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      Multi-parent populations in crops: a toolbox integrating genomics and genetic mapping with breeding

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          Abstract

          Crop populations derived from experimental crosses enable the genetic dissection of complex traits and support modern plant breeding. Among these, multi-parent populations now play a central role. By mixing and recombining the genomes of multiple founders, multi-parent populations combine many commonly sought beneficial properties of genetic mapping populations. For example, they have high power and resolution for mapping quantitative trait loci, high genetic diversity and minimal population structure. Many multi-parent populations have been constructed in crop species, and their inbred germplasm and associated phenotypic and genotypic data serve as enduring resources. Their utility has grown from being a tool for mapping quantitative trait loci to a means of providing germplasm for breeding programmes. Genomics approaches, including de novo genome assemblies and gene annotations for the population founders, have allowed the imputation of rich sequence information into the descendent population, expanding the breadth of research and breeding applications of multi-parent populations. Here, we report recent successes from crop multi-parent populations in crops. We also propose an ideal genotypic, phenotypic and germplasm ‘package’ that multi-parent populations should feature to optimise their use as powerful community resources for crop research, development and breeding.

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          A global reference for human genetic variation

          The 1000 Genomes Project set out to provide a comprehensive description of common human genetic variation by applying whole-genome sequencing to a diverse set of individuals from multiple populations. Here we report completion of the project, having reconstructed the genomes of 2,504 individuals from 26 populations using a combination of low-coverage whole-genome sequencing, deep exome sequencing, and dense microarray genotyping. We characterized a broad spectrum of genetic variation, in total over 88 million variants (84.7 million single nucleotide polymorphisms (SNPs), 3.6 million short insertions/deletions (indels), and 60,000 structural variants), all phased onto high-quality haplotypes. This resource includes >99% of SNP variants with a frequency of >1% for a variety of ancestries. We describe the distribution of genetic variation across the global sample, and discuss the implications for common disease studies.
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            An Integrated Encyclopedia of DNA Elements in the Human Genome

            Summary The human genome encodes the blueprint of life, but the function of the vast majority of its nearly three billion bases is unknown. The Encyclopedia of DNA Elements (ENCODE) project has systematically mapped regions of transcription, transcription factor association, chromatin structure, and histone modification. These data enabled us to assign biochemical functions for 80% of the genome, in particular outside of the well-studied protein-coding regions. Many discovered candidate regulatory elements are physically associated with one another and with expressed genes, providing new insights into the mechanisms of gene regulation. The newly identified elements also show a statistical correspondence to sequence variants linked to human disease, and can thereby guide interpretation of this variation. Overall the project provides new insights into the organization and regulation of our genes and genome, and an expansive resource of functional annotations for biomedical research.
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              GCTA: a tool for genome-wide complex trait analysis.

              For most human complex diseases and traits, SNPs identified by genome-wide association studies (GWAS) explain only a small fraction of the heritability. Here we report a user-friendly software tool called genome-wide complex trait analysis (GCTA), which was developed based on a method we recently developed to address the "missing heritability" problem. GCTA estimates the variance explained by all the SNPs on a chromosome or on the whole genome for a complex trait rather than testing the association of any particular SNP to the trait. We introduce GCTA's five main functions: data management, estimation of the genetic relationships from SNPs, mixed linear model analysis of variance explained by the SNPs, estimation of the linkage disequilibrium structure, and GWAS simulation. We focus on the function of estimating the variance explained by all the SNPs on the X chromosome and testing the hypotheses of dosage compensation. The GCTA software is a versatile tool to estimate and partition complex trait variation with large GWAS data sets.
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                Author and article information

                Contributors
                m.f.scott@ucl.ac.uk
                f.ladejobi@ucl.ac.uk
                Journal
                Heredity (Edinb)
                Heredity (Edinb)
                Heredity
                Springer International Publishing (Cham )
                0018-067X
                1365-2540
                3 July 2020
                3 July 2020
                December 2020
                : 125
                : 6
                : 396-416
                Affiliations
                [1 ]GRID grid.83440.3b, ISNI 0000000121901201, UCL Genetics Institute, ; Gower Street, London, WC1E 6BT UK
                [2 ]GRID grid.9435.b, ISNI 0000 0004 0457 9566, University of Reading, ; Reading, RG6 6AH UK
                [3 ]GRID grid.7155.6, ISNI 0000 0001 2260 6941, Faculty of Agriculture, , Alexandria University, ; Alexandria, 23714 Egypt
                [4 ]GRID grid.17595.3f, ISNI 0000 0004 0383 6532, The John Bingham Laboratory, NIAB, ; 93 Lawrence Weaver Road, Cambridge, CB3 0LE UK
                [5 ]GRID grid.4991.5, ISNI 0000 0004 1936 8948, Department of Plant Sciences, , University of Oxford, ; South Parks Road, Oxford, OX1 3RB UK
                [6 ]GRID grid.1010.0, ISNI 0000 0004 1936 7304, School of Agriculture, Food and Wine, , University of Adelaide, ; Glen Osmond, SA 5064 Australia
                [7 ]GRID grid.35937.3b, ISNI 0000 0001 2270 9879, Natural History Museum, ; London, UK
                [8 ]GRID grid.263145.7, ISNI 0000 0004 1762 600X, Institute of Life Sciences, Scuola Superiore Sant’Anna, ; Pisa, Italy
                [9 ]GRID grid.9909.9, ISNI 0000 0004 1936 8403, Faculty of Biological Sciences, University of Leeds, ; Leeds, LS2 9JT UK
                [10 ]Instituto de Agrobiotecnología y Biología Molecular (IABIMO), INTA-CONICET, Nicolas Repetto y Los Reseros s/n, 1686 Hurlingham, Buenos Aires Argentina
                [11 ]GRID grid.426884.4, ISNI 0000 0001 0170 6644, SRUC, ; West Mains Road, Kings Buildings, Edinburgh, EH9 3JG UK
                [12 ]GRID grid.419337.b, ISNI 0000 0000 9323 1772, Center of Excellence in Genomics and Systems Biology, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), ; Hyderabad, India
                [13 ]GRID grid.466870.b, ISNI 0000 0001 0039 8483, International Center for Biosaline Agriculture, Academic City, ; Dubai, United Arab Emirates
                [14 ]GRID grid.1016.6, CSIRO, ; GPO Box 1700, Canberra, ACT 2601 Australia
                Author information
                http://orcid.org/0000-0002-7182-9946
                http://orcid.org/0000-0002-0508-0578
                http://orcid.org/0000-0002-7584-5636
                http://orcid.org/0000-0001-5519-4357
                http://orcid.org/0000-0002-5564-7480
                http://orcid.org/0000-0002-1109-4730
                http://orcid.org/0000-0003-4889-056X
                http://orcid.org/0000-0001-6595-281X
                http://orcid.org/0000-0001-7463-3044
                http://orcid.org/0000-0002-4562-9131
                http://orcid.org/0000-0002-1022-9330
                Article
                336
                10.1038/s41437-020-0336-6
                7784848
                32616877
                18dac74e-168e-4bed-a32f-99ff022e106c
                © The Author(s) 2020

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 27 January 2020
                : 16 June 2020
                : 16 June 2020
                Funding
                Funded by: FundRef https://doi.org/10.13039/501100000268, RCUK | Biotechnology and Biological Sciences Research Council (BBSRC);
                Award ID: BB/P024726/1
                Award ID: BB/M011585/1
                Award ID: BB/P024726/1
                Award ID: BB/M013995/1
                Award ID: BB/P027849/1
                Award ID: BB/M011666/1
                Award ID: BB/P010741/1
                Award ID: BB/M011666/1
                Award ID: BB/P024726/1
                Award ID: BB/M011585/1
                Award Recipient :
                Categories
                Review Article
                Custom metadata
                © The Genetics Society 2020

                Human biology
                plant genetics,plant breeding,agricultural genetics,quantitative trait
                Human biology
                plant genetics, plant breeding, agricultural genetics, quantitative trait

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