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      Genome-wide association study and genomic prediction using parental and breeding populations of Japanese pear ( Pyrus pyrifolia Nakai)

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          Abstract

          Breeding of fruit trees is hindered by their large size and long juvenile period. Genome-wide association study (GWAS) and genomic selection (GS) are promising methods for circumventing this hindrance, but preparing new large datasets for these methods may not always be practical. Here, we evaluated the potential of breeding populations evaluated routinely in breeding programs for GWAS and GS. We used a pear parental population of 86 varieties and breeding populations of 765 trees from 16 full-sib families, which were phenotyped for 18 traits and genotyped for 1,506 single nucleotide polymorphisms (SNPs). The power of GWAS and accuracy of genomic prediction were improved when we combined data from the breeding populations and the parental population. The accuracy of genomic prediction was improved further when full-sib data of the target family were available. The results suggest that phenotype data collected in breeding programs can be beneficial for GWAS and GS when they are combined with genome-wide marker data. The potential of GWAS and GS will be further extended if we can build a system for routine collection of the phenotype and marker genotype data for breeding populations.

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          Linkage disequilibrium--understanding the evolutionary past and mapping the medical future.

          Linkage disequilibrium--the nonrandom association of alleles at different loci--is a sensitive indicator of the population genetic forces that structure a genome. Because of the explosive growth of methods for assessing genetic variation at a fine scale, evolutionary biologists and human geneticists are increasingly exploiting linkage disequilibrium in order to understand past evolutionary and demographic events, to map genes that are associated with quantitative characters and inherited diseases, and to understand the joint evolution of linked sets of genes. This article introduces linkage disequilibrium, reviews the population genetic processes that affect it and describes some of its uses. At present, linkage disequilibrium is used much more extensively in the study of humans than in non-humans, but that is changing as technological advances make extensive genomic studies feasible in other species.
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            An efficient multi-locus mixed model approach for genome-wide association studies in structured populations

            Population structure causes genome-wide linkage disequilibrium between unlinked loci, leading to statistical confounding in genome-wide association studies. Mixed models have been shown to handle the confounding effects of a diffuse background of large numbers of loci of small effect well, but do not always account for loci of larger effect. Here we propose a multi-locus mixed model as a general method for mapping complex traits in structured populations. Simulations suggest that our method outperforms existing methods, in terms of power as well as false discovery rate. We apply our method to human and Arabidopsis thaliana data, identifying novel associations in known candidates as well as evidence for allelic heterogeneity. We also demonstrate how a priori knowledge from an A. thaliana linkage mapping study can be integrated into our method using a Bayesian approach. Our implementation is computationally efficient, making the analysis of large datasets (n > 10000) practicable.
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              Kernel Smoothing

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                Author and article information

                Contributors
                aiwata@mail.ecc.u-tokyo.ac.jp
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                10 August 2018
                10 August 2018
                2018
                : 8
                : 11994
                Affiliations
                [1 ]ISNI 0000 0001 2151 536X, GRID grid.26999.3d, Laboratory of Biometry and Bioinformatics, Department of Agricultural and Environmental Biology, Graduate School of Agricultural and Life Sciences, , The University of Tokyo, ; 1-1-1 Yayoi, Bunkyo, Tokyo, 113-8657 Japan
                [2 ]ISNI 0000 0001 2222 0432, GRID grid.416835.d, Institute of Fruit Tree and Tea Science, , National Agriculture and Food Research Organization (NARO), ; 2-1 Fujimoto, Tsukuba, Ibaraki, 305-8605 Japan
                [3 ]ISNI 0000 0001 2222 0432, GRID grid.416835.d, Institute of Crop Science, , NARO, ; 2-1-2 Kannondai, Tsukuba, Ibaraki, 305-8518 Japan
                Author information
                http://orcid.org/0000-0003-1707-9539
                http://orcid.org/0000-0002-6747-7036
                Article
                30154
                10.1038/s41598-018-30154-w
                6086889
                30097588
                9383de45-9205-4b53-926a-a496519adf57
                © The Author(s) 2018

                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
                : 30 April 2018
                : 25 July 2018
                Funding
                Funded by: Ministry of Agriculture, Forestry and Fisheries of Japan (Genomics-based Technology for Agricultural Improvement, NGB-1005 and 2008)
                Funded by: Ministry of Agriculture, Forestry and Fisheries of Japan (Genomics-based Technology for Agricultural Improvement, NGB-2010)
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