974. [PMID: 35143486 PMCID: PMC8830729 DOI: 10.1371/journal.pgen.1009974] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2021] [Accepted: 12/01/2021] [Indexed: 11/18/2022] Open
Abstract
Wide-ranging animals, including migratory species, are significantly threatened by the effects of habitat fragmentation and habitat loss. In the case of terrestrial mammals, this results in nearly a quarter of species being at risk of extinction. Caribou are one such example of a wide-ranging, migratory, terrestrial, and endangered mammal. In populations of caribou, the proportion of individuals considered as "migrants" can vary dramatically. There is therefore a possibility that, under the condition that migratory behavior is genetically determined, those individuals or populations that are migratory will be further impacted by humans, and this impact could result in the permanent loss of the migratory trait in some populations. However, genetic determination of migration has not previously been studied in an endangered terrestrial mammal. We examined migratory behavior of 139 GPS-collared endangered caribou in western North America and carried out genomic scans for the same individuals. Here we determine a genetic subdivision of caribou into a Northern and a Southern genetic cluster. We also detect >50 SNPs associated with migratory behavior, which are in genes with hypothesized roles in determining migration in other organisms. Furthermore, we determine that propensity to migrate depends upon the proportion of ancestry in individual caribou, and thus on the evolutionary history of its migratory and sedentary subspecies. If, as we report, migratory behavior is influenced by genes, caribou could be further impacted by the loss of the migratory trait in some isolated populations already at low numbers. Our results indicating an ancestral genetic component also suggest that the migratory trait and their associated genetic mutations could not be easily re-established when lost in a population.
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Affiliation(s)
- Maria Cavedon
- Faculty of Environmental Design, University of Calgary, Calgary, Alberta, Canada
| | - Bridgett vonHoldt
- Department of Ecology & Evolutionary Biology, Princeton University, Princeton, New Jersey, United States of America
| | - Mark Hebblewhite
- Wildlife Biology Program, Department of Ecosystem and Conservation Sciences, College of Forestry and Conservation, University of Montana, Missoula, Montana, United States of America
| | - Troy Hegel
- Yukon Department of Environment, Whitehorse, Yukon, Canada
| | - Elizabeth Heppenheimer
- Department of Ecology & Evolutionary Biology, Princeton University, Princeton, New Jersey, United States of America
| | - Dave Hervieux
- Fish and Wildlife Stewardship Branch, Alberta Environment and Parks, Grande Prairie, Alberta, Canada
| | - Stefano Mariani
- School of Natural Sciences and Psychology, Liverpool John Moores University, Liverpool, United Kingdom
| | - Helen Schwantje
- Wildlife and Habitat Branch, Ministry of Forests, Lands, Natural Resource Operations and Rural Development, Government of British Columbia, Nanaimo, British Columbia, Canada
| | - Robin Steenweg
- Pacific Region, Canadian Wildlife Service, Environment and Climate Change Canada, Delta, British Columbia, Canada
| | - Jessica Theoret
- Faculty of Environmental Design, University of Calgary, Calgary, Alberta, Canada
| | - Megan Watters
- Land and Resource Specialist, Fort St. John, British Columbia, Canada
| | - Marco Musiani
- Department of Biological Sciences, Faculty of Science and Veterinary Medicine (Joint Appointment), University of Calgary, Calgary, Alberta, Canada
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28
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Zhang Z, Ma P, Zhang Z, Wang Z, Wang Q, Pan Y. The construction of a haplotype reference panel using extremely low coverage whole genome sequences and its application in genome-wide association studies and genomic prediction in Duroc pigs. Genomics 2021; 114:340-350. [PMID: 34929285 DOI: 10.1016/j.ygeno.2021.12.016] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2021] [Revised: 10/11/2021] [Accepted: 12/15/2021] [Indexed: 12/30/2022]
Abstract
Extremely low coverage whole genome sequencing (lcWGS) is an economical technique to obtain high-density single nucleotide polymorphisms (SNPs). Here, we explored the feasibility of constructing a haplotype reference panel (lcHRP) using lcWGS and evaluated the effects of lcHRP through a genome-wide association study (GWAS) and genomic prediction in pigs. A total of 297 and 974 Duroc pigs were genotyped using lcWGS and a 50 K SNP array, respectively. We obtained 19,306,498 SNPs using lcWGS with an accuracy of 0.984. With the help of lcHRP, the accuracy of imputation from the SNP array to lcWGS was 0.922. Compared to the SNP array findings, those from the imputation-based GWAS identified more signals across four traits. With the integration of the top 1% imputation-based GWAS findings as genomic features, the accuracies of genomic prediction was improved by 6.0% to 13.2%. This study showed the great potential of lcWGS in pigs' molecular breeding.
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Affiliation(s)
- Zhe Zhang
- Department of Animal Science, College of Animal Sciences, Zhejiang University, Hangzhou 310058, PR China
| | - Peipei Ma
- Department of Animal Science, School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai 200240, PR China
| | - Zhenyang Zhang
- Department of Animal Science, College of Animal Sciences, Zhejiang University, Hangzhou 310058, PR China
| | - Zhen Wang
- Department of Animal Science, College of Animal Sciences, Zhejiang University, Hangzhou 310058, PR China
| | - Qishan Wang
- Department of Animal Science, College of Animal Sciences, Zhejiang University, Hangzhou 310058, PR China.
| | - Yuchun Pan
- Department of Animal Science, College of Animal Sciences, Zhejiang University, Hangzhou 310058, PR China; Hainan Institute, Zhejiang University, Yongyou Industry Park, Yazhou Bay Sci-Tech City, Sanya 572000, China.
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Gusev A, Groha S, Taraszka K, Semenov YR, Zaitlen N. Constructing germline research cohorts from the discarded reads of clinical tumor sequences. Genome Med 2021; 13:179. [PMID: 34749793 PMCID: PMC8576948 DOI: 10.1186/s13073-021-00999-4] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2021] [Accepted: 10/28/2021] [Indexed: 12/02/2022] Open
Abstract
Background Hundreds of thousands of cancer patients have had targeted (panel) tumor sequencing to identify clinically meaningful mutations. In addition to improving patient outcomes, this activity has led to significant discoveries in basic and translational domains. However, the targeted nature of clinical tumor sequencing has a limited scope, especially for germline genetics. In this work, we assess the utility of discarded, off-target reads from tumor-only panel sequencing for the recovery of genome-wide germline genotypes through imputation. Methods We developed a framework for inference of germline variants from tumor panel sequencing, including imputation, quality control, inference of genetic ancestry, germline polygenic risk scores, and HLA alleles. We benchmarked our framework on 833 individuals with tumor sequencing and matched germline SNP array data. We then applied our approach to a prospectively collected panel sequencing cohort of 25,889 tumors. Results We demonstrate high to moderate accuracy of each inferred feature relative to direct germline SNP array genotyping: individual common variants were imputed with a mean accuracy (correlation) of 0.86, genetic ancestry was inferred with a correlation of > 0.98, polygenic risk scores were inferred with a correlation of > 0.90, and individual HLA alleles were inferred with a correlation of > 0.80. We demonstrate a minimal influence on the accuracy of somatic copy number alterations and other tumor features. We showcase the feasibility and utility of our framework by analyzing 25,889 tumors and identifying the relationships between genetic ancestry, polygenic risk, and tumor characteristics that could not be studied with conventional on-target tumor data. Conclusions We conclude that targeted tumor sequencing can be leveraged to build rich germline research cohorts from existing data and make our analysis pipeline publicly available to facilitate this effort. Supplementary Information The online version contains supplementary material available at 10.1186/s13073-021-00999-4.
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Affiliation(s)
- Alexander Gusev
- Division of Population Sciences, Dana-Farber Cancer Institute and Harvard Medical School, Boston, MA, USA. .,Division of Genetics, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA. .,The Broad Institute of MIT & Harvard, Cambridge, MA, USA.
| | - Stefan Groha
- Division of Population Sciences, Dana-Farber Cancer Institute and Harvard Medical School, Boston, MA, USA.,The Broad Institute of MIT & Harvard, Cambridge, MA, USA
| | - Kodi Taraszka
- Departments of Neurology and Computational Medicine, UCLA, Los Angeles, CA, USA
| | - Yevgeniy R Semenov
- Department of Dermatology, Massachusetts General Hospital, Boston, MA, USA
| | - Noah Zaitlen
- Departments of Neurology and Computational Medicine, UCLA, Los Angeles, CA, USA.
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Emde AK, Phipps-Green A, Cadzow M, Gallagher CS, Major TJ, Merriman ME, Topless RK, Takei R, Dalbeth N, Murphy R, Stamp LK, de Zoysa J, Wilcox PL, Fox K, Wasik KA, Merriman TR, Castel SE. Mid-pass whole genome sequencing enables biomedical genetic studies of diverse populations. BMC Genomics 2021; 22:666. [PMID: 34719381 PMCID: PMC8559369 DOI: 10.1186/s12864-021-07949-9] [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: 08/10/2021] [Accepted: 08/25/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Historically, geneticists have relied on genotyping arrays and imputation to study human genetic variation. However, an underrepresentation of diverse populations has resulted in arrays that poorly capture global genetic variation, and a lack of reference panels. This has contributed to deepening global health disparities. Whole genome sequencing (WGS) better captures genetic variation but remains prohibitively expensive. Thus, we explored WGS at "mid-pass" 1-7x coverage. RESULTS Here, we developed and benchmarked methods for mid-pass sequencing. When applied to a population without an existing genomic reference panel, 4x mid-pass performed consistently well across ethnicities, with high recall (98%) and precision (97.5%). CONCLUSION Compared to array data imputed into 1000 Genomes, mid-pass performed better across all metrics and identified novel population-specific variants with potential disease relevance. We hope our work will reduce financial barriers for geneticists from underrepresented populations to characterize their genomes prior to biomedical genetic applications.
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Affiliation(s)
| | | | - Murray Cadzow
- Department of Biochemistry, University of Otago, Dunedin, New Zealand
| | | | - Tanya J Major
- Department of Biochemistry, University of Otago, Dunedin, New Zealand
| | | | - Ruth K Topless
- Department of Biochemistry, University of Otago, Dunedin, New Zealand
| | - Riku Takei
- Department of Biochemistry, University of Otago, Dunedin, New Zealand
- Division of Clinical Immunology and Rheumatology, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Nicola Dalbeth
- Department of Medicine, University of Auckland, Auckland, New Zealand
| | - Rinki Murphy
- Department of Medicine, University of Auckland, Auckland, New Zealand
| | - Lisa K Stamp
- University of Otago Christchurch, Christchurch, New Zealand
| | - Janak de Zoysa
- Department of Medicine, University of Auckland, Auckland, New Zealand
| | - Philip L Wilcox
- Department of Mathematics and Statistics, University of Otago, Dunedin, New Zealand
| | - Keolu Fox
- Departments of Anthropology and Global Health, University of California, San Diego, CA, USA
| | | | - Tony R Merriman
- Department of Biochemistry, University of Otago, Dunedin, New Zealand.
- Division of Clinical Immunology and Rheumatology, University of Alabama at Birmingham, Birmingham, AL, USA.
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Casellas J, Martín de Hijas-Villalba M, Vázquez-Gómez M, Id-Lahoucine S. Low-coverage whole-genome sequencing in livestock species for individual traceability and parentage testing. Livest Sci 2021. [DOI: 10.1016/j.livsci.2021.104629] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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Wu XL, Zhao S. Editorial: Advances in Genomics of Crossbred Farm Animals. Front Genet 2021; 12:709483. [PMID: 34422012 PMCID: PMC8375302 DOI: 10.3389/fgene.2021.709483] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2021] [Accepted: 07/13/2021] [Indexed: 01/01/2023] Open
Affiliation(s)
- Xiao-Lin Wu
- Council on Dairy Cattle Breeding, Bowie, MD, United States.,Department of Animal and Dairy Sciences, University of Wisconsin, Madison, WI, United States
| | - Shuhong Zhao
- College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, China
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Ward LD, Tu HC, Quenneville CB, Tsour S, Flynn-Carroll AO, Parker MM, Deaton AM, Haslett PAJ, Lotta LA, Verweij N, Ferreira MAR, Baras A, Hinkle G, Nioi P. GWAS of serum ALT and AST reveals an association of SLC30A10 Thr95Ile with hypermanganesemia symptoms. Nat Commun 2021; 12:4571. [PMID: 34315874 PMCID: PMC8316433 DOI: 10.1038/s41467-021-24563-1] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2020] [Accepted: 06/23/2021] [Indexed: 02/07/2023] Open
Abstract
Understanding mechanisms of hepatocellular damage may lead to new treatments for liver disease, and genome-wide association studies (GWAS) of alanine aminotransferase (ALT) and aspartate aminotransferase (AST) serum activities have proven useful for investigating liver biology. Here we report 100 loci associating with both enzymes, using GWAS across 411,048 subjects in the UK Biobank. The rare missense variant SLC30A10 Thr95Ile (rs188273166) associates with the largest elevation of both enzymes, and this association replicates in the DiscovEHR study. SLC30A10 excretes manganese from the liver to the bile duct, and rare homozygous loss of function causes the syndrome hypermanganesemia with dystonia-1 (HMNDYT1) which involves cirrhosis. Consistent with hematological symptoms of hypermanganesemia, SLC30A10 Thr95Ile carriers have increased hematocrit and risk of iron deficiency anemia. Carriers also have increased risk of extrahepatic bile duct cancer. These results suggest that genetic variation in SLC30A10 adversely affects more individuals than patients with diagnosed HMNDYT1.
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Affiliation(s)
- Lucas D. Ward
- grid.417897.40000 0004 0506 3000Alnylam Pharmaceuticals, Cambridge, MA USA
| | - Ho-Chou Tu
- grid.417897.40000 0004 0506 3000Alnylam Pharmaceuticals, Cambridge, MA USA
| | | | - Shira Tsour
- grid.417897.40000 0004 0506 3000Alnylam Pharmaceuticals, Cambridge, MA USA
| | | | - Margaret M. Parker
- grid.417897.40000 0004 0506 3000Alnylam Pharmaceuticals, Cambridge, MA USA
| | - Aimee M. Deaton
- grid.417897.40000 0004 0506 3000Alnylam Pharmaceuticals, Cambridge, MA USA
| | | | - Luca A. Lotta
- grid.418961.30000 0004 0472 2713Regeneron Genetics Center, Tarrytown, NY USA
| | - Niek Verweij
- grid.418961.30000 0004 0472 2713Regeneron Genetics Center, Tarrytown, NY USA
| | | | | | | | - Aris Baras
- grid.418961.30000 0004 0472 2713Regeneron Genetics Center, Tarrytown, NY USA
| | - Gregory Hinkle
- grid.417897.40000 0004 0506 3000Alnylam Pharmaceuticals, Cambridge, MA USA
| | - Paul Nioi
- grid.417897.40000 0004 0506 3000Alnylam Pharmaceuticals, Cambridge, MA USA
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Yang R, Guo X, Zhu D, Tan C, Bian C, Ren J, Huang Z, Zhao Y, Cai G, Liu D, Wu Z, Wang Y, Li N, Hu X. Accelerated deciphering of the genetic architecture of agricultural economic traits in pigs using a low-coverage whole-genome sequencing strategy. Gigascience 2021; 10:giab048. [PMID: 34282453 PMCID: PMC8290195 DOI: 10.1093/gigascience/giab048] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2020] [Revised: 03/21/2021] [Accepted: 06/15/2021] [Indexed: 11/20/2022] Open
Abstract
BACKGROUND Uncovering the genetic architecture of economic traits in pigs is important for agricultural breeding. However, high-density haplotype reference panels are unavailable in most agricultural species, limiting accurate genotype imputation in large populations. Moreover, the infinitesimal model of quantitative traits implies that weak association signals tend to be spread across most of the genome, further complicating the genetic analysis. Hence, there is a need to develop new methods for sequencing large cohorts without large reference panels. RESULTS We describe a Tn5-based highly accurate, cost- and time-efficient, low-coverage sequencing method to obtain 11.3 million whole-genome single-nucleotide polymorphisms in 2,869 Duroc boars at a mean depth of 0.73×. On the basis of these single-nucleotide polymorphisms, a genome-wide association study was performed, resulting in 14 quantitative trait loci (QTLs) for 7 of 21 important agricultural traits in pigs. These QTLs harbour genes, such as ABCD4 for total teat number and HMGA1 for back fat thickness, and provided a starting point for further investigation. The inheritance models of the different traits varied greatly. Most follow the minor-polygene model, but this can be attributed to different reasons, such as the shaping of genetic architecture by artificial selection for this population and sufficiently interconnected minor gene regulatory networks. CONCLUSIONS Genome-wide association study results for 21 important agricultural traits identified 14 QTLs/genes and showed their genetic architectures, providing guidance for genetic improvement harnessing genomic features. The Tn5-based low-coverage sequencing method can be applied to large-scale genome studies for any species without a good reference panel and can be used for agricultural breeding.
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Affiliation(s)
- Ruifei Yang
- State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, No. 2 Yuanmingyuan west road, Haidian district, Beijing 100193, China
| | - Xiaoli Guo
- State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, No. 2 Yuanmingyuan west road, Haidian district, Beijing 100193, China
| | - Di Zhu
- State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, No. 2 Yuanmingyuan west road, Haidian district, Beijing 100193, China
| | - Cheng Tan
- National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, No. 483 Wushan road, Tianhe district, Guangdong 510640, China
| | - Cheng Bian
- State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, No. 2 Yuanmingyuan west road, Haidian district, Beijing 100193, China
| | - Jiangli Ren
- State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, No. 2 Yuanmingyuan west road, Haidian district, Beijing 100193, China
| | - Zhuolin Huang
- State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, No. 2 Yuanmingyuan west road, Haidian district, Beijing 100193, China
| | - Yiqiang Zhao
- State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, No. 2 Yuanmingyuan west road, Haidian district, Beijing 100193, China
| | - Gengyuan Cai
- National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, No. 483 Wushan road, Tianhe district, Guangdong 510640, China
| | - Dewu Liu
- National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, No. 483 Wushan road, Tianhe district, Guangdong 510640, China
| | - Zhenfang Wu
- National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, No. 483 Wushan road, Tianhe district, Guangdong 510640, China
| | - Yuzhe Wang
- State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, No. 2 Yuanmingyuan west road, Haidian district, Beijing 100193, China
- National Research Facility for Phenotypic and Genotypic Analysis of Model Animals (Beijing), China Agricultural University, No. 2 Yuanmingyuan west road, Haidian district, Beijing 100193, China
| | - Ning Li
- State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, No. 2 Yuanmingyuan west road, Haidian district, Beijing 100193, China
| | - Xiaoxiang Hu
- State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, No. 2 Yuanmingyuan west road, Haidian district, Beijing 100193, China
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Hafeez AN, Arora S, Ghosh S, Gilbert D, Bowden RL, Wulff BBH. Creation and judicious application of a wheat resistance gene atlas. MOLECULAR PLANT 2021; 14:1053-1070. [PMID: 33991673 DOI: 10.1016/j.molp.2021.05.014] [Citation(s) in RCA: 47] [Impact Index Per Article: 15.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/27/2021] [Revised: 04/12/2021] [Accepted: 05/11/2021] [Indexed: 05/18/2023]
Abstract
Disease-resistance (R) gene cloning in wheat (Triticum aestivum) has been accelerated by the recent surge of genomic resources, facilitated by advances in sequencing technologies and bioinformatics. However, with the challenges of population growth and climate change, it is vital not only to clone and functionally characterize a few handfuls of R genes, but also to do so at a scale that would facilitate the breeding and deployment of crops that can recognize the wide range of pathogen effectors that threaten agroecosystems. Pathogen populations are continually changing, and breeders must have tools and resources available to rapidly respond to those changes if we are to safeguard our daily bread. To meet this challenge, we propose the creation of a wheat R-gene atlas by an international community of researchers and breeders. The atlas would consist of an online directory from which sources of resistance could be identified and deployed to achieve more durable resistance to the major wheat pathogens, such as wheat rusts, blotch diseases, powdery mildew, and wheat blast. We present a costed proposal detailing how the interacting molecular components governing disease resistance could be captured from both the host and the pathogen through biparental mapping, mutational genomics, and whole-genome association genetics. We explore options for the configuration and genotyping of diversity panels of hexaploid and tetraploid wheat, as well as their wild relatives and major pathogens, and discuss how the atlas could inform a dynamic, durable approach to R-gene deployment. Set against the current magnitude of wheat yield losses worldwide, recently estimated at 21%, this endeavor presents one route for bringing R genes from the lab to the field at a considerable speed and quantity.
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Affiliation(s)
| | - Sanu Arora
- John Innes Centre, Norwich Research Park, Norwich, UK
| | - Sreya Ghosh
- John Innes Centre, Norwich Research Park, Norwich, UK
| | - David Gilbert
- John Innes Centre, Norwich Research Park, Norwich, UK
| | - Robert L Bowden
- USDA-ARS, Hard Winter Wheat Genetics Research Unit, Manhattan, KS 66506, USA
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36
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Zhang M, Liu Y, Zhou H, Watkins J, Zhou J. A novel nonlinear dimension reduction approach to infer population structure for low-coverage sequencing data. BMC Bioinformatics 2021; 22:348. [PMID: 34174829 PMCID: PMC8236193 DOI: 10.1186/s12859-021-04265-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2020] [Accepted: 06/11/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Low-depth sequencing allows researchers to increase sample size at the expense of lower accuracy. To incorporate uncertainties while maintaining statistical power, we introduce MCPCA_PopGen to analyze population structure of low-depth sequencing data. RESULTS The method optimizes the choice of nonlinear transformations of dosages to maximize the Ky Fan norm of the covariance matrix. The transformation incorporates the uncertainty in calling between heterozygotes and the common homozygotes for loci having a rare allele and is more linear when both variants are common. CONCLUSIONS We apply MCPCA_PopGen to samples from two indigenous Siberian populations and reveal hidden population structure accurately using only a single chromosome. The MCPCA_PopGen package is available on https://github.com/yiwenstat/MCPCA_PopGen .
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Affiliation(s)
- Miao Zhang
- Interdisciplinary Program in Statistics and Data Science, University of Arizona, 617 N. Santa Rita Ave., 85721 Tucson, USA
| | - Yiwen Liu
- Department of Mathematics, University of Arizona, 617 N. Santa Rita Ave., 85721 Tucson, USA
| | - Hua Zhou
- Department of Biostatistics, University of California, Los Angeles, 650 Charles E. Young Dr. South, 90095 Los Angeles, USA
| | - Joseph Watkins
- Department of Mathematics, University of Arizona, 617 N. Santa Rita Ave., 85721 Tucson, USA
- Interdisciplinary Program in Statistics and Data Science, University of Arizona, 617 N. Santa Rita Ave., 85721 Tucson, USA
| | - Jin Zhou
- Department of Epidemiology and Biostatistics, University of Arizona, 1295 N. Martin Ave., 85724 Tucson, USA
- Interdisciplinary Program in Statistics and Data Science, University of Arizona, 617 N. Santa Rita Ave., 85721 Tucson, USA
- Department of Medicine, UCLA David Geffen School of Medicine, Los Angeles, CA USA
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37
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Martin AR, Atkinson EG, Chapman SB, Stevenson A, Stroud RE, Abebe T, Akena D, Alemayehu M, Ashaba FK, Atwoli L, Bowers T, Chibnik LB, Daly MJ, DeSmet T, Dodge S, Fekadu A, Ferriera S, Gelaye B, Gichuru S, Injera WE, James R, Kariuki SM, Kigen G, Koenen KC, Kwobah E, Kyebuzibwa J, Majara L, Musinguzi H, Mwema RM, Neale BM, Newman CP, Newton CRJC, Pickrell JK, Ramesar R, Shiferaw W, Stein DJ, Teferra S, van der Merwe C, Zingela Z. Low-coverage sequencing cost-effectively detects known and novel variation in underrepresented populations. Am J Hum Genet 2021; 108:656-668. [PMID: 33770507 PMCID: PMC8059370 DOI: 10.1016/j.ajhg.2021.03.012] [Citation(s) in RCA: 41] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2020] [Accepted: 03/05/2021] [Indexed: 12/21/2022] Open
Abstract
Genetic studies in underrepresented populations identify disproportionate numbers of novel associations. However, most genetic studies use genotyping arrays and sequenced reference panels that best capture variation most common in European ancestry populations. To compare data generation strategies best suited for underrepresented populations, we sequenced the whole genomes of 91 individuals to high coverage as part of the Neuropsychiatric Genetics of African Population-Psychosis (NeuroGAP-Psychosis) study with participants from Ethiopia, Kenya, South Africa, and Uganda. We used a downsampling approach to evaluate the quality of two cost-effective data generation strategies, GWAS arrays versus low-coverage sequencing, by calculating the concordance of imputed variants from these technologies with those from deep whole-genome sequencing data. We show that low-coverage sequencing at a depth of ≥4× captures variants of all frequencies more accurately than all commonly used GWAS arrays investigated and at a comparable cost. Lower depths of sequencing (0.5-1×) performed comparably to commonly used low-density GWAS arrays. Low-coverage sequencing is also sensitive to novel variation; 4× sequencing detects 45% of singletons and 95% of common variants identified in high-coverage African whole genomes. Low-coverage sequencing approaches surmount the problems induced by the ascertainment of common genotyping arrays, effectively identify novel variation particularly in underrepresented populations, and present opportunities to enhance variant discovery at a cost similar to traditional approaches.
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Affiliation(s)
- Alicia R Martin
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA; Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA; Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA.
| | - Elizabeth G Atkinson
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA; Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA; Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA
| | - Sinéad B Chapman
- Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA
| | - Anne Stevenson
- Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA; Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
| | - Rocky E Stroud
- Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA; Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
| | - Tamrat Abebe
- Department of Microbiology, Immunology, and Parasitology, School of Medicine, College of Health Sciences, Addis Ababa University, Addis Ababa, Ethiopia
| | - Dickens Akena
- Department of Psychiatry, School of Medicine, College of Health Sciences, Makerere University, Kampala, Uganda
| | - Melkam Alemayehu
- Department of Psychiatry, School of Medicine, College of Health Sciences, Addis Ababa University, Addis Ababa, Ethiopia
| | - Fred K Ashaba
- Department of Immunology & Molecular Biology, College of Health Sciences, Makerere University, Kampala, Uganda
| | - Lukoye Atwoli
- Department of Mental Health, School of Medicine, Moi University College of Health Sciences, Eldoret, Kenya
| | - Tera Bowers
- Broad Genomics, Broad Institute of MIT and Harvard, 320 Charles Street, Cambridge, MA 02141, USA
| | - Lori B Chibnik
- Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA; Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA; Department of Neurology, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Mark J Daly
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA; Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA; Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA; Institute for Molecular Medicine Finland, Helsinki 00014, Finland
| | - Timothy DeSmet
- Broad Genomics, Broad Institute of MIT and Harvard, 320 Charles Street, Cambridge, MA 02141, USA
| | - Sheila Dodge
- Broad Genomics, Broad Institute of MIT and Harvard, 320 Charles Street, Cambridge, MA 02141, USA
| | - Abebaw Fekadu
- Department of Psychiatry, School of Medicine, College of Health Sciences, Addis Ababa University, Addis Ababa, Ethiopia; Centre for Innovative Drug Development & Therapeutic Trials for Africa, Addis Ababa University, Addis Ababa, Ethiopia
| | - Steven Ferriera
- Broad Genomics, Broad Institute of MIT and Harvard, 320 Charles Street, Cambridge, MA 02141, USA
| | - Bizu Gelaye
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
| | - Stella Gichuru
- Department of Mental Health, Moi Teaching and Referral Hospital, Eldoret, Kenya
| | - Wilfred E Injera
- Department of Immunology, School of Medicine, Moi University College of Health Sciences, Eldoret, Kenya
| | - Roxanne James
- Department of Psychiatry and Mental Health, University of Cape Town, Cape Town, South Africa
| | - Symon M Kariuki
- Neurosciences Unit, Clinical Department, KEMRI-Wellcome Trust Research Programme-Coast, Kilifi, Kenya; Department of Psychiatry, University of Oxford, Oxford OX3 7JX, UK
| | - Gabriel Kigen
- Department of Pharmacology and Toxicology, School of Medicine, Moi University College of Health Sciences, Eldoret, Kenya
| | - Karestan C Koenen
- Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA; Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
| | - Edith Kwobah
- Department of Mental Health, Moi Teaching and Referral Hospital, Eldoret, Kenya
| | - Joseph Kyebuzibwa
- Department of Psychiatry, School of Medicine, College of Health Sciences, Makerere University, Kampala, Uganda
| | - Lerato Majara
- Department of Psychiatry and Mental Health, University of Cape Town, Cape Town, South Africa; SA MRC Human Genetics Research Unit, Division of Human Genetics, Institute of Infectious Disease and Molecular Medicine, Faculty of Health Sciences, University of Cape Town, Observatory 7925, South Africa
| | - Henry Musinguzi
- Department of Immunology & Molecular Biology, College of Health Sciences, Makerere University, Kampala, Uganda
| | - Rehema M Mwema
- Neurosciences Unit, Clinical Department, KEMRI-Wellcome Trust Research Programme-Coast, Kilifi, Kenya
| | - Benjamin M Neale
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA; Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA; Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA
| | - Carter P Newman
- Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA; Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
| | - Charles R J C Newton
- Neurosciences Unit, Clinical Department, KEMRI-Wellcome Trust Research Programme-Coast, Kilifi, Kenya; Department of Psychiatry, University of Oxford, Oxford OX3 7JX, UK
| | | | - Raj Ramesar
- SA MRC Genomic and Precision Medicine Research Unit, Division of Human Genetics, Department of Pathology, Institute of Infectious Diseases and Molecular Medicine, University of Cape Town, Cape Town, South Africa
| | - Welelta Shiferaw
- Department of Microbiology, Immunology, and Parasitology, School of Medicine, College of Health Sciences, Addis Ababa University, Addis Ababa, Ethiopia
| | - Dan J Stein
- Department of Psychiatry and Mental Health, University of Cape Town, Cape Town, South Africa; SA MRC Unit on Risk & Resilience in Mental Disorders, University of Cape Town and Neuroscience Institute, Cape Town, South Africa
| | - Solomon Teferra
- Department of Psychiatry, School of Medicine, College of Health Sciences, Addis Ababa University, Addis Ababa, Ethiopia
| | - Celia van der Merwe
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA; Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA; Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA; Department of Psychiatry and Mental Health, University of Cape Town, Cape Town, South Africa
| | - Zukiswa Zingela
- Department of Psychiatry and Human Behavioral Sciences, Walter Sisulu University, Mthatha, South Africa
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Wasik K, Berisa T, Pickrell JK, Li JH, Fraser DJ, King K, Cox C. Comparing low-pass sequencing and genotyping for trait mapping in pharmacogenetics. BMC Genomics 2021; 22:197. [PMID: 33743587 PMCID: PMC7981957 DOI: 10.1186/s12864-021-07508-2] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2020] [Accepted: 03/05/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Low pass sequencing has been proposed as a cost-effective alternative to genotyping arrays to identify genetic variants that influence multifactorial traits in humans. For common diseases this typically has required both large sample sizes and comprehensive variant discovery. Genotyping arrays are also routinely used to perform pharmacogenetic (PGx) experiments where sample sizes are likely to be significantly smaller, but clinically relevant effect sizes likely to be larger. RESULTS To assess how low pass sequencing would compare to array based genotyping for PGx we compared a low-pass assay (in which 1x coverage or less of a target genome is sequenced) along with software for genotype imputation to standard approaches. We sequenced 79 individuals to 1x genome coverage and genotyped the same samples on the Affymetrix Axiom Biobank Precision Medicine Research Array (PMRA). We then down-sampled the sequencing data to 0.8x, 0.6x, and 0.4x coverage, and performed imputation. Both the genotype data and the sequencing data were further used to impute human leukocyte antigen (HLA) genotypes for all samples. We compared the sequencing data and the genotyping array data in terms of four metrics: overall concordance, concordance at single nucleotide polymorphisms in pharmacogenetics-related genes, concordance in imputed HLA genotypes, and imputation r2. Overall concordance between the two assays ranged from 98.2% (for 0.4x coverage sequencing) to 99.2% (for 1x coverage sequencing), with qualitatively similar numbers for the subsets of variants most important in pharmacogenetics. At common single nucleotide polymorphisms (SNPs), the mean imputation r2 from the genotyping array was 0.90, which was comparable to the imputation r2 from 0.4x coverage sequencing, while the mean imputation r2 from 1x sequencing data was 0.96. CONCLUSIONS These results indicate that low-pass sequencing to a depth above 0.4x coverage attains higher power for association studies when compared to the PMRA and should be considered as a competitive alternative to genotyping arrays for trait mapping in pharmacogenetics.
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Affiliation(s)
| | | | | | | | | | - Karen King
- PAREXEL Genomic Medicine, Durham, NC, 27713, USA
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Li JH, Mazur CA, Berisa T, Pickrell JK. Low-pass sequencing increases the power of GWAS and decreases measurement error of polygenic risk scores compared to genotyping arrays. Genome Res 2021; 31:529-537. [PMID: 33536225 PMCID: PMC8015847 DOI: 10.1101/gr.266486.120] [Citation(s) in RCA: 47] [Impact Index Per Article: 15.7] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2020] [Accepted: 02/01/2021] [Indexed: 01/08/2023]
Abstract
Low-pass sequencing (sequencing a genome to an average depth less than 1× coverage) combined with genotype imputation has been proposed as an alternative to genotyping arrays for trait mapping and calculation of polygenic scores. To empirically assess the relative performance of these technologies for different applications, we performed low-pass sequencing (targeting coverage levels of 0.5× and 1×) and array genotyping (using the Illumina Global Screening Array [GSA]) on 120 DNA samples derived from African- and European-ancestry individuals that are part of the 1000 Genomes Project. We then imputed both the sequencing data and the genotyping array data to the 1000 Genomes Phase 3 haplotype reference panel using a leave-one-out design. We evaluated overall imputation accuracy from these different assays as well as overall power for GWAS from imputed data and computed polygenic risk scores for coronary artery disease and breast cancer using previously derived weights. We conclude that low-pass sequencing plus imputation, in addition to providing a substantial increase in statistical power for genome-wide association studies, provides increased accuracy for polygenic risk prediction at effective coverages of ∼0.5× and higher compared to the Illumina GSA.
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Affiliation(s)
- Jeremiah H Li
- Gencove, Incorporated, New York, New York 10016, USA
| | - Chase A Mazur
- Gencove, Incorporated, New York, New York 10016, USA
| | - Tomaz Berisa
- Gencove, Incorporated, New York, New York 10016, USA
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Efficient phasing and imputation of low-coverage sequencing data using large reference panels. Nat Genet 2021; 53:120-126. [PMID: 33414550 DOI: 10.1038/s41588-020-00756-0] [Citation(s) in RCA: 130] [Impact Index Per Article: 43.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2020] [Accepted: 11/20/2020] [Indexed: 01/28/2023]
Abstract
Low-coverage whole-genome sequencing followed by imputation has been proposed as a cost-effective genotyping approach for disease and population genetics studies. However, its competitiveness against SNP arrays is undermined because current imputation methods are computationally expensive and unable to leverage large reference panels. Here, we describe a method, GLIMPSE, for phasing and imputation of low-coverage sequencing datasets from modern reference panels. We demonstrate its remarkable performance across different coverages and human populations. GLIMPSE achieves imputation of a genome for less than US$1 in computational cost, considerably outperforming other methods and improving imputation accuracy over the full allele frequency range. As a proof of concept, we show that 1× coverage enables effective gene expression association studies and outperforms dense SNP arrays in rare variant burden tests. Overall, this study illustrates the promising potential of low-coverage imputation and suggests a paradigm shift in the design of future genomic studies.
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Host genetics and infectious disease: new tools, insights and translational opportunities. Nat Rev Genet 2020; 22:137-153. [PMID: 33277640 PMCID: PMC7716795 DOI: 10.1038/s41576-020-00297-6] [Citation(s) in RCA: 74] [Impact Index Per Article: 18.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/14/2020] [Indexed: 12/22/2022]
Abstract
Understanding how human genetics influence infectious disease susceptibility offers the opportunity for new insights into pathogenesis, potential drug targets, risk stratification, response to therapy and vaccination. As new infectious diseases continue to emerge, together with growing levels of antimicrobial resistance and an increasing awareness of substantial differences between populations in genetic associations, the need for such work is expanding. In this Review, we illustrate how our understanding of the host–pathogen relationship is advancing through holistic approaches, describing current strategies to investigate the role of host genetic variation in established and emerging infections, including COVID-19, the need for wider application to diverse global populations mirroring the burden of disease, the impact of pathogen and vector genetic diversity and a broad array of immune and inflammation phenotypes that can be mapped as traits in health and disease. Insights from study of inborn errors of immunity and multi-omics profiling together with developments in analytical methods are further advancing our knowledge of this important area. Infectious diseases are an ever-present global threat. In this Review, Kwok, Mentzer and Knight discuss our latest understanding of how human genetics influence susceptibility to disease. Furthermore, they discuss emerging progress in the interplay between host and pathogen genetics, molecular responses to infection and vaccination, and opportunities to bring these aspects together for rapid responses to emerging diseases such as COVID-19.
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Chen SF, Dias R, Evans D, Salfati EL, Liu S, Wineinger NE, Torkamani A. Genotype imputation and variability in polygenic risk score estimation. Genome Med 2020; 12:100. [PMID: 33225976 PMCID: PMC7682022 DOI: 10.1186/s13073-020-00801-x] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2020] [Accepted: 11/09/2020] [Indexed: 01/10/2023] Open
Abstract
BACKGROUND Polygenic risk scores (PRSs) are a summarization of an individual's genetic risk for a disease or trait. These scores are being generated in research and commercial settings to study how they may be used to guide healthcare decisions. PRSs should be updated as genetic knowledgebases improve; however, no guidelines exist for their generation or updating. METHODS Here, we characterize the variability introduced in PRS calculation by a common computational process used in their generation-genotype imputation. We evaluated PRS variability when performing genotype imputation using 3 different pre-phasing tools (Beagle, Eagle, SHAPEIT) and 2 different imputation tools (Beagle, Minimac4), relative to a WGS-based gold standard. Fourteen different PRSs spanning different disease architectures and PRS generation approaches were evaluated. RESULTS We find that genotype imputation can introduce variability in calculated PRSs at the individual level without any change to the underlying genetic model. The degree of variability introduced by genotype imputation differs across algorithms, where pre-phasing algorithms with stochastic elements introduce the greatest degree of score variability. In most cases, PRS variability due to imputation is minor (< 5 percentile rank change) and does not influence the interpretation of the score. PRS percentile fluctuations are also reduced in the more informative tails of the PRS distribution. However, in rare instances, PRS instability at the individual level can result in singular PRS calculations that differ substantially from a whole genome sequence-based gold standard score. CONCLUSIONS Our study highlights some challenges in applying population genetics tools to individual-level genetic analysis including return of results. Rare individual-level variability events are masked by a high degree of overall score reproducibility at the population level. In order to avoid PRS result fluctuations during updates, we suggest that deterministic imputation processes or the average of multiple iterations of stochastic imputation processes be used to generate and deliver PRS results.
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Affiliation(s)
- Shang-Fu Chen
- Scripps Research Translational Institute, Scripps Research, La Jolla, CA, 92037, USA
- Department of Integrative Structural and Computational Biology, Scripps Research, La Jolla, CA, 92037, USA
| | - Raquel Dias
- Scripps Research Translational Institute, Scripps Research, La Jolla, CA, 92037, USA
- Department of Integrative Structural and Computational Biology, Scripps Research, La Jolla, CA, 92037, USA
| | - Doug Evans
- Scripps Research Translational Institute, Scripps Research, La Jolla, CA, 92037, USA
- Department of Integrative Structural and Computational Biology, Scripps Research, La Jolla, CA, 92037, USA
| | - Elias L Salfati
- Scripps Research Translational Institute, Scripps Research, La Jolla, CA, 92037, USA
- Department of Integrative Structural and Computational Biology, Scripps Research, La Jolla, CA, 92037, USA
| | - Shuchen Liu
- Scripps Research Translational Institute, Scripps Research, La Jolla, CA, 92037, USA
- Department of Integrative Structural and Computational Biology, Scripps Research, La Jolla, CA, 92037, USA
| | - Nathan E Wineinger
- Scripps Research Translational Institute, Scripps Research, La Jolla, CA, 92037, USA
- Department of Integrative Structural and Computational Biology, Scripps Research, La Jolla, CA, 92037, USA
| | - Ali Torkamani
- Scripps Research Translational Institute, Scripps Research, La Jolla, CA, 92037, USA.
- Department of Integrative Structural and Computational Biology, Scripps Research, La Jolla, CA, 92037, USA.
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Díaz-de Usera A, Lorenzo-Salazar JM, Rubio-Rodríguez LA, Muñoz-Barrera A, Guillen-Guio B, Marcelino-Rodríguez I, García-Olivares V, Mendoza-Alvarez A, Corrales A, Íñigo-Campos A, González-Montelongo R, Flores C. Evaluation of Whole-Exome Enrichment Solutions: Lessons from the High-End of the Short-Read Sequencing Scale. J Clin Med 2020; 9:jcm9113656. [PMID: 33202991 PMCID: PMC7696786 DOI: 10.3390/jcm9113656] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2020] [Revised: 11/10/2020] [Accepted: 11/10/2020] [Indexed: 12/13/2022] Open
Abstract
Whole-exome sequencing has become a popular technique in research and clinical settings, assisting in disease diagnosis and increasing the understanding of disease pathogenesis. In this study, we aimed to compare common enrichment capture solutions available in the market. Peripheral blood-purified DNA samples were enriched with SureSelectQXT V6 (Agilent) and various Illumina solutions: TruSeq DNA Nano, TruSeq DNA Exome, Nextera DNA Exome, and Illumina DNA Prep with Enrichment, and sequenced on a HiSeq 4000. We found that their percentage of duplicate reads was as much as 2 times higher than previously reported values for the previous HiSeq series. SureSelectQXT and Illumina DNA Prep with Enrichment showed the best average on-target coverage, which improved when off-target regions were included. At high coverage levels and in shared bases, these two solutions and TruSeq DNA Exome provided three of the best performances. With respect to the number of small variants detected, SureSelectQXT presented the lowest number of detected variants in target regions. When off-target regions were considered, its ability equalized to other solutions. Our results show SureSelectQXT and Illumina DNA Prep with Enrichment to be the best enrichment capture solutions.
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Affiliation(s)
- Ana Díaz-de Usera
- Genomics Division, Instituto Tecnológico y de Energías Renovables (ITER), 38600 Santa Cruz de Tenerife, Spain; (A.D.-d.U.); (J.M.L.-S.); (L.A.R.-R.); (A.M.-B.); (V.G.-O.); (A.Í.-C.); (R.G.-M.)
| | - Jose M. Lorenzo-Salazar
- Genomics Division, Instituto Tecnológico y de Energías Renovables (ITER), 38600 Santa Cruz de Tenerife, Spain; (A.D.-d.U.); (J.M.L.-S.); (L.A.R.-R.); (A.M.-B.); (V.G.-O.); (A.Í.-C.); (R.G.-M.)
| | - Luis A. Rubio-Rodríguez
- Genomics Division, Instituto Tecnológico y de Energías Renovables (ITER), 38600 Santa Cruz de Tenerife, Spain; (A.D.-d.U.); (J.M.L.-S.); (L.A.R.-R.); (A.M.-B.); (V.G.-O.); (A.Í.-C.); (R.G.-M.)
| | - Adrián Muñoz-Barrera
- Genomics Division, Instituto Tecnológico y de Energías Renovables (ITER), 38600 Santa Cruz de Tenerife, Spain; (A.D.-d.U.); (J.M.L.-S.); (L.A.R.-R.); (A.M.-B.); (V.G.-O.); (A.Í.-C.); (R.G.-M.)
| | - Beatriz Guillen-Guio
- Research Unit, Hospital Universitario N.S. de Candelaria, Universidad de La Laguna, 38010 Santa Cruz de Tenerife, Spain; (B.G.-G.); (I.M.-R.); (A.M.-A.); (A.C.)
| | - Itahisa Marcelino-Rodríguez
- Research Unit, Hospital Universitario N.S. de Candelaria, Universidad de La Laguna, 38010 Santa Cruz de Tenerife, Spain; (B.G.-G.); (I.M.-R.); (A.M.-A.); (A.C.)
- Instituto de Tecnologías Biomédicas (ITB), Universidad de La Laguna, 38200 San Cristóbal de La Laguna, Spain
| | - Víctor García-Olivares
- Genomics Division, Instituto Tecnológico y de Energías Renovables (ITER), 38600 Santa Cruz de Tenerife, Spain; (A.D.-d.U.); (J.M.L.-S.); (L.A.R.-R.); (A.M.-B.); (V.G.-O.); (A.Í.-C.); (R.G.-M.)
| | - Alejandro Mendoza-Alvarez
- Research Unit, Hospital Universitario N.S. de Candelaria, Universidad de La Laguna, 38010 Santa Cruz de Tenerife, Spain; (B.G.-G.); (I.M.-R.); (A.M.-A.); (A.C.)
| | - Almudena Corrales
- Research Unit, Hospital Universitario N.S. de Candelaria, Universidad de La Laguna, 38010 Santa Cruz de Tenerife, Spain; (B.G.-G.); (I.M.-R.); (A.M.-A.); (A.C.)
- CIBER de Enfermedades Respiratorias, Instituto de Salud Carlos III, 28029 Madrid, Spain
| | - Antonio Íñigo-Campos
- Genomics Division, Instituto Tecnológico y de Energías Renovables (ITER), 38600 Santa Cruz de Tenerife, Spain; (A.D.-d.U.); (J.M.L.-S.); (L.A.R.-R.); (A.M.-B.); (V.G.-O.); (A.Í.-C.); (R.G.-M.)
| | - Rafaela González-Montelongo
- Genomics Division, Instituto Tecnológico y de Energías Renovables (ITER), 38600 Santa Cruz de Tenerife, Spain; (A.D.-d.U.); (J.M.L.-S.); (L.A.R.-R.); (A.M.-B.); (V.G.-O.); (A.Í.-C.); (R.G.-M.)
| | - Carlos Flores
- Genomics Division, Instituto Tecnológico y de Energías Renovables (ITER), 38600 Santa Cruz de Tenerife, Spain; (A.D.-d.U.); (J.M.L.-S.); (L.A.R.-R.); (A.M.-B.); (V.G.-O.); (A.Í.-C.); (R.G.-M.)
- Research Unit, Hospital Universitario N.S. de Candelaria, Universidad de La Laguna, 38010 Santa Cruz de Tenerife, Spain; (B.G.-G.); (I.M.-R.); (A.M.-A.); (A.C.)
- Instituto de Tecnologías Biomédicas (ITB), Universidad de La Laguna, 38200 San Cristóbal de La Laguna, Spain
- CIBER de Enfermedades Respiratorias, Instituto de Salud Carlos III, 28029 Madrid, Spain
- Correspondence: ; Tel.: +34-922-602938
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Hui R, D'Atanasio E, Cassidy LM, Scheib CL, Kivisild T. Evaluating genotype imputation pipeline for ultra-low coverage ancient genomes. Sci Rep 2020; 10:18542. [PMID: 33122697 PMCID: PMC7596702 DOI: 10.1038/s41598-020-75387-w] [Citation(s) in RCA: 39] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2020] [Accepted: 10/12/2020] [Indexed: 01/12/2023] Open
Abstract
Although ancient DNA data have become increasingly more important in studies about past populations, it is often not feasible or practical to obtain high coverage genomes from poorly preserved samples. While methods of accurate genotype imputation from > 1 × coverage data have recently become a routine, a large proportion of ancient samples remain unusable for downstream analyses due to their low coverage. Here, we evaluate a two-step pipeline for the imputation of common variants in ancient genomes at 0.05-1 × coverage. We use the genotype likelihood input mode in Beagle and filter for confident genotypes as the input to impute missing genotypes. This procedure, when tested on ancient genomes, outperforms a single-step imputation from genotype likelihoods, suggesting that current genotype callers do not fully account for errors in ancient sequences and additional quality controls can be beneficial. We compared the effect of various genotype likelihood calling methods, post-calling, pre-imputation and post-imputation filters, different reference panels, as well as different imputation tools. In a Neolithic Hungarian genome, we obtain ~ 90% imputation accuracy for heterozygous common variants at coverage 0.05 × and > 97% accuracy at coverage 0.5 ×. We show that imputation can mitigate, though not eliminate reference bias in ultra-low coverage ancient genomes.
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Affiliation(s)
- Ruoyun Hui
- McDonald Institute for Archaeological Research, University of Cambridge, Cambridge, UK
| | - Eugenia D'Atanasio
- Department of Human Genetics, Katholieke Universiteit Leuven, Herestraat 49 - box 602, 3000, Leuven, Belgium
- Istituto di Biologia e Patologia Molecolari, Consiglio Nazionale delle Ricerche, Rome, Italy
| | - Lara M Cassidy
- Smurfit Institute of Genetics, Trinity College Dublin, Dublin, Ireland
| | - Christiana L Scheib
- Estonian Biocentre, Institute of Genomics, University of Tartu, Tartu, Estonia
- St John's College, St John's Street, Cambridge, CB2 1TP, UK
| | - Toomas Kivisild
- Department of Human Genetics, Katholieke Universiteit Leuven, Herestraat 49 - box 602, 3000, Leuven, Belgium.
- Estonian Biocentre, Institute of Genomics, University of Tartu, Tartu, Estonia.
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GWAS Analysis of 17,019 Korean Women Identifies the Variants Associated with Facial Pigmented Spots. J Invest Dermatol 2020; 141:555-562. [PMID: 32835660 DOI: 10.1016/j.jid.2020.08.007] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2020] [Revised: 08/09/2020] [Accepted: 08/14/2020] [Indexed: 12/20/2022]
Abstract
Variation in skin pigmentation can be affected by both environmental factors and intrinsic factors such as age, gender, and genetic variation. Recent GWASs revealed that genetic variants of genes functionally related to a pigmentation pathway were associated with skin pigmentary traits. However, these GWASs focused on populations with European ancestry, and only a few studies have been performed on Asian populations, limiting our understanding of the genetic basis of skin pigmentary traits in Asians. To evaluate the genetic variants associated with facial pigmented spots, we conducted a GWAS analysis of objectively measured facial pigmented spots in 17,019 Korean women. This large-scale GWAS identified several genomic loci that were significantly associated with facial pigmented spots (five previously reported loci and two previously unreported loci, to our knowledge), which were detected by UV light: BNC2 at 9p22 (rs16935073; P-value = 2.11 × 10-46), PPARGC1B at 5q32 (rs32579; P-value = 9.04 × 10-42), 10q26 (rs11198112; P-value = 9.66 × 10-38), MC1R at 16q24 (rs2228479; P-value = 6.62 × 10-21), lnc01877 at 2q33 (rs12693889; P-value = 1.59 × 10-11), CDKN2B-AS1 at 9p21 (rs643319; P-value = 7.76 × 10-9), and MFSD12 at 19p13 (rs2240751; P-value = 9.70 × 10-9). Further functional characterization of the candidate genes needs to be done to fully evaluate their contribution to facial pigmented spots.
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Ros-Freixedes R, Whalen A, Gorjanc G, Mileham AJ, Hickey JM. Evaluation of sequencing strategies for whole-genome imputation with hybrid peeling. Genet Sel Evol 2020; 52:18. [PMID: 32248818 PMCID: PMC7132986 DOI: 10.1186/s12711-020-00537-7] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2019] [Accepted: 03/27/2020] [Indexed: 11/26/2022] Open
Abstract
BACKGROUND For assembling large whole-genome sequence datasets for routine use in research and breeding, the sequencing strategy should be adapted to the methods that will be used later for variant discovery and imputation. In this study, we used simulation to explore the impact that the sequencing strategy and level of sequencing investment have on the overall accuracy of imputation using hybrid peeling, a pedigree-based imputation method that is well suited for large livestock populations. METHODS We simulated marker array and whole-genome sequence data for 15 populations with simulated or real pedigrees that had different structures. In these populations, we evaluated the effect on imputation accuracy of seven methods for selecting which individuals to sequence, the generation of the pedigree to which the sequenced individuals belonged, the use of variable or uniform coverage, and the trade-off between the number of sequenced individuals and their sequencing coverage. For each population, we considered four levels of investment in sequencing that were proportional to the size of the population. RESULTS Imputation accuracy depended greatly on pedigree depth. The distribution of the sequenced individuals across the generations of the pedigree underlay the performance of the different methods used to select individuals to sequence and it was critical for achieving high imputation accuracy in both early and late generations. Imputation accuracy was highest with a uniform coverage across the sequenced individuals of 2× rather than variable coverage. An investment equivalent to the cost of sequencing 2% of the population at 2× provided high imputation accuracy. The gain in imputation accuracy from additional investment decreased with larger populations and higher levels of investment. However, to achieve the same imputation accuracy, a proportionally greater investment must be used in the smaller populations compared to the larger ones. CONCLUSIONS Suitable sequencing strategies for subsequent imputation with hybrid peeling involve sequencing ~2% of the population at a uniform coverage 2×, distributed preferably across all generations of the pedigree, except for the few earliest generations that lack genotyped ancestors. Such sequencing strategies are beneficial for generating whole-genome sequence data in populations with deep pedigrees of closely related individuals.
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Affiliation(s)
- Roger Ros-Freixedes
- The Roslin Institute and Royal (Dick) School of Veterinary Studies, The University of Edinburgh, Easter Bush, Midlothian, Scotland, UK
- Departament de Ciència Animal, Universitat de Lleida-Agrotecnio Center, Lleida, Spain
| | - Andrew Whalen
- The Roslin Institute and Royal (Dick) School of Veterinary Studies, The University of Edinburgh, Easter Bush, Midlothian, Scotland, UK
| | - Gregor Gorjanc
- The Roslin Institute and Royal (Dick) School of Veterinary Studies, The University of Edinburgh, Easter Bush, Midlothian, Scotland, UK
| | | | - John M. Hickey
- The Roslin Institute and Royal (Dick) School of Veterinary Studies, The University of Edinburgh, Easter Bush, Midlothian, Scotland, UK
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Chen Q, Qu K, Ma Z, Zhan J, Zhang F, Shen J, Ning Q, Jia P, Zhang J, Chen N, Chen H, Huang B, Lei C. Genome-Wide Association Study Identifies Genomic Loci Associated With Neurotransmitter Concentration in Cattle. Front Genet 2020; 11:139. [PMID: 32292413 PMCID: PMC7119242 DOI: 10.3389/fgene.2020.00139] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2019] [Accepted: 02/06/2020] [Indexed: 11/30/2022] Open
Abstract
Abnormal neurotransmitter concentration is one of the factors that affect the health status, behavioral personality, and welfare level of animals, but the genetic basis of the abnormality is still largely unknown. The objective of this study is to identify putative genomic loci associated with neurotransmitter concentration in cattle. We measured serotonin (5HT), dopamine (DA), cortisol, glutamate (Glu), and ACTH concentrations in blood serum using double-antibody sandwich ELISA in 30 Brahman cattle and 127 Yunling cattle. Interestingly, we found that ACTH concentration was positively correlated with body weight, cannon circumference, and hip width (P < 0.05). Genome-wide association study (GWAS) was performed with mixed linear models using autosomal SNPs derived from the whole-genome sequence. We identified five, five, two, three, and five suggestive loci associated with 5HT, DA, cortisol, Glu, and ACTH concentration, respectively. These 20 associated loci implicated 18 candidate genes. For Glu concentration, the most significant association locus was assigned to MCHR1, a G-coupled receptor that could modulate glutamate release. For dopamine concentration, a very strong association locus was located in the intron of SLC18A2, which is a critical mediator of dopamine dynamics. However, for ACTH concentration, a very strong association locus was assigned to HTR1F, a G protein-coupled receptor that can influence the release of ACTH. Other candidate genes of interest identified for neurotransmitter concentration were PRMT6, GADD45A, PCCA, ANGPT1, ACCS, LOC100336971, TNR, GSDMA, CNTN3, CARMIL1, CDKAL1, RBFOX1, PCDH15, and LGALS12. Our findings will provide targets for the genetic improvement of neurotransmitter-related traits in domestic cattle and basic materials for studying the mechanism of neurotransmitter synthesis, release, and transport in human and animals.
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Affiliation(s)
- Qiuming Chen
- Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling, China
| | - Kaixing Qu
- Yunnan Academy of Grassland and Animal Science, Kunming, China
| | - Zhijie Ma
- Academy of Animal Science and Veterinary Medicine, Qinghai University, Xining, China
| | - Jingxi Zhan
- Yunnan Academy of Grassland and Animal Science, Kunming, China
| | - Fengwei Zhang
- Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling, China
| | - Jiafei Shen
- Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling, China
| | - Qingqing Ning
- Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling, China
| | - Peng Jia
- Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling, China
| | - Jicai Zhang
- Yunnan Academy of Grassland and Animal Science, Kunming, China
| | - Ningbo Chen
- Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling, China
| | - Hong Chen
- Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling, China
| | - Bizhi Huang
- Yunnan Academy of Grassland and Animal Science, Kunming, China
| | - Chuzhao Lei
- Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling, China
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Homburger JR, Neben CL, Mishne G, Zhou AY, Kathiresan S, Khera AV. Low coverage whole genome sequencing enables accurate assessment of common variants and calculation of genome-wide polygenic scores. Genome Med 2019; 11:74. [PMID: 31771638 PMCID: PMC6880438 DOI: 10.1186/s13073-019-0682-2] [Citation(s) in RCA: 52] [Impact Index Per Article: 10.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2019] [Accepted: 11/01/2019] [Indexed: 01/01/2023] Open
Abstract
BACKGROUND Inherited susceptibility to common, complex diseases may be caused by rare, pathogenic variants ("monogenic") or by the cumulative effect of numerous common variants ("polygenic"). Comprehensive genome interpretation should enable assessment for both monogenic and polygenic components of inherited risk. The traditional approach requires two distinct genetic testing technologies-high coverage sequencing of known genes to detect monogenic variants and a genome-wide genotyping array followed by imputation to calculate genome-wide polygenic scores (GPSs). We assessed the feasibility and accuracy of using low coverage whole genome sequencing (lcWGS) as an alternative to genotyping arrays to calculate GPSs. METHODS First, we performed downsampling and imputation of WGS data from ten individuals to assess concordance with known genotypes. Second, we assessed the correlation between GPSs for 3 common diseases-coronary artery disease (CAD), breast cancer (BC), and atrial fibrillation (AF)-calculated using lcWGS and genotyping array in 184 samples. Third, we assessed concordance of lcWGS-based genotype calls and GPS calculation in 120 individuals with known genotypes, selected to reflect diverse ancestral backgrounds. Fourth, we assessed the relationship between GPSs calculated using lcWGS and disease phenotypes in a cohort of 11,502 individuals of European ancestry. RESULTS We found imputation accuracy r2 values of greater than 0.90 for all ten samples-including those of African and Ashkenazi Jewish ancestry-with lcWGS data at 0.5×. GPSs calculated using lcWGS and genotyping array followed by imputation in 184 individuals were highly correlated for each of the 3 common diseases (r2 = 0.93-0.97) with similar score distributions. Using lcWGS data from 120 individuals of diverse ancestral backgrounds, we found similar results with respect to imputation accuracy and GPS correlations. Finally, we calculated GPSs for CAD, BC, and AF using lcWGS in 11,502 individuals of European ancestry, confirming odds ratios per standard deviation increment ranging 1.28 to 1.59, consistent with previous studies. CONCLUSIONS lcWGS is an alternative technology to genotyping arrays for common genetic variant assessment and GPS calculation. lcWGS provides comparable imputation accuracy while also overcoming the ascertainment bias inherent to variant selection in genotyping array design.
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Affiliation(s)
| | - Cynthia L Neben
- Color Genomics, 831 Mitten Road, Suite 100, Burlingame, CA, 94010, USA
| | - Gilad Mishne
- Color Genomics, 831 Mitten Road, Suite 100, Burlingame, CA, 94010, USA
| | - Alicia Y Zhou
- Color Genomics, 831 Mitten Road, Suite 100, Burlingame, CA, 94010, USA
| | | | - Amit V Khera
- Center for Genomic Medicine and Cardiology Division, Department of Medicine, Massachusetts General Hospital, Simches Research Building | CPZN 6.256, Boston, MA, 02114, USA.
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA.
- Harvard Medical School, Boston, MA, 02115, USA.
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49
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The transferability of lipid loci across African, Asian and European cohorts. Nat Commun 2019; 10:4330. [PMID: 31551420 PMCID: PMC6760173 DOI: 10.1038/s41467-019-12026-7] [Citation(s) in RCA: 56] [Impact Index Per Article: 11.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2019] [Accepted: 08/19/2019] [Indexed: 02/02/2023] Open
Abstract
Most genome-wide association studies are based on samples of European descent. We assess whether the genetic determinants of blood lipids, a major cardiovascular risk factor, are shared across populations. Genetic correlations for lipids between European-ancestry and Asian cohorts are not significantly different from 1. A genetic risk score based on LDL-cholesterol-associated loci has consistent effects on serum levels in samples from the UK, Uganda and Greece (r = 0.23-0.28, p < 1.9 × 10-14). Overall, there is evidence of reproducibility for ~75% of the major lipid loci from European discovery studies, except triglyceride loci in the Ugandan samples (10% of loci). Individual transferable loci are identified using trans-ethnic colocalization. Ten of fourteen loci not transferable to the Ugandan population have pleiotropic associations with BMI in Europeans; none of the transferable loci do. The non-transferable loci might affect lipids by modifying food intake in environments rich in certain nutrients, which suggests a potential role for gene-environment interactions.
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50
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Martin AR, Kanai M, Kamatani Y, Okada Y, Neale BM, Daly MJ. Clinical use of current polygenic risk scores may exacerbate health disparities. Nat Genet 2019; 51:584-591. [PMID: 30926966 PMCID: PMC6563838 DOI: 10.1038/s41588-019-0379-x] [Citation(s) in RCA: 1399] [Impact Index Per Article: 279.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2018] [Accepted: 02/07/2019] [Indexed: 02/06/2023]
Abstract
Polygenic risk scores (PRS) are poised to improve biomedical outcomes via precision medicine. However, the major ethical and scientific challenge surrounding clinical implementation of PRS is that those available today are several times more accurate in individuals of European ancestry than other ancestries. This disparity is an inescapable consequence of Eurocentric biases in genome-wide association studies, thus highlighting that-unlike clinical biomarkers and prescription drugs, which may individually work better in some populations but do not ubiquitously perform far better in European populations-clinical uses of PRS today would systematically afford greater improvement for European-descent populations. Early diversifying efforts show promise in leveling this vast imbalance, even when non-European sample sizes are considerably smaller than the largest studies to date. To realize the full and equitable potential of PRS, greater diversity must be prioritized in genetic studies, and summary statistics must be publically disseminated to ensure that health disparities are not increased for those individuals already most underserved.
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Affiliation(s)
- Alicia R Martin
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA.
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA.
- Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA, USA.
| | - Masahiro Kanai
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Laboratory for Statistical Analysis, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Yoichiro Kamatani
- Laboratory for Statistical Analysis, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
- Kyoto-McGill International Collaborative School in Genomic Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Yukinori Okada
- Laboratory for Statistical Analysis, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
- Laboratory of Statistical Immunology, Immunology Frontier Research Center (WPI-IFReC), Osaka University, Suita, Japan
| | - Benjamin M Neale
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Mark J Daly
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
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