1
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Liang H, Sedillo JC, Schrodi SJ, Ikeda A. Structural variants in linkage disequilibrium with GWAS-significant SNPs. Heliyon 2024; 10:e32053. [PMID: 38882374 PMCID: PMC11177133 DOI: 10.1016/j.heliyon.2024.e32053] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2024] [Revised: 05/17/2024] [Accepted: 05/28/2024] [Indexed: 06/18/2024] Open
Abstract
With the recent expansion of structural variant identification in the human genome, understanding the role of these impactful variants in disease architecture is critically important. Currently, a large proportion of genome-wide-significant genome-wide association study (GWAS) single nucleotide polymorphisms (SNPs) are functionally unresolved, raising the possibility that some of these SNPs are associated with disease through linkage disequilibrium with causal structural variants. Hence, understanding the linkage disequilibrium between newly discovered structural variants and statistically significant SNPs may provide a resource for further investigation into disease-associated regions in the genome. Here we present a resource cataloging structural variant-significant SNP pairs in high linkage disequilibrium. The database is composed of (i) SNPs that have exhibited genome-wide significant association with traits, primarily disease phenotypes, (ii) newly released structural variants (SVs), and (iii) linkage disequilibrium values calculated from unphased data. All data files including those detailing SV and GWAS SNP associations and results of GWAS-SNP-SV pairs are available at the SV-SNP LD Database and can be accessed at 'https://github.com/hliang-SchrodiLab/SV_SNPs. Our analysis results represent a useful fine mapping tool for interrogating SVs in linkage disequilibrium with disease-associated SNPs. We anticipate that this resource may play an important role in subsequent studies which investigate incorporating disease causing SVs into disease risk prediction models.
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Affiliation(s)
- Hao Liang
- Department of Medical Genetics, University of Wisconsin-Madison, Madison, WI, USA
| | - Joni C Sedillo
- Department of Medical Genetics, University of Wisconsin-Madison, Madison, WI, USA
- Computation and Informatics in Biology and Medicine, University of Wisconsin-Madison, Madison, WI, USA
| | - Steven J Schrodi
- Department of Medical Genetics, University of Wisconsin-Madison, Madison, WI, USA
- Computation and Informatics in Biology and Medicine, University of Wisconsin-Madison, Madison, WI, USA
| | - Akihiro Ikeda
- Department of Medical Genetics, University of Wisconsin-Madison, Madison, WI, USA
- McPherson Eye Research Institute, University of Wisconsin-Madison, Madison, WI, USA
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2
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Hujoel MLA, Handsaker RE, Sherman MA, Kamitaki N, Barton AR, Mukamel RE, Terao C, McCarroll SA, Loh PR. Protein-altering variants at copy number-variable regions influence diverse human phenotypes. Nat Genet 2024; 56:569-578. [PMID: 38548989 PMCID: PMC11018521 DOI: 10.1038/s41588-024-01684-z] [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: 06/23/2023] [Accepted: 02/08/2024] [Indexed: 04/09/2024]
Abstract
Copy number variants (CNVs) are among the largest genetic variants, yet CNVs have not been effectively ascertained in most genetic association studies. Here we ascertained protein-altering CNVs from UK Biobank whole-exome sequencing data (n = 468,570) using haplotype-informed methods capable of detecting subexonic CNVs and variation within segmental duplications. Incorporating CNVs into analyses of rare variants predicted to cause gene loss of function (LOF) identified 100 associations of predicted LOF variants with 41 quantitative traits. A low-frequency partial deletion of RGL3 exon 6 conferred one of the strongest protective effects of gene LOF on hypertension risk (odds ratio = 0.86 (0.82-0.90)). Protein-coding variation in rapidly evolving gene families within segmental duplications-previously invisible to most analysis methods-generated some of the human genome's largest contributions to variation in type 2 diabetes risk, chronotype and blood cell traits. These results illustrate the potential for new genetic insights from genomic variation that has escaped large-scale analysis to date.
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Affiliation(s)
- Margaux L A Hujoel
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
- Center for Data Sciences, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
| | - Robert E Handsaker
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Boston, MA, USA
- Department of Genetics, Harvard Medical School, Boston, MA, USA
| | - Maxwell A Sherman
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Center for Data Sciences, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA
- Serinus Biosciences Inc., New York, NY, USA
| | - Nolan Kamitaki
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Center for Data Sciences, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Alison R Barton
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Center for Data Sciences, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Department of Human Evolutionary Biology, Harvard University, Cambridge, MA, USA
| | - Ronen E Mukamel
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Center for Data Sciences, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, 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
- Department of Applied Genetics, School of Pharmaceutical Sciences, University of Shizuoka, Shizuoka, Japan
| | - Steven A McCarroll
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Boston, MA, USA
- Department of Genetics, Harvard Medical School, Boston, MA, USA
| | - Po-Ru Loh
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
- Center for Data Sciences, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
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3
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Auwerx C, Jõeloo M, Sadler MC, Tesio N, Ojavee S, Clark CJ, Mägi R, Reymond A, Kutalik Z. Rare copy-number variants as modulators of common disease susceptibility. Genome Med 2024; 16:5. [PMID: 38185688 PMCID: PMC10773105 DOI: 10.1186/s13073-023-01265-5] [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: 07/17/2023] [Accepted: 11/27/2023] [Indexed: 01/09/2024] Open
Abstract
BACKGROUND Copy-number variations (CNVs) have been associated with rare and debilitating genomic disorders (GDs) but their impact on health later in life in the general population remains poorly described. METHODS Assessing four modes of CNV action, we performed genome-wide association scans (GWASs) between the copy-number of CNV-proxy probes and 60 curated ICD-10 based clinical diagnoses in 331,522 unrelated white British UK Biobank (UKBB) participants with replication in the Estonian Biobank. RESULTS We identified 73 signals involving 40 diseases, all of which indicating that CNVs increased disease risk and caused earlier onset. We estimated that 16% of these associations are indirect, acting by increasing body mass index (BMI). Signals mapped to 45 unique, non-overlapping regions, nine of which being linked to known GDs. Number and identity of genes affected by CNVs modulated their pathogenicity, with many associations being supported by colocalization with both common and rare single-nucleotide variant association signals. Dissection of association signals provided insights into the epidemiology of known gene-disease pairs (e.g., deletions in BRCA1 and LDLR increased risk for ovarian cancer and ischemic heart disease, respectively), clarified dosage mechanisms of action (e.g., both increased and decreased dosage of 17q12 impacted renal health), and identified putative causal genes (e.g., ABCC6 for kidney stones). Characterization of the pleiotropic pathological consequences of recurrent CNVs at 15q13, 16p13.11, 16p12.2, and 22q11.2 in adulthood indicated variable expressivity of these regions and the involvement of multiple genes. Finally, we show that while the total burden of rare CNVs-and especially deletions-strongly associated with disease risk, it only accounted for ~ 0.02% of the UKBB disease burden. These associations are mainly driven by CNVs at known GD CNV regions, whose pleiotropic effect on common diseases was broader than anticipated by our CNV-GWAS. CONCLUSIONS Our results shed light on the prominent role of rare CNVs in determining common disease susceptibility within the general population and provide actionable insights for anticipating later-onset comorbidities in carriers of recurrent CNVs.
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Affiliation(s)
- Chiara Auwerx
- Center for Integrative Genomics, University of Lausanne, Genopode building, 1015, Lausanne, Switzerland.
- Department of Computational Biology, University of Lausanne, Genopode building, 1015, Lausanne, Switzerland.
- Swiss Institute of Bioinformatics, 1015, Lausanne, Switzerland.
- University Center for Primary Care and Public Health, 1005, Lausanne, Switzerland.
| | - Maarja Jõeloo
- Institute of Molecular and Cell Biology, University of Tartu, 51010, Tartu, Estonia
- Estonian Genome Centre, Institute of Genomics, University of Tartu, 51010, Tartu, Estonia
| | - Marie C Sadler
- Department of Computational Biology, University of Lausanne, Genopode building, 1015, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, 1015, Lausanne, Switzerland
- University Center for Primary Care and Public Health, 1005, Lausanne, Switzerland
| | - Nicolò Tesio
- Center for Integrative Genomics, University of Lausanne, Genopode building, 1015, Lausanne, Switzerland
| | - Sven Ojavee
- Department of Computational Biology, University of Lausanne, Genopode building, 1015, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, 1015, Lausanne, Switzerland
| | - Charlie J Clark
- Center for Integrative Genomics, University of Lausanne, Genopode building, 1015, Lausanne, Switzerland
| | - Reedik Mägi
- Estonian Genome Centre, Institute of Genomics, University of Tartu, 51010, Tartu, Estonia
| | - Alexandre Reymond
- Center for Integrative Genomics, University of Lausanne, Genopode building, 1015, Lausanne, Switzerland.
| | - Zoltán Kutalik
- Department of Computational Biology, University of Lausanne, Genopode building, 1015, Lausanne, Switzerland.
- Swiss Institute of Bioinformatics, 1015, Lausanne, Switzerland.
- University Center for Primary Care and Public Health, 1005, Lausanne, Switzerland.
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4
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Lee S, Kim J, Ohn JH. Exploring quantitative traits-associated copy number deletions through reanalysis of UK10K consortium whole genome sequencing cohorts. BMC Genomics 2023; 24:787. [PMID: 38110883 PMCID: PMC10729411 DOI: 10.1186/s12864-023-09903-3] [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: 08/01/2023] [Accepted: 12/13/2023] [Indexed: 12/20/2023] Open
Abstract
OBJECTIVES We performed comprehensive association analyses of common high-confidence gnomAD-reported copy number deletions (CNDs) with 60 quantitative traits from UK10K consortium WGS data. METHODS The study made use of data generated by the UK10K Consortium. UK10K consortium WGS data consist of TwinsUK (n = 1754, middle-aged females) and ALSPAC (n = 1867, birth to adolescence) cohorts. UK10K consortium called 18,739 CNDs (hg19) with GenomeSTRiP software. After filtering out variants with minor allele frequency < 0.05 or HWE P < 1.0 × 10- 6, 1222 (TwinsUK) and 1211 (ALSPAC) CNDs remained for association analyses with 60 normalized quantitative traits. RESULTS We identified 23 genome-wide significant associations at 13 loci, among which 2 associations reached experiment-wide significance. We found that two common deletions in chromosome 4, located between WDR1 and ZNF518B (23.3 kb, dbVar ID:nssv15888957, 4:10211262-10,234,569 and 9.8 kb, dbVar ID:nssv15888975, 4:10392422-10,402,191), were associated with uric acid levels (P = 5.23 × 10- 11 and 2.29 × 10- 8, respectively). We also discovered a novel deletion spanning chromosome 18 (823 bp, dbVar ID: nssv15841628, 8:74347187-74,348,010) associated with low HDL cholesterol levels (P = 4.15 × 10- 7). Additionally, we observed two red blood cell traits-associated loci with genome-wide significance, a 13.2 kb deletion in 7q22.1 (nssv15922542) and a 3.7 kb deletion in 12q24.12 (nssv15813226), both of which were located in regions previously reported to be associated with red blood cell traits. Two deletions in 11q11 (nssv15803200 and nssv15802240), where clusters of multiple olfactory receptor genes exist, and a deletion (nssv15929560) upstream to DOCK5 were associated with childhood obesity. Finally, when defining Trait-Associated copy number Deletions (TADs) as CNDs with phenotype associations at sub-threshold significance (P < 10- 3), we identified 157 (97.5%) out of 161 TADs in non-coding regions, with a mean size of 4 kb (range: 209 - 47,942 bp). CONCLUSION We conducted a reanalysis of the UK10K Whole Genome Sequencing cohort, which led to the identification of multiple high confidence copy number deletions associated with quantitative traits. These deletions have standard dbVar IDs and replicate previous findings, as well as reveal novel loci that require further replication studies.
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Affiliation(s)
- Sejoon Lee
- Precision Medicine Center, Future Innovation Research Division, Seoul National University Bundang Hospital, 173-82, Gumi-ro, Bundang-gu, Seongnam, Gyeonggi-do, 13620, South Korea
- Department of Pathology, Seoul National University Bundang Hospital, 173-82, Gumi-ro, Bundang-gu, Seongnam, Gyeonggi-do, 13620, South Korea
| | - Jinho Kim
- Precision Medicine Center, Future Innovation Research Division, Seoul National University Bundang Hospital, 173-82, Gumi-ro, Bundang-gu, Seongnam, Gyeonggi-do, 13620, South Korea
- Department of Laboratory Medicine, Seoul National University Bundang Hospital, 173-82, Gumi-ro, Bundang-gu, Seongnam, Gyeonggi-do, 13620, South Korea
| | - Jung Hun Ohn
- Precision Medicine Center, Future Innovation Research Division, Seoul National University Bundang Hospital, 173-82, Gumi-ro, Bundang-gu, Seongnam, Gyeonggi-do, 13620, South Korea.
- Department of Internal Medicine, Seoul National University Bundang Hospital, 173-82, Gumi-ro, Bundang-gu, Seongnam, Gyeonggi-do, 13620, South Korea.
- Department of Internal Medicine, College of Medicine, Seoul National University, 103, Daehak-ro, Jongno-gu, Seoul, 03080, South Korea.
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5
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Hujoel ML, Handsaker RE, Sherman MA, Kamitaki N, Barton AR, Mukamel RE, Terao C, McCarroll SA, Loh PR. Hidden protein-altering variants influence diverse human phenotypes. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.06.07.544066. [PMID: 37333244 PMCID: PMC10274781 DOI: 10.1101/2023.06.07.544066] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/20/2023]
Abstract
Structural variants (SVs) comprise the largest genetic variants, altering from 50 base pairs to megabases of DNA. However, SVs have not been effectively ascertained in most genetic association studies, leaving a key gap in our understanding of human complex trait genetics. We ascertained protein-altering SVs from UK Biobank whole-exome sequencing data (n=468,570) using haplotype-informed methods capable of detecting sub-exonic SVs and variation within segmental duplications. Incorporating SVs into analyses of rare variants predicted to cause gene loss-of-function (pLoF) identified 100 associations of pLoF variants with 41 quantitative traits. A low-frequency partial deletion of RGL3 exon 6 appeared to confer one of the strongest protective effects of gene LoF on hypertension risk (OR = 0.86 [0.82-0.90]). Protein-coding variation in rapidly-evolving gene families within segmental duplications-previously invisible to most analysis methods-appeared to generate some of the human genome's largest contributions to variation in type 2 diabetes risk, chronotype, and blood cell traits. These results illustrate the potential for new genetic insights from genomic variation that has escaped large-scale analysis to date.
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Affiliation(s)
- Margaux L.A. Hujoel
- Division of Genetics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
- Center for Data Sciences, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Robert E. Handsaker
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard University, Boston, MA, USA
- Department of Genetics, Harvard Medical School, Boston, MA, USA
| | - Maxwell A. Sherman
- Division of Genetics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
- Center for Data Sciences, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Nolan Kamitaki
- Division of Genetics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
- Center for Data Sciences, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Alison R. Barton
- Division of Genetics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
- Center for Data Sciences, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Ronen E. Mukamel
- Division of Genetics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
- Center for Data Sciences, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, 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
- Department of Applied Genetics, School of Pharmaceutical Sciences, University of Shizuoka, Shizuoka, Japan
| | - Steven A. McCarroll
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard University, Boston, MA, USA
- Department of Genetics, Harvard Medical School, Boston, MA, USA
| | - Po-Ru Loh
- Division of Genetics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
- Center for Data Sciences, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
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6
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Bezdvornykh I, Cherkasov N, Kanapin A, Samsonova A. A collection of read depth profiles at structural variant breakpoints. Sci Data 2023; 10:186. [PMID: 37024526 PMCID: PMC10079824 DOI: 10.1038/s41597-023-02076-4] [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: 07/18/2022] [Accepted: 03/16/2023] [Indexed: 04/08/2023] Open
Abstract
SWaveform, a newly created open genome-wide resource for read depth signal in the vicinity of structural variant (SV) breakpoints, aims to boost development of computational tools and algorithms for discovery of genomic rearrangement events from sequencing data. SVs are a dominant force shaping genomes and substantially contributing to genetic diversity. Still, there are challenges in reliable and efficient genotyping of SVs from whole genome sequencing data, thus delaying translation into clinical applications and wasting valuable resources. SWaveform includes a database containing ~7 M of read depth profiles at SV breakpoints extracted from 911 sequencing samples generated by the Human Genome Diversity Project, generalised patterns of the signal at breakpoints, an interface for navigation and download, as well as a toolbox for local deployment with user's data. The dataset can be of immense value to bioinformatics and engineering communities as it empowers smooth application of intelligent signal processing and machine learning techniques for discovery of genomic rearrangement events and thus opens the floodgates for development of innovative algorithms and software.
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Affiliation(s)
- Igor Bezdvornykh
- Institute of Translational Biomedicine, Saint Petersburg State University, Saint Petersburg, 199004, Russia
| | - Nikolay Cherkasov
- Institute of Translational Biomedicine, Saint Petersburg State University, Saint Petersburg, 199004, Russia
| | - Alexander Kanapin
- Institute of Translational Biomedicine, Saint Petersburg State University, Saint Petersburg, 199004, Russia
| | - Anastasia Samsonova
- Institute of Translational Biomedicine, Saint Petersburg State University, Saint Petersburg, 199004, Russia.
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7
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Nguyen TV, Vander Jagt CJ, Wang J, Daetwyler HD, Xiang R, Goddard ME, Nguyen LT, Ross EM, Hayes BJ, Chamberlain AJ, MacLeod IM. In it for the long run: perspectives on exploiting long-read sequencing in livestock for population scale studies of structural variants. Genet Sel Evol 2023; 55:9. [PMID: 36721111 PMCID: PMC9887926 DOI: 10.1186/s12711-023-00783-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Accepted: 01/23/2023] [Indexed: 02/02/2023] Open
Abstract
Studies have demonstrated that structural variants (SV) play a substantial role in the evolution of species and have an impact on Mendelian traits in the genome. However, unlike small variants (< 50 bp), it has been challenging to accurately identify and genotype SV at the population scale using short-read sequencing. Long-read sequencing technologies are becoming competitively priced and can address several of the disadvantages of short-read sequencing for the discovery and genotyping of SV. In livestock species, analysis of SV at the population scale still faces challenges due to the lack of resources, high costs, technological barriers, and computational limitations. In this review, we summarize recent progress in the characterization of SV in the major livestock species, the obstacles that still need to be overcome, as well as the future directions in this growing field. It seems timely that research communities pool resources to build global population-scale long-read sequencing consortiums for the major livestock species for which the application of genomic tools has become cost-effective.
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Affiliation(s)
- Tuan V. Nguyen
- grid.452283.a0000 0004 0407 2669Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC 3083 Australia
| | - Christy J. Vander Jagt
- grid.452283.a0000 0004 0407 2669Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC 3083 Australia
| | - Jianghui Wang
- grid.452283.a0000 0004 0407 2669Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC 3083 Australia
| | - Hans D. Daetwyler
- grid.452283.a0000 0004 0407 2669Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC 3083 Australia ,grid.1018.80000 0001 2342 0938School of Applied Systems Biology, La Trobe University, Bundoora, VIC 3083 Australia
| | - Ruidong Xiang
- grid.452283.a0000 0004 0407 2669Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC 3083 Australia ,grid.1008.90000 0001 2179 088XFaculty of Veterinary & Agricultural Science, The University of Melbourne, Parkville, VIC 3052 Australia
| | - Michael E. Goddard
- grid.452283.a0000 0004 0407 2669Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC 3083 Australia ,grid.1008.90000 0001 2179 088XFaculty of Veterinary & Agricultural Science, The University of Melbourne, Parkville, VIC 3052 Australia
| | - Loan T. Nguyen
- grid.1003.20000 0000 9320 7537Queensland Alliance for Agriculture and Food Innovation, University of Queensland, St Lucia, QLD 4072 Australia
| | - Elizabeth M. Ross
- grid.1003.20000 0000 9320 7537Queensland Alliance for Agriculture and Food Innovation, University of Queensland, St Lucia, QLD 4072 Australia
| | - Ben J. Hayes
- grid.1003.20000 0000 9320 7537Queensland Alliance for Agriculture and Food Innovation, University of Queensland, St Lucia, QLD 4072 Australia
| | - Amanda J. Chamberlain
- grid.452283.a0000 0004 0407 2669Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC 3083 Australia ,grid.1018.80000 0001 2342 0938School of Applied Systems Biology, La Trobe University, Bundoora, VIC 3083 Australia
| | - Iona M. MacLeod
- grid.452283.a0000 0004 0407 2669Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC 3083 Australia
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8
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Ramirez-Diaz J, Cenadelli S, Bornaghi V, Bongioni G, Montedoro SM, Achilli A, Capelli C, Rincon JC, Milanesi M, Passamonti MM, Colli L, Barbato M, Williams JL, Marsan PA. Identification of genomic regions associated with total and progressive sperm motility in Italian Holstein bulls. J Dairy Sci 2023; 106:407-420. [PMID: 36400619 DOI: 10.3168/jds.2021-21700] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Accepted: 08/10/2022] [Indexed: 11/17/2022]
Abstract
Sperm motility is directly related to the ability of sperm to move through the female reproductive tract to reach the ovum. Sperm motility is a complex trait that is influenced by environmental and genetic factors and is associated with male fertility, oocyte penetration rate, and reproductive success of cattle. In this study we carried out a GWAS in Italian Holstein bulls to identify candidate regions and genes associated with variations in progressive and total motility (PM and TM, respectively). After quality control, the final data set consisted of 5,960 records from 949 bulls having semen collected in 10 artificial insemination stations and genotyped at 412,737 SNPs (call rate >95%; minor allele frequency >5%). (Co)variance components were estimated using single trait mixed models, and associations between SNPs and phenotypes were assessed using a genomic BLUP approach. Ten windows that explained the greatest percentage of genetic variance were located on Bos taurus autosomes 1, 2, 4, 6, 7, 23, and 26 for TM and Bos taurus autosomes 1, 2, 4, 6, 8, 16, 23, and 26 for PM. A total of 150 genes for TM and 72 genes for PM were identified within these genomic regions. Gene Ontology enrichment analyses identified significant Gene Ontology terms involved with energy homeostasis, membrane functions, sperm-egg interactions, protection against oxidative stress, olfactory receptors, and immune system. There was significant enrichment of quantitative trait loci for fertility, calving ease, immune response, feed intake, and carcass weight within the candidate windows. These results contribute to understanding the architecture of the genetic control of sperm motility and may aid in the development of strategies to identify subfertile bulls and improve reproductive success.
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Affiliation(s)
- J Ramirez-Diaz
- Department of Animal Sciences, Food and Nutrition (DIANA), Università Cattolica del Sacro Cuore, Piacenza, Italy 29122; Institute of Agricultural Biology and Biotechnology (IBBA), Consiglio Nazionale di Ricerca, Milano, Italy.
| | - S Cenadelli
- Institute Lazzaro Spallanzani, Rivolta d'Adda (CR), Cremona, Italy
| | - V Bornaghi
- Institute Lazzaro Spallanzani, Rivolta d'Adda (CR), Cremona, Italy
| | - G Bongioni
- Institute Lazzaro Spallanzani, Rivolta d'Adda (CR), Cremona, Italy
| | - S M Montedoro
- Institute Lazzaro Spallanzani, Rivolta d'Adda (CR), Cremona, Italy
| | - A Achilli
- Department of Biology and Biotechnology, Università degli Studi di Pavia, Pavia, Italy
| | - C Capelli
- Department of Chemical, Life and Environmental Sustainability Sciences, Università degli Studi di Parma, Parma, Italy
| | - J C Rincon
- Department of Animal Science, Universidad Nacional de Colombia, Palmira, Valle del Cauca, Colombia
| | - M Milanesi
- Department for Innovation in Biological, Agri-food and Forestry Systems (DIBAF), Università degli Studi della Tuscia, Viterbo, Italy
| | - M M Passamonti
- Department of Animal Sciences, Food and Nutrition (DIANA), Università Cattolica del Sacro Cuore, Piacenza, Italy 29122
| | - L Colli
- Department of Animal Sciences, Food and Nutrition (DIANA), Università Cattolica del Sacro Cuore, Piacenza, Italy 29122
| | - M Barbato
- Department of Animal Sciences, Food and Nutrition (DIANA), Università Cattolica del Sacro Cuore, Piacenza, Italy 29122
| | - J L Williams
- Department of Animal Sciences, Food and Nutrition (DIANA), Università Cattolica del Sacro Cuore, Piacenza, Italy 29122
| | - P Ajmone Marsan
- Department of Animal Sciences, Food and Nutrition (DIANA), Università Cattolica del Sacro Cuore, Piacenza, Italy 29122
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9
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Wheeler MM, Stilp AM, Rao S, Halldórsson BV, Beyter D, Wen J, Mihkaylova AV, McHugh CP, Lane J, Jiang MZ, Raffield LM, Jun G, Sedlazeck FJ, Metcalf G, Yao Y, Bis JB, Chami N, de Vries PS, Desai P, Floyd JS, Gao Y, Kammers K, Kim W, Moon JY, Ratan A, Yanek LR, Almasy L, Becker LC, Blangero J, Cho MH, Curran JE, Fornage M, Kaplan RC, Lewis JP, Loos RJF, Mitchell BD, Morrison AC, Preuss M, Psaty BM, Rich SS, Rotter JI, Tang H, Tracy RP, Boerwinkle E, Abecasis GR, Blackwell TW, Smith AV, Johnson AD, Mathias RA, Nickerson DA, Conomos MP, Li Y, Þorsteinsdóttir U, Magnússon MK, Stefansson K, Pankratz ND, Bauer DE, Auer PL, Reiner AP. Whole genome sequencing identifies structural variants contributing to hematologic traits in the NHLBI TOPMed program. Nat Commun 2022; 13:7592. [PMID: 36481753 PMCID: PMC9732337 DOI: 10.1038/s41467-022-35354-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2021] [Accepted: 11/29/2022] [Indexed: 12/13/2022] Open
Abstract
Genome-wide association studies have identified thousands of single nucleotide variants and small indels that contribute to variation in hematologic traits. While structural variants are known to cause rare blood or hematopoietic disorders, the genome-wide contribution of structural variants to quantitative blood cell trait variation is unknown. Here we utilized whole genome sequencing data in ancestrally diverse participants of the NHLBI Trans Omics for Precision Medicine program (N = 50,675) to detect structural variants associated with hematologic traits. Using single variant tests, we assessed the association of common and rare structural variants with red cell-, white cell-, and platelet-related quantitative traits and observed 21 independent signals (12 common and 9 rare) reaching genome-wide significance. The majority of these associations (N = 18) replicated in independent datasets. In genome-editing experiments, we provide evidence that a deletion associated with lower monocyte counts leads to disruption of an S1PR3 monocyte enhancer and decreased S1PR3 expression.
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Affiliation(s)
- Marsha M Wheeler
- Department of Genome Sciences, University of Washington, Seattle, WA, 98105, USA
| | - Adrienne M Stilp
- Department of Biostatistics, University of Washington, Seattle, WA, 98105, USA
| | - Shuquan Rao
- Division of Hematology/Oncology, Boston Children's Hospital, Boston, MA, 02115, USA
- Department of Pediatric Oncology, Dana-Farber Cancer Institute, Boston, MA, 02115, USA
- Harvard Stem Cell Institute, Boston, MA, 02138, USA
- Broad Institute, Cambridge, MA, 02142, USA
- Department of Pediatrics, Harvard Medical School, Boston, MA, 02115, USA
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, 300020, China
| | - Bjarni V Halldórsson
- deCODE genetics/Amgen Inc., Reykjavik, Iceland
- School of Technology, Reykjavik University, Reykjavík, Iceland
| | | | - Jia Wen
- Departments of Biostatistics, Genetics, Computer Science, Applied Physical Sciences, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Anna V Mihkaylova
- Department of Biostatistics, University of Washington, Seattle, WA, 98105, USA
| | - Caitlin P McHugh
- Department of Biostatistics, University of Washington, Seattle, WA, 98105, USA
| | - John Lane
- Department of Laboratory Medicine and Pathology, University of Minnesota Medical School, Minneapolis, MN, 55455, USA
| | - Min-Zhi Jiang
- Departments of Biostatistics, Genetics, Computer Science, Applied Physical Sciences, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Laura M Raffield
- Department of Genetics, University of North Carolina, Chapel Hill, NC, 27599, USA
| | - Goo Jun
- Human Genetics Center, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA
| | - Fritz J Sedlazeck
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA
| | - Ginger Metcalf
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA
| | - Yao Yao
- Division of Hematology/Oncology, Boston Children's Hospital, Boston, MA, 02115, USA
- Department of Pediatric Oncology, Dana-Farber Cancer Institute, Boston, MA, 02115, USA
- Harvard Stem Cell Institute, Boston, MA, 02138, USA
- Broad Institute, Cambridge, MA, 02142, USA
- Department of Pediatrics, Harvard Medical School, Boston, MA, 02115, USA
| | - Joshua B Bis
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, 98101, USA
| | - Nathalie Chami
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Paul S de Vries
- Human Genetics Center, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA
- Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA
| | - Pinkal Desai
- Division of Hematology and Oncology, Weill Cornell Medical College, New York, NY, 10065, USA
| | - James S Floyd
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, 98101, USA
| | - Yan Gao
- Jackson Heart Study, Department of Medicine, University of Mississippi, Jackson, MS, 39216, USA
| | - Kai Kammers
- GeneSTAR Research Program, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, 21287, USA
| | - Wonji Kim
- Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, MA, 2115, USA
| | - Jee-Young Moon
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, 10461, USA
| | - Aakrosh Ratan
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, 22908, USA
| | - Lisa R Yanek
- GeneSTAR Research Program, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, 21287, USA
| | - Laura Almasy
- Children's Hospital of Philadelphia and University of Pennsylvania School of Medicine, Philadelphia, PA, 19104, USA
| | - Lewis C Becker
- GeneSTAR Research Program, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, 21287, USA
| | - John Blangero
- Department of Human Genetics and South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley School of Medicine, Brownsville, TX, 78520, USA
| | - Michael H Cho
- Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, MA, 2115, USA
| | - Joanne E Curran
- Department of Human Genetics and South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley School of Medicine, Brownsville, TX, 78520, USA
| | - Myriam Fornage
- Brown Foundation Institute of Molecular Medicine, McGovern Medical School, University of Texas Health Science Center at Houston, Houston, TX, 77030, USA
| | - Robert C Kaplan
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, 10461, USA
| | - Joshua P Lewis
- Department of Medicine, Division of Endocrinology, Diabetes, and Nutrition, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Ruth J F Loos
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- The Mindich Child Health and Development Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Braxton D Mitchell
- Department of Medicine, Division of Endocrinology, Diabetes, and Nutrition, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Alanna C Morrison
- Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA
| | - Michael Preuss
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Bruce M Psaty
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, 98101, USA
| | - Stephen S Rich
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, 22908, USA
| | - Jerome I Rotter
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, 90502, USA
| | - Hua Tang
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, 94305, USA
| | - Russell P Tracy
- Departments of Pathology & Laboratory Medicine and Biochemistry, Larner College of Medicine at the University of Vermont, Colchester, VT, 5446, USA
| | - Eric Boerwinkle
- Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA
| | - Goncalo R Abecasis
- TOPMed Informatics Research Center, University of Michigan, Department of Biostatistics, Ann Arbor, MI, 48109, USA
| | - Thomas W Blackwell
- TOPMed Informatics Research Center, University of Michigan, Department of Biostatistics, Ann Arbor, MI, 48109, USA
| | - Albert V Smith
- TOPMed Informatics Research Center, University of Michigan, Department of Biostatistics, Ann Arbor, MI, 48109, USA
| | - Andrew D Johnson
- Population Sciences Branch, Division of Intramural Research, National Heart, Lung and Blood Institute, Framingham, MA, 1702, USA
| | - Rasika A Mathias
- GeneSTAR Research Program, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, 21287, USA
| | - Deborah A Nickerson
- Department of Genome Sciences, University of Washington, Seattle, WA, 98105, USA
| | - Matthew P Conomos
- Department of Biostatistics, University of Washington, Seattle, WA, 98105, USA
| | - Yun Li
- Departments of Biostatistics, Genetics, Computer Science, Applied Physical Sciences, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Unnur Þorsteinsdóttir
- deCODE genetics/Amgen Inc., Reykjavik, Iceland
- Faculty of Medicine, University of Iceland, 101, Reykjavik, Iceland
| | - Magnús K Magnússon
- deCODE genetics/Amgen Inc., Reykjavik, Iceland
- Faculty of Medicine, University of Iceland, 101, Reykjavik, Iceland
| | - Kari Stefansson
- deCODE genetics/Amgen Inc., Reykjavik, Iceland
- Faculty of Medicine, University of Iceland, 101, Reykjavik, Iceland
| | - Nathan D Pankratz
- Department of Laboratory Medicine and Pathology, University of Minnesota Medical School, Minneapolis, MN, 55455, USA
| | - Daniel E Bauer
- Division of Hematology/Oncology, Boston Children's Hospital, Boston, MA, 02115, USA
- Department of Pediatric Oncology, Dana-Farber Cancer Institute, Boston, MA, 02115, USA
- Harvard Stem Cell Institute, Boston, MA, 02138, USA
- Broad Institute, Cambridge, MA, 02142, USA
- Department of Pediatrics, Harvard Medical School, Boston, MA, 02115, USA
| | - Paul L Auer
- Division of Biostatistics, Institute for Health and Equity, and Cancer Center, Medical College of Wisconsin, Milwaukee, WI, 53226, USA.
| | - Alex P Reiner
- Department of Epidemiology, University of Washington, Seattle, WA, 98105, USA.
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10
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Hujoel MLA, Sherman MA, Barton AR, Mukamel RE, Sankaran VG, Terao C, Loh PR. Influences of rare copy-number variation on human complex traits. Cell 2022; 185:4233-4248.e27. [PMID: 36306736 PMCID: PMC9800003 DOI: 10.1016/j.cell.2022.09.028] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2021] [Revised: 07/22/2022] [Accepted: 09/19/2022] [Indexed: 11/06/2022]
Abstract
The human genome contains hundreds of thousands of regions harboring copy-number variants (CNV). However, the phenotypic effects of most such polymorphisms are unknown because only larger CNVs have been ascertainable from SNP-array data generated by large biobanks. We developed a computational approach leveraging haplotype sharing in biobank cohorts to more sensitively detect CNVs. Applied to UK Biobank, this approach accounted for approximately half of all rare gene inactivation events produced by genomic structural variation. This CNV call set enabled a detailed analysis of associations between CNVs and 56 quantitative traits, identifying 269 independent associations (p < 5 × 10-8) likely to be causally driven by CNVs. Putative target genes were identifiable for nearly half of the loci, enabling insights into dosage sensitivity of these genes and uncovering several gene-trait relationships. These results demonstrate the ability of haplotype-informed analysis to provide insights into the genetic basis of human complex traits.
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Affiliation(s)
- Margaux L A Hujoel
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA; Center for Data Sciences, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA; Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
| | - Maxwell A Sherman
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA; Center for Data Sciences, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA; Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA; Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Alison R Barton
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA; Center for Data Sciences, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA; Bioinformatics and Integrative Genomics Program, Harvard Medical School, Boston, MA, USA
| | - Ronen E Mukamel
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA; Center for Data Sciences, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA; Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Vijay G Sankaran
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA; Division of Hematology/Oncology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA; Department of Pediatric Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, 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; Department of Applied Genetics, School of Pharmaceutical Sciences, University of Shizuoka, Shizuoka, Japan
| | - Po-Ru Loh
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA; Center for Data Sciences, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA; Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
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11
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Wang Y, Ling Y, Gong J, Zhao X, Zhou H, Xie B, Lou H, Zhuang X, Jin L, Fan S, Zhang G, Xu S. PGG.SV: a whole-genome-sequencing-based structural variant resource and data analysis platform. Nucleic Acids Res 2022; 51:D1109-D1116. [PMID: 36243989 PMCID: PMC9825616 DOI: 10.1093/nar/gkac905] [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: 08/14/2022] [Revised: 09/21/2022] [Accepted: 10/04/2022] [Indexed: 01/30/2023] Open
Abstract
Structural variations (SVs) play important roles in human evolution and diseases, but there is a lack of data resources concerning representative samples, especially for East Asians. Taking advantage of both next-generation sequencing and third-generation sequencing data at the whole-genome level, we developed the database PGG.SV to provide a practical platform for both regionally and globally representative structural variants. In its current version, PGG.SV archives 584 277 SVs obtained from whole-genome sequencing data of 6048 samples, including 1030 long-read sequencing genomes representing 177 global populations. PGG.SV provides (i) high-quality SVs with fine-scale and precise genomic locations in both GRCh37 and GRCh38, covering underrepresented SVs in existing sequencing and microarray data; (ii) hierarchical estimation of SV prevalence in geographical populations; (iii) informative annotations of SV-related genes, potential functions and clinical effects; (iv) an analysis platform to facilitate SV-based case-control association studies and (v) various visualization tools for understanding the SV structures in the human genome. Taken together, PGG.SV provides a user-friendly online interface, easy-to-use analysis tools and a detailed presentation of results. PGG.SV is freely accessible via https://www.biosino.org/pggsv.
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Affiliation(s)
| | | | | | - Xiaohan Zhao
- State Key Laboratory of Genetic Engineering, Center for Evolutionary Biology, Collaborative Innovation Center of Genetics and Development, School of Life Sciences, Fudan University, Shanghai 200438, China,Human Phenome Institute, Zhangjiang Fudan International Innovation Center, and Ministry of Education Key Laboratory of Contemporary Anthropology, Fudan University, Shanghai 201203, China
| | - Hanwen Zhou
- Key Laboratory of Computational Biology, National Genomics Data Center & Bio-Med Big Data Center, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Bo Xie
- Key Laboratory of Computational Biology, National Genomics Data Center & Bio-Med Big Data Center, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Haiyi Lou
- State Key Laboratory of Genetic Engineering, Center for Evolutionary Biology, Collaborative Innovation Center of Genetics and Development, School of Life Sciences, Fudan University, Shanghai 200438, China
| | - Xinhao Zhuang
- Key Laboratory of Computational Biology, National Genomics Data Center & Bio-Med Big Data Center, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Li Jin
- State Key Laboratory of Genetic Engineering, Center for Evolutionary Biology, Collaborative Innovation Center of Genetics and Development, School of Life Sciences, Fudan University, Shanghai 200438, China,Human Phenome Institute, Zhangjiang Fudan International Innovation Center, and Ministry of Education Key Laboratory of Contemporary Anthropology, Fudan University, Shanghai 201203, China
| | | | - Shaohua Fan
- Correspondence may also be addressed to Shaohua Fan.
| | - Guoqing Zhang
- Correspondence may also be addressed to Guoqing Zhang.
| | - Shuhua Xu
- To whom correspondence should be addressed. Tel: +86 21 31246617; Fax: +86 21 31246617;
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12
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Cuenca-Guardiola J, de la Morena-Barrio B, García JL, Sanchis-Juan A, Corral J, Fernández-Breis JT. Improvement of large copy number variant detection by whole genome nanopore sequencing. J Adv Res 2022:S2090-1232(22)00241-7. [PMID: 36323370 PMCID: PMC10403694 DOI: 10.1016/j.jare.2022.10.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2022] [Revised: 10/18/2022] [Accepted: 10/22/2022] [Indexed: 11/11/2022] Open
Abstract
INTRODUCTION Whole-genome sequencing using nanopore technologies can uncover structural variants, which are DNA rearrangements larger than 50 base pairs. Nanopore technologies can also characterize their boundaries with single-base accuracy, owing to the kilobase-long reads that encompass either full variants or their junctions. Other methods, such as next-generation short read sequencing or PCR assays, are limited in their capabilities to detect or characterize structural variants. However, the existing software for nanopore sequencing data analysis still reports incomplete variant sets, which also contain erroneous calls, a considerable obstacle for the molecular diagnosis or accurate genotyping of populations. METHODS We compared multiple factors affecting variant calling, such as reference genome version, aligner (minimap2, NGMLR, and lra) choice, and variant caller combinations (Sniffles, CuteSV, SVIM, and NanoVar), to find the optimal group of tools for calling large (>50 kb) deletions and duplications, using data from seven patients exhibiting gross gene defects on SERPINC1 and from a reference variant set as the control. The goal was to obtain the most complete, yet reasonably specific group of large variants using a single cell of PromethION sequencing, which yielded lower depth coverage than short-read sequencing. We also used a custom method for the statistical analysis of the coverage value to refine the resulting datasets. RESULTS We found that for large deletions and duplications (>50 kb), the existing software performed worse than for smaller ones, in terms of both sensitivity and specificity, and newer tools had not improved this. Our novel software, disCoverage, could polish variant callers' results, improving specificity by up to 62% and sensitivity by 15%, the latter requiring other data or samples. CONCLUSION We analyzed the current situation of >50-kb copy number variants with nanopore sequencing, which could be improved. The methods presented in this work could help to identify the known deletions and duplications in a set of patients, while also helping to filter out erroneous calls for these variants, which might aid the efforts to characterize a not-yet well-known fraction of genetic variability in the human genome.
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13
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Mohd Saad NS, Neik TX, Thomas WJW, Amas JC, Cantila AY, Craig RJ, Edwards D, Batley J. Advancing designer crops for climate resilience through an integrated genomics approach. CURRENT OPINION IN PLANT BIOLOGY 2022; 67:102220. [PMID: 35489163 DOI: 10.1016/j.pbi.2022.102220] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/13/2021] [Revised: 03/15/2022] [Accepted: 03/25/2022] [Indexed: 06/14/2023]
Abstract
Climate change and exponential population growth are exposing an immediate need for developing future crops that are highly resilient and adaptable to changing environments to maintain global food security in the next decade. Rigorous selection from long domestication history has rendered cultivated crops genetically disadvantaged, raising concerns in their ability to adapt to these new challenges and limiting their usefulness in breeding programmes. As a result, future crop improvement efforts must rely on integrating various genomic strategies ranging from high-throughput sequencing to machine learning, in order to exploit germplasm diversity and overcome bottlenecks created by domestication, expansive multi-dimensional phenotypes, arduous breeding processes, complex traits and big data.
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Affiliation(s)
- Nur Shuhadah Mohd Saad
- UWA School of Biological Sciences and the UWA Institute of Agriculture, University of Western Australia, Crawley, WA, Australia
| | - Ting Xiang Neik
- Sunway College Kuala Lumpur, Bandar Sunway, 47500, Selangor, Malaysia
| | - William J W Thomas
- UWA School of Biological Sciences and the UWA Institute of Agriculture, University of Western Australia, Crawley, WA, Australia
| | - Junrey C Amas
- UWA School of Biological Sciences and the UWA Institute of Agriculture, University of Western Australia, Crawley, WA, Australia
| | - Aldrin Y Cantila
- UWA School of Biological Sciences and the UWA Institute of Agriculture, University of Western Australia, Crawley, WA, Australia
| | - Ryan J Craig
- UWA School of Biological Sciences and the UWA Institute of Agriculture, University of Western Australia, Crawley, WA, Australia
| | - David Edwards
- UWA School of Biological Sciences and the UWA Institute of Agriculture, University of Western Australia, Crawley, WA, Australia
| | - Jacqueline Batley
- UWA School of Biological Sciences and the UWA Institute of Agriculture, University of Western Australia, Crawley, WA, Australia.
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14
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Quan C, Lu H, Lu Y, Zhou G. Population-scale genotyping of structural variation in the era of long-read sequencing. Comput Struct Biotechnol J 2022; 20:2639-2647. [PMID: 35685364 PMCID: PMC9163579 DOI: 10.1016/j.csbj.2022.05.047] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Revised: 05/24/2022] [Accepted: 05/24/2022] [Indexed: 11/29/2022] Open
Abstract
Population-scale studies of structural variation (SV) are growing rapidly worldwide with the development of long-read sequencing technology, yielding a considerable number of novel SVs and complete gap-closed genome assemblies. Herein, we highlight recent studies using a hybrid sequencing strategy and present the challenges toward large-scale genotyping for SVs due to the reference bias. Genotyping SVs at a population scale remains challenging, which severely impacts genotype-based population genetic studies or genome-wide association studies of complex diseases. We summarize academic efforts to improve genotype quality through linear or graph representations of reference and alternative alleles. Graph-based genotypers capable of integrating diverse genetic information are effectively applied to large and diverse cohorts, contributing to unbiased downstream analysis. Meanwhile, there is still an urgent need in this field for efficient tools to construct complex graphs and perform sequence-to-graph alignments.
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Affiliation(s)
- Cheng Quan
- Department of Genetics & Integrative Omics, State Key Laboratory of Proteomics, National Center for Protein Sciences, Beijing Institute of Radiation Medicine, Beijing 100850, PR China
| | - Hao Lu
- Department of Genetics & Integrative Omics, State Key Laboratory of Proteomics, National Center for Protein Sciences, Beijing Institute of Radiation Medicine, Beijing 100850, PR China
| | - Yiming Lu
- Department of Genetics & Integrative Omics, State Key Laboratory of Proteomics, National Center for Protein Sciences, Beijing Institute of Radiation Medicine, Beijing 100850, PR China
- Hebei University, Baoding, Hebei Province 071002, PR China
- Corresponding authors at: Department of Genetics & Integrative Omics, State Key Laboratory of Proteomics, National Center for Protein Sciences, Beijing Institute of Radiation Medicine, 27 Taiping Road, Beijing 100850, PR China (G. Zhou). Department of Genetics & Integrative Omics, State Key Laboratory of Proteomics, National Center for Protein Sciences, Beijing Institute of Radiation Medicine, 27 Taiping Road, Beijing, 100850, PR China (Y. Lu).
| | - Gangqiao Zhou
- Department of Genetics & Integrative Omics, State Key Laboratory of Proteomics, National Center for Protein Sciences, Beijing Institute of Radiation Medicine, Beijing 100850, PR China
- Collaborative Innovation Center for Personalized Cancer Medicine, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu Province 211166, PR China
- Medical College of Guizhou University, Guiyang, Guizhou Province 550025, PR China
- Hebei University, Baoding, Hebei Province 071002, PR China
- Corresponding authors at: Department of Genetics & Integrative Omics, State Key Laboratory of Proteomics, National Center for Protein Sciences, Beijing Institute of Radiation Medicine, 27 Taiping Road, Beijing 100850, PR China (G. Zhou). Department of Genetics & Integrative Omics, State Key Laboratory of Proteomics, National Center for Protein Sciences, Beijing Institute of Radiation Medicine, 27 Taiping Road, Beijing, 100850, PR China (Y. Lu).
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15
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Auwerx C, Lepamets M, Sadler MC, Patxot M, Stojanov M, Baud D, Mägi R, Porcu E, Reymond A, Kutalik Z, Metspalu A, Milani L, Mägi R, Nelis M. The individual and global impact of copy-number variants on complex human traits. Am J Hum Genet 2022; 109:647-668. [PMID: 35240056 PMCID: PMC9069145 DOI: 10.1016/j.ajhg.2022.02.010] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2021] [Accepted: 02/09/2022] [Indexed: 12/25/2022] Open
Abstract
The impact of copy-number variations (CNVs) on complex human traits remains understudied. We called CNVs in 331,522 UK Biobank participants and performed genome-wide association studies (GWASs) between the copy number of CNV-proxy probes and 57 continuous traits, revealing 131 signals spanning 47 phenotypes. Our analysis recapitulated well-known associations (e.g., 1q21 and height), revealed the pleiotropy of recurrent CNVs (e.g., 26 and 16 traits for 16p11.2-BP4-BP5 and 22q11.21, respectively), and suggested gene functionalities (e.g., MARF1 in female reproduction). Forty-eight CNV signals (38%) overlapped with single-nucleotide polymorphism (SNP)-GWASs signals for the same trait. For instance, deletion of PDZK1, which encodes a urate transporter scaffold protein, decreased serum urate levels, while deletion of RHD, which encodes the Rhesus blood group D antigen, associated with hematological traits. Other signals overlapped Mendelian disorder regions, suggesting variable expressivity and broad impact of these loci, as illustrated by signals mapping to Rotor syndrome (SLCO1B1/3), renal cysts and diabetes syndrome (HNF1B), or Charcot-Marie-Tooth (PMP22) loci. Total CNV burden negatively impacted 35 traits, leading to increased adiposity, liver/kidney damage, and decreased intelligence and physical capacity. Thirty traits remained burden associated after correcting for CNV-GWAS signals, pointing to a polygenic CNV architecture. The burden negatively correlated with socio-economic indicators, parental lifespan, and age (survivorship proxy), suggesting a contribution to decreased longevity. Together, our results showcase how studying CNVs can expand biological insights, emphasizing the critical role of this mutational class in shaping human traits and arguing in favor of a continuum between Mendelian and complex diseases.
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Affiliation(s)
- Chiara Auwerx
- Center for Integrative Genomics, University of Lausanne, Lausanne 1015, Switzerland; Department of Computational Biology, University of Lausanne, Lausanne 1015, Switzerland; Swiss Institute of Bioinformatics, Lausanne 1015, Switzerland; University Center for Primary Care and Public Health, Lausanne 1010, Switzerland
| | - Maarja Lepamets
- Institute of Molecular and Cell Biology, University of Tartu, Tartu 51010, Estonia; Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu 51010, Estonia
| | - Marie C Sadler
- Swiss Institute of Bioinformatics, Lausanne 1015, Switzerland; University Center for Primary Care and Public Health, Lausanne 1010, Switzerland
| | - Marion Patxot
- Department of Computational Biology, University of Lausanne, Lausanne 1015, Switzerland
| | - Miloš Stojanov
- Materno-fetal and Obstetrics Research Unit, Department Woman-Mother-Child, CHUV, Lausanne 1011, Switzerland
| | - David Baud
- Materno-fetal and Obstetrics Research Unit, Department Woman-Mother-Child, CHUV, Lausanne 1011, Switzerland
| | - Reedik Mägi
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu 51010, Estonia
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- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu 51010, Estonia
| | - Eleonora Porcu
- Center for Integrative Genomics, University of Lausanne, Lausanne 1015, Switzerland; Swiss Institute of Bioinformatics, Lausanne 1015, Switzerland; University Center for Primary Care and Public Health, Lausanne 1010, Switzerland
| | - Alexandre Reymond
- Center for Integrative Genomics, University of Lausanne, Lausanne 1015, Switzerland.
| | - Zoltán Kutalik
- Department of Computational Biology, University of Lausanne, Lausanne 1015, Switzerland; Swiss Institute of Bioinformatics, Lausanne 1015, Switzerland; University Center for Primary Care and Public Health, Lausanne 1010, Switzerland.
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16
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Ganel L, Chen L, Christ R, Vangipurapu J, Young E, Das I, Kanchi K, Larson D, Regier A, Abel H, Kang CJ, Scott A, Havulinna A, Chiang CWK, Service S, Freimer N, Palotie A, Ripatti S, Kuusisto J, Boehnke M, Laakso M, Locke A, Stitziel NO, Hall IM. Mitochondrial genome copy number measured by DNA sequencing in human blood is strongly associated with metabolic traits via cell-type composition differences. Hum Genomics 2021; 15:34. [PMID: 34099068 PMCID: PMC8185936 DOI: 10.1186/s40246-021-00335-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2021] [Accepted: 05/26/2021] [Indexed: 01/14/2023] Open
Abstract
BACKGROUND Mitochondrial genome copy number (MT-CN) varies among humans and across tissues and is highly heritable, but its causes and consequences are not well understood. When measured by bulk DNA sequencing in blood, MT-CN may reflect a combination of the number of mitochondria per cell and cell-type composition. Here, we studied MT-CN variation in blood-derived DNA from 19184 Finnish individuals using a combination of genome (N = 4163) and exome sequencing (N = 19034) data as well as imputed genotypes (N = 17718). RESULTS We identified two loci significantly associated with MT-CN variation: a common variant at the MYB-HBS1L locus (P = 1.6 × 10-8), which has previously been associated with numerous hematological parameters; and a burden of rare variants in the TMBIM1 gene (P = 3.0 × 10-8), which has been reported to protect against non-alcoholic fatty liver disease. We also found that MT-CN is strongly associated with insulin levels (P = 2.0 × 10-21) and other metabolic syndrome (metS)-related traits. Using a Mendelian randomization framework, we show evidence that MT-CN measured in blood is causally related to insulin levels. We then applied an MT-CN polygenic risk score (PRS) derived from Finnish data to the UK Biobank, where the association between the PRS and metS traits was replicated. Adjusting for cell counts largely eliminated these signals, suggesting that MT-CN affects metS via cell-type composition. CONCLUSION These results suggest that measurements of MT-CN in blood-derived DNA partially reflect differences in cell-type composition and that these differences are causally linked to insulin and related traits.
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Affiliation(s)
- Liron Ganel
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, USA
- Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA
| | - Lei Chen
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, USA
- Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA
| | - Ryan Christ
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, USA
| | - Jagadish Vangipurapu
- Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland, Kuopio, Finland
| | - Erica Young
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, USA
- Department of Medicine, Cardiovascular Division, Washington University School of Medicine, St. Louis, MO, USA
| | - Indraniel Das
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, USA
| | - Krishna Kanchi
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, USA
| | - David Larson
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, USA
- Department of Genetics, Washington University School of Medicine, St. Louis, MO, USA
| | - Allison Regier
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, USA
- Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA
| | - Haley Abel
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, USA
- Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA
- Department of Genetics, Washington University School of Medicine, St. Louis, MO, USA
| | - Chul Joo Kang
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, USA
| | - Alexandra Scott
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, USA
- Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA
| | - Aki Havulinna
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
- Finnish Institute for Health and Welfare (THL), Helsinki, Finland
| | - Charleston W K Chiang
- Center for Genetic Epidemiology, Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- Quantitative and Computational Biology Section, Department of Biological Sciences, University of Southern California, Los Angeles, CA, USA
| | - Susan Service
- Center for Neurobehavioral Genetics, Jane and Terry Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles, Los Angeles, CA, USA
| | - Nelson Freimer
- Center for Neurobehavioral Genetics, Jane and Terry Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles, Los Angeles, CA, USA
| | - Aarno Palotie
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
- Analytical and Translational Genetics Unit (ATGU), Psychiatric & Neurodevelopmental Genetics Unit, Departments of Psychiatry and Neurology, Massachusetts General Hospital, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Samuli Ripatti
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Johanna Kuusisto
- Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland, Kuopio, Finland
- Department of Medicine, Kuopio University Hospital, Kuopio, Finland
| | - Michael Boehnke
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI, USA
| | - Markku Laakso
- Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland, Kuopio, Finland
- Department of Medicine, Kuopio University Hospital, Kuopio, Finland
| | - Adam Locke
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, USA
- Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA
| | - Nathan O Stitziel
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, USA.
- Department of Medicine, Cardiovascular Division, Washington University School of Medicine, St. Louis, MO, USA.
- Department of Genetics, Washington University School of Medicine, St. Louis, MO, USA.
| | - Ira M Hall
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, USA.
- Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA.
- Department of Genetics, Yale University School of Medicine, New Haven, CT, USA.
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17
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Tang H, He Z. Advances and challenges in quantitative delineation of the genetic architecture of complex traits. QUANTITATIVE BIOLOGY 2021; 9:168-184. [PMID: 35492964 PMCID: PMC9053444 DOI: 10.15302/j-qb-021-0249] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Background Genome-wide association studies (GWAS) have been widely adopted in studies of human complex traits and diseases. Results This review surveys areas of active research: quantifying and partitioning trait heritability, fine mapping functional variants and integrative analysis, genetic risk prediction of phenotypes, and the analysis of sequencing studies that have identified millions of rare variants. Current challenges and opportunities are highlighted. Conclusion GWAS have fundamentally transformed the field of human complex trait genetics. Novel statistical and computational methods have expanded the scope of GWAS and have provided valuable insights on the genetic architecture underlying complex phenotypes.
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Affiliation(s)
- Hua Tang
- Department of Genetics, Stanford University, Stanford, CA 94305, USA
| | - Zihuai He
- Department of Neurology and Neurological Sciences, Stanford University, Stanford, CA 94305, USA
- Quantitative Sciences Unit, Department of Medicine, Stanford University, Stanford, CA 94305, USA
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