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Ritchie SC, Taylor HJ, Liang Y, Manikpurage HD, Pennells L, Foguet C, Abraham G, Gibson JT, Jiang X, Liu Y, Xu Y, Kim LG, Mahajan A, McCarthy MI, Kaptoge S, Lambert SA, Wood A, Sim X, Collins FS, Denny JC, Danesh J, Butterworth AS, Di Angelantonio E, Inouye M. Integrated clinical risk prediction of type 2 diabetes with a multifactorial polygenic risk score. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.08.22.24312440. [PMID: 39228710 PMCID: PMC11370520 DOI: 10.1101/2024.08.22.24312440] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/05/2024]
Abstract
Combining information from multiple GWASs for a disease and its risk factors has proven a powerful approach for development of polygenic risk scores (PRSs). This may be particularly useful for type 2 diabetes (T2D), a highly polygenic and heterogeneous disease where the additional predictive value of a PRS is unclear. Here, we use a meta-scoring approach to develop a metaPRS for T2D that incorporated genome-wide associations from both European and non-European genetic ancestries and T2D risk factors. We evaluated the performance of this metaPRS and benchmarked it against existing genome-wide PRS in 620,059 participants and 50,572 T2D cases amongst six diverse genetic ancestries from UK Biobank, INTERVAL, the All of Us Research Program, and the Singapore Multi-Ethnic Cohort. We show that our metaPRS was the most powerful PRS for predicting T2D in European population-based cohorts and had comparable performance to the top ancestry-specific PRS, highlighting its transferability. In UK Biobank, we show the metaPRS had stronger predictive power for 10-year risk than all individual risk factors apart from BMI and biomarkers of dysglycemia. The metaPRS modestly improved T2D risk stratification of QDiabetes risk scores for 10-year risk prediction, particularly when prioritising individuals for blood tests of dysglycemia. Overall, we present a highly predictive and transferrable PRS for T2D and demonstrate that the potential for PRS to incrementally improve T2D risk prediction when incorporated into UK guideline-recommended screening and risk prediction with a clinical risk score.
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Affiliation(s)
- Scott C. Ritchie
- Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
- British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge, UK
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK
| | - Henry J. Taylor
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
- Center for Precision Health Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Yujian Liang
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore
| | - Hasanga D. Manikpurage
- Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
| | - Lisa Pennells
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
| | - Carles Foguet
- Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
| | - Gad Abraham
- Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia
- Department of Clinical Pathology, University of Melbourne, Parkville, Victoria, Australia
| | - Joel T. Gibson
- Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
| | - Xilin Jiang
- Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
- British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge, UK
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, US
| | - Yang Liu
- Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
| | - Yu Xu
- Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
- British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge, UK
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK
| | - Lois G. Kim
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
- National Institute for Health and Care Research Blood and Transplant Research Unit in Donor Health and Behaviour, University of Cambridge, Cambridge, UK
| | - Anubha Mahajan
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK, OX3 7BN
- OMNI Human Genetics, Genentech, Inc., 1 DNA Way, South San Francisco, CA 94080, USA
| | - Mark I. McCarthy
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK, OX3 7BN
- OMNI Human Genetics, Genentech, Inc., 1 DNA Way, South San Francisco, CA 94080, USA
| | - Stephen Kaptoge
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
| | - Samuel A Lambert
- Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Angela Wood
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
- British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge, UK
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK
- National Institute for Health and Care Research Blood and Transplant Research Unit in Donor Health and Behaviour, University of Cambridge, Cambridge, UK
- Cambridge Centre of Artificial Intelligence in Medicine, University of Cambridge, Cambridge, UK
| | - Xueling Sim
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore
| | - Francis S. Collins
- Center for Precision Health Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Joshua C. Denny
- Center for Precision Health Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
- All of Us Research Program, National Institutes of Health, Bethesda, MD, USA
| | - John Danesh
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
- British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge, UK
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK
- National Institute for Health and Care Research Blood and Transplant Research Unit in Donor Health and Behaviour, University of Cambridge, Cambridge, UK
- Department of Human Genetics, Wellcome Sanger Institute, Hinxton, UK
| | - Adam S. Butterworth
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
- British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge, UK
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK
- National Institute for Health and Care Research Blood and Transplant Research Unit in Donor Health and Behaviour, University of Cambridge, Cambridge, UK
| | - Emanuele Di Angelantonio
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
- British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge, UK
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK
- National Institute for Health and Care Research Blood and Transplant Research Unit in Donor Health and Behaviour, University of Cambridge, Cambridge, UK
- Health Data Science Research Centre, Human Technopole, Milan, Italy
| | - Michael Inouye
- Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
- British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge, UK
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK
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Senthivel V, Jolly B, Vr A, Bajaj A, Bhoyar R, Imran M, Vignesh H, Divakar MK, Sharma G, Rai N, Kumar K, Mp J, Krishna M, Shenthar J, Ali M, Abqari S, Nadri G, Scaria V, Naik N, Sivasubbu S. Whole genome sequencing of families diagnosed with cardiac channelopathies reveals structural variants missed by whole exome sequencing. J Hum Genet 2024; 69:455-465. [PMID: 38890497 DOI: 10.1038/s10038-024-01265-2] [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: 10/24/2023] [Revised: 03/07/2024] [Accepted: 06/02/2024] [Indexed: 06/20/2024]
Abstract
Cardiac channelopathies are a group of heritable disorders that affect the heart's electrical activity due to genetic variations present in genes coding for ion channels. With the advent of new sequencing technologies, molecular diagnosis of these disorders in patients has paved the way for early identification, therapeutic management and family screening. The objective of this retrospective study was to understand the efficacy of whole-genome sequencing in diagnosing patients with suspected cardiac channelopathies who were reported negative after whole exome sequencing and analysis. We employed a 3-tier analysis approach to identify nonsynonymous variations and loss-of-function variations missed by exome sequencing, and structural variations that are better resolved only by sequencing whole genomes. By performing whole genome sequencing and analyzing 25 exome-negative cardiac channelopathy patients, we identified 3 pathogenic variations. These include a heterozygous likely pathogenic nonsynonymous variation, CACNA1C:NM_000719:exon19:c.C2570G:p. P857R, which causes autosomal dominant long QT syndrome in the absence of Timothy syndrome, a heterozygous loss-of-function variation CASQ2:NM_001232.4:c.420+2T>C classified as pathogenic, and a 9.2 kb structural variation that spans exon 2 of the KCNQ1 gene, which is likely to cause Jervell-Lange-Nielssen syndrome. In addition, we also identified a loss-of-function variation and 16 structural variations of unknown significance (VUS). Further studies are required to elucidate the role of these identified VUS in gene regulation and decipher the underlying genetic and molecular mechanisms of these disorders. Our present study serves as a pilot for understanding the utility of WGS over clinical exomes in diagnosing cardiac channelopathy disorders.
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Affiliation(s)
- Vigneshwar Senthivel
- CSIR- Institute of Genomics and Integrative Biology, Mathura Road, Sukhdev Vihar, New Delhi, 110025, India
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, 201002, India
| | - Bani Jolly
- CSIR- Institute of Genomics and Integrative Biology, Mathura Road, Sukhdev Vihar, New Delhi, 110025, India
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, 201002, India
| | - Arvinden Vr
- CSIR- Institute of Genomics and Integrative Biology, Mathura Road, Sukhdev Vihar, New Delhi, 110025, India
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, 201002, India
| | - Anjali Bajaj
- CSIR- Institute of Genomics and Integrative Biology, Mathura Road, Sukhdev Vihar, New Delhi, 110025, India
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, 201002, India
| | - Rahul Bhoyar
- CSIR- Institute of Genomics and Integrative Biology, Mathura Road, Sukhdev Vihar, New Delhi, 110025, India
| | - Mohamed Imran
- CSIR- Institute of Genomics and Integrative Biology, Mathura Road, Sukhdev Vihar, New Delhi, 110025, India
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, 201002, India
| | - Harie Vignesh
- CSIR- Institute of Genomics and Integrative Biology, Mathura Road, Sukhdev Vihar, New Delhi, 110025, India
| | - Mohit Kumar Divakar
- CSIR- Institute of Genomics and Integrative Biology, Mathura Road, Sukhdev Vihar, New Delhi, 110025, India
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, 201002, India
| | - Gautam Sharma
- Department of Cardiology, All India Institute of Medical Sciences, New Delhi, 110029, India
| | - Nitin Rai
- Department of Cardiology, All India Institute of Medical Sciences, New Delhi, 110029, India
| | - Kapil Kumar
- Department of Cardiology, All India Institute of Medical Sciences, New Delhi, 110029, India
| | - Jayakrishnan Mp
- Government Medical College, Kozhikode, Kerala, 673008, India
| | - Maniram Krishna
- Tiny Hearts Fetal and Pediatric Clinic, Thanjavur, Tamil Nadu, 613001, India
| | - Jeyaprakash Shenthar
- Sri Jayadeva Institute of Cardiovascular Sciences and Research, Bengaluru, Karnataka, 560069, India
| | - Muzaffar Ali
- Sri Jayadeva Institute of Cardiovascular Sciences and Research, Bengaluru, Karnataka, 560069, India
| | - Shaad Abqari
- Jawaharlal Nehru Medical College and Hospital, Aligarh Muslim University, Aligarh, Uttar Pradesh, 202002, India
| | - Gulnaz Nadri
- Jawaharlal Nehru Medical College and Hospital, Aligarh Muslim University, Aligarh, Uttar Pradesh, 202002, India
| | - Vinod Scaria
- CSIR- Institute of Genomics and Integrative Biology, Mathura Road, Sukhdev Vihar, New Delhi, 110025, India
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, 201002, India
| | - Nitish Naik
- Department of Cardiology, All India Institute of Medical Sciences, New Delhi, 110029, India.
| | - Sridhar Sivasubbu
- CSIR- Institute of Genomics and Integrative Biology, Mathura Road, Sukhdev Vihar, New Delhi, 110025, India.
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, 201002, India.
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3
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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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Jolly B, Scaria V. Ethnic differences in pharmacogenomic variants: a south Asian perspective. Pharmacogenomics 2024; 25:171-174. [PMID: 38511426 DOI: 10.2217/pgs-2024-0026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2024] [Accepted: 02/14/2024] [Indexed: 03/22/2024] Open
Affiliation(s)
- Bani Jolly
- Karkinos Healthcare Private Limited (KHPL), Aurbis Business Parks, Bellandur, Bengaluru, Karnataka, 560103, India
| | - Vinod Scaria
- Vishwanath Cancer Care Foundation (VCCF), Neelkanth Business Park Kirol Village, West Mumbai, Maharashtra, 400086, India
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Yang L, Metzger GA, Padilla Del Valle R, Delgadillo Rubalcaba D, McLaughlin RN. Evolutionary insights from profiling LINE-1 activity at allelic resolution in a single human genome. EMBO J 2024; 43:112-131. [PMID: 38177314 PMCID: PMC10883270 DOI: 10.1038/s44318-023-00007-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2023] [Revised: 10/18/2023] [Accepted: 11/10/2023] [Indexed: 01/06/2024] Open
Abstract
Transposable elements have created the majority of the sequence in many genomes. In mammals, LINE-1 retrotransposons have been expanding for more than 100 million years as distinct, consecutive lineages; however, the drivers of this recurrent lineage emergence and disappearance are unknown. Most human genome assemblies provide a record of this ancient evolution, but fail to resolve ongoing LINE-1 retrotranspositions. Utilizing the human CHM1 long-read-based haploid assembly, we identified and cloned all full-length, intact LINE-1s, and found 29 LINE-1s with measurable in vitro retrotransposition activity. Among individuals, these LINE-1s varied in their presence, their allelic sequences, and their activity. We found that recently retrotransposed LINE-1s tend to be active in vitro and polymorphic in the population relative to more ancient LINE-1s. However, some rare allelic forms of old LINE-1s retain activity, suggesting older lineages can persist longer than expected. Finally, in LINE-1s with in vitro activity and in vivo fitness, we identified mutations that may have increased replication in ancient genomes and may prove promising candidates for mechanistic investigations of the drivers of LINE-1 evolution and which LINE-1 sequences contribute to human disease.
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Affiliation(s)
- Lei Yang
- Pacific Northwest Research Institute, Seattle, WA, USA
| | | | - Ricky Padilla Del Valle
- Pacific Northwest Research Institute, Seattle, WA, USA
- Molecular and Cellular Biology Graduate Program, University of Washington, Seattle, WA, USA
| | | | - Richard N McLaughlin
- Pacific Northwest Research Institute, Seattle, WA, USA.
- Molecular and Cellular Biology Graduate Program, University of Washington, Seattle, WA, USA.
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Ma R, Guan X, Teng N, Du Y, Ou S, Li X. Construction of ceRNA prognostic model based on the CCR7/CCL19 chemokine axis as a biomarker in breast cancer. BMC Med Genomics 2023; 16:254. [PMID: 37864213 PMCID: PMC10590005 DOI: 10.1186/s12920-023-01683-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2023] [Accepted: 10/02/2023] [Indexed: 10/22/2023] Open
Abstract
BACKGROUND The study of CCR7/CCL19 chemokine axis and breast cancer (BC) prognosis and metastasis is a current hot topic. We constructed a ceRNA network and risk-prognosis model based on CCR7/CCL19. METHODS Based on the lncRNA, miRNA and mRNA expression data downloaded from the TCGA database, we used the starbase website to find the lncRNA and miRNA of CCR7/CCL19 and established the ceRNA network. The 1008 BC samples containing survival data were divided into Train group (504 cases) and Test group (504 cases) using R "caret" package. Then we constructed a prognostic risk model using RNA screened by univariate Cox analysis in the Train group and validated it in the Test and All groups. In addition, we explored the correlation between riskScores and clinical trials and immune-related factors (22 immune-infiltrating cells, tumor microenvironment, 13 immune-related pathways and 24 HLA genes). After transfection with knockdown CCR7, we observed the activity and migration ability of MDA-MB-231 and MCF-7 cells using CCK8, scratch assays and angiogenesis assays. Finally, qPCR was used to detect the expression levels of five RNAs in the prognostic risk model in MDA-MB-231 and MCF-7 cell. RESULTS Patients with high expression of CCR7 and CCL19 had significantly higher overall survival times than those with low expression. The ceRNA network is constructed by 3 pairs of mRNA-miRNA pairs and 8 pairs of miRNA-lncRNA. After multivariate Cox analysis, we obtained a risk prognostic model: riskScore= -1.544 *`TRG-AS1`+ 0.936 * AC010327.5 + 0.553 *CCR7 -0.208 *CCL19 -0.315 *`hsa-let-7b-5p. Age, stage and riskScore can all be used as independent risk factors for BC prognosis. By drug sensitivity analysis, we found 5 drugs targeting CCR7 (convolamine, amikacin, AH-23,848, ondansetron, flucloxacillin). After transfection with knockdown CCR7, we found a significant reduction in cell activity and migration capacity in MDA-MB-231 cells. CONCLUSION We constructed the first prognostic model based on the CCR7/CCL19 chemokine axis in BC and explored its role in immune infiltration, tumor microenvironment, and HLA genes.
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Affiliation(s)
- Rufei Ma
- Department of Epidemiology, Dalian Medical University, Dalian, China
| | - Xiuliang Guan
- Department of Epidemiology, Dalian Medical University, Dalian, China
| | - Nan Teng
- Department of Epidemiology, Dalian Medical University, Dalian, China
| | - Yue Du
- Department of Epidemiology, Dalian Medical University, Dalian, China
| | - Shu Ou
- Department of Epidemiology, Dalian Medical University, Dalian, China
| | - Xiaofeng Li
- Department of Epidemiology, Dalian Medical University, Dalian, China.
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7
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Wall JD, Sathirapongsasuti JF, Gupta R, Rasheed A, Venkatesan R, Belsare S, Menon R, Phalke S, Mittal A, Fang J, Tanneeru D, Deshmukh M, Bassi A, Robinson J, Chaudhary R, Murugan S, Ul-Asar Z, Saleem I, Ishtiaq U, Fatima A, Sheikh SS, Hameed S, Ishaq M, Rasheed SZ, Memon FUR, Jalal A, Abbas S, Frossard P, Fuchsberger C, Forer L, Schoenherr S, Bei Q, Bhangale T, Tom J, Gadde SGK, B V P, Naik NK, Wang M, Kwok PY, Khera AV, Lakshmi BR, Butterworth AS, Chowdhury R, Danesh J, di Angelantonio E, Naheed A, Goyal V, Kandadai RM, Kumar H, Borgohain R, Mukherjee A, Wadia PM, Yadav R, Desai S, Kumar N, Biswas A, Pal PK, Muthane UB, Das SK, Ramprasad VL, Kukkle PL, Seshagiri S, Kathiresan S, Ghosh A, Mohan V, Saleheen D, Stawiski EW, Peterson AS. South Asian medical cohorts reveal strong founder effects and high rates of homozygosity. Nat Commun 2023; 14:3377. [PMID: 37291107 PMCID: PMC10250394 DOI: 10.1038/s41467-023-38766-1] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2021] [Accepted: 05/15/2023] [Indexed: 06/10/2023] Open
Abstract
The benefits of large-scale genetic studies for healthcare of the populations studied are well documented, but these genetic studies have traditionally ignored people from some parts of the world, such as South Asia. Here we describe whole genome sequence (WGS) data from 4806 individuals recruited from the healthcare delivery systems of Pakistan, India and Bangladesh, combined with WGS from 927 individuals from isolated South Asian populations. We characterize population structure in South Asia and describe a genotyping array (SARGAM) and imputation reference panel that are optimized for South Asian genomes. We find evidence for high rates of reproductive isolation, endogamy and consanguinity that vary across the subcontinent and that lead to levels of rare homozygotes that reach 100 times that seen in outbred populations. Founder effects increase the power to associate functional variants with disease processes and make South Asia a uniquely powerful place for population-scale genetic studies.
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Affiliation(s)
- Jeffrey D Wall
- Institute for Human Genetics, University of California, San Francisco, CA, 94143, USA.
- Dept of Ornithology and Mammology, California Academy of Sciences, San Francisco, CA, 94118, USA.
| | - J Fah Sathirapongsasuti
- MedGenome Inc., Foster City, CA, 94404, USA
- GenomeAsia 100K Foundation, Foster City, CA, 94404, USA
| | - Ravi Gupta
- MedGenome Labs Pvt. Ltd., Bengaluru, Karnataka, 560099, India
| | - Asif Rasheed
- Center for Non-Communicable Disease, Karachi, Karachi City, Sindh, 75300, Pakistan
| | - Radha Venkatesan
- Madras Diabetes Research Foundation and Dr. Mohan's Diabetes Specialties Centre, Chennai, Tamil Nadu, 600086, India
| | - Saurabh Belsare
- Institute for Human Genetics, University of California, San Francisco, CA, 94143, USA
| | - Ramesh Menon
- MedGenome Labs Pvt. Ltd., Bengaluru, Karnataka, 560099, India
| | - Sameer Phalke
- MedGenome Labs Pvt. Ltd., Bengaluru, Karnataka, 560099, India
| | | | - John Fang
- Thermo Fisher Scientific, Santa Clara, CA, 95051, USA
| | - Deepak Tanneeru
- MedGenome Labs Pvt. Ltd., Bengaluru, Karnataka, 560099, India
| | | | - Akshi Bassi
- MedGenome Labs Pvt. Ltd., Bengaluru, Karnataka, 560099, India
| | - Jacqueline Robinson
- Institute for Human Genetics, University of California, San Francisco, CA, 94143, USA
| | | | | | - Zameer Ul-Asar
- Center for Non-Communicable Disease, Karachi, Karachi City, Sindh, 75300, Pakistan
| | - Imran Saleem
- Center for Non-Communicable Disease, Karachi, Karachi City, Sindh, 75300, Pakistan
| | - Unzila Ishtiaq
- Center for Non-Communicable Disease, Karachi, Karachi City, Sindh, 75300, Pakistan
| | - Areej Fatima
- Center for Non-Communicable Disease, Karachi, Karachi City, Sindh, 75300, Pakistan
| | | | | | | | | | | | - Anjum Jalal
- Faisalabad Institute of Cardiology, Faisalabad, Pakistan
| | - Shahid Abbas
- Faisalabad Institute of Cardiology, Faisalabad, Pakistan
| | - Philippe Frossard
- Center for Non-Communicable Disease, Karachi, Karachi City, Sindh, 75300, Pakistan
| | - Christian Fuchsberger
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, 48109, USA
- Institute for Biomedicine, Eurac Research, Bolzano, Italy
- Institute of Genetic Epidemiology, Department of Genetics and Pharmacology, Medical University of Innsbruck, Innsbruck, Austria
| | - Lukas Forer
- Institute of Genetic Epidemiology, Department of Genetics and Pharmacology, Medical University of Innsbruck, Innsbruck, Austria
| | - Sebastian Schoenherr
- Institute of Genetic Epidemiology, Department of Genetics and Pharmacology, Medical University of Innsbruck, Innsbruck, Austria
| | - Qixin Bei
- Department of Molecular Biology, Genentech, South San Francisco, CA, 94080, USA
| | - Tushar Bhangale
- Department of Human Genetics, Genentech, South San Francisco, CA, 94080, USA
| | - Jennifer Tom
- Product Development Data Sciences, Genentech, South San Francisco, CA, 94080, USA
| | | | - Priya B V
- Narayana Nethralaya Foundation, Bengaluru, Karnataka, 560010, India
| | | | - Minxian Wang
- Program in Medical and Population Genetics & Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA
| | - Pui-Yan Kwok
- Institute for Human Genetics, University of California, San Francisco, CA, 94143, USA
- Cardiovascular Research Institute and Department of Dermatology, University of California San Francisco, San Francisco, CA, 94143, USA
- Institute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan
| | - Amit V Khera
- Harvard Medical School, Boston, MA, 02115, USA
- Division of Cardiology, Department of Medicine, Brigham and Women's Hospital, MA, 02115, Boston, USA
- Verve Therapeutics, Cambridge, MA, 02139, USA
| | - B R Lakshmi
- MDCRC, Royal Care Super Speciality Hospital 1/520, Neelambur, Coimbatore, Tamil Nadu, 641062, India
| | - Adam S Butterworth
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- National Institute for Health Research Blood and Transplant Research Unit in Donor Health and Genomics, University of Cambridge, Cambridge, UK
- National Institute for Health Research Cambridge Biomedical Research Centre, University of Cambridge and Cambridge University Hospitals, Cambridge, UK
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK
| | - Rajiv Chowdhury
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - John Danesh
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- National Institute for Health Research Blood and Transplant Research Unit in Donor Health and Genomics, University of Cambridge, Cambridge, UK
- National Institute for Health Research Cambridge Biomedical Research Centre, University of Cambridge and Cambridge University Hospitals, Cambridge, UK
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK
- Department of Human Genetics, Wellcome Sanger Institute, Hinxton, UK
| | - Emanuele di Angelantonio
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- National Institute for Health Research Blood and Transplant Research Unit in Donor Health and Genomics, University of Cambridge, Cambridge, UK
- National Institute for Health Research Cambridge Biomedical Research Centre, University of Cambridge and Cambridge University Hospitals, Cambridge, UK
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK
| | - Aliya Naheed
- Initiative for Non Communicable Diseases, Health Systems and Population Studies Division, icddr,b, Dhaka, Bangladesh
| | - Vinay Goyal
- All India Institute of Medical Sciences (AIIMS), New Delhi, India
- Medanta Hospital, New Delhi, India
- Medanta, The Medicity, Gurgaon, India
| | | | | | - Rupam Borgohain
- Nizams Institute of Medical Sciences (NIMS), Hyderabad, India
| | - Adreesh Mukherjee
- Bangur Institute of Neurosciences and Institute of Post Graduate Medical Education and Research (IPGME&R), Kolkata, India
| | | | - Ravi Yadav
- National Institute of Mental Health and Neurosciences (NIMHANS), Bengaluru, India
| | - Soaham Desai
- Shree Krishna Hospital and Pramukhaswami Medical College, Bhaikaka University, Karamsad, Gujarat, India
| | - Niraj Kumar
- All India Institute of Medical Sciences, Rishikesh, India
| | - Atanu Biswas
- Bangur Institute of Neurosciences and Institute of Post Graduate Medical Education and Research (IPGME&R), Kolkata, India
| | - Pramod Kumar Pal
- National Institute of Mental Health and Neurosciences (NIMHANS), Bengaluru, India
| | - Uday B Muthane
- Parkinson and Ageing Research Foundation, Bengaluru, India
| | - Shymal K Das
- Bangur Institute of Neurosciences and Institute of Post Graduate Medical Education and Research (IPGME&R), Kolkata, India
| | | | - Prashanth L Kukkle
- All India Institute of Medical Sciences, Rishikesh, India
- Manipal Hospital, Miller Road, Bengaluru, India
- Parkinson's Disease and Movement Disorders Clinic, Bengaluru, India
| | - Somasekar Seshagiri
- GenomeAsia 100K Foundation, Foster City, CA, 94404, USA
- Department of Molecular Biology, Genentech, South San Francisco, CA, 94080, USA
| | - Sekar Kathiresan
- Program in Medical and Population Genetics & Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA
- Verve Therapeutics, Cambridge, MA, 02139, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, 02114, USA
| | - Arkasubhra Ghosh
- Narayana Nethralaya Foundation, Bengaluru, Karnataka, 560010, India
| | - V Mohan
- Madras Diabetes Research Foundation and Dr. Mohan's Diabetes Specialties Centre, Chennai, Tamil Nadu, 600086, India
| | - Danish Saleheen
- Center for Non-Communicable Disease, Karachi, Karachi City, Sindh, 75300, Pakistan
- Seymour, Paul and Gloria Milstein Division of Cardiology at Columbia University, New York, NY, 10032, USA
| | - Eric W Stawiski
- MedGenome Inc., Foster City, CA, 94404, USA
- GenomeAsia 100K Foundation, Foster City, CA, 94404, USA
- Department of Molecular Biology, Genentech, South San Francisco, CA, 94080, USA
- Caribou Biosciences, Berkeley, CA, 94710, USA
| | - Andrew S Peterson
- MedGenome Inc., Foster City, CA, 94404, USA.
- GenomeAsia 100K Foundation, Foster City, CA, 94404, USA.
- Department of Molecular Biology, Genentech, South San Francisco, CA, 94080, USA.
- Broadwing Bio, South San Francisco, CA, 94080, USA.
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8
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Liao K, Carlson J, Zöllner S. The effect of mutation subtypes on the allele frequency spectrum and population genetics inference. G3 (BETHESDA, MD.) 2023; 13:jkad035. [PMID: 36759699 PMCID: PMC10085755 DOI: 10.1093/g3journal/jkad035] [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: 12/15/2022] [Revised: 01/23/2023] [Accepted: 01/26/2023] [Indexed: 02/11/2023]
Abstract
Population genetics has adapted as technological advances in next-generation sequencing have resulted in an exponential increase of genetic data. A common approach to efficiently analyze genetic variation present in large sequencing data is through the allele frequency spectrum, defined as the distribution of allele frequencies in a sample. While the frequency spectrum serves to summarize patterns of genetic variation, it implicitly assumes mutation types (A→C vs C→T) as interchangeable. However, mutations of different types arise and spread due to spatial and temporal variation in forces such as mutation rate and biased gene conversion that result in heterogeneity in the distribution of allele frequencies across sites. In this work, we explore the impact of this simplification on multiple aspects of population genetic modeling. As a site's mutation rate is strongly affected by flanking nucleotides, we defined a mutation subtype by the base pair change and adjacent nucleotides (e.g. AAA→ATA) and systematically assessed the heterogeneity in the frequency spectrum across 96 distinct 3-mer mutation subtypes using n = 3556 whole-genome sequenced individuals of European ancestry. We observed substantial variation across the subtype-specific frequency spectra, with some of the variation being influenced by molecular factors previously identified for single base mutation types. Estimates of model parameters from demographic inference performed for each mutation subtype's AFS individually varied drastically across the 96 subtypes. In local patterns of variation, a combination of regional subtype composition and local genomic factors shaped the regional frequency spectrum across genomic regions. Our results illustrate how treating variants in large sequencing samples as interchangeable may confound population genetic frameworks and encourages us to consider the unique evolutionary mechanisms of analyzed polymorphisms.
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Affiliation(s)
- Kevin Liao
- Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Jedidiah Carlson
- Department of Integrative Biology, University of Texas at Austin, Austin, TX 78712, USA
- Department of Population Health, University of Texas at Austin, Austin, TX 78712, USA
| | - Sebastian Zöllner
- Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA
- Department of Psychiatry, University of Michigan, Ann Arbor, MI 48109, USA
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9
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Ishigaki K, Sakaue S, Terao C, Luo Y, Sonehara K, Yamaguchi K, Amariuta T, Too CL, Laufer VA, Scott IC, Viatte S, Takahashi M, Ohmura K, Murasawa A, Hashimoto M, Ito H, Hammoudeh M, Emadi SA, Masri BK, Halabi H, Badsha H, Uthman IW, Wu X, Lin L, Li T, Plant D, Barton A, Orozco G, Verstappen SMM, Bowes J, MacGregor AJ, Honda S, Koido M, Tomizuka K, Kamatani Y, Tanaka H, Tanaka E, Suzuki A, Maeda Y, Yamamoto K, Miyawaki S, Xie G, Zhang J, Amos CI, Keystone E, Wolbink G, van der Horst-Bruinsma I, Cui J, Liao KP, Carroll RJ, Lee HS, Bang SY, Siminovitch KA, de Vries N, Alfredsson L, Rantapää-Dahlqvist S, Karlson EW, Bae SC, Kimberly RP, Edberg JC, Mariette X, Huizinga T, Dieudé P, Schneider M, Kerick M, Denny JC, Matsuda K, Matsuo K, Mimori T, Matsuda F, Fujio K, Tanaka Y, Kumanogoh A, Traylor M, Lewis CM, Eyre S, Xu H, Saxena R, Arayssi T, Kochi Y, Ikari K, Harigai M, Gregersen PK, Yamamoto K, Louis Bridges S, Padyukov L, Martin J, Klareskog L, Okada Y, Raychaudhuri S. Multi-ancestry genome-wide association analyses identify novel genetic mechanisms in rheumatoid arthritis. Nat Genet 2022; 54:1640-1651. [PMID: 36333501 PMCID: PMC10165422 DOI: 10.1038/s41588-022-01213-w] [Citation(s) in RCA: 92] [Impact Index Per Article: 46.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2021] [Accepted: 09/26/2022] [Indexed: 11/06/2022]
Abstract
Rheumatoid arthritis (RA) is a highly heritable complex disease with unknown etiology. Multi-ancestry genetic research of RA promises to improve power to detect genetic signals, fine-mapping resolution and performances of polygenic risk scores (PRS). Here, we present a large-scale genome-wide association study (GWAS) of RA, which includes 276,020 samples from five ancestral groups. We conducted a multi-ancestry meta-analysis and identified 124 loci (P < 5 × 10-8), of which 34 are novel. Candidate genes at the novel loci suggest essential roles of the immune system (for example, TNIP2 and TNFRSF11A) and joint tissues (for example, WISP1) in RA etiology. Multi-ancestry fine-mapping identified putatively causal variants with biological insights (for example, LEF1). Moreover, PRS based on multi-ancestry GWAS outperformed PRS based on single-ancestry GWAS and had comparable performance between populations of European and East Asian ancestries. Our study provides several insights into the etiology of RA and improves the genetic predictability of RA.
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Affiliation(s)
- Kazuyoshi Ishigaki
- Center for Data Sciences, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Laboratory for Human Immunogenetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Saori Sakaue
- Center for Data Sciences, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Chikashi Terao
- Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
- Clinical Research Center, Shizuoka General Hospital, Shizuoka, Japan
- The Department of Applied Genetics, The School of Pharmaceutical Sciences, University of Shizuoka, Shizuoka, Japan
| | - Yang Luo
- Center for Data Sciences, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Kyuto Sonehara
- 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
| | - Kensuke Yamaguchi
- Department of Genomic Function and Diversity, Medical Research Institute, Tokyo Medical and Dental University, Tokyo, Japan
- Laboratory for Autoimmune Diseases, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Tiffany Amariuta
- Center for Data Sciences, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Halıcıoğlu Data Science Institute, University of California San Diego, La Jolla, CA, USA
- Department of Medicine, University of California San Diego, La Jolla, CA, USA
| | - Chun Lai Too
- Immunogenetics Unit, Allergy and Immunology Research Center, Institute for Medical Research, National Institutes of Health Complex, Ministry of Health, Kuala Lumpur, Malaysia
- Department of Medicine, Division of Rheumatology, Karolinska Institutet and Karolinska University Hospital, Stockholm, Sweden
| | - Vincent A Laufer
- Department of Clinical Immunology and Rheumatology, University of Alabama at Birmingham School of Medicine, Birmingham, AL, USA
- Department of Pathology, Michigan Medicine, Ann Arbor, MI, USA
| | - Ian C Scott
- Haywood Academic Rheumatology Centre, Haywood Hospital, Midlands Partnership NHS Foundation Trust, Burslem, UK
- Primary Care Centre Versus Arthritis, School of Medicine, Keele University, Keele, UK
| | - Sebastien Viatte
- Centre for Genetics and Genomics Versus Arthritis, Division of Musculoskeletal and Dermatological Sciences, School of Biological Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester Academic Health Science Centre, Manchester, UK
- Lydia Becker Institute of Immunology and Inflammation, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK
- NIHR Manchester Biomedical Research Centre, Manchester University Foundation Trust, Manchester, UK
| | - Meiko Takahashi
- Center for Genomic Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Koichiro Ohmura
- Department of Rheumatology and Clinical immunology, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Akira Murasawa
- Department of Rheumatology, Niigata Rheumatic Center, Niigata, Japan
| | - Motomu Hashimoto
- Department of Advanced Medicine for Rheumatic Diseases, Kyoto University Graduate School of Medicine, Kyoto, Japan
- Department of Clinical Immunology, Graduate School of Medicine, Osaka City University, Osaka, Japan
| | - Hiromu Ito
- Department of Advanced Medicine for Rheumatic Diseases, Kyoto University Graduate School of Medicine, Kyoto, Japan
- Department of Orthopaedic Surgery, Kurashiki Central Hospital, Kurashiki, Japan
| | - Mohammed Hammoudeh
- Rheumatology Division, Department of Internal Medicine, Hamad Medical Corporation, Doha, Qatar
| | - Samar Al Emadi
- Rheumatology Division, Department of Internal Medicine, Hamad Medical Corporation, Doha, Qatar
| | - Basel K Masri
- Department of Internal Medicine, Jordan Hospital, Amman, Jordan
| | - Hussein Halabi
- Section of Rheumatology, Department of Internal Medicine, King Faisal Specialist Hospital and Research Center, Jeddah, Saudi Arabia
| | - Humeira Badsha
- Dr. Humeira Badsha Medical Center, Emirates Hospital, Dubai, United Arab Emirates
| | - Imad W Uthman
- Department of Rheumatology, American University of Beirut, Beirut, Lebanon
| | - Xin Wu
- Department of Rheumatology and Immunology, Shanghai Changzeng Hospital, The Second Military Medical University, Shanghai, China
| | - Li Lin
- Department of Rheumatology and Immunology, Shanghai Changzeng Hospital, The Second Military Medical University, Shanghai, China
| | - Ting Li
- Department of Rheumatology and Immunology, Shanghai Changzeng Hospital, The Second Military Medical University, Shanghai, China
| | - Darren Plant
- Centre for Genetics and Genomics Versus Arthritis, Division of Musculoskeletal and Dermatological Sciences, School of Biological Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester Academic Health Science Centre, Manchester, UK
| | - Anne Barton
- Centre for Genetics and Genomics Versus Arthritis, Division of Musculoskeletal and Dermatological Sciences, School of Biological Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester Academic Health Science Centre, Manchester, UK
- NIHR Manchester Biomedical Research Centre, Manchester University Foundation Trust, Manchester, UK
| | - Gisela Orozco
- Centre for Genetics and Genomics Versus Arthritis, Division of Musculoskeletal and Dermatological Sciences, School of Biological Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester Academic Health Science Centre, Manchester, UK
- NIHR Manchester Biomedical Research Centre, Manchester University Foundation Trust, Manchester, UK
| | - Suzanne M M Verstappen
- NIHR Manchester Biomedical Research Centre, Manchester University Foundation Trust, Manchester, UK
- Centre for Epidemiology Versus Arthritis, Centre for Musculoskeletal Research, Division of Musculoskeletal and Dermatological Sciences, The University of Manchester, Manchester, UK
| | - John Bowes
- Centre for Genetics and Genomics Versus Arthritis, Division of Musculoskeletal and Dermatological Sciences, School of Biological Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester Academic Health Science Centre, Manchester, UK
- NIHR Manchester Biomedical Research Centre, Manchester University Foundation Trust, Manchester, UK
| | | | - Suguru Honda
- Institute of Rheumatology, Tokyo Women's Medical University Hospital, Tokyo, Japan
- Department of Rheumatology, Tokyo Women's Medical University School of Medicine, Tokyo, Japan
| | - Masaru Koido
- Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Kohei Tomizuka
- Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Yoichiro Kamatani
- Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
- Laboratory of Complex Trait Genomics, Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan
| | - Hiroaki Tanaka
- The First Department of Internal Medicine, School of Medicine, University of Occupational and Environmental Health Japan, Kitakyushu, Japan
| | - Eiichi Tanaka
- Institute of Rheumatology, Tokyo Women's Medical University Hospital, Tokyo, Japan
- Department of Rheumatology, Tokyo Women's Medical University School of Medicine, Tokyo, Japan
| | - Akari Suzuki
- Laboratory for Autoimmune Diseases, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Yuichi Maeda
- Integrated Frontier Research for Medical Science Division, Institute for Open and Transdisciplinary Research Initiatives, Osaka University, Suita, Japan
- Department of Respiratory Medicine and Clinical Immunology, Osaka University Graduate School of Medicine, Suita, Japan
- Department of Immunopathology, Immunology Frontier Research Center (WPI-IFReC), Osaka University, Suita, Japan
| | - Kenichi Yamamoto
- 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
- Department of Pediatrics, Osaka University Graduate School of Medicine, Suita, Japan
| | - Satoru Miyawaki
- Department of Neurosurgery, Faculty of Medicine, the University of Tokyo, Tokyo, Japan
| | - Gang Xie
- Lunenfeld-Tanenbaum Research Institute, Toronto, Ontario, Canada
| | - Jinyi Zhang
- Lunenfeld-Tanenbaum Research Institute, Toronto, Ontario, Canada
- Department of Medicine, University of Toronto, Toronto, Ontario, Canada
| | | | | | - Gertjan Wolbink
- Department of Rheumatology, Amsterdam Rheumatology and Immunology Center (ARC), Reade, Amsterdam, the Netherlands
| | - Irene van der Horst-Bruinsma
- Department of Rheumatology & Clinical Immunology/ARC, Amsterdam Institute for Infection and Immunity, Amsterdam UMC location Vrije Universiteit, Amsterdam, the Netherlands
| | - Jing Cui
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Katherine P Liao
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Massachusetts Veterans Epidemiology Research and Information Center, VA Boston Healthcare System, Boston, MA, USA
| | - Robert J Carroll
- Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Hye-Soon Lee
- Department of Rheumatology, Hanyang University Hospital for Rheumatic Diseases, Seoul, Korea
- Hanyang University Institute for Rheumatology Research, Seoul, Korea
| | - So-Young Bang
- Department of Rheumatology, Hanyang University Hospital for Rheumatic Diseases, Seoul, Korea
- Hanyang University Institute for Rheumatology Research, Seoul, Korea
| | - Katherine A Siminovitch
- Lunenfeld-Tanenbaum Research Institute, Toronto, Ontario, Canada
- Departments of Medicine and Immunology, University of Toronto, Toronto, Ontario, Canada
| | - Niek de Vries
- Department of Rheumatology & Clinical Immunology/ARC, Amsterdam Institute for Infection and Immunity, Amsterdam UMC location AMC/University of Amsterdam, Amsterdam, the Netherlands
| | - Lars Alfredsson
- Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
| | | | - Elizabeth W Karlson
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Sang-Cheol Bae
- Department of Rheumatology, Hanyang University Hospital for Rheumatic Diseases, Seoul, Korea
- Hanyang University Institute for Rheumatology Research, Seoul, Korea
| | - Robert P Kimberly
- Center for Clinical and Translational Science, Division of Clinical Immunology and Rheumatology, Department of Medicine, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Jeffrey C Edberg
- Center for Clinical and Translational Science, Division of Clinical Immunology and Rheumatology, Department of Medicine, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Xavier Mariette
- Department of Rheumatology, Université Paris-Saclay, Assistance Pubique - Hôpitaux de Paris, Hôpital Bicêtre, INSERM UMR1184, Le Kremlin Bicêtre, France
| | - Tom Huizinga
- Leiden University Medical Center, Leiden, the Netherlands
| | - Philippe Dieudé
- University of Paris Cité, Inserm, PHERE, F-75018, Paris, France
- Department of Rheumatology, Hôpital Bichat, APHP, Paris, France
| | - Matthias Schneider
- Department of Rheumatology & Hiller Research Unit Rheumatology, UKD, Heinrich-Heine University, Düsseldorf, Germany
| | - Martin Kerick
- Institute of Parasitology and Biomedicine Lopez-Neyra, CSIC, Granada, Spain
| | - Joshua C Denny
- Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, TN, USA
- All of Us Research Program, Office of the Director, National Institutes of Health, Bethesda, MD, USA
- Department of Medicine, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Koichi Matsuda
- Laboratory of Genome Technology, Human Genome Center, Institute of Medical Science, The University of Tokyo, Tokyo, Japan
- Laboratory of Clinical Genome Sequencing, Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan
| | - Keitaro Matsuo
- Division of Cancer Epidemiology and Prevention, Department of Preventive Medicine, Aichi Cancer Center Research Institute, Nagoya, Japan
- Department of Cancer Epidemiology, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Tsuneyo Mimori
- Department of Rheumatology and Clinical immunology, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Fumihiko Matsuda
- Center for Genomic Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Keishi Fujio
- Department of Allergy and Rheumatology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Yoshiya Tanaka
- The First Department of Internal Medicine, School of Medicine, University of Occupational and Environmental Health Japan, Kitakyushu, Japan
| | - Atsushi Kumanogoh
- Integrated Frontier Research for Medical Science Division, Institute for Open and Transdisciplinary Research Initiatives, Osaka University, Suita, Japan
- Centre for Genetics and Genomics Versus Arthritis, Division of Musculoskeletal and Dermatological Sciences, School of Biological Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester Academic Health Science Centre, Manchester, UK
- Department of Respiratory Medicine and Clinical Immunology, Osaka University Graduate School of Medicine, Suita, Japan
- Department of Immunopathology, Immunology Frontier Research Center (WPI-IFReC), Osaka University, Suita, Japan
| | - Matthew Traylor
- Department of Medical & Molecular Genetics, King's College London, London, UK
- Department of Genetics, Novo Nordisk Research Centre Oxford, Oxford, UK
- Clinical Pharmacology, William Harvey Research Institute, Queen Mary University of London, London, UK
| | - Cathryn M Lewis
- Department of Medical & Molecular Genetics, King's College London, London, UK
- Social, Genetic and Developmental Psychiatry Centre, King's College London, London, UK
| | - Stephen Eyre
- Centre for Genetics and Genomics Versus Arthritis, Division of Musculoskeletal and Dermatological Sciences, School of Biological Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester Academic Health Science Centre, Manchester, UK
- NIHR Manchester Biomedical Research Centre, Manchester University Foundation Trust, Manchester, UK
| | - Huji Xu
- Department of Rheumatology and Immunology, Shanghai Changzeng Hospital, The Second Military Medical University, Shanghai, China
- School of Clinical Medicine Tsinghua University, Beijing, China
- Peking-Tsinghua Center for Life Sciences, Tsinghua University, Beijing, China
| | - Richa Saxena
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Thurayya Arayssi
- Department of Internal Medicine, Weill Cornell Medicine-Qatar, Education City, Doha, Qatar
| | - Yuta Kochi
- Department of Genomic Function and Diversity, Medical Research Institute, Tokyo Medical and Dental University, Tokyo, Japan
- Laboratory for Autoimmune Diseases, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Katsunori Ikari
- Institute of Rheumatology, Tokyo Women's Medical University Hospital, Tokyo, Japan
- Department of Orthopedic Surgery, Tokyo Women's Medical University School of Medicine, Tokyo, Japan
- Division of Multidisciplinary Management of Rheumatic Diseases, Tokyo Women's Medical University, Tokyo, Japan
| | - Masayoshi Harigai
- Institute of Rheumatology, Tokyo Women's Medical University Hospital, Tokyo, Japan
- Division of Rheumatology, Department of Internal Medicine, Tokyo Women's Medical University School of Medicine, Tokyo, Japan
| | - Peter K Gregersen
- Robert S. Boas Center for Genomics and Human Genetics, Feinstein Institutes for Medical Research, Northwell Health, Manhasset, NY, USA
| | - Kazuhiko Yamamoto
- Laboratory for Autoimmune Diseases, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - S Louis Bridges
- Department of Medicine, Hospital for Special Surgery, New York, NY, USA
- Division of Rheumatology, Weill Cornell Medicine, New York, NY, USA
| | - Leonid Padyukov
- Department of Medicine, Division of Rheumatology, Karolinska Institutet and Karolinska University Hospital, Stockholm, Sweden
| | - Javier Martin
- Institute of Parasitology and Biomedicine Lopez-Neyra, CSIC, Granada, Spain
| | - Lars Klareskog
- Department of Medicine, Division of Rheumatology, Karolinska Institutet and Karolinska University Hospital, Stockholm, Sweden
| | - 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.
| | - Soumya Raychaudhuri
- Center for Data Sciences, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA.
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
- Centre for Genetics and Genomics Versus Arthritis, Division of Musculoskeletal and Dermatological Sciences, School of Biological Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester Academic Health Science Centre, Manchester, UK.
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10
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Phillips C, de la Puente M, Ruiz-Ramirez J, Staniewska A, Ambroa-Conde A, Freire-Aradas A, Mosquera-Miguel A, Rodriguez A, Lareu MV. Eurasiaplex-2: Shifting the focus to SNPs with high population specificity increases the power of forensic ancestry marker sets. Forensic Sci Int Genet 2022; 61:102780. [PMID: 36174251 DOI: 10.1016/j.fsigen.2022.102780] [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: 06/03/2022] [Revised: 09/16/2022] [Accepted: 09/18/2022] [Indexed: 11/27/2022]
Abstract
To compile a new South Asian-informative panel of forensic ancestry SNPs, we changed the strategy for selecting the most powerful markers for this purpose by targeting polymorphisms with near absolute specificity - when the South Asian-informative allele identified is absent from all other populations or present at frequencies below 0.001 (one in a thousand). More than 120 candidate SNPs were identified from 1000 Genomes datasets satisfying an allele frequency screen of ≥ 0.1 (10 % or more) allele frequency in South Asians, and ≤ 0.001 (0.1 % or less) in African, East Asian, and European populations. From the candidate pool of markers, a final panel of 36 SNPs, widely distributed across most autosomes, were selected that had allele frequencies in the five 1000 Genomes South Asian populations ranging from 0.4 to 0.15. Slightly lower average allele frequencies, but consistent patterns of informativeness were observed in gnomAD South Asian datasets used to validate the 1000 Genomes variant annotations. We named the panel of 36 South Asian-specific SNPs Eurasiaplex-2, and the informativeness of the panel was evaluated by compiling worldwide population data from 4097 samples in four genome variation databases that largely complement the global sampling of 1000 Genomes. Consistent patterns of allele frequency distribution, which were specific to South Asia, were observed in all populations in, or closely sited to, the Indian sub-continent. Pakistani populations from the HGDP-CEPH panel had markedly lower allele frequencies, highlighting the need to develop a statistical system to evaluate the ancestry inference value of counting the number of population-specific alleles present in an individual.
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Affiliation(s)
- C Phillips
- Forensic Genetics Unit, Institute of Forensic Sciences, University of Santiago de Compostela, Spain; Institute of Anthropology and Ethnology, Adam Mickiewicz University in Poznań, Poland..
| | - M de la Puente
- Forensic Genetics Unit, Institute of Forensic Sciences, University of Santiago de Compostela, Spain
| | - J Ruiz-Ramirez
- Forensic Genetics Unit, Institute of Forensic Sciences, University of Santiago de Compostela, Spain
| | - A Staniewska
- Institute of Anthropology and Ethnology, Adam Mickiewicz University in Poznań, Poland
| | - A Ambroa-Conde
- Forensic Genetics Unit, Institute of Forensic Sciences, University of Santiago de Compostela, Spain
| | - A Freire-Aradas
- Forensic Genetics Unit, Institute of Forensic Sciences, University of Santiago de Compostela, Spain
| | - A Mosquera-Miguel
- Forensic Genetics Unit, Institute of Forensic Sciences, University of Santiago de Compostela, Spain
| | - A Rodriguez
- Forensic Genetics Unit, Institute of Forensic Sciences, University of Santiago de Compostela, Spain
| | - M V Lareu
- Forensic Genetics Unit, Institute of Forensic Sciences, University of Santiago de Compostela, Spain
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11
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Abdul Wahab SA, Yakob Y, Mohd Khalid MKN, Ali N, Leong HY, Ngu LH. Molecular, Biochemical, and Clinical Characterization of Thirteen Patients with Glycogen Storage Disease 1a in Malaysia. Genet Res (Camb) 2022; 2022:5870092. [PMID: 36160031 PMCID: PMC9489408 DOI: 10.1155/2022/5870092] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2022] [Revised: 08/11/2022] [Accepted: 08/13/2022] [Indexed: 02/06/2023] Open
Abstract
Background Glycogen storage disease type 1a (GSD1a) is a rare autosomal recessive metabolic disorder characterized by hypoglycaemia, growth retardation, lactic acidosis, hepatomegaly, hyperlipidemia, and nephromegaly. GSD1a is caused by a mutation in the G6PC gene encoding glucose-6-phosphatase (G6Pase); an enzyme that catalyses the hydrolysis of glucose-6-phosphate (G6P) to phosphate and glucose. Objective To elaborate on the clinical findings, biochemical data, molecular genetic analysis, and short-term prognosis of 13 GSD1a patients in Malaysia. Methods The information about 13 clinically classified GSD1a patients was retrospectively studied. The G6PC mutation analysis was performed by PCR-DNA sequencing. Results Patients were presented with hepatomegaly (92%), hypoglycaemia (38%), poor weight gain (23%), and short stature (15%). Mutation analysis revealed nine heterozygous mutations; eight previously reported mutations (c.155 A > T, c.209 G > A, c.226 A > T, c.248 G > A, c.648 G > T, c.706 T > A, c.1022 T > A, c.262delG) and a novel mutation (c.325 T > C). The most common mutation found in Malaysian patients was c.648 G > T in ten patients (77%) of mostly Malay ethnicity, followed by c.248 G > A in 4 patients of Chinese ethnicity (30%). A novel missense mutation (c.325 T > C) was predicted to be disease-causing by various in silico software. Conclusions The establishment of G6PC molecular genetic testing will enable the detection of presymptomatic patients, assisting in genetic counselling while avoiding the invasive methods of liver biopsy.
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Affiliation(s)
- Siti Aishah Abdul Wahab
- Molecular Diagnostics Unit, Specialised Diagnostic Centre, Institute for Medical Research, National Institute of Health, Ministry of Health, Jalan Pahang 50586, Kuala Lumpur, Malaysia
| | - Yusnita Yakob
- Molecular Diagnostics Unit, Specialised Diagnostic Centre, Institute for Medical Research, National Institute of Health, Ministry of Health, Jalan Pahang 50586, Kuala Lumpur, Malaysia
| | - Mohd Khairul Nizam Mohd Khalid
- IEM & Genetic Unit, Nutrition Metabolic and Cardiovascular Research Centre, Institute for Medical Research, National Institute of Health, Ministry of Health, Kuala Lumpur, Malaysia
| | - Noraishah Ali
- Department of Genetics, Hospital Kuala Lumpur, Ministry of Health, Kuala Lumpur, Malaysia
| | - Huey Yin Leong
- Department of Genetics, Hospital Kuala Lumpur, Ministry of Health, Kuala Lumpur, Malaysia
| | - Lock Hock Ngu
- Department of Genetics, Hospital Kuala Lumpur, Ministry of Health, Kuala Lumpur, Malaysia
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12
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Khoruddin NA, Lim WF, Teh LK, Noorizhab MNF, Mohd Yusof FZ, Salleh MZ. Toward precision nutrition: A cross‐sectional study on the genetic risks of nutrients deficiencies and eating behaviors among the Orang Asli and Malays. PRECISION MEDICAL SCIENCES 2022. [DOI: 10.1002/prm2.12081] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Affiliation(s)
- Nurul Ain Khoruddin
- Integrative Pharmacogenomics Institute (iPROMISE) Universiti Teknologi MARA (UiTM) Selangor Bandar Puncak Alam Malaysia
- Faculty of Applied Sciences Universiti Teknologi MARA (UiTM) Selangor Shah Alam Malaysia
| | - Wai Feng Lim
- Integrative Pharmacogenomics Institute (iPROMISE) Universiti Teknologi MARA (UiTM) Selangor Bandar Puncak Alam Malaysia
| | - Lay Kek Teh
- Integrative Pharmacogenomics Institute (iPROMISE) Universiti Teknologi MARA (UiTM) Selangor Bandar Puncak Alam Malaysia
- Faculty of Pharmacy Universiti Teknologi MARA (UiTM) Selangor Bandar Puncak Alam Malaysia
| | - Mohd Nur Fakhruzzaman Noorizhab
- Integrative Pharmacogenomics Institute (iPROMISE) Universiti Teknologi MARA (UiTM) Selangor Bandar Puncak Alam Malaysia
- Faculty of Pharmacy Universiti Teknologi MARA (UiTM) Selangor Bandar Puncak Alam Malaysia
| | - Farida Zuraina Mohd Yusof
- Integrative Pharmacogenomics Institute (iPROMISE) Universiti Teknologi MARA (UiTM) Selangor Bandar Puncak Alam Malaysia
- Faculty of Applied Sciences Universiti Teknologi MARA (UiTM) Selangor Shah Alam Malaysia
| | - Mohd Zaki Salleh
- Integrative Pharmacogenomics Institute (iPROMISE) Universiti Teknologi MARA (UiTM) Selangor Bandar Puncak Alam Malaysia
- Faculty of Pharmacy Universiti Teknologi MARA (UiTM) Selangor Bandar Puncak Alam Malaysia
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13
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Mary L, Leclerc D, Gilot D, Belaud-Rotureau MA, Jaillard S. The TALE never ends: A comprehensive overview of the role of PBX1, a TALE transcription factor, in human developmental defects. Hum Mutat 2022; 43:1125-1148. [PMID: 35451537 DOI: 10.1002/humu.24388] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2021] [Revised: 03/25/2022] [Accepted: 04/20/2022] [Indexed: 11/07/2022]
Abstract
PBX1 is a highly conserved atypical homeodomain transcription factor (TF) belonging to the TALE (three amino acid loop extension) family. Dimerized with other TALE proteins, it can interact with numerous partners and reach dozens of regulating sequences, suggesting its role as a pioneer factor. PBX1 is expressed throughout the embryonic stages (as early as the blastula stage) in vertebrates. In human, PBX1 germline variations are linked to syndromic renal anomalies (CAKUTHED). In this review, we summarized available data on PBX1 functions, PBX1-deficient animal models, and PBX1 germline variations in humans. Two types of genetic alterations were identified in PBX1 gene. PBX1 missense variations generate a severe phenotype including lung hypoplasia, cardiac malformations, and sexual development defects (DSDs). Conversely, truncating variants generate milder phenotypes (mainly cryptorchidism and deafness). We suggest that defects in PBX1 interactions with various partners, including proteins from the HOX (HOXA7, HOXA10, etc.), WNT (WNT9B, WNT3), and Polycomb (BMI1, EED) families are responsible for abnormal proliferation and differentiation of the embryonic mesenchyme. These alterations could explain most of the defects observed in humans. However, some phenotype variability (especially DSDs) remains poorly understood. Further studies are needed to explore the TALE family in greater depth.
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Affiliation(s)
- Laura Mary
- Service de Cytogénétique et Biologie Cellulaire, CHU Rennes, Rennes, France
- INSERM, EHESP, IRSET (Institut de recherche en santé, environnement et travail)- UMR_S 1085, Université Rennes 1, Rennes, France
| | - Delphine Leclerc
- Inserm U1242, Centre de lutte contre le cancer Eugène Marquis, Université de Rennes, Rennes, France
| | - David Gilot
- Service de Cytogénétique et Biologie Cellulaire, CHU Rennes, Rennes, France
- Inserm U1242, Centre de lutte contre le cancer Eugène Marquis, Université de Rennes, Rennes, France
| | - Marc-Antoine Belaud-Rotureau
- Service de Cytogénétique et Biologie Cellulaire, CHU Rennes, Rennes, France
- INSERM, EHESP, IRSET (Institut de recherche en santé, environnement et travail)- UMR_S 1085, Université Rennes 1, Rennes, France
| | - Sylvie Jaillard
- Service de Cytogénétique et Biologie Cellulaire, CHU Rennes, Rennes, France
- INSERM, EHESP, IRSET (Institut de recherche en santé, environnement et travail)- UMR_S 1085, Université Rennes 1, Rennes, France
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14
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Aghakhanian F, Hoh BP, Yew CW, Kumar Subbiah V, Xue Y, Tyler-Smith C, Ayub Q, Phipps ME. Sequence analyses of Malaysian Indigenous communities reveal historical admixture between Hoabinhian hunter-gatherers and Neolithic farmers. Sci Rep 2022; 12:13743. [PMID: 35962005 PMCID: PMC9374673 DOI: 10.1038/s41598-022-17884-8] [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: 11/19/2021] [Accepted: 04/08/2022] [Indexed: 11/09/2022] Open
Abstract
Southeast Asia comprises 11 countries that span mainland Asia across to numerous islands that stretch from the Andaman Sea to the South China Sea and Indian Ocean. This region harbors an impressive diversity of history, culture, religion and biology. Indigenous people of Malaysia display substantial phenotypic, linguistic, and anthropological diversity. Despite this remarkable diversity which has been documented for centuries, the genetic history and structure of indigenous Malaysians remain under-studied. To have a better understanding about the genetic history of these people, especially Malaysian Negritos, we sequenced whole genomes of 15 individuals belonging to five indigenous groups from Peninsular Malaysia and one from North Borneo to high coverage (30X). Our results demonstrate that indigenous populations of Malaysia are genetically close to East Asian populations. We show that present-day Malaysian Negritos can be modeled as an admixture of ancient Hoabinhian hunter-gatherers and Neolithic farmers. We observe gene flow from South Asian populations into the Malaysian indigenous groups, but not into Dusun of North Borneo. Our study proposes that Malaysian indigenous people originated from at least three distinct ancestral populations related to the Hoabinhian hunter-gatherers, Neolithic farmers and Austronesian speakers.
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Affiliation(s)
- Farhang Aghakhanian
- MUM Genomics Facility, Monash University Malaysia, 47500, Bandar Sunway, Selangor Darul Ehsan, Malaysia.,TropMed and Biology Multidisciplinary Platform, Monash University Malaysia, 47500, Bandar Sunway, Selangor, Malaysia.,Department of Medicine, Institute for Global Health and Infectious Diseases, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.,Faculty of Medicine and Health Sciences, UCSI University, Jalan Menara Gading, Taman Connaught, 56000, Cheras, Kuala Lumpur, Malaysia.,Jeffrey Cheah School of Medicine and Health Sciences, Monash University Malaysia, Jalan Lagoon Selatan, 46150, Bandar Sunway, Selangor, Malaysia
| | - Boon-Peng Hoh
- Faculty of Medicine and Health Sciences, UCSI University, Jalan Menara Gading, Taman Connaught, 56000, Cheras, Kuala Lumpur, Malaysia
| | - Chee-Wei Yew
- Biotechnology Research Institute, University Malaysia Sabah, Jalan UMS, 88400, Kota Kinabalu, Sabah, Malaysia
| | - Vijay Kumar Subbiah
- Biotechnology Research Institute, University Malaysia Sabah, Jalan UMS, 88400, Kota Kinabalu, Sabah, Malaysia
| | - Yali Xue
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, CB10 1SA, UK
| | - Chris Tyler-Smith
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, CB10 1SA, UK
| | - Qasim Ayub
- MUM Genomics Facility, Monash University Malaysia, 47500, Bandar Sunway, Selangor Darul Ehsan, Malaysia.,TropMed and Biology Multidisciplinary Platform, Monash University Malaysia, 47500, Bandar Sunway, Selangor, Malaysia
| | - Maude E Phipps
- MUM Genomics Facility, Monash University Malaysia, 47500, Bandar Sunway, Selangor Darul Ehsan, Malaysia. .,Jeffrey Cheah School of Medicine and Health Sciences, Monash University Malaysia, Jalan Lagoon Selatan, 46150, Bandar Sunway, Selangor, Malaysia.
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15
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Zhang X, Ji X, Li C, Yang T, Huang J, Zhao Y, Wu Y, Ma S, Pang Y, Huang Y, He Y, Su B. A Late Pleistocene human genome from Southwest China. Curr Biol 2022; 32:3095-3109.e5. [PMID: 35839766 DOI: 10.1016/j.cub.2022.06.016] [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: 12/24/2021] [Revised: 05/27/2022] [Accepted: 06/08/2022] [Indexed: 11/25/2022]
Abstract
Southern East Asia is the dispersal center regarding the prehistoric settlement and migrations of modern humans in Asia-Pacific regions. However, the settlement pattern and population structure of paleolithic humans in this region remain elusive, and ancient DNA can provide direct information. Here, we sequenced the genome of a Late Pleistocene hominin (MZR), dated ∼14.0 thousand years ago from Red Deer Cave located in Southwest China, which was previously reported possessing mosaic features of modern and archaic hominins. MZR is the first Late Pleistocene genome from southern East Asia. Our results indicate that MZR is a modern human who represents an early diversified lineage in East Asia. The mtDNA of MZR belongs to an extinct basal lineage of the M9 haplogroup, reflecting a rich matrilineal diversity in southern East Asia during the Late Pleistocene. Combined with the published data, we detected clear genetic stratification in ancient southern populations of East/Southeast Asia and some degree of south-versus-north divergency during the Late Pleistocene, and MZR was identified as a southern East Asian who exhibits genetic continuity to present day populations. Markedly, MZR is linked deeply to the East Asian ancestry that contributed to First Americans.
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Affiliation(s)
- Xiaoming Zhang
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming 650201, China; Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming 650201, China
| | - Xueping Ji
- Kunming Natural History Museum of Zoology, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming 650223, China; Department of Paleoanthropology, Yunnan Institute of Cultural Relics and Archaeology, Kunming 650118, China.
| | - Chunmei Li
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming 650201, China; Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming 650201, China
| | - Tingyu Yang
- Biomedical Pioneering Innovation Center (BIOPIC) and Beijing Advanced Innovation Center for Genomics (ICG), Peking University, Beijing 100871, China
| | - Jiahui Huang
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming 650201, China; Kunming College of Life Science, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yinhui Zhao
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming 650201, China; Kunming College of Life Science, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yun Wu
- Department of Paleoanthropology, Yunnan Institute of Cultural Relics and Archaeology, Kunming 650118, China; School of History, Wuhan University, Wuhan 430072, China; Archaeological Institute for Yangtze Civilization, Wuhan University, Wuhan 430072, China
| | - Shiwu Ma
- Mengzi Institute of Cultural Relics, Mengzi, Yunnan Province 661100, China
| | - Yuhong Pang
- Biomedical Pioneering Innovation Center (BIOPIC) and Beijing Advanced Innovation Center for Genomics (ICG), Peking University, Beijing 100871, China
| | - Yanyi Huang
- Biomedical Pioneering Innovation Center (BIOPIC) and Beijing Advanced Innovation Center for Genomics (ICG), Peking University, Beijing 100871, China
| | - Yaoxi He
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming 650201, China; Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming 650201, China.
| | - Bing Su
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming 650201, China; Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming 650201, China.
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16
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Damert A. SVA retrotransposons and a low copy repeat in humans and great apes: a mobile connection. Mol Biol Evol 2022; 39:6586216. [PMID: 35574660 PMCID: PMC9132208 DOI: 10.1093/molbev/msac103] [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] [Indexed: 11/18/2022] Open
Abstract
Segmental duplications (SDs) constitute a considerable fraction of primate genomes. They contribute to genetic variation and provide raw material for evolution. Groups of SDs are characterized by the presence of shared core duplicons. One of these core duplicons, low copy repeat (lcr)16a, has been shown to be particularly active in the propagation of interspersed SDs in primates. The underlying mechanisms are, however, only partially understood. Alu short interspersed elements (SINEs) are frequently found at breakpoints and have been implicated in the expansion of SDs. Detailed analysis of lcr16a-containing SDs shows that the hominid-specific SVA (SINE-R-VNTR-Alu) retrotransposon is an integral component of the core duplicon in Asian and African great apes. In orang-utan, it provides breakpoints and contributes to both interchromosomal and intrachromosomal lcr16a mobility by inter-element recombination. Furthermore, the data suggest that in hominines (human, chimpanzee, gorilla) SVA recombination-mediated integration of a circular intermediate is the founding event of a lineage-specific lcr16a expansion. One of the hominine lcr16a copies displays large flanking direct repeats, a structural feature shared by other SDs in the human genome. Taken together, the results obtained extend the range of SVAs’ contribution to genome evolution from RNA-mediated transduction to DNA-based recombination. In addition, they provide further support for a role of circular intermediates in SD mobilization.
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Affiliation(s)
- Annette Damert
- Infection Biology Unit and Primate Genetics Laboratory, German Primate Center, Leibniz Institute for Primate Research, Göttingen, Germany
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17
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Tshabalala M, Mellet J, Vather K, Nelson D, Mohamed F, Christoffels A, Pepper MS. High Resolution HLA ∼A, ∼B, ∼C, ∼DRB1, ∼DQA1, and ∼DQB1 Diversity in South African Populations. Front Genet 2022; 13:711944. [PMID: 35309124 PMCID: PMC8931603 DOI: 10.3389/fgene.2022.711944] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2021] [Accepted: 01/17/2022] [Indexed: 01/10/2023] Open
Abstract
Background: Lack of HLA data in southern African populations hampers disease association studies and our understanding of genetic diversity in these populations. We aimed to determine HLA diversity in South African populations using high resolution HLA ∼A, ∼B, ∼C, ∼DRB1, ∼DQA1 and ∼DQB1 data, from 3005 previously typed individuals. Methods: We determined allele and haplotype frequencies, deviations from Hardy-Weinberg equilibrium (HWE), linkage disequilibrium (LD) and neutrality test. South African HLA class I data was additionally compared to other global populations using non-metrical multidimensional scaling (NMDS), genetic distances and principal component analysis (PCA). Results: All loci strongly (p < 0.0001) deviated from HWE, coupled with excessive heterozygosity in most loci. Two of the three most frequent alleles, HLA ∼DQA1*05:02 (0.2584) and HLA ∼C*17:01 (0.1488) were previously reported in South African populations at lower frequencies. NMDS showed genetic distinctness of South African populations. Phylogenetic analysis and PCA clustered our current dataset with previous South African studies. Additionally, South Africans seem to be related to other sub-Saharan populations using HLA class I allele frequencies. Discussion and Conclusion: Despite the retrospective nature of the study, data missingness, the imbalance of sample sizes for each locus and haplotype pairs, and induced methodological difficulties, this study provides a unique and large HLA dataset of South Africans, which might be a useful resource to support anthropological studies, disease association studies, population based vaccine development and donor recruitment programs. We additionally provide simulated high resolution HLA class I data to augment the mixed resolution typing results generated from this study.
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Affiliation(s)
- Mqondisi Tshabalala
- Department of Immunology, Institute for Cellular and Molecular Medicine, Faculty of Health Sciences, University of Pretoria, Pretoria, South Africa
- South African Medical Research Council (SAMRC) Extramural Unit for Stem Cell Research and Therapy, Faculty of Health Sciences, University of Pretoria, Pretoria, South Africa
| | - Juanita Mellet
- Department of Immunology, Institute for Cellular and Molecular Medicine, Faculty of Health Sciences, University of Pretoria, Pretoria, South Africa
- South African Medical Research Council (SAMRC) Extramural Unit for Stem Cell Research and Therapy, Faculty of Health Sciences, University of Pretoria, Pretoria, South Africa
| | - Kuben Vather
- South African National Blood Service (SANBS), Roodepoort, South Africa
| | - Derrick Nelson
- South African National Blood Service (SANBS), Roodepoort, South Africa
| | - Fathima Mohamed
- South African National Blood Service (SANBS), Roodepoort, South Africa
| | - Alan Christoffels
- SAMRC Bioinformatics Unit, South African National Bioinformatics Institute, University of the Western Cape, Bellville, South Africa
| | - Michael S. Pepper
- Department of Immunology, Institute for Cellular and Molecular Medicine, Faculty of Health Sciences, University of Pretoria, Pretoria, South Africa
- South African Medical Research Council (SAMRC) Extramural Unit for Stem Cell Research and Therapy, Faculty of Health Sciences, University of Pretoria, Pretoria, South Africa
- *Correspondence: Michael S. Pepper,
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18
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Karmin M, Flores R, Saag L, Hudjashov G, Brucato N, Crenna-Darusallam C, Larena M, Endicott PL, Jakobsson M, Lansing JS, Sudoyo H, Leavesley M, Metspalu M, Ricaut FX, Cox MP. Episodes of Diversification and Isolation in Island Southeast Asian and Near Oceanian Male Lineages. Mol Biol Evol 2022; 39:msac045. [PMID: 35294555 PMCID: PMC8926390 DOI: 10.1093/molbev/msac045] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Island Southeast Asia (ISEA) and Oceania host one of the world's richest assemblages of human phenotypic, linguistic, and cultural diversity. Despite this, the region's male genetic lineages are globally among the last to remain unresolved. We compiled ∼9.7 Mb of Y chromosome (chrY) sequence from a diverse sample of over 380 men from this region, including 152 first reported here. The granularity of this data set allows us to fully resolve and date the regional chrY phylogeny. This new high-resolution tree confirms two main population bursts: multiple rapid diversifications following the region's initial settlement ∼50 kya, and extensive expansions <6 kya. Notably, ∼40-25 kya the deep rooting local lineages of C-M130, M-P256, and S-B254 show almost no further branching events in ISEA, New Guinea, and Australia, matching a similar pause in diversification seen in maternal mitochondrial DNA lineages. The main local lineages start diversifying ∼25 kya, at the time of the last glacial maximum. This improved chrY topology highlights localized events with important historical implications, including pre-Holocene contact between Mainland and ISEA, potential interactions between Australia and the Papuan world, and a sustained period of diversification following the flooding of the ancient Sunda and Sahul continents as the insular landscape observed today formed. The high-resolution phylogeny of the chrY presented here thus enables a detailed exploration of past isolation, interaction, and change in one of the world's least understood regions.
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Affiliation(s)
- Monika Karmin
- School of Natural Sciences, Massey University, Palmerston North, New Zealand
- Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Rodrigo Flores
- Institute of Genomics, University of Tartu, Tartu, Estonia
- Institute of Computer Science, University of Tartu, Tartu, Estonia
| | - Lauri Saag
- Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Georgi Hudjashov
- School of Natural Sciences, Massey University, Palmerston North, New Zealand
- Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Nicolas Brucato
- Laboratoire Evolution et Diversité Biologique (EDB UMR 5174), Université de Toulouse Midi-Pyrénées, CNRS, IRD, UPS, Toulouse, France
| | - Chelzie Crenna-Darusallam
- Genome Diversity and Disease Laboratory, Eijkman Institute for Molecular Biology, Jakarta, Indonesia
| | - Maximilian Larena
- Department of Organismal Biology, University of Uppsala, Uppsala, Sweden
| | - Phillip L Endicott
- Institute of Genomics, University of Tartu, Tartu, Estonia
- Department Hommes Natures Societies, Musée de l’Homme, Paris, France
| | - Mattias Jakobsson
- Department of Organismal Biology, University of Uppsala, Uppsala, Sweden
| | - J Stephen Lansing
- Complexity Science Hub Vienna, Vienna, Austria
- Santa Fe Institute Center for Advanced Study in the Behavioral Sciences, Stanford University, Santa Fe, USA
| | - Herawati Sudoyo
- Genome Diversity and Disease Laboratory, Eijkman Institute for Molecular Biology, Jakarta, Indonesia
| | - Matthew Leavesley
- School of Humanities and Social Sciences, University of Papua New Guinea, National Capital District, Papua New Guinea
- CABAH and College of Arts, Society and Education, James Cook University, Cairns, QLD, Australia
| | - Mait Metspalu
- Institute of Genomics, University of Tartu, Tartu, Estonia
| | - François-Xavier Ricaut
- Laboratoire Evolution et Diversité Biologique (EDB UMR 5174), Université de Toulouse Midi-Pyrénées, CNRS, IRD, UPS, Toulouse, France
| | - Murray P Cox
- School of Natural Sciences, Massey University, Palmerston North, New Zealand
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19
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Hoh BP, Deng L, Xu S. The Peopling and Migration History of the Natives in Peninsular Malaysia and Borneo: A Glimpse on the Studies Over the Past 100 years. Front Genet 2022; 13:767018. [PMID: 35154269 PMCID: PMC8829068 DOI: 10.3389/fgene.2022.767018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2021] [Accepted: 01/07/2022] [Indexed: 12/05/2022] Open
Abstract
Southeast Asia (SEA) has one of the longest records of modern human habitation out-of-Africa. Located at the crossroad of the mainland and islands of SEA, Peninsular Malaysia is an important piece of puzzle to the map of peopling and migration history in Asia, a question that is of interest to many anthropologists, archeologists, and population geneticists. This review aims to revisit our understanding to the population genetics of the natives from Peninsular Malaysia and Borneo over the past century based on the chronology of the technology advancement: 1) Anthropological and Physical Characterization; 2) Blood Group Markers; 3) Protein Markers; 4) Mitochondrial and Autosomal DNA Markers; and 5) Whole Genome Analysis. Subsequently some missing gaps of the study are identified. In the later part of this review, challenges of studying the population genetics of natives will be elaborated. Finally, we conclude our review by reiterating the importance of unveiling migration history and genetic diversity of the indigenous populations as a steppingstone towards comprehending disease evolution and etiology.
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Affiliation(s)
- Boon-Peng Hoh
- Faculty of Medicine and Health Sciences, UCSI University, UCSI Hospital, Port Dickson, Malaysia
| | - Lian Deng
- State Key Laboratory of Genetic Engineering, Center for Evolutionary Biology, Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Fudan University, Shanghai, China
| | - Shuhua Xu
- State Key Laboratory of Genetic Engineering, Center for Evolutionary Biology, Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Fudan University, Shanghai, China
- Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
- Department of Liver Surgery and Transplantation Liver Cancer Institute, Zhongshan Hospital, Fudan University, Shanghai, China
- Ministry of Education Key Laboratory of Contemporary Anthropology, School of Life Sciences, Human Phenome Institute, Fudan University, Shanghai, China
- School of Life Science and Technology, ShanghaiTech University, Shanghai, China
- Jiangsu Key Laboratory of Phylogenomics and Comparative Genomics, School of Life Sciences, Jiangsu Normal University, Xuzhou, China
- Henan Institute of Medical and Pharmaceutical Sciences, Zhengzhou University, Zhengzhou, China
- Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming, China
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20
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Wahyudi F, Aghakhanian F, Rahman S, Teo YY, Szpak M, Dhaliwal J, Ayub Q. Prioritising positively selected variants in whole-genome sequencing data using FineMAV. BMC Bioinformatics 2021; 22:604. [PMID: 34922440 PMCID: PMC8684245 DOI: 10.1186/s12859-021-04506-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2021] [Accepted: 11/30/2021] [Indexed: 11/17/2022] Open
Abstract
Background In population genomics, polymorphisms that are highly differentiated between geographically separated populations are often suggestive of Darwinian positive selection. Genomic scans have highlighted several such regions in African and non-African populations, but only a handful of these have functional data that clearly associates candidate variations driving the selection process. Fine-Mapping of Adaptive Variation (FineMAV) was developed to address this in a high-throughput manner using population based whole-genome sequences generated by the 1000 Genomes Project. It pinpoints positively selected genetic variants in sequencing data by prioritizing high frequency, population-specific and functional derived alleles. Results We developed a stand-alone software that implements the FineMAV statistic. To graphically visualise the FineMAV scores, it outputs the statistics as bigWig files, which is a common file format supported by many genome browsers. It is available as a command-line and graphical user interface. The software was tested by replicating the FineMAV scores obtained using 1000 Genomes Project African, European, East and South Asian populations and subsequently applied to whole-genome sequencing datasets from Singapore and China to highlight population specific variants that can be subsequently modelled. The software tool is publicly available at https://github.com/fadilla-wahyudi/finemav. Conclusions The software tool described here determines genome-wide FineMAV scores, using low or high-coverage whole-genome sequencing datasets, that can be used to prioritize a list of population specific, highly differentiated candidate variants for in vitro or in vivo functional screens. The tool displays these scores on the human genome browsers for easy visualisation, annotation and comparison between different genomic regions in worldwide human populations. Supplementary Information The online version contains supplementary material available at 10.1186/s12859-021-04506-9.
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Affiliation(s)
- Fadilla Wahyudi
- School of Science, Monash University Malaysia, 47500, Bandar Sunway, Selangor Darul Ehsan, Malaysia
| | - Farhang Aghakhanian
- Monash University Malaysia Genomics Facility, 47500, Bandar Sunway, Selangor Darul Ehsan, Malaysia.,Genes and Human Disease Research Program, Oklahoma Medical Research Foundation,, Oklahoma City, OK, 73104, USA
| | - Sadequr Rahman
- School of Science, Monash University Malaysia, 47500, Bandar Sunway, Selangor Darul Ehsan, Malaysia.,Tropical Medicine and Biology Multidisciplinary Platform, Monash University Malaysia, 47500, Bandar Sunway, Selangor Darul Ehsan, Malaysia
| | - Yik-Ying Teo
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore
| | - Michał Szpak
- European Bioinformatics Institute, Hinxton, CB10 1SA, UK.,Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, CB10 1SA, UK
| | - Jasbir Dhaliwal
- School of Information Technology, Monash University Malaysia, 47500, Bandar Sunway, Selangor Darul Ehsan, Malaysia.
| | - Qasim Ayub
- School of Science, Monash University Malaysia, 47500, Bandar Sunway, Selangor Darul Ehsan, Malaysia. .,Monash University Malaysia Genomics Facility, 47500, Bandar Sunway, Selangor Darul Ehsan, Malaysia. .,Tropical Medicine and Biology Multidisciplinary Platform, Monash University Malaysia, 47500, Bandar Sunway, Selangor Darul Ehsan, Malaysia.
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21
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Ghafouri-Fard S, Khoshbakht T, Hussen BM, Taheri M, Mokhtari M. A Review on the Role of AFAP1-AS1 in the Pathoetiology of Cancer. Front Oncol 2021; 11:777849. [PMID: 34912717 PMCID: PMC8666534 DOI: 10.3389/fonc.2021.777849] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Accepted: 11/09/2021] [Indexed: 12/17/2022] Open
Abstract
AFAP1-AS1 is a long non-coding RNA which partakes in the pathoetiology of several cancers. The sense protein coding gene from this locus partakes in the regulation of cytophagy, cell motility, invasive characteristics of cells and metastatic ability. In addition to acting in concert with AFAP1, AFAP1-AS1 can sequester a number of cancer-related miRNAs, thus affecting activity of signaling pathways involved in cancer progression. Most of animal studies have confirmed that AFAP1-AS1 silencing can reduce tumor volume and invasive behavior of tumor cells in the xenograft models. Moreover, statistical analyses in the human subjects have shown strong correlation between expression levels of this lncRNA and clinical outcomes. In the present work, we review the impact of AFAP1-AS1 in the carcinogenesis.
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Affiliation(s)
- Soudeh Ghafouri-Fard
- Department of Medical Genetics, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Tayybeh Khoshbakht
- Phytochemistry Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Bashdar Mahmud Hussen
- Department of Pharmacognosy, College of Pharmacy, Hawler Medical University, Erbil, Iraq
| | - Mohammad Taheri
- Urology and Nephrology Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran.,Institute of Human Genetics, Jena University Hospital, Jena, Germany
| | - Majid Mokhtari
- Skull Base Research Center, Loghman Hakim Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran
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22
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Breton G, Johansson ACV, Sjödin P, Schlebusch CM, Jakobsson M. Comparison of sequencing data processing pipelines and application to underrepresented African human populations. BMC Bioinformatics 2021; 22:488. [PMID: 34627144 PMCID: PMC8502359 DOI: 10.1186/s12859-021-04407-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2020] [Accepted: 09/27/2021] [Indexed: 11/10/2022] Open
Abstract
Background Population genetic studies of humans make increasing use of high-throughput sequencing in order to capture diversity in an unbiased way. There is an abundance of sequencing technologies, bioinformatic tools and the available genomes are increasing in number. Studies have evaluated and compared some of these technologies and tools, such as the Genome Analysis Toolkit (GATK) and its “Best Practices” bioinformatic pipelines. However, studies often focus on a few genomes of Eurasian origin in order to detect technical issues. We instead surveyed the use of the GATK tools and established a pipeline for processing high coverage full genomes from a diverse set of populations, including Sub-Saharan African groups, in order to reveal challenges from human diversity and stratification. Results We surveyed 29 studies using high-throughput sequencing data, and compared their strategies for data pre-processing and variant calling. We found that processing of data is very variable across studies and that the GATK “Best Practices” are seldom followed strictly. We then compared three versions of a GATK pipeline, differing in the inclusion of an indel realignment step and with a modification of the base quality score recalibration step. We applied the pipelines on a diverse set of 28 individuals. We compared the pipelines in terms of count of called variants and overlap of the callsets. We found that the pipelines resulted in similar callsets, in particular after callset filtering. We also ran one of the pipelines on a larger dataset of 179 individuals. We noted that including more individuals at the joint genotyping step resulted in different counts of variants. At the individual level, we observed that the average genome coverage was correlated to the number of variants called. Conclusions We conclude that applying the GATK “Best Practices” pipeline, including their recommended reference datasets, to underrepresented populations does not lead to a decrease in the number of called variants compared to alternative pipelines. We recommend to aim for coverage of > 30X if identifying most variants is important, and to work with large sample sizes at the variant calling stage, also for underrepresented individuals and populations. Supplementary Information The online version contains supplementary material available at 10.1186/s12859-021-04407-x.
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Affiliation(s)
- Gwenna Breton
- Human Evolution, Department of Organismal Biology, Evolutionary Biology Centre, Uppsala University, Norbyvägen 18C, 752 36, Uppsala, Sweden.
| | - Anna C V Johansson
- Department of Cell and Molecular Biology, National Bioinformatics Infrastructure Sweden, Science for Life Laboratory, Uppsala University, Husargatan 3, 752 37, Uppsala, Sweden
| | - Per Sjödin
- Human Evolution, Department of Organismal Biology, Evolutionary Biology Centre, Uppsala University, Norbyvägen 18C, 752 36, Uppsala, Sweden
| | - Carina M Schlebusch
- Human Evolution, Department of Organismal Biology, Evolutionary Biology Centre, Uppsala University, Norbyvägen 18C, 752 36, Uppsala, Sweden.,Palaeo-Research Institute, University of Johannesburg, P.O. Box 524, Auckland Park, 2006, South Africa.,Science for Life Laboratory, Uppsala, Sweden
| | - Mattias Jakobsson
- Human Evolution, Department of Organismal Biology, Evolutionary Biology Centre, Uppsala University, Norbyvägen 18C, 752 36, Uppsala, Sweden. .,Palaeo-Research Institute, University of Johannesburg, P.O. Box 524, Auckland Park, 2006, South Africa. .,Science for Life Laboratory, Uppsala, Sweden.
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23
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Khoruddin NA, Noorizhab MN, Teh LK, Mohd Yusof FZ, Salleh MZ. Pathogenic nsSNPs that increase the risks of cancers among the Orang Asli and Malays. Sci Rep 2021; 11:16158. [PMID: 34373545 PMCID: PMC8352870 DOI: 10.1038/s41598-021-95618-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2021] [Accepted: 07/26/2021] [Indexed: 02/07/2023] Open
Abstract
Single-nucleotide polymorphisms (SNPs) are the most common genetic variations for various complex human diseases, including cancers. Genome-wide association studies (GWAS) have identified numerous SNPs that increase cancer risks, such as breast cancer, colorectal cancer, and leukemia. These SNPs were cataloged for scientific use. However, GWAS are often conducted on certain populations in which the Orang Asli and Malays were not included. Therefore, we have developed a bioinformatic pipeline to mine the whole-genome sequence databases of the Orang Asli and Malays to determine the presence of pathogenic SNPs that might increase the risks of cancers among them. Five different in silico tools, SIFT, PROVEAN, Poly-Phen-2, Condel, and PANTHER, were used to predict and assess the functional impacts of the SNPs. Out of the 80 cancer-related nsSNPs from the GWAS dataset, 52 nsSNPs were found among the Orang Asli and Malays. They were further analyzed using the bioinformatic pipeline to identify the pathogenic variants. Three nsSNPs; rs1126809 (TYR), rs10936600 (LRRC34), and rs757978 (FARP2), were found as the most damaging cancer pathogenic variants. These mutations alter the protein interface and change the allosteric sites of the respective proteins. As TYR, LRRC34, and FARP2 genes play important roles in numerous cellular processes such as cell proliferation, differentiation, growth, and cell survival; therefore, any impairment on the protein function could be involved in the development of cancer. rs1126809, rs10936600, and rs757978 are the important pathogenic variants that increase the risks of cancers among the Orang Asli and Malays. The roles and impacts of these variants in cancers will require further investigations using in vitro cancer models.
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Affiliation(s)
- Nurul Ain Khoruddin
- Integrative Pharmacogenomics Institute (iPROMISE), Universiti Teknologi MARA (UiTM), Selangor Branch, Puncak Alam Campus, 42300, Puncak Alam, Selangor, Malaysia
- Faculty of Applied Sciences, Universiti Teknologi MARA (UiTM), Shah Alam Campus, Selangor, Malaysia
| | - Mohd NurFakhruzzaman Noorizhab
- Integrative Pharmacogenomics Institute (iPROMISE), Universiti Teknologi MARA (UiTM), Selangor Branch, Puncak Alam Campus, 42300, Puncak Alam, Selangor, Malaysia
- Faculty of Pharmacy, Universiti Teknologi MARA (UiTM), Selangor Branch, Puncak Alam Campus, 42300, Puncak Alam, Selangor, Malaysia
| | - Lay Kek Teh
- Integrative Pharmacogenomics Institute (iPROMISE), Universiti Teknologi MARA (UiTM), Selangor Branch, Puncak Alam Campus, 42300, Puncak Alam, Selangor, Malaysia
- Faculty of Pharmacy, Universiti Teknologi MARA (UiTM), Selangor Branch, Puncak Alam Campus, 42300, Puncak Alam, Selangor, Malaysia
| | - Farida Zuraina Mohd Yusof
- Integrative Pharmacogenomics Institute (iPROMISE), Universiti Teknologi MARA (UiTM), Selangor Branch, Puncak Alam Campus, 42300, Puncak Alam, Selangor, Malaysia
- Faculty of Applied Sciences, Universiti Teknologi MARA (UiTM), Shah Alam Campus, Selangor, Malaysia
| | - Mohd Zaki Salleh
- Integrative Pharmacogenomics Institute (iPROMISE), Universiti Teknologi MARA (UiTM), Selangor Branch, Puncak Alam Campus, 42300, Puncak Alam, Selangor, Malaysia.
- Faculty of Pharmacy, Universiti Teknologi MARA (UiTM), Selangor Branch, Puncak Alam Campus, 42300, Puncak Alam, Selangor, Malaysia.
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24
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Jones JL, Corbett MA, Yeaman E, Zhao D, Gecz J, Gasperini RJ, Charlesworth JC, Mackey DA, Elder JE, Craig JE, Burdon KP. A 127 kb truncating deletion of PGRMC1 is a novel cause of X-linked isolated paediatric cataract. Eur J Hum Genet 2021; 29:1206-1215. [PMID: 33867527 PMCID: PMC8385038 DOI: 10.1038/s41431-021-00889-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2020] [Revised: 03/10/2021] [Accepted: 04/02/2021] [Indexed: 02/02/2023] Open
Abstract
Inherited paediatric cataract is a rare Mendelian disease that results in visual impairment or blindness due to a clouding of the eye's crystalline lens. Here we report an Australian family with isolated paediatric cataract, which we had previously mapped to Xq24. Linkage at Xq24-25 (LOD = 2.53) was confirmed, and the region refined with a denser marker map. In addition, two autosomal regions with suggestive evidence of linkage were observed. A segregating 127 kb deletion (chrX:g.118373226_118500408del) in the Xq24-25 linkage region was identified from whole-genome sequencing data. This deletion completely removed a commonly deleted long non-coding RNA gene LOC101928336 and truncated the protein coding progesterone receptor membrane component 1 (PGRMC1) gene following exon 1. A literature search revealed a report of two unrelated males with non-syndromic intellectual disability, as well as congenital cataract, who had contiguous gene deletions that accounted for their intellectual disability but also disrupted the PGRMC1 gene. A morpholino-induced pgrmc1 knockdown in a zebrafish model produced significant cataract formation, supporting a role for PGRMC1 in lens development and cataract formation. We hypothesise that the loss of PGRMC1 causes cataract through disrupted PGRMC1-CYP51A1 protein-protein interactions and altered cholesterol biosynthesis. The cause of paediatric cataract in this family is the truncating deletion of PGRMC1, which we report as a novel cataract gene.
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Affiliation(s)
- Johanna L. Jones
- grid.1009.80000 0004 1936 826XMenzies Institute for Medical Research, University of Tasmania, Hobart, TAS Australia
| | - Mark A. Corbett
- grid.1010.00000 0004 1936 7304Adelaide Medical School, Robinson Research Institute, University of Adelaide, Adelaide, SA Australia
| | - Elise Yeaman
- grid.1009.80000 0004 1936 826XMenzies Institute for Medical Research, University of Tasmania, Hobart, TAS Australia
| | - Duran Zhao
- grid.1009.80000 0004 1936 826XMenzies Institute for Medical Research, University of Tasmania, Hobart, TAS Australia
| | - Jozef Gecz
- grid.1010.00000 0004 1936 7304Adelaide Medical School, Robinson Research Institute, University of Adelaide, Adelaide, SA Australia
| | - Robert J. Gasperini
- grid.1009.80000 0004 1936 826XSchool of Medicine, University of Tasmania, Hobart, TAS Australia
| | - Jac C. Charlesworth
- grid.1009.80000 0004 1936 826XMenzies Institute for Medical Research, University of Tasmania, Hobart, TAS Australia
| | - David A. Mackey
- grid.1489.40000 0000 8737 8161Centre for Ophthalmology and Visual Science, University of Western Australia, Lions Eye Institute, Perth, WA Australia
| | - James E. Elder
- grid.1008.90000 0001 2179 088XDepartment of Paediatrics, University of Melbourne, Melbourne, VIC Australia
| | - Jamie E. Craig
- grid.1014.40000 0004 0367 2697Department of Ophthalmology, Flinders University, Bedford Park, SA Australia
| | - Kathryn P. Burdon
- grid.1009.80000 0004 1936 826XMenzies Institute for Medical Research, University of Tasmania, Hobart, TAS Australia ,grid.1014.40000 0004 0367 2697Department of Ophthalmology, Flinders University, Bedford Park, SA Australia
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25
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Daw Elbait G, Henschel A, Tay GK, Al Safar HS. A Population-Specific Major Allele Reference Genome From The United Arab Emirates Population. Front Genet 2021; 12:660428. [PMID: 33968136 PMCID: PMC8102833 DOI: 10.3389/fgene.2021.660428] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2021] [Accepted: 03/19/2021] [Indexed: 12/30/2022] Open
Abstract
The ethnic composition of the population of a country contributes to the uniqueness of each national DNA sequencing project and, ideally, individual reference genomes are required to reduce the confounding nature of ethnic bias. This work represents a representative Whole Genome Sequencing effort of an understudied population. Specifically, high coverage consensus sequences from 120 whole genomes and 33 whole exomes were used to construct the first ever population specific major allele reference genome for the United Arab Emirates (UAE). When this was applied and compared to the archetype hg19 reference, assembly of local Emirati genomes was reduced by ∼19% (i.e., some 1 million fewer calls). In compiling the United Arab Emirates Reference Genome (UAERG), sets of annotated 23,038,090 short (novel: 1,790,171) and 137,713 structural (novel: 8,462) variants; their allele frequencies (AFs) and distribution across the genome were identified. Population-specific genetic characteristics including loss-of-function variants, admixture, and ancestral haplogroup distribution were identified and reported here. We also detect a strong correlation between F ST and admixture components in the UAE. This baseline study was conceived to establish a high-quality reference genome and a genetic variations resource to enable the development of regional population specific initiatives and thus inform the application of population studies and precision medicine in the UAE.
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Affiliation(s)
- Gihan Daw Elbait
- Center for Biotechnology, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates
| | - Andreas Henschel
- Center for Biotechnology, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates
- Department of Electrical Engineering and Computer Science, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates
| | - Guan K. Tay
- Center for Biotechnology, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates
- Department of Biomedical Engineering, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates
- Division of Psychiatry, Faculty of Health and Medical Sciences, The University of Western Australia, Crawley, WA, Australia
- School of Medical and Health Sciences, Edith Cowan University, Joondalup, WA, Australia
| | - Habiba S. Al Safar
- Center for Biotechnology, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates
- Department of Biomedical Engineering, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates
- Department of Genetics and Molecular Biology, College of Medicine and Health Sciences, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates
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26
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Janse van Rensburg WJ, de Kock A, Bester C, Kloppers JF. HLA major allele group frequencies in a diverse population of the Free State Province, South Africa. Heliyon 2021; 7:e06850. [PMID: 33981900 PMCID: PMC8085688 DOI: 10.1016/j.heliyon.2021.e06850] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2020] [Revised: 02/11/2021] [Accepted: 04/14/2021] [Indexed: 12/17/2022] Open
Abstract
Background Human Leucocyte Antigens (HLA) play a vital role in disease pathogenesis and transplant rejection. HLA-typing is a useful tool in predicting disease progression and to identify potential organ donors. Due to human migration and known ethnic variation, frequent targeted HLA sequencing of specific populations is crucial to increase their representation in global reference panels. Materials and methods We performed a retrospective file audit of all HLA-typings done in our setting from 2005-2019. We discuss data for the major HLA-A, HLA-B, HLA-C, and HLA-DRB1 allele groups. Results Overall, the most common allele groups were HLA-A∗02, HLA-B∗15, HLA-C∗07 and HLA-DRB1∗03. For the African descent group, the most common alleles were HLA-A∗30, HLA-B∗15, HLA-C∗07 and HLA-DRB1∗03. For the European descent group, the most common alleles were HLA-A∗02, HLA-B∗07, HLA-C∗07 and HLA-DRB1∗15. For the mixed ancestry group, the most common allele groups were HLA-A∗02, HLA-B∗15, HLA-C∗02 and HLA-DRB1∗13. HLA-B∗44 was identified as the most common allele group in patients with renal failure. Discussion and conclusion The significant variation within the HLA frequencies between the different ethnic groups highlights the value of population-specific HLA-typing. Furthermore, the identification of HLA-B∗44 as a prominent HLA in our renal failure population warrants in-depth investigation of this group.
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Affiliation(s)
- Walter J Janse van Rensburg
- Human Molecular Biology Unit, School of Biomedical Sciences, Faculty of Health Sciences, University of the Free State, Bloemfontein, South Africa
| | - André de Kock
- Department of Haematology and Cell Biology, School of Pathology, Faculty of Health Sciences, University of the Free State, Bloemfontein, South Africa.,National Health Laboratory Service, Universitas Academic Business Unit, Bloemfontein, South Africa
| | - Chené Bester
- Human Molecular Biology Unit, School of Biomedical Sciences, Faculty of Health Sciences, University of the Free State, Bloemfontein, South Africa
| | - Jean F Kloppers
- Department of Haematology and Cell Biology, School of Pathology, Faculty of Health Sciences, University of the Free State, Bloemfontein, South Africa.,National Health Laboratory Service, Universitas Academic Business Unit, Bloemfontein, South Africa
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27
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Liu X. Human Prehistoric Demography Revealed by the Polymorphic Pattern of CpG Transitions. Mol Biol Evol 2021; 37:2691-2698. [PMID: 32369585 DOI: 10.1093/molbev/msaa112] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023] Open
Abstract
The prehistoric demography of human populations is an essential piece of information for illustrating our evolution. Despite its importance and the advancement of ancient DNA studies, our knowledge of human evolution is still limited, which is also the case for relatively recent population dynamics during and around the Holocene. Here, we inferred detailed demographic histories from 1 to 40 ka for 24 population samples using an improved model-flexible method with 36 million genome-wide noncoding CpG sites. Our results showed many population growth events that were likely due to the Neolithic Revolution (i.e., the shift from hunting and gathering to agriculture and settlement). Our results help to provide a clearer picture of human prehistoric demography, confirming the significant impact of agriculture on population expansion, and provide new hypotheses and directions for future research.
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Affiliation(s)
- Xiaoming Liu
- USF Genomics & College of Public Health, University of South Florida, Tampa, FL
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28
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Jain A, Sharma D, Bajaj A, Gupta V, Scaria V. Founder variants and population genomes-Toward precision medicine. ADVANCES IN GENETICS 2021; 107:121-152. [PMID: 33641745 DOI: 10.1016/bs.adgen.2020.11.004] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
Human migration and community specific cultural practices have contributed to founder events and enrichment of the variants associated with genetic diseases. While many founder events in isolated populations have remained uncharacterized, the application of genomics in clinical settings as well as for population scale studies in the recent years have provided an unprecedented push towards identification of founder variants associated with human health and disease. The discovery and characterization of founder variants could have far reaching implications not only in understanding the history or genealogy of the disease, but also in implementing evidence based policies and genetic testing frameworks. This further enables precise diagnosis and prevention in an attempt towards precision medicine. This review provides an overview of founder variants along with methods and resources cataloging them. We have also discussed the public health implications and examples of prevalent disease associated founder variants in specific populations.
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Affiliation(s)
- Abhinav Jain
- CSIR-Institute of Genomics and Integrative Biology, New Delhi, India; Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, Uttar Pradesh, India
| | - Disha Sharma
- CSIR-Institute of Genomics and Integrative Biology, New Delhi, India
| | - Anjali Bajaj
- CSIR-Institute of Genomics and Integrative Biology, New Delhi, India; Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, Uttar Pradesh, India
| | - Vishu Gupta
- CSIR-Institute of Genomics and Integrative Biology, New Delhi, India; Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, Uttar Pradesh, India
| | - Vinod Scaria
- CSIR-Institute of Genomics and Integrative Biology, New Delhi, India; Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, Uttar Pradesh, India.
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Sun J, Li YX, Ma PC, Yan S, Cheng HZ, Fan ZQ, Deng XH, Ru K, Wang CC, Chen G, Wei LH. Shared paternal ancestry of Han, Tai-Kadai-speaking, and Austronesian-speaking populations as revealed by the high resolution phylogeny of O1a-M119 and distribution of its sub-lineages within China. AMERICAN JOURNAL OF PHYSICAL ANTHROPOLOGY 2021; 174:686-700. [PMID: 33555039 DOI: 10.1002/ajpa.24240] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/27/2020] [Revised: 01/06/2021] [Accepted: 01/12/2021] [Indexed: 12/27/2022]
Abstract
OBJECTIVES The aim of this research was to explore the origin, diversification, and demographic history of O1a-M119 over the past 10,000 years, as well as its role during the formation of East Asian and Southeast Asian populations, particularly the Han, Tai-Kadai-speaking, and Austronesian-speaking populations. MATERIALS AND METHODS Y-chromosome sequences (n = 141) of the O1a-M119 lineage, including 17 newly generated in this study, were used to reconstruct a revised phylogenetic tree with age estimates, and identify sub-lineages. The geographic distribution of 12 O1a-M119 sub-lineages was summarized, based on 7325 O1a-M119 individuals identified among 60,009 Chinese males. RESULTS A revised phylogenetic tree, age estimation, and distribution maps indicated continuous expansion of haplogroup O1a-M119 over the past 10,000 years, and differences in demographic history across geographic regions. We propose several sub-lineages of O1a-M119 as founding paternal lineages of Han, Tai-Kadai-speaking, and Austronesian-speaking populations. The sharing of several young O1a-M119 sub-lineages with expansion times less than 6000 years between these three population groups supports a partial common ancestry for them in the Neolithic Age; however, the paternal genetic divergence pattern is much more complex than previous hypotheses based on ethnology, archeology, and linguistics. DISCUSSION Our analyses contribute to a better understanding of the demographic history of O1a-M119 sub-lineages over the past 10,000 years during the emergence of Han, Austronesians, Tai-Kadai-speaking populations. The data described in this study will assist in understanding of the history of Han, Tai-Kadai-speaking, and Austronesian-speaking populations from ethnology, archeology, and linguistic perspectives in the future.
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Affiliation(s)
- Jin Sun
- Xingyi Normal University for Nationalities, Xingyi, China
- Department of Anthropology and Ethnology, Institute of Anthropology, School of Sociology and Anthropology, Xiamen University, Xiamen, China
| | - Ying-Xiang Li
- Department of Anthropology and Ethnology, Institute of Anthropology, School of Sociology and Anthropology, Xiamen University, Xiamen, China
| | - Peng-Cheng Ma
- School of Life Sciences, Jilin University, Changchun, China
| | - Shi Yan
- School of Ethnology and Sociology, Minzu University of China, Beijing, China
| | - Hui-Zhen Cheng
- Department of Anthropology and Ethnology, Institute of Anthropology, School of Sociology and Anthropology, Xiamen University, Xiamen, China
| | - Zhi-Quan Fan
- Department of Anthropology and Ethnology, Institute of Anthropology, School of Sociology and Anthropology, Xiamen University, Xiamen, China
| | - Xiao-Hua Deng
- Department of Anthropology and Ethnology, Institute of Anthropology, School of Sociology and Anthropology, Xiamen University, Xiamen, China
- Center for collation and studies of Fujian local literature, Fujian University of Technology, Fuzhou, China
| | - Kai Ru
- Enlighten Co., Ltd., Shanghai, China
| | - Chuan-Chao Wang
- Department of Anthropology and Ethnology, Institute of Anthropology, School of Sociology and Anthropology, Xiamen University, Xiamen, China
| | - Gang Chen
- Hunan Key Lab of Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha, China
| | - Lan-Hai Wei
- Department of Anthropology and Ethnology, Institute of Anthropology, School of Sociology and Anthropology, Xiamen University, Xiamen, China
- B&R International Joint Laboratory for Eurasian Anthropology, Fudan University, Shanghai, China
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Sun J, Ma PC, Cheng HZ, Wang CZ, Li YL, Cui YQ, Yao HB, Wen SQ, Wei LH. Post-last glacial maximum expansion of Y-chromosome haplogroup C2a-L1373 in northern Asia and its implications for the origin of Native Americans. AMERICAN JOURNAL OF PHYSICAL ANTHROPOLOGY 2021; 174:363-374. [PMID: 33241578 DOI: 10.1002/ajpa.24173] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/08/2020] [Revised: 10/10/2020] [Accepted: 11/04/2020] [Indexed: 02/05/2023]
Abstract
OBJECTIVES Subbranches of Y-chromosome haplogroup C2a-L1373 are founding paternal lineages in northern Asia and Native American populations. Our objective was to investigate C2a-L1373 differentiation in northern Asia and its implications for Native American origins. MATERIALS AND METHODS Sequences of rare subbranches (n = 43) and ancient individuals (n = 37) of C2a-L1373 (including P39 and MPB373), were used to construct phylogenetic trees with age estimation by BEAST software. RESULTS C2a-L1373 expanded rapidly approximately 17.7,000-14.3,000 years ago (kya) after the last glacial maximum (LGM), generating numerous sublineages which became founding paternal lineages of modern northern Asian and Native American populations (C2a-P39 and C2a-MPB373). The divergence pattern supports possible initiation of differentiation in low latitude regions of northern Asia and northward diffusion after the LGM. There is a substantial gap between the divergence times of C2a-MPB373 (approximately 22.4 or 17.7 kya) and C2a-P39 (approximately 14.3 kya), indicating two possible migration waves. DISCUSSION We discussed the decreasing time interval of "Beringian standstill" (2.5 ky or smaller) and its reduced significance. We also discussed the multiple possibilities for the peopling of the Americas: the "Long-term Beringian standstill model," the "Short-term Beringian standstill model," and the "Multiple waves of migration model." Our results support the argument from ancient DNA analyses that the direct ancestor group of Native Americans is an admixture of "Ancient Northern Siberians" and Paleolithic communities from the Amur region, which appeared during the post-LGM era, rather than ancient populations in greater Beringia, or an adjacent region, before the LGM.
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Affiliation(s)
- Jin Sun
- Department of Anthropology and Ethnology, Institute of Anthropology, Xiamen University, Xiamen, China
- Xingyi Normal University for Nationalities, Xingyi, China
| | - Peng-Cheng Ma
- School of Life Sciences, Jilin University, Changchun, China
| | - Hui-Zhen Cheng
- Department of Anthropology and Ethnology, Institute of Anthropology, Xiamen University, Xiamen, China
| | - Chi-Zao Wang
- Department of Radiology, The First Affiliated Hospital of Shantou University Medical College, Shantou, China
| | - Yong-Lan Li
- School of Ethnology and Anthropology, Inner Mongolia Normal University, Hohhot, China
| | - Yin-Qiu Cui
- School of Life Sciences, Jilin University, Changchun, China
| | - Hong-Bin Yao
- Key Laboratory of Evidence Science of Gansu Province, Gansu University of Political Science and Law, Lanzhou, China
| | - Shao-Qing Wen
- Institute of Archaeological Science, Fudan University, Shanghai, China
- B&R International Joint Laboratory for Eurasian Anthropology, Fudan University, Shanghai, China
| | - Lan-Hai Wei
- Department of Anthropology and Ethnology, Institute of Anthropology, Xiamen University, Xiamen, China
- B&R International Joint Laboratory for Eurasian Anthropology, Fudan University, Shanghai, China
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Peña-Chilet M, Roldán G, Perez-Florido J, Ortuño FM, Carmona R, Aquino V, Lopez-Lopez D, Loucera C, Fernandez-Rueda JL, Gallego A, García-Garcia F, González-Neira A, Pita G, Núñez-Torres R, Santoyo-López J, Ayuso C, Minguez P, Avila-Fernandez A, Corton M, Moreno-Pelayo MÁ, Morin M, Gallego-Martinez A, Lopez-Escamez JA, Borrego S, Antiñolo G, Amigo J, Salgado-Garrido J, Pasalodos-Sanchez S, Morte B, Carracedo Á, Alonso Á, Dopazo J. CSVS, a crowdsourcing database of the Spanish population genetic variability. Nucleic Acids Res 2021; 49:D1130-D1137. [PMID: 32990755 PMCID: PMC7778906 DOI: 10.1093/nar/gkaa794] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2020] [Revised: 09/08/2020] [Accepted: 09/10/2020] [Indexed: 01/01/2023] Open
Abstract
The knowledge of the genetic variability of the local population is of utmost importance in personalized medicine and has been revealed as a critical factor for the discovery of new disease variants. Here, we present the Collaborative Spanish Variability Server (CSVS), which currently contains more than 2000 genomes and exomes of unrelated Spanish individuals. This database has been generated in a collaborative crowdsourcing effort collecting sequencing data produced by local genomic projects and for other purposes. Sequences have been grouped by ICD10 upper categories. A web interface allows querying the database removing one or more ICD10 categories. In this way, aggregated counts of allele frequencies of the pseudo-control Spanish population can be obtained for diseases belonging to the category removed. Interestingly, in addition to pseudo-control studies, some population studies can be made, as, for example, prevalence of pharmacogenomic variants, etc. In addition, this genomic data has been used to define the first Spanish Genome Reference Panel (SGRP1.0) for imputation. This is the first local repository of variability entirely produced by a crowdsourcing effort and constitutes an example for future initiatives to characterize local variability worldwide. CSVS is also part of the GA4GH Beacon network. CSVS can be accessed at: http://csvs.babelomics.org/.
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Affiliation(s)
- María Peña-Chilet
- Clinical Bioinformatics Area, Fundación Progreso y Salud (FPS), Hospital Virgen del Rocío, Sevilla 41013, Spain
- Bioinformatics in Rare Diseases (BiER), Center for Biomedical Network Research on Rare Diseases (CIBERER), ISCIII, Sevilla 41013, Spain
- Computational Systems Medicine group, Institute of Biomedicine of Seville (IBIS) Hospital Virgen del Rocío, Sevilla 41013, Spain
| | - Gema Roldán
- Clinical Bioinformatics Area, Fundación Progreso y Salud (FPS), Hospital Virgen del Rocío, Sevilla 41013, Spain
| | - Javier Perez-Florido
- Clinical Bioinformatics Area, Fundación Progreso y Salud (FPS), Hospital Virgen del Rocío, Sevilla 41013, Spain
- Computational Systems Medicine group, Institute of Biomedicine of Seville (IBIS) Hospital Virgen del Rocío, Sevilla 41013, Spain
- Functional Genomics Node, FPS/ELIXIR-ES, Hospital Virgen del Rocío, Sevilla 41013, Spain
| | - Francisco M Ortuño
- Clinical Bioinformatics Area, Fundación Progreso y Salud (FPS), Hospital Virgen del Rocío, Sevilla 41013, Spain
- Computational Systems Medicine group, Institute of Biomedicine of Seville (IBIS) Hospital Virgen del Rocío, Sevilla 41013, Spain
- Functional Genomics Node, FPS/ELIXIR-ES, Hospital Virgen del Rocío, Sevilla 41013, Spain
| | - Rosario Carmona
- Clinical Bioinformatics Area, Fundación Progreso y Salud (FPS), Hospital Virgen del Rocío, Sevilla 41013, Spain
| | - Virginia Aquino
- Clinical Bioinformatics Area, Fundación Progreso y Salud (FPS), Hospital Virgen del Rocío, Sevilla 41013, Spain
| | - Daniel Lopez-Lopez
- Clinical Bioinformatics Area, Fundación Progreso y Salud (FPS), Hospital Virgen del Rocío, Sevilla 41013, Spain
- Computational Systems Medicine group, Institute of Biomedicine of Seville (IBIS) Hospital Virgen del Rocío, Sevilla 41013, Spain
| | - Carlos Loucera
- Clinical Bioinformatics Area, Fundación Progreso y Salud (FPS), Hospital Virgen del Rocío, Sevilla 41013, Spain
- Computational Systems Medicine group, Institute of Biomedicine of Seville (IBIS) Hospital Virgen del Rocío, Sevilla 41013, Spain
| | - Jose L Fernandez-Rueda
- Clinical Bioinformatics Area, Fundación Progreso y Salud (FPS), Hospital Virgen del Rocío, Sevilla 41013, Spain
| | | | - Francisco García-Garcia
- Unidad de Bioinformática y Bioestadística, Centro de Investigación Príncipe Felipe (CIPF), Valencia 46012, Spain
| | - Anna González-Neira
- Human Genotyping Unit–Centro Nacional de Genotipado (CEGEN), Human Cancer Genetics Programme, Spanish National Cancer Research Centre (CNIO), Madrid 28029, Spain
| | - Guillermo Pita
- Human Genotyping Unit–Centro Nacional de Genotipado (CEGEN), Human Cancer Genetics Programme, Spanish National Cancer Research Centre (CNIO), Madrid 28029, Spain
| | - Rocío Núñez-Torres
- Human Genotyping Unit–Centro Nacional de Genotipado (CEGEN), Human Cancer Genetics Programme, Spanish National Cancer Research Centre (CNIO), Madrid 28029, Spain
| | | | - Carmen Ayuso
- Department of Genetics, Instituto de Investigación Sanitaria-Fundación Jiménez Díaz University Hospital, Universidad Autónoma de Madrid (IIS-FJD, UAM), Madrid 28040, Spain
| | - Pablo Minguez
- Department of Genetics, Instituto de Investigación Sanitaria-Fundación Jiménez Díaz University Hospital, Universidad Autónoma de Madrid (IIS-FJD, UAM), Madrid 28040, Spain
- Center for Biomedical Network Research on Rare Diseases (CIBERER), ISCIII, Madrid 28040, Spain
| | - Almudena Avila-Fernandez
- Department of Genetics, Instituto de Investigación Sanitaria-Fundación Jiménez Díaz University Hospital, Universidad Autónoma de Madrid (IIS-FJD, UAM), Madrid 28040, Spain
| | - Marta Corton
- Department of Genetics, Instituto de Investigación Sanitaria-Fundación Jiménez Díaz University Hospital, Universidad Autónoma de Madrid (IIS-FJD, UAM), Madrid 28040, Spain
| | - Miguel Ángel Moreno-Pelayo
- Servicio de Genética, Ramón y Cajal Institute of Health Research (IRYCIS) and Biomedical Network Research Centre on Rare Diseases (CIBERER), Madrid 28034, Spain
| | - Matías Morin
- Servicio de Genética, Ramón y Cajal Institute of Health Research (IRYCIS) and Biomedical Network Research Centre on Rare Diseases (CIBERER), Madrid 28034, Spain
| | - Alvaro Gallego-Martinez
- Otology & Neurotology Group CTS 495, Department of Genomic Medicine, Centre for Genomics and Oncological Research (GENYO), Pfizer University of Granada, Granada 18016, Spain
- Department of Otolaryngology, Instituto de Investigación Biosanitaria, IBS. GRANADA, Hospital Universitario Virgen de las Nieves, Universidad de Granada, Granada 18016, Spain
| | - Jose A Lopez-Escamez
- Otology & Neurotology Group CTS 495, Department of Genomic Medicine, Centre for Genomics and Oncological Research (GENYO), Pfizer University of Granada, Granada 18016, Spain
- Department of Otolaryngology, Instituto de Investigación Biosanitaria, IBS. GRANADA, Hospital Universitario Virgen de las Nieves, Universidad de Granada, Granada 18016, Spain
| | - Salud Borrego
- Department of Maternofetal Medicine, Genetics and Reproduction, Institute of Biomedicine of Seville (IBIS), University Hospital Virgen del Rocío/CSIC/University of Seville, Seville 41013, Spain
- Centre for Biomedical Network Research on Rare Diseases (CIBERER), Seville 41013, Spain
| | - Guillermo Antiñolo
- Department of Maternofetal Medicine, Genetics and Reproduction, Institute of Biomedicine of Seville (IBIS), University Hospital Virgen del Rocío/CSIC/University of Seville, Seville 41013, Spain
- Centre for Biomedical Network Research on Rare Diseases (CIBERER), Seville 41013, Spain
| | - Jorge Amigo
- Fundación Pública Galega de Medicina Xenómica, SERGAS, IDIS, Santiago de Compostela 15706, Spain
| | - Josefa Salgado-Garrido
- Navarrabiomed-IdiSNA, Complejo Hospitalario de Navarra, Universidad Pública de Navarra (UPNA), IdiSNA (Navarra Institute for Health Research), Pamplona, Navarra 31008, Spain
| | - Sara Pasalodos-Sanchez
- Navarrabiomed-IdiSNA, Complejo Hospitalario de Navarra, Universidad Pública de Navarra (UPNA), IdiSNA (Navarra Institute for Health Research), Pamplona, Navarra 31008, Spain
| | - Beatriz Morte
- Undiagnosed Rare Diseases Programme (ENoD). Center for Biomedical Research on Rare Diseases (CIBERER), ISCIII, Madrid 28029, Spain
| | - Ángel Carracedo
- Fundación Pública Galega de Medicina Xenómica, SERGAS, IDIS, Santiago de Compostela 15706, Spain
- Grupo de Medicina Xenómica, Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), CIMUS, Universidade de Santiago de Compostela, Santiago de Compostela, España
| | - Ángel Alonso
- Navarrabiomed-IdiSNA, Complejo Hospitalario de Navarra, Universidad Pública de Navarra (UPNA), IdiSNA (Navarra Institute for Health Research), Pamplona, Navarra 31008, Spain
| | - Joaquín Dopazo
- Clinical Bioinformatics Area, Fundación Progreso y Salud (FPS), Hospital Virgen del Rocío, Sevilla 41013, Spain
- Bioinformatics in Rare Diseases (BiER), Center for Biomedical Network Research on Rare Diseases (CIBERER), ISCIII, Sevilla 41013, Spain
- Computational Systems Medicine group, Institute of Biomedicine of Seville (IBIS) Hospital Virgen del Rocío, Sevilla 41013, Spain
- Functional Genomics Node, FPS/ELIXIR-ES, Hospital Virgen del Rocío, Sevilla 41013, Spain
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Chai JF, Kao SL, Wang C, Lim VJY, Khor IW, Dou J, Podgornaia AI, Chothani S, Cheng CY, Sabanayagam C, Wong TY, van Dam RM, Liu J, Reilly DF, Paterson AD, Sim X. Genome-Wide Association for HbA1c in Malay Identified Deletion on SLC4A1 that Influences HbA1c Independent of Glycemia. J Clin Endocrinol Metab 2020; 105:5906591. [PMID: 32936915 DOI: 10.1210/clinem/dgaa658] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/08/2020] [Accepted: 09/15/2020] [Indexed: 12/30/2022]
Abstract
CONTEXT Glycated hemoglobin A1c (HbA1c) level is used to screen and diagnose diabetes. Genetic determinants of HbA1c can vary across populations and many of the genetic variants influencing HbA1c level were specific to populations. OBJECTIVE To discover genetic variants associated with HbA1c level in nondiabetic Malay individuals. DESIGN AND PARTICIPANTS We conducted a genome-wide association study (GWAS) analysis for HbA1c using 2 Malay studies, the Singapore Malay Eye Study (SiMES, N = 1721 on GWAS array) and the Living Biobank study (N = 983 on GWAS array and whole-exome sequenced). We built a Malay-specific reference panel to impute ethnic-specific variants and validate the associations with HbA1c at ethnic-specific variants. RESULTS Meta-analysis of the 1000 Genomes imputed array data identified 4 loci at genome-wide significance (P < 5 × 10-8). Of the 4 loci, 3 (ADAM15, LINC02226, JUP) were novel for HbA1c associations. At the previously reported HbA1c locus ATXN7L3-G6PC3, association analysis using the exome data fine-mapped the HbA1c associations to a 27-bp deletion (rs769664228) at SLC4A1 that reduced HbA1c by 0.38 ± 0.06% (P = 3.5 × 10-10). Further imputation of this variant in SiMES confirmed the association with HbA1c at SLC4A1. We also showed that these genetic variants influence HbA1c level independent of glucose level. CONCLUSION We identified a deletion at SLC4A1 associated with HbA1c in Malay. The nonglycemic lowering of HbA1c at rs769664228 might cause individuals carrying this variant to be underdiagnosed for diabetes or prediabetes when HbA1c is used as the only diagnostic test for diabetes.
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Affiliation(s)
- Jin-Fang Chai
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore
| | - Shih-Ling Kao
- Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore and National University Health System, Singapore, Singapore
| | - Chaolong Wang
- Department of Epidemiology and Biostatistics, Key Laboratory for Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
- Genome Institute of Singapore, Agency for Science, Technology and Research, Singapore
| | - Victor Jun-Yu Lim
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore
| | - Ing Wei Khor
- Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore and National University Health System, Singapore, Singapore
| | - Jinzhuang Dou
- Department of Epidemiology and Biostatistics, Key Laboratory for Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
- Genome Institute of Singapore, Agency for Science, Technology and Research, Singapore
| | | | - Sonia Chothani
- Genome Institute of Singapore, Agency for Science, Technology and Research, Singapore
| | - Ching-Yu Cheng
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
- Ophthalmology & Visual Sciences Academic Clinical Program (Eye ACP), Duke-NUS Medical School, Singapore, Singapore
- Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore and National University Health System, Singapore, Singapore
| | - Charumathi Sabanayagam
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
- Ophthalmology & Visual Sciences Academic Clinical Program (Eye ACP), Duke-NUS Medical School, Singapore, Singapore
| | - Tien-Yin Wong
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
- Ophthalmology & Visual Sciences Academic Clinical Program (Eye ACP), Duke-NUS Medical School, Singapore, Singapore
- Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore and National University Health System, Singapore, Singapore
| | - Rob M van Dam
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore
- Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore and National University Health System, Singapore, Singapore
- Department of Nutrition, Harvard T.H Chan School of Public Health, Boston, Massachusetts
| | - Jianjun Liu
- Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore and National University Health System, Singapore, Singapore
- Genome Institute of Singapore, Agency for Science, Technology and Research, Singapore
| | - Dermot F Reilly
- Merck Research Laboratories, Kenilworth, New Jersey
- Janssen Pharmaceuticals Inc, Titusville, New Jersey
| | - Andrew D Paterson
- Program in Genetics and Genome Biology, The Hospital for Sick Children, Toronto, Canada
- Divisions of Epidemiology and Biostatistics, Dalla Lana School of Public Health, University of Toronto, Canada
| | - Xueling Sim
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore
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Sun J, Wei LH, Wang LX, Huang YZ, Yan S, Cheng HZ, Ong RTH, Saw WY, Fan ZQ, Deng XH, Lu Y, Zhang C, Xu SH, Jin L, Teo YY, Li H. Paternal gene pool of Malays in Southeast Asia and its applications for the early expansion of Austronesians. Am J Hum Biol 2020; 33:e23486. [PMID: 32851723 DOI: 10.1002/ajhb.23486] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2019] [Revised: 06/16/2020] [Accepted: 07/10/2020] [Indexed: 11/08/2022] Open
Abstract
OBJECTIVES The origin and differentiation of Austronesian populations and their languages have long fascinated linguists, archeologists, and geneticists. However, the founding process of Austronesians and when they separated from their close relatives, such as the Daic and Austro-Asiatic populations in the mainland of Asia, remain unclear. In this study, we explored the paternal origin of Malays in Southeast Asia and the early differentiation of Austronesians. MATERIALS AND METHODS We generated whole Y-chromosome sequences of 50 Malays and co-analyzed 200 sequences from other Austronesians and related populations. We generated a revised phylogenetic tree with time estimation. RESULTS We identified six founding paternal lineages among the studied Malays samples. These founding lineages showed a surprisingly coincident expansion age at 5000 to 6000 years ago. We also found numerous mostly close related samples of the founding lineages of Malays among populations from Mainland of Asia. CONCLUSION Our analyses provided a refined phylogenetic resolution for the dominant paternal lineages of Austronesians found by previous studies. We suggested that the co-expansion of numerous founding paternal lineages corresponds to the initial differentiation of the most recent common ancestor of modern Austronesians. The splitting time and divergence pattern in perspective of paternal Y-chromosome evidence are highly consistent with the previous theories of ethnologists, linguists, and archeologists.
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Affiliation(s)
- Jin Sun
- Department of Anthropology and Ethnology, Institute of Anthropology, Xiamen University, Xiamen, China
| | - Lan-Hai Wei
- Department of Anthropology and Ethnology, Institute of Anthropology, Xiamen University, Xiamen, China.,B&R International Joint Laboratory for Eurasian Anthropology, Fudan University, Shanghai, China
| | | | - Yun-Zhi Huang
- MOE Key Laboratory of Contemporary Anthropology, School of Life Sciences, Fudan University, Shanghai, China
| | - Shi Yan
- Human Phenome Institute, Fudan University, Shanghai, China
| | - Hui-Zhen Cheng
- Department of Anthropology and Ethnology, Institute of Anthropology, Xiamen University, Xiamen, China
| | - Rick Twee-Hee Ong
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore
| | - Woei-Yuh Saw
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore.,Life Sciences Institute, National University of Singapore, Singapore, Singapore
| | - Zhi-Quan Fan
- Department of Anthropology and Ethnology, Institute of Anthropology, Xiamen University, Xiamen, China
| | - Xiao-Hua Deng
- Department of Anthropology and Ethnology, Institute of Anthropology, Xiamen University, Xiamen, China.,Center for collation and studies of Fujian local literature, Fujian University of Technology, Fuzhou, China
| | - Yan Lu
- Chinese Academy of Sciences (CAS) Key Laboratory of Computational Biology, Max Planck Independent Research Group on Population Genomics, CAS-MPG Partner Institute for Computational Biology (PICB), Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, CAS, Shanghai, China
| | - Chao Zhang
- Chinese Academy of Sciences (CAS) Key Laboratory of Computational Biology, Max Planck Independent Research Group on Population Genomics, CAS-MPG Partner Institute for Computational Biology (PICB), Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, CAS, Shanghai, China.,School of Life Science and Technology, Shanghai Tech University, Shanghai, China
| | - Shu-Hua Xu
- Chinese Academy of Sciences (CAS) Key Laboratory of Computational Biology, Max Planck Independent Research Group on Population Genomics, CAS-MPG Partner Institute for Computational Biology (PICB), Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, CAS, Shanghai, China.,School of Life Science and Technology, Shanghai Tech University, Shanghai, China.,Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming, China
| | - Li Jin
- MOE Key Laboratory of Contemporary Anthropology, School of Life Sciences, Fudan University, Shanghai, China.,Human Phenome Institute, Fudan University, Shanghai, China
| | - Yik-Ying Teo
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore.,Life Sciences Institute, National University of Singapore, Singapore, Singapore.,Genome Institute of Singapore, Agency for Science, Technology and Research, Singapore, Singapore.,NUS Graduate School for Integrative Science and Engineering, National University of Singapore, Singapore, Singapore.,Department of Statistics and Applied Probability, National University of Singapore, Singapore, Singapore
| | - Hui Li
- B&R International Joint Laboratory for Eurasian Anthropology, Fudan University, Shanghai, China.,MOE Key Laboratory of Contemporary Anthropology, School of Life Sciences, Fudan University, Shanghai, China.,Human Phenome Institute, Fudan University, Shanghai, China
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Hallast P, Agdzhoyan A, Balanovsky O, Xue Y, Tyler-Smith C. A Southeast Asian origin for present-day non-African human Y chromosomes. Hum Genet 2020; 140:299-307. [PMID: 32666166 PMCID: PMC7864842 DOI: 10.1007/s00439-020-02204-9] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2020] [Accepted: 07/02/2020] [Indexed: 12/17/2022]
Abstract
The genomes of present-day humans outside Africa originated almost entirely from a single out-migration ~ 50,000–70,000 years ago, followed by mixture with Neanderthals contributing ~ 2% to all non-Africans. However, the details of this initial migration remain poorly understood because no ancient DNA analyses are available from this key time period, and interpretation of present-day autosomal data is complicated due to subsequent population movements/reshaping. One locus, however, does retain male-specific information from this early period: the Y chromosome, where a detailed calibrated phylogeny has been constructed. Three present-day Y lineages were carried by the initial migration: the rare haplogroup D, the moderately rare C, and the very common FT lineage which now dominates most non-African populations. Here, we show that phylogenetic analyses of haplogroup C, D and FT sequences, including very rare deep-rooting lineages, together with phylogeographic analyses of ancient and present-day non-African Y chromosomes, all point to East/Southeast Asia as the origin 50,000–55,000 years ago of all known surviving non-African male lineages (apart from recent migrants). This observation contrasts with the expectation of a West Eurasian origin predicted by a simple model of expansion from a source near Africa, and can be interpreted as resulting from extensive genetic drift in the initial population or replacement of early western Y lineages from the east, thus informing and constraining models of the initial expansion.
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Affiliation(s)
- Pille Hallast
- Institute of Biomedicine and Translational Medicine, University of Tartu, 50411, Tartu, Estonia. .,Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SA, UK.
| | - Anastasia Agdzhoyan
- Vavilov Institute of General Genetics, Moscow, 119991, Russia.,Research Centre for Medical Genetics, Moscow, 115522, Russia
| | - Oleg Balanovsky
- Vavilov Institute of General Genetics, Moscow, 119991, Russia.,Research Centre for Medical Genetics, Moscow, 115522, Russia.,Biobank of North Eurasia, Moscow, 115201, Russia
| | - Yali Xue
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SA, UK
| | - Chris Tyler-Smith
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SA, UK.
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Abdullah UYH, Ibrahim HM, Mahmud NB, Salleh MZ, Teh LK, Noorizhab MNFB, Zilfalil BA, Jassim HM, Wilairat P, Fucharoen S. Genotype-Phenotype Correlation of β-Thalassemia in Malaysian Population: Toward Effective Genetic Counseling. Hemoglobin 2020; 44:184-189. [PMID: 32586164 DOI: 10.1080/03630269.2020.1781652] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
Abstract
Effective prevention of β-thalassemia (β-thal) requires strategies to detect at-risk couples. This is the first study attempting to assess the prevalence of silent β-thal carriers in the Malaysian population. Hematological and clinical parameters were evaluated in healthy blood donors and patients with β-thal trait, Hb E (HBB: c.79G>A)/β-thal and β-thal major (β-TM). β-Globin gene sequencing was carried out for 52 healthy blood donors, 48 patients with Hb E/β-thal, 34 patients with β-TM and 38 patients with β-thal trait. The prevalence of silent β-thal carrier phenotypes found in 25.0% of healthy Malaysian blood donors indicates the need for clinician's awareness of this type in evaluating β-thal in Malaysia. Patients with β-TM present at a significantly younger age at initial diagnosis and require more blood transfusions compared to those with Hb E/β-thal. The time at which genomic DNA was extracted after blood collection, particularly from patients with β-TM and Hb E/β-thal, was found to be an important determinant of the quality of the results of the β-globin sequencing. Public education and communication campaigns are recommended as apparently healthy individuals have few or no symptoms and normal or borderline hematological parameters. β-Globin gene mutation characterization and screening for silent β-thal carriers in regions prevalent with β-thal are recommended to develop more effective genetic counseling and management of β-thal.
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Affiliation(s)
- Uday Y H Abdullah
- Department of Pathology, Faculty of Medicine, Universiti Sultan Zainal Abidin, Kuala Terengganu, Malaysia
| | - Hishamshah M Ibrahim
- Paediatric Haematology, Oncology and Haemopoietic Stem Cell Transplant Unit, Hospital Kuala Lumpur (HKL) Jalan Pahang, Kuala Lumpur, Malaysia
| | - Noraesah Binti Mahmud
- Department of Pathology, Hospital Kuala Lumpur (HKL) Jalan Pahang, Kuala Lumpur, Malaysia
| | - Mohamad Zaki Salleh
- Integrative Pharmacogenomics Institute (iPROMISE), Universiti Teknologi Mara (UiTM), Bandar Puncak Alam, Malaysia
| | - Lay Kek Teh
- Integrative Pharmacogenomics Institute (iPROMISE), Universiti Teknologi Mara (UiTM), Bandar Puncak Alam, Malaysia
| | | | - Bin Alwi Zilfalil
- Department of Paediatric, School of Medical Science, Health Campus, Univesiti Sains Malaysia (USM), Kelantan, Malaysia
| | - Haitham Muhammed Jassim
- Department of Emergency, Rockingham Peel Group, South Metropolitan Health Service, Rockingham, Australia
| | - Prapin Wilairat
- National Doping Control Centre, Mahidol University, Bangkok, Thailand
| | - Suthat Fucharoen
- Thalassaemia Research Centre, Institute of Molecular Biosciences, Mahidol University, Nakornpathom, Thailand
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36
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Botton MR, Lu X, Zhao G, Repnikova E, Seki Y, Gaedigk A, Schadt EE, Edelmann L, Scott SA. Structural variation at the CYP2C locus: Characterization of deletion and duplication alleles. Hum Mutat 2020; 40:e37-e51. [PMID: 31260137 DOI: 10.1002/humu.23855] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2019] [Revised: 06/11/2019] [Accepted: 06/25/2019] [Indexed: 12/27/2022]
Abstract
The human CYP2C locus harbors the polymorphic CYP2C18, CYP2C19, CYP2C9, and CYP2C8 genes, and of these, CYP2C19 and CYP2C9 are directly involved in the metabolism of ~15% of all medications. All variant CYP2C19 and CYP2C9 star (*) allele haplotypes currently cataloged by the Pharmacogene Variation (PharmVar) Consortium are defined by sequence variants. To determine if structural variation also occurs at the CYP2C locus, the 10q23.33 region was interrogated across deidentified clinical chromosomal microarray (CMA) data from 20,642 patients tested at two academic medical centers. Fourteen copy number variants that affected the coding region of CYP2C genes were detected in the clinical CMA cohorts, which ranged in size from 39.2 to 1,043.3 kb. Selected deletions and duplications were confirmed by MLPA or ddPCR. Analysis of the clinical CMA and an additional 78,839 cases from the Database of Genomic Variants (DGV) and ClinGen (total n = 99,481) indicated that the carrier frequency of a CYP2C structural variant is ~1 in 1,000, with ~1 in 2,000 being a CYP2C19 full gene or partial-gene deletion carrier, designated by PharmVar as CYP2C19*36 and *37, respectively. Although these structural variants are rare in the general population, their detection will likely improve metabolizer phenotype prediction when interrogated for research and/or clinical testing.
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Affiliation(s)
- Mariana R Botton
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York.,Sema4, A Mount Sinai venture, Stamford, Connecticut
| | - Xingwu Lu
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York.,Sema4, A Mount Sinai venture, Stamford, Connecticut
| | - Geping Zhao
- Sema4, A Mount Sinai venture, Stamford, Connecticut
| | - Elena Repnikova
- Clinical Genetics and Genomics Laboratories, Children's Mercy Hospital Kansas City, Kansas City, Missouri.,School of Medicine, University of Missouri-Kansas City, Kansas City, Missouri
| | | | - Andrea Gaedigk
- Division of Clinical Pharmacology, Toxicology & Therapeutic Innovation, Children's Mercy Kansas City, Kansas City, Missouri
| | - Eric E Schadt
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York.,Sema4, A Mount Sinai venture, Stamford, Connecticut
| | - Lisa Edelmann
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York.,Sema4, A Mount Sinai venture, Stamford, Connecticut
| | - Stuart A Scott
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York.,Sema4, A Mount Sinai venture, Stamford, Connecticut
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37
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Jamaluddin J, Mohd Khair NK, Vinodamaney SD, Othman Z, Abubakar S. Copy number variation of CCL3L1 among three major ethnic groups in Malaysia. BMC Genet 2020; 21:1. [PMID: 31900126 PMCID: PMC6942282 DOI: 10.1186/s12863-019-0803-3] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2018] [Accepted: 12/17/2019] [Indexed: 11/30/2022] Open
Abstract
Background C-C motif Chemokine Ligand 3 Like 1 (CCL3L1) is a multiallelic copy number variable, which plays a crucial role in immunoregulatory and hosts defense through the production of macrophage inflammatory protein (MIP)-1α. Variable range of the CCL3L1 copies from 0 to 14 copies have been documented in several different populations. However, there is still lack of report on the range of CCL3L1 copy number exclusively among Malaysians who are a multi-ethnic population. Thus, this study aims to extensively examine the distribution of CCL3L1 copy number in the three major populations from Malaysia namely Malay, Chinese and Indian. A diploid copy number of CCL3L1 for 393 Malaysians (Malay = 178, Indian = 90, and Chinese = 125) was quantified using Paralogue Ratio Tests (PRTs) and then validated with microsatellites analysis. Results To our knowledge, this is the first report on the CCL3L1 copy number that has been attempted among Malaysians and the Chinese ethnic group exhibits a diverse pattern of CCL3L1 distribution copy number from the Malay and Indian (p < 0.0001). The CCL3L1 ranged from 0 to 8 copies for both the Malay and Indian ethnic groups while 0 to 10 copies for the Chinese ethnic. Consequently, the CCL3L1 copy number among major ethnic groups in the Malaysian population is found to be significantly varied when compared to the European population (p < 0.0001). The mean/median reported for the Malay, Chinese, Indian, and European are 2.759/2.869, 3.453/3.290, 2.437/1.970 and 2.001/1.940 respectively. Conclusion This study reveals the existence of genetic variation of CCL3L1 in the Malaysian population, and suggests by examining genetic diversity on the ethnicity, and specific geographical region could help in reconstructing human evolutionary history and for the prediction of disease risk related to the CCL3L1 copy number.
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Affiliation(s)
- Jalilah Jamaluddin
- Department of Biomedical Science, Faculty of Medicine and Health Sciences, Universiti Putra Malaysia, UPM, 43400, Serdang, Selangor, Malaysia
| | - Nur Khairina Mohd Khair
- Department of Biomedical Science, Faculty of Medicine and Health Sciences, Universiti Putra Malaysia, UPM, 43400, Serdang, Selangor, Malaysia
| | - Shameni Devi Vinodamaney
- Department of Biomedical Science, Faculty of Medicine and Health Sciences, Universiti Putra Malaysia, UPM, 43400, Serdang, Selangor, Malaysia
| | - Zulkefley Othman
- Department of Biomedical Science, Faculty of Medicine and Health Sciences, Universiti Putra Malaysia, UPM, 43400, Serdang, Selangor, Malaysia
| | - Suhaili Abubakar
- Department of Biomedical Science, Faculty of Medicine and Health Sciences, Universiti Putra Malaysia, UPM, 43400, Serdang, Selangor, Malaysia.
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38
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Ha J, Martinson R, Iwamoto SK, Nishi A. Hemoglobin E, malaria and natural selection. EVOLUTION MEDICINE AND PUBLIC HEALTH 2019; 2019:232-241. [PMID: 31890210 PMCID: PMC6925914 DOI: 10.1093/emph/eoz034] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 02/26/2019] [Accepted: 11/26/2019] [Indexed: 12/24/2022]
Abstract
It is known that there has been positive natural selection for hemoglobin S and C in humans despite negative health effects, due to its role in malaria resistance. However, it is not well understood, if there has been natural selection for hemoglobin E (HbE), which is a common variant in Southeast Asia. Therefore, we reviewed previous studies and discussed the potential role of natural selection in the prevalence of HbE. Our review shows that in vitro studies, evolutionary genetics studies and epidemiologic studies largely support an involvement of natural selection in the evolution of HbE and a protective role of HbE against malaria infection. However, the evidence is inconsistent, provided from different regions, and insufficient to perform an aggregated analysis such as a meta-analysis. In addition, few candidate gene, genome-wide association or epistasis studies, which have been made possible with the use of big data in the post-genomic era, have investigated HbE. The biological pathways linking HbE and malaria infection have not yet been fully elucidated. Therefore, further research is necessary before it can be concluded that there was positive natural selection for HbE due to protection against malaria. Lay summary: Our review shows that evidence largely supports an involvement of natural selection in the evolution of HbE and a protective role of HbE against malaria. However, the evidence is not consistent. Further research is necessary before it is concluded.
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Affiliation(s)
- Jiwoo Ha
- Division of Biotechnology, College of Life Sciences and Biotechnology, Korea University, Seoul 02841, Korea
| | - Ryan Martinson
- Department of Ecology and Evolutionary Biology, University of California, Los Angeles, CA 90025, USA
| | - Sage K Iwamoto
- College of Letters & Science, University of California Berkeley, Berkeley, CA 94720-2930, USA
| | - Akihiro Nishi
- Department of Epidemiology, UCLA Fielding School of Public Health, Los Angeles, CA 90095, USA
- Corresponding author. Department of Epidemiology, UCLA Fielding School of Public Health, 650 Charles E Young Dr S, Los Angeles, CA 90095, USA. Tel: +1-310-206-7164; Fax: +1-310-206-6039; E-mail:
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39
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The GenomeAsia 100K Project enables genetic discoveries across Asia. Nature 2019; 576:106-111. [PMID: 31802016 PMCID: PMC7054211 DOI: 10.1038/s41586-019-1793-z] [Citation(s) in RCA: 213] [Impact Index Per Article: 42.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2018] [Accepted: 10/11/2019] [Indexed: 12/30/2022]
Abstract
The underrepresentation of non-Europeans in human genetic studies so far has limited the diversity of individuals in genomic datasets and led to reduced medical relevance for a large proportion of the world’s population. Population-specific reference genome datasets as well as genome-wide association studies in diverse populations are needed to address this issue. Here we describe the pilot phase of the GenomeAsia 100K Project. This includes a whole-genome sequencing reference dataset from 1,739 individuals of 219 population groups and 64 countries across Asia. We catalogue genetic variation, population structure, disease associations and founder effects. We also explore the use of this dataset in imputation, to facilitate genetic studies in populations across Asia and worldwide. Using whole-genome sequencing data from 1,739 individuals, the GenomeAsia 100K Project catalogues genetic variation, population structure and disease associations to facilitate genetic studies in Asian populations and increase representation in genetics studies worldwide.
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40
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Lantieri F, Gimelli S, Viaggi C, Stathaki E, Malacarne M, Santamaria G, Grossi A, Mosconi M, Sloan-Béna F, Prato AP, Coviello D, Ceccherini I. Copy number variations in candidate genomic regions confirm genetic heterogeneity and parental bias in Hirschsprung disease. Orphanet J Rare Dis 2019; 14:270. [PMID: 31767031 PMCID: PMC6878652 DOI: 10.1186/s13023-019-1205-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2018] [Accepted: 09/13/2019] [Indexed: 11/26/2022] Open
Abstract
Background Hirschsprung Disease (HSCR) is a congenital defect of the intestinal innervations characterized by complex inheritance. Many susceptibility genes including RET, the major HSCR gene, and several linked regions and associated loci have been shown to contribute to disease pathogenesis. Nonetheless, a proportion of patients still remains unexplained. Copy Number Variations (CNVs) have already been involved in HSCR, and for this reason we performed Comparative Genomic Hybridization (CGH), using a custom array with high density probes. Results A total of 20 HSCR candidate regions/genes was tested in 55 sporadic patients and four patients with already known chromosomal aberrations. Among 83 calls, 12 variants were experimentally validated, three of which involving the HSCR crucial genes SEMA3A/3D, NRG1, and PHOX2B. Conversely RET involvement in HSCR does not seem to rely on the presence of CNVs while, interestingly, several gains and losses did co-occur with another RET defect, thus confirming that more than one predisposing event is necessary for HSCR to develop. New loci were also shown to be involved, such as ALDH1A2, already found to play a major role in the enteric nervous system. Finally, all the inherited CNVs were of maternal origin. Conclusions Our results confirm a wide genetic heterogeneity in HSCR occurrence and support a role of candidate genes in expression regulation and cell signaling, thus contributing to depict further the molecular complexity of the genomic regions involved in the Enteric Nervous System development. The observed maternal transmission bias for HSCR associated CNVs supports the hypothesis that in females these variants might be more tolerated, requiring additional alterations to develop HSCR disease.
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Affiliation(s)
- Francesca Lantieri
- Dipartimento di Scienze della Salute, sezione di Biostatistica, Universita' degli Studi di Genova, 16132, Genoa, Italy
| | - Stefania Gimelli
- Department of Medical Genetic and Laboratories, University Hospitals of Geneva, Geneva, Switzerland
| | - Chiara Viaggi
- S.C. Laboratorio Genetica Umana, Ospedali Galliera, Genoa, Italy
| | - Elissavet Stathaki
- Department of Medical Genetic and Laboratories, University Hospitals of Geneva, Geneva, Switzerland
| | - Michela Malacarne
- S.C. Laboratorio Genetica Umana, Ospedali Galliera, Genoa, Italy.,Present address: U.O.C. Laboratorio di Genetica Umana, IRCCS Istituto Giannina Gaslini, Genoa, 16148, Italy
| | - Giuseppe Santamaria
- U.O.C. Genetica Medica, IRCCS, Istituto Giannina Gaslini, 16148, Genoa, Italy
| | - Alice Grossi
- U.O.C. Genetica Medica, IRCCS, Istituto Giannina Gaslini, 16148, Genoa, Italy
| | - Manuela Mosconi
- UOC Chirurgia Pediatrica, Istituto Giannina Gaslini, 16148, Genoa, Italy
| | - Frédérique Sloan-Béna
- Department of Medical Genetic and Laboratories, University Hospitals of Geneva, Geneva, Switzerland
| | - Alessio Pini Prato
- UOC Chirurgia Pediatrica, Istituto Giannina Gaslini, 16148, Genoa, Italy.,Present address: Children Hospital, AON SS Antonio e Biagio e Cesare Arrigo, Alessandria, Italy
| | - Domenico Coviello
- S.C. Laboratorio Genetica Umana, Ospedali Galliera, Genoa, Italy.,Present address: U.O.C. Laboratorio di Genetica Umana, IRCCS Istituto Giannina Gaslini, Genoa, 16148, Italy
| | - Isabella Ceccherini
- U.O.C. Genetica Medica, IRCCS, Istituto Giannina Gaslini, 16148, Genoa, Italy.
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41
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Deng L, Lou H, Zhang X, Thiruvahindrapuram B, Lu D, Marshall CR, Liu C, Xie B, Xu W, Wong LP, Yew CW, Farhang A, Ong RTH, Hoque MZ, Thuhairah AR, Jong B, Phipps ME, Scherer SW, Teo YY, Kumar SV, Hoh BP, Xu S. Analysis of five deep-sequenced trio-genomes of the Peninsular Malaysia Orang Asli and North Borneo populations. BMC Genomics 2019; 20:842. [PMID: 31718558 PMCID: PMC6852992 DOI: 10.1186/s12864-019-6226-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2019] [Accepted: 10/25/2019] [Indexed: 12/18/2022] Open
Abstract
Background Recent advances in genomic technologies have facilitated genome-wide investigation of human genetic variations. However, most efforts have focused on the major populations, yet trio genomes of indigenous populations from Southeast Asia have been under-investigated. Results We analyzed the whole-genome deep sequencing data (~ 30×) of five native trios from Peninsular Malaysia and North Borneo, and characterized the genomic variants, including single nucleotide variants (SNVs), small insertions and deletions (indels) and copy number variants (CNVs). We discovered approximately 6.9 million SNVs, 1.2 million indels, and 9000 CNVs in the 15 samples, of which 2.7% SNVs, 2.3% indels and 22% CNVs were novel, implying the insufficient coverage of population diversity in existing databases. We identified a higher proportion of novel variants in the Orang Asli (OA) samples, i.e., the indigenous people from Peninsular Malaysia, than that of the North Bornean (NB) samples, likely due to more complex demographic history and long-time isolation of the OA groups. We used the pedigree information to identify de novo variants and estimated the autosomal mutation rates to be 0.81 × 10− 8 – 1.33 × 10− 8, 1.0 × 10− 9 – 2.9 × 10− 9, and ~ 0.001 per site per generation for SNVs, indels, and CNVs, respectively. The trio-genomes also allowed for haplotype phasing with high accuracy, which serves as references to the future genomic studies of OA and NB populations. In addition, high-frequency inherited CNVs specific to OA or NB were identified. One example is a 50-kb duplication in DEFA1B detected only in the Negrito trios, implying plausible effects on host defense against the exposure of diverse microbial in tropical rainforest environment of these hunter-gatherers. The CNVs shared between OA and NB groups were much fewer than those specific to each group. Nevertheless, we identified a 142-kb duplication in AMY1A in all the 15 samples, and this gene is associated with the high-starch diet. Moreover, novel insertions shared with archaic hominids were identified in our samples. Conclusion Our study presents a full catalogue of the genome variants of the native Malaysian populations, which is a complement of the genome diversity in Southeast Asians. It implies specific population history of the native inhabitants, and demonstrated the necessity of more genome sequencing efforts on the multi-ethnic native groups of Malaysia and Southeast Asia.
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Affiliation(s)
- Lian Deng
- Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Haiyi Lou
- Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Xiaoxi Zhang
- Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China.,School of Life Science and Technology, ShanghaiTech University, Shanghai, 201210, China
| | | | - Dongsheng Lu
- Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Christian R Marshall
- The Centre for Applied Genomics, The Hospital for Sick Children, Toronto, Ontario, Canada.,Genome Diagnostics, Department of Paediatric Laboratory Medicine, The Hospital for Sick Children, Toronto, Ontario, Canada.,Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Ontario, Canada
| | - Chang Liu
- Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Bo Xie
- Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Wanxing Xu
- Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China.,School of Life Science and Technology, ShanghaiTech University, Shanghai, 201210, China
| | - Lai-Ping Wong
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore, 117597, Singapore
| | - Chee-Wei Yew
- Biotechnology Research Institute, Universiti Malaysia Sabah, Jalan UMS, 88400, Kota Kinabalu, Sabah, Malaysia
| | - Aghakhanian Farhang
- Jefrey Cheah School of Medicine and Health Sciences, Monash University Malaysia, Jalan Lagoon Selatan, Sunway, 46150, Subang Jaya, Selangor, Malaysia.,Tropical Medicine and Biology Platform, Monash University Malaysia, Jalan Lagoon Selatan, 46150 Sunway, Subang Jaya, Selangor, Malaysia
| | - Rick Twee-Hee Ong
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore, 117597, Singapore
| | - Mohammad Zahirul Hoque
- Faculty of Medicine and Health Sciences, Universiti Malaysia Sabah, Jalan UMS, 88400, Kota Kinabalu, Sabah, Malaysia
| | - Abdul Rahman Thuhairah
- Clinical Pathology Diagnostic Centre Research Laboratory, Faculty of Medicine, Universiti Teknologi MARA, Sungai Buloh Campus, 47000 Sg Buloh, Subang Jaya, Selangor, Malaysia
| | - Bhak Jong
- Personal Genomics Institute, Genome Research Foundation, Suwon, Republic of Korea.,Geromics, Ulsan, 44919, Republic of Korea.,Biomedical Engineering Department, The Genomics Institute, UNIST, Ulsan, Republic of Korea
| | - Maude E Phipps
- Tropical Medicine and Biology Platform, Monash University Malaysia, Jalan Lagoon Selatan, 46150 Sunway, Subang Jaya, Selangor, Malaysia
| | - Stephen W Scherer
- The Centre for Applied Genomics, The Hospital for Sick Children, Toronto, Ontario, Canada.,Genome Diagnostics, Department of Paediatric Laboratory Medicine, The Hospital for Sick Children, Toronto, Ontario, Canada.,Program in Genetics and Genome Biology, The Hospital for Sick Children, Toronto, Ontario, Canada.,Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada
| | - Yik-Ying Teo
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore, 117597, Singapore.,NUS Graduate School for Integrative Science and Engineering, National University of Singapore, Singapore, 117456, Singapore.,Life Sciences Institute, National University of Singapore, Singapore, Singapore.,Department of Statistics and Applied Probability, National University of Singapore, Singapore, Singapore.,Genome Institute of Singapore, Agency for Science, Technology and Research, Singapore, 138672, Singapore
| | - Subbiah Vijay Kumar
- Biotechnology Research Institute, Universiti Malaysia Sabah, Jalan UMS, 88400, Kota Kinabalu, Sabah, Malaysia.
| | - Boon-Peng Hoh
- Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China. .,Faculty of Medicine and Health Sciences, UCSI University, Jalan Menara Gading, Taman Connaught, Cheras, 56000, Kuala Lumpur, Malaysia.
| | - Shuhua Xu
- Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China. .,School of Life Science and Technology, ShanghaiTech University, Shanghai, 201210, China. .,Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming, 650223, China. .,Collaborative Innovation Center of Genetics and Development, Shanghai, 200438, China. .,Human Phenome Institute, Fudan University, Shanghai, 201203, China.
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Rose AM, Krishan A, Chakarova CF, Moya L, Chambers SK, Hollands M, Illingworth JC, Williams SMG, McCabe HE, Shah AZ, Palmer CNA, Chakravarti A, Berg JN, Batra J, Bhattacharya SS. MSR1 repeats modulate gene expression and affect risk of breast and prostate cancer. Ann Oncol 2019; 29:1292-1303. [PMID: 29509840 DOI: 10.1093/annonc/mdy082] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022] Open
Abstract
Background MSR1 repeats are a 36-38 bp minisatellite element that have recently been implicated in the regulation of gene expression, through copy number variation (CNV). Patients and methods Bioinformatic and experimental methods were used to assess the distribution of MSR1 across the genome, evaluate the regulatory potential of such elements and explore the role of MSR1 elements in cancer, particularly non-familial breast cancer and prostate cancer. Results MSR1s are predominately located at chromosome 19 and are functionally enriched in regulatory regions of the genome, particularly regions implicated in short-range regulatory activities (H3K27ac, H3K4me1 and H3K4me3). MSR1-regulated genes were found to have specific molecular roles, such as serine-protease activity (P = 4.80 × 10-7) and ion channel activity (P = 2.7 × 10-4). The kallikrein locus was found to contain a large number of MSR1 clusters, and at least six of these showed CNV. An MSR1 cluster was identified within KLK14, with 9 and 11 copies being normal variants. A significant association with the 9-copy allele and non-familial breast cancer was found in two independent populations (P = 0.004; P = 0.03). In the white British population, the minor allele conferred an increased risk of 1.21-3.51 times for all non-familial disease, or 1.7-5.3 times in early-onset disease. The 9-copy allele was also found to be associated with increased risk of prostate cancer in an independent population (odds ratio = 1.27-1.56; P =0.009). Conclusions MSR1 repeats act as molecular switches that modulate gene expression. It is likely that CNV of MSR1 will affect risk of development of various forms of cancer, including that of breast and prostate. The MSR1 cluster at KLK14 represents the strongest risk factor identified to date in non-familial breast cancer and a significant risk factor for prostate cancer. Analysis of MSR1 genotype will allow development of precise stratification of disease risk and provide a novel target for therapeutic agents.
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Affiliation(s)
- A M Rose
- Department of Genetics, UCL Institute of Ophthalmology, University College London, London, UK.
| | - A Krishan
- Cell Therapy and Regenerative Medicine, CABIMER, Seville, Spain
| | - C F Chakarova
- Department of Genetics, UCL Institute of Ophthalmology, University College London, London, UK
| | - L Moya
- Australian Prostate Cancer Research Centre - Queensland, Translational Research Institute, Brisbane; Cancer Program, School of Biomedical Sciences, Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane
| | - S K Chambers
- Menzies Health Institute Queensland, Griffith University, Southport; Cancer Research Centre, Cancer Council Queensland, Brisbane, Australia
| | - M Hollands
- UCL Medical School, University College London, London
| | | | | | - H E McCabe
- Clinical Genetics, Ninewells Hospital & Medical School, University of Dundee, Dundee
| | - A Z Shah
- Department of Genetics, UCL Institute of Ophthalmology, University College London, London, UK
| | - C N A Palmer
- Centre for Pharmacogenetics and Pharmacogenomics, Ninewells Hospital and School of Medicine, University of Dundee, Dundee, UK
| | - A Chakravarti
- Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, USA
| | - J N Berg
- Clinical Genetics, Ninewells Hospital & Medical School, University of Dundee, Dundee
| | - J Batra
- Australian Prostate Cancer Research Centre - Queensland, Translational Research Institute, Brisbane; Cancer Program, School of Biomedical Sciences, Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane
| | - S S Bhattacharya
- Department of Genetics, UCL Institute of Ophthalmology, University College London, London, UK; Cell Therapy and Regenerative Medicine, CABIMER, Seville, Spain
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43
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Zhang C, Gao Y, Ning Z, Lu Y, Zhang X, Liu J, Xie B, Xue Z, Wang X, Yuan K, Ge X, Pan Y, Liu C, Tian L, Wang Y, Lu D, Hoh BP, Xu S. PGG.SNV: understanding the evolutionary and medical implications of human single nucleotide variations in diverse populations. Genome Biol 2019; 20:215. [PMID: 31640808 PMCID: PMC6805450 DOI: 10.1186/s13059-019-1838-5] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2019] [Accepted: 09/26/2019] [Indexed: 12/23/2022] Open
Abstract
Despite the tremendous growth of the DNA sequencing data in the last decade, our understanding of the human genome is still in its infancy. To understand the implications of genetic variants in the light of population genetics and molecular evolution, we developed a database, PGG.SNV ( https://www.pggsnv.org ), which gives much higher weight to previously under-investigated indigenous populations in Asia. PGG.SNV archives 265 million SNVs across 220,147 present-day genomes and 1018 ancient genomes, including 1009 newly sequenced genomes, representing 977 global populations. Moreover, estimation of population genetic diversity and evolutionary parameters is available in PGG.SNV, a unique feature compared with other databases.
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Affiliation(s)
- Chao Zhang
- Chinese Academy of Sciences (CAS) Key Laboratory of Computational Biology, Max Planck Independent Research Group on Population Genomics, CAS-MPG Partner Institute for Computational Biology (PICB), Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, CAS, Shanghai, 200031, China
- Present Address: Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Yang Gao
- Chinese Academy of Sciences (CAS) Key Laboratory of Computational Biology, Max Planck Independent Research Group on Population Genomics, CAS-MPG Partner Institute for Computational Biology (PICB), Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, CAS, Shanghai, 200031, China
- School of Life Science and Technology, ShanghaiTech University, Shanghai, 201210, China
| | - Zhilin Ning
- Chinese Academy of Sciences (CAS) Key Laboratory of Computational Biology, Max Planck Independent Research Group on Population Genomics, CAS-MPG Partner Institute for Computational Biology (PICB), Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, CAS, Shanghai, 200031, China
| | - Yan Lu
- Chinese Academy of Sciences (CAS) Key Laboratory of Computational Biology, Max Planck Independent Research Group on Population Genomics, CAS-MPG Partner Institute for Computational Biology (PICB), Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, CAS, Shanghai, 200031, China
| | - Xiaoxi Zhang
- Chinese Academy of Sciences (CAS) Key Laboratory of Computational Biology, Max Planck Independent Research Group on Population Genomics, CAS-MPG Partner Institute for Computational Biology (PICB), Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, CAS, Shanghai, 200031, China
- School of Life Science and Technology, ShanghaiTech University, Shanghai, 201210, China
| | - Jiaojiao Liu
- Chinese Academy of Sciences (CAS) Key Laboratory of Computational Biology, Max Planck Independent Research Group on Population Genomics, CAS-MPG Partner Institute for Computational Biology (PICB), Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, CAS, Shanghai, 200031, China
- School of Life Science and Technology, ShanghaiTech University, Shanghai, 201210, China
| | - Bo Xie
- Chinese Academy of Sciences (CAS) Key Laboratory of Computational Biology, Max Planck Independent Research Group on Population Genomics, CAS-MPG Partner Institute for Computational Biology (PICB), Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, CAS, Shanghai, 200031, China
| | - Zhe Xue
- Chinese Academy of Sciences (CAS) Key Laboratory of Computational Biology, Max Planck Independent Research Group on Population Genomics, CAS-MPG Partner Institute for Computational Biology (PICB), Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, CAS, Shanghai, 200031, China
| | - Xiaoji Wang
- Chinese Academy of Sciences (CAS) Key Laboratory of Computational Biology, Max Planck Independent Research Group on Population Genomics, CAS-MPG Partner Institute for Computational Biology (PICB), Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, CAS, Shanghai, 200031, China
| | - Kai Yuan
- Chinese Academy of Sciences (CAS) Key Laboratory of Computational Biology, Max Planck Independent Research Group on Population Genomics, CAS-MPG Partner Institute for Computational Biology (PICB), Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, CAS, Shanghai, 200031, China
| | - Xueling Ge
- Chinese Academy of Sciences (CAS) Key Laboratory of Computational Biology, Max Planck Independent Research Group on Population Genomics, CAS-MPG Partner Institute for Computational Biology (PICB), Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, CAS, Shanghai, 200031, China
| | - Yuwen Pan
- Chinese Academy of Sciences (CAS) Key Laboratory of Computational Biology, Max Planck Independent Research Group on Population Genomics, CAS-MPG Partner Institute for Computational Biology (PICB), Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, CAS, Shanghai, 200031, China
| | - Chang Liu
- Chinese Academy of Sciences (CAS) Key Laboratory of Computational Biology, Max Planck Independent Research Group on Population Genomics, CAS-MPG Partner Institute for Computational Biology (PICB), Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, CAS, Shanghai, 200031, China
| | - Lei Tian
- Chinese Academy of Sciences (CAS) Key Laboratory of Computational Biology, Max Planck Independent Research Group on Population Genomics, CAS-MPG Partner Institute for Computational Biology (PICB), Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, CAS, Shanghai, 200031, China
| | - Yuchen Wang
- Chinese Academy of Sciences (CAS) Key Laboratory of Computational Biology, Max Planck Independent Research Group on Population Genomics, CAS-MPG Partner Institute for Computational Biology (PICB), Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, CAS, Shanghai, 200031, China
| | - Dongsheng Lu
- Chinese Academy of Sciences (CAS) Key Laboratory of Computational Biology, Max Planck Independent Research Group on Population Genomics, CAS-MPG Partner Institute for Computational Biology (PICB), Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, CAS, Shanghai, 200031, China
| | - Boon-Peng Hoh
- Chinese Academy of Sciences (CAS) Key Laboratory of Computational Biology, Max Planck Independent Research Group on Population Genomics, CAS-MPG Partner Institute for Computational Biology (PICB), Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, CAS, Shanghai, 200031, China
- Faculty of Medicine and Health Sciences, UCSI University, Jalan Menara Gading, Taman Connaught, Cheras, 56000, Kuala Lumpur, Malaysia
| | - Shuhua Xu
- Chinese Academy of Sciences (CAS) Key Laboratory of Computational Biology, Max Planck Independent Research Group on Population Genomics, CAS-MPG Partner Institute for Computational Biology (PICB), Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, CAS, Shanghai, 200031, China.
- School of Life Science and Technology, ShanghaiTech University, Shanghai, 201210, China.
- Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming, 650223, China.
- Collaborative Innovation Center of Genetics and Development, Shanghai, 200438, China.
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44
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Large-Scale Whole-Genome Sequencing of Three Diverse Asian Populations in Singapore. Cell 2019; 179:736-749.e15. [DOI: 10.1016/j.cell.2019.09.019] [Citation(s) in RCA: 76] [Impact Index Per Article: 15.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2019] [Revised: 06/24/2019] [Accepted: 09/19/2019] [Indexed: 12/19/2022]
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45
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Wang XK, Liao XW, Yang CK, Yu TD, Liu ZQ, Gong YZ, Huang KT, Zeng XM, Han CY, Zhu GZ, Qin W, Peng T. Diagnostic and prognostic biomarkers of Human Leukocyte Antigen complex for hepatitis B virus-related hepatocellular carcinoma. J Cancer 2019; 10:5173-5190. [PMID: 31602270 PMCID: PMC6775598 DOI: 10.7150/jca.29655] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2018] [Accepted: 07/25/2019] [Indexed: 01/11/2023] Open
Abstract
Background: Hepatitis B virus infection had been identified its relationship with liver diseases, including liver tumors. We aimed to explore diagnostic and prognostic values between the Human Leukocyte Antigen (HLA) complex and hepatocellular carcinoma (HCC). Methods: We used the GSE14520 dataset to explore diagnostic and prognostic significance between HLA complex and HCC. A nomogram was constructed to predict survival probability of HCC prognosis. Gene set enrichment analysis was explored using gene ontologies and metabolic pathways. Validation of prognostic values of the HLA complex was performed in the Kaplan-Meier Plotter website. Results: We found that HLA-C showed the diagnostic value (P <0.0001, area under curve: 0.784, sensitivity: 93.14%, specificity: 62.26%). In addition, HLA-DQA1 and HLA-F showed prognostic values for overall survival, and HLA-A, HLA-C, HLA-DPA1 and HLA-DQA1 showed prognostic values for recurrence-free survival (all P ≤ 0.05, elevated 0.927, 0.992, 1.023, 0.918, 0.937 multiples compared to non-tumor tissues, respectively). Gene set enrichment analysis found that they were involved in antigen processing and toll like receptor signalling pathway, etc. The nomogram was evaluated for survival probability of HCC prognosis. Validation analysis indicated that HLA-C, HLA-DPA1, HLA-E, HLA-F and HLA-G were associated with HCC prognosis of overall survival (all P ≤ 0.05, elevated 0.988 and 0.997 multiples compared to non-tumor tissues, respectively). Conclusion: HLA-C might be a diagnostic and prognostic biomarker for HCC. HLA-DPA1 and HLA-F might be prognostic biomarkers for HCC.
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Affiliation(s)
- Xiang-Kun Wang
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning, 530021, Guangxi Province, China
| | - Xi-Wen Liao
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning, 530021, Guangxi Province, China
| | - Cheng-Kun Yang
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning, 530021, Guangxi Province, China
| | - Ting-Dong Yu
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning, 530021, Guangxi Province, China
| | - Zheng-Qian Liu
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning, 530021, Guangxi Province, China
| | - Yi-Zhen Gong
- Department of Colorectal and Anal Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning, 530021, Guangxi Province, China
| | - Ke-Tuan Huang
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning, 530021, Guangxi Province, China
| | - Xian-Min Zeng
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning, 530021, Guangxi Province, China
| | - Chuang-Ye Han
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning, 530021, Guangxi Province, China
| | - Guang-Zhi Zhu
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning, 530021, Guangxi Province, China
| | - Wei Qin
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning, 530021, Guangxi Province, China
| | - Tao Peng
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning, 530021, Guangxi Province, China
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Zhang C, Gao Y, Liu J, Xue Z, Lu Y, Deng L, Tian L, Feng Q, Xu S. PGG.Population: a database for understanding the genomic diversity and genetic ancestry of human populations. Nucleic Acids Res 2019; 46:D984-D993. [PMID: 29112749 PMCID: PMC5753384 DOI: 10.1093/nar/gkx1032] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2017] [Accepted: 10/17/2017] [Indexed: 12/16/2022] Open
Abstract
There are a growing number of studies focusing on delineating genetic variations that are associated with complex human traits and diseases due to recent advances in next-generation sequencing technologies. However, identifying and prioritizing disease-associated causal variants relies on understanding the distribution of genetic variations within and among populations. The PGG.Population database documents 7122 genomes representing 356 global populations from 107 countries and provides essential information for researchers to understand human genomic diversity and genetic ancestry. These data and information can facilitate the design of research studies and the interpretation of results of both evolutionary and medical studies involving human populations. The database is carefully maintained and constantly updated when new data are available. We included miscellaneous functions and a user-friendly graphical interface for visualization of genomic diversity, population relationships (genetic affinity), ancestral makeup, footprints of natural selection, and population history etc. Moreover, PGG.Population provides a useful feature for users to analyze data and visualize results in a dynamic style via online illustration. The long-term ambition of the PGG.Population, together with the joint efforts from other researchers who contribute their data to our database, is to create a comprehensive depository of geographic and ethnic variation of human genome, as well as a platform bringing influence on future practitioners of medicine and clinical investigators. PGG.Population is available at https://www.pggpopulation.org.
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Affiliation(s)
- Chao Zhang
- Chinese Academy of Sciences (CAS) Key Laboratory of Computational Biology, Max Planck Independent Research Group on Population Genomics, CAS-MPG Partner Institute for Computational Biology (PICB), Shanghai Institutes for Biological Sciences, CAS, Shanghai 200031, China.,University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yang Gao
- Chinese Academy of Sciences (CAS) Key Laboratory of Computational Biology, Max Planck Independent Research Group on Population Genomics, CAS-MPG Partner Institute for Computational Biology (PICB), Shanghai Institutes for Biological Sciences, CAS, Shanghai 200031, China.,University of Chinese Academy of Sciences, Beijing 100049, China.,School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, China
| | - Jiaojiao Liu
- Chinese Academy of Sciences (CAS) Key Laboratory of Computational Biology, Max Planck Independent Research Group on Population Genomics, CAS-MPG Partner Institute for Computational Biology (PICB), Shanghai Institutes for Biological Sciences, CAS, Shanghai 200031, China.,University of Chinese Academy of Sciences, Beijing 100049, China.,School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, China
| | - Zhe Xue
- Chinese Academy of Sciences (CAS) Key Laboratory of Computational Biology, Max Planck Independent Research Group on Population Genomics, CAS-MPG Partner Institute for Computational Biology (PICB), Shanghai Institutes for Biological Sciences, CAS, Shanghai 200031, China
| | - Yan Lu
- Chinese Academy of Sciences (CAS) Key Laboratory of Computational Biology, Max Planck Independent Research Group on Population Genomics, CAS-MPG Partner Institute for Computational Biology (PICB), Shanghai Institutes for Biological Sciences, CAS, Shanghai 200031, China
| | - Lian Deng
- Chinese Academy of Sciences (CAS) Key Laboratory of Computational Biology, Max Planck Independent Research Group on Population Genomics, CAS-MPG Partner Institute for Computational Biology (PICB), Shanghai Institutes for Biological Sciences, CAS, Shanghai 200031, China.,University of Chinese Academy of Sciences, Beijing 100049, China
| | - Lei Tian
- Chinese Academy of Sciences (CAS) Key Laboratory of Computational Biology, Max Planck Independent Research Group on Population Genomics, CAS-MPG Partner Institute for Computational Biology (PICB), Shanghai Institutes for Biological Sciences, CAS, Shanghai 200031, China.,University of Chinese Academy of Sciences, Beijing 100049, China
| | - Qidi Feng
- Chinese Academy of Sciences (CAS) Key Laboratory of Computational Biology, Max Planck Independent Research Group on Population Genomics, CAS-MPG Partner Institute for Computational Biology (PICB), Shanghai Institutes for Biological Sciences, CAS, Shanghai 200031, China.,University of Chinese Academy of Sciences, Beijing 100049, China
| | - Shuhua Xu
- Chinese Academy of Sciences (CAS) Key Laboratory of Computational Biology, Max Planck Independent Research Group on Population Genomics, CAS-MPG Partner Institute for Computational Biology (PICB), Shanghai Institutes for Biological Sciences, CAS, Shanghai 200031, China.,University of Chinese Academy of Sciences, Beijing 100049, China.,School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, China.,Collaborative Innovation Center of Genetics and Development, Shanghai 200438, China
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47
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Le VS, Tran KT, Bui HTP, Le HTT, Nguyen CD, Do DH, Ly HTT, Pham LTD, Dao LTM, Nguyen LT. A Vietnamese human genetic variation database. Hum Mutat 2019; 40:1664-1675. [PMID: 31180159 DOI: 10.1002/humu.23835] [Citation(s) in RCA: 30] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2019] [Revised: 05/14/2019] [Accepted: 06/05/2019] [Indexed: 12/29/2022]
Abstract
Large scale human genome projects have created tremendous human genome databases for some well-studied populations. Vietnam has about 95 million people (the 14th largest country by population in the world) of which more than 86% are Kinh people. To date, genetic studies for Vietnamese people mostly rely on genetic information from other populations. Building a Vietnamese human genetic variation database is a must for properly interpreting Vietnamese genetic variants. To this end, we sequenced 105 whole genomes and 200 whole exomes of 305 unrelated Kinh Vietnamese (KHV) people. We also included 101 other previously published KHV genomes to build a Vietnamese human genetic variation database of 406 KHV people. The KHV database contains 24.81 million variants (22.47 million single nucleotide polymorphisms (SNPs) and 2.34 million indels) of which 0.71 million variants are novel. It includes more than 99.3% of variants with a frequency of >1% in the KHV population. Noticeably, the KHV database revealed 107 variants reported in the human genome mutation database as pathological mutations with a frequency above 1% in the KHV population. The KHV database (available at https://genomes.vn) would be beneficial for genetic studies and medical applications not only for the Vietnamese population but also for other closely related populations.
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Affiliation(s)
- Vinh S Le
- Vinmec Research Institute of Stem Cell and Gene Technology, Hanoi, Vietnam.,Department of Gene Technology, Vinmec International Hospital Times City, Hanoi, Vietnam.,Faculty of Information Technology, University of Engineering and Technology, Vietnam National University Hanoi, Hanoi, Vietnam
| | - Kien T Tran
- Vinmec Research Institute of Stem Cell and Gene Technology, Hanoi, Vietnam
| | - Hoa T P Bui
- Vinmec Research Institute of Stem Cell and Gene Technology, Hanoi, Vietnam.,Department of Gene Technology, Vinmec International Hospital Times City, Hanoi, Vietnam.,School of Environment and Life Science, University of Salford, Manchester, United Kingdom
| | - Huong T T Le
- Vinmec Research Institute of Stem Cell and Gene Technology, Hanoi, Vietnam.,Department of Gene Technology, Vinmec International Hospital Times City, Hanoi, Vietnam
| | - Canh D Nguyen
- Faculty of Information Technology, University of Engineering and Technology, Vietnam National University Hanoi, Hanoi, Vietnam
| | - Duong H Do
- Vinmec Research Institute of Stem Cell and Gene Technology, Hanoi, Vietnam.,Department of Gene Technology, Vinmec International Hospital Times City, Hanoi, Vietnam
| | - Ha T T Ly
- Vinmec Research Institute of Stem Cell and Gene Technology, Hanoi, Vietnam.,Department of Gene Technology, Vinmec International Hospital Times City, Hanoi, Vietnam
| | - Linh T D Pham
- Vinmec Research Institute of Stem Cell and Gene Technology, Hanoi, Vietnam
| | - Lan T M Dao
- Vinmec Research Institute of Stem Cell and Gene Technology, Hanoi, Vietnam
| | - Liem T Nguyen
- Vinmec Research Institute of Stem Cell and Gene Technology, Hanoi, Vietnam
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Leong HY, Abdul Azize NA, Chew HB, Keng WT, Thong MK, Mohd Khalid MKN, Hung LC, Mohamed Zainudin N, Ramlee A, Md Haniffa MA, Yakob Y, Ngu LH. Clinical, biochemical and genetic profiles of patients with mucopolysaccharidosis type IVA (Morquio A syndrome) in Malaysia: the first national natural history cohort study. Orphanet J Rare Dis 2019; 14:143. [PMID: 31200731 PMCID: PMC6570902 DOI: 10.1186/s13023-019-1105-6] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2019] [Accepted: 05/26/2019] [Indexed: 01/12/2023] Open
Abstract
BACKGROUND Mucopolysaccharidosis IVA (MPS IVA) is an autosomal recessive lysosomal storage disease due to N-acetylgalactosamine-6-sulfatase (GALNS) deficiency. It results in accumulation of the glycosaminoglycans, keratan sulfate and chondroitin-6-sulfate, leading to skeletal and other systemic impairments. Data on MPS IVA in Asian populations are scarce. METHODS This is a multicentre descriptive case series of 21 patients comprising all MPS IVA patients in Malaysia. Mutational analysis was performed by PCR and Sanger sequencing of the GALNS gene in 17 patients. RESULTS The patients (15 females and 6 males) had a mean age (± SD) of 15.5 (± 8.1) years. Mean age at symptom onset was 2.6 (± 2.1) years and at confirmed diagnosis was 6.9 (± 4.5) years. The study cohort included patients from all the main ethnic groups in Malaysia - 57% Malay, 29% Chinese and 14% Indian. Common presenting symptoms included pectus carinatum (57%) and genu valgum (43%). Eight patients (38%) had undergone surgery, most commonly knee surgeries (29%) and cervical spine decompression (24%). Patients had limited endurance with lower mean walking distances with increasing age. GALNS gene analysis identified 18 distinct mutations comprising 13 missense, three nonsense, one small deletion and one splice site mutation. Of these, eight were novel mutations (Tyr133Ser, Glu158Valfs*12, Gly168*, Gly168Val, Trp184*, Leu271Pro, Glu320Lys, Leu508Pro). Mutations in exons 1, 5 and 9 accounted for 51% of the mutant alleles identified. CONCLUSIONS All the MPS IVA patients in this study had clinical impairments. A better understanding of the natural history and the clinical and genetic spectrum of MPS IVA in this population may assist early diagnosis, improve management and permit timely genetic counselling and prenatal diagnosis.
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Affiliation(s)
- Huey Yin Leong
- Genetics Department, Hospital Kuala Lumpur, Ministry of Health Malaysia, Jalan Pahang, 50586 Kuala Lumpur, Malaysia
| | - Nor Azimah Abdul Azize
- Unit of Molecular Diagnostics & Protein, Institute for Medical Research, Ministry of Health Malaysia, Kuala Lumpur, Malaysia
| | - Hui Bein Chew
- Genetics Department, Hospital Kuala Lumpur, Ministry of Health Malaysia, Jalan Pahang, 50586 Kuala Lumpur, Malaysia
| | - Wee Teik Keng
- Genetics Department, Hospital Kuala Lumpur, Ministry of Health Malaysia, Jalan Pahang, 50586 Kuala Lumpur, Malaysia
| | - Meow Keong Thong
- Department of Paediatrics, Faculty of Medicine, University Malaya, Kuala Lumpur, Malaysia
| | - Mohd Khairul Nizam Mohd Khalid
- Unit of Molecular Diagnostics & Protein, Institute for Medical Research, Ministry of Health Malaysia, Kuala Lumpur, Malaysia
| | - Liang Choo Hung
- Paediatric Department, Hospital Kuala Lumpur, Ministry of Health Malaysia, Kuala Lumpur, Malaysia
| | - Norzila Mohamed Zainudin
- Paediatric Department, Hospital Kuala Lumpur, Ministry of Health Malaysia, Kuala Lumpur, Malaysia
| | - Azura Ramlee
- Ophthalmology Department, Hospital Selayang, Ministry of Health Malaysia, Selayang, Malaysia
| | - Muzhirah Aisha Md Haniffa
- Genetics Department, Hospital Kuala Lumpur, Ministry of Health Malaysia, Jalan Pahang, 50586 Kuala Lumpur, Malaysia
| | - Yusnita Yakob
- Unit of Molecular Diagnostics & Protein, Institute for Medical Research, Ministry of Health Malaysia, Kuala Lumpur, Malaysia
| | - Lock Hock Ngu
- Genetics Department, Hospital Kuala Lumpur, Ministry of Health Malaysia, Jalan Pahang, 50586 Kuala Lumpur, Malaysia
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49
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Empirical evaluation of variant calling accuracy using ultra-deep whole-genome sequencing data. Sci Rep 2019; 9:1784. [PMID: 30741997 PMCID: PMC6370902 DOI: 10.1038/s41598-018-38346-0] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2018] [Accepted: 12/19/2018] [Indexed: 11/09/2022] Open
Abstract
In the design of whole-genome sequencing (WGS) studies, sequencing depth is a crucial parameter to define variant calling accuracy and study cost, with no standard recommendations having been established. We empirically evaluated the variant calling accuracy of the WGS pipeline using ultra-deep WGS data (approximately 410×). We randomly sampled sequence reads and constructed a series of simulation WGS datasets with a variety of gradual depths (n = 54; from 0.05× to 410×). Next, we evaluated the genotype concordances of the WGS data with those in the SNP microarray data or the WGS data using all the sequence reads. In addition, we assessed the accuracy of HLA allele genotyping using the WGS data with multiple software tools (PHLAT, HLA-VBseq, HLA-HD, and SNP2HLA). The WGS data with higher depths showed higher concordance rates, and >13.7× depth achieved as high as >99% of concordance. Comparisons with the WGS data using all the sequence reads showed that SNVs achieved >95% of concordance at 17.6× depth, whereas indels showed only 60% concordance. For the accuracy of HLA allele genotyping using the WGS data, 13.7× depth showed sufficient accuracy while performance heterogeneity among the software tools was observed (the highest concordance of 96.9% was observed with HLA-HD). Improvement in HLA genotyping accuracy by further increasing the depths was limited. These results suggest a medium degree of the WGS depth setting (approximately 15×) to achieve both accurate SNV calling and cost-effectiveness, whereas relatively higher depths are required for accurate indel calling.
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Moon S, Kim YJ, Han S, Hwang MY, Shin DM, Park MY, Lu Y, Yoon K, Jang HM, Kim YK, Park TJ, Song DS, Park JK, Lee JE, Kim BJ. The Korea Biobank Array: Design and Identification of Coding Variants Associated with Blood Biochemical Traits. Sci Rep 2019; 9:1382. [PMID: 30718733 PMCID: PMC6361960 DOI: 10.1038/s41598-018-37832-9] [Citation(s) in RCA: 166] [Impact Index Per Article: 33.2] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2018] [Accepted: 12/11/2018] [Indexed: 02/05/2023] Open
Abstract
We introduce the design and implementation of a new array, the Korea Biobank Array (referred to as KoreanChip), optimized for the Korean population and demonstrate findings from GWAS of blood biochemical traits. KoreanChip comprised >833,000 markers including >247,000 rare-frequency or functional variants estimated from >2,500 sequencing data in Koreans. Of the 833 K markers, 208 K functional markers were directly genotyped. Particularly, >89 K markers were presented in East Asians. KoreanChip achieved higher imputation performance owing to the excellent genomic coverage of 95.38% for common and 73.65% for low-frequency variants. From GWAS (Genome-wide association study) using 6,949 individuals, 28 associations were successfully recapitulated. Moreover, 9 missense variants were newly identified, of which we identified new associations between a common population-specific missense variant, rs671 (p.Glu457Lys) of ALDH2, and two traits including aspartate aminotransferase (P = 5.20 × 10−13) and alanine aminotransferase (P = 4.98 × 10−8). Furthermore, two novel missense variants of GPT with rare frequency in East Asians but extreme rarity in other populations were associated with alanine aminotransferase (rs200088103; p.Arg133Trp, P = 2.02 × 10−9 and rs748547625; p.Arg143Cys, P = 1.41 × 10−6). These variants were successfully replicated in 6,000 individuals (P = 5.30 × 10−8 and P = 1.24 × 10−6). GWAS results suggest the promising utility of KoreanChip with a substantial number of damaging variants to identify new population-specific disease-associated rare/functional variants.
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Affiliation(s)
- Sanghoon Moon
- Division of Genome Research, Center for Genome Science, National Institute of Health, Chungcheongbuk-do, 28159, Republic of Korea
| | - Young Jin Kim
- Division of Genome Research, Center for Genome Science, National Institute of Health, Chungcheongbuk-do, 28159, Republic of Korea
| | - Sohee Han
- Division of Genome Research, Center for Genome Science, National Institute of Health, Chungcheongbuk-do, 28159, Republic of Korea
| | - Mi Yeong Hwang
- Division of Genome Research, Center for Genome Science, National Institute of Health, Chungcheongbuk-do, 28159, Republic of Korea
| | - Dong Mun Shin
- Division of Genome Research, Center for Genome Science, National Institute of Health, Chungcheongbuk-do, 28159, Republic of Korea
| | | | | | - Kyungheon Yoon
- Division of Genome Research, Center for Genome Science, National Institute of Health, Chungcheongbuk-do, 28159, Republic of Korea
| | - Hye-Mi Jang
- Division of Genome Research, Center for Genome Science, National Institute of Health, Chungcheongbuk-do, 28159, Republic of Korea
| | - Yun Kyoung Kim
- Division of Genome Research, Center for Genome Science, National Institute of Health, Chungcheongbuk-do, 28159, Republic of Korea
| | - Tae-Joon Park
- Division of Genome Research, Center for Genome Science, National Institute of Health, Chungcheongbuk-do, 28159, Republic of Korea
| | - Dae Sub Song
- Division of Epidemiology and Health Index, Center for Genome Science, National Institute of Health, Chungcheongbuk-do, 28159, Republic of Korea
| | - Jae Kyung Park
- Division of Epidemiology and Health Index, Center for Genome Science, National Institute of Health, Chungcheongbuk-do, 28159, Republic of Korea
| | - Jong-Eun Lee
- DNA link, Incorporated, Seoul, 03759, Republic of Korea
| | - Bong-Jo Kim
- Division of Genome Research, Center for Genome Science, National Institute of Health, Chungcheongbuk-do, 28159, Republic of Korea.
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