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Lin YC, Mayer M, Valle Torres D, Pook T, Hölker AC, Presterl T, Ouzunova M, Schön CC. Genomic prediction within and across maize landrace derived populations using haplotypes. FRONTIERS IN PLANT SCIENCE 2024; 15:1351466. [PMID: 38584949 PMCID: PMC10995330 DOI: 10.3389/fpls.2024.1351466] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/06/2023] [Accepted: 03/05/2024] [Indexed: 04/09/2024]
Abstract
Genomic prediction (GP) using haplotypes is considered advantageous compared to GP solely reliant on single nucleotide polymorphisms (SNPs), owing to haplotypes' enhanced ability to capture ancestral information and their higher linkage disequilibrium with quantitative trait loci (QTL). Many empirical studies supported the advantages of haplotype-based GP over SNP-based approaches. Nevertheless, the performance of haplotype-based GP can vary significantly depending on multiple factors, including the traits being studied, the genetic structure of the population under investigation, and the particular method employed for haplotype construction. In this study, we compared haplotype and SNP based prediction accuracies in four populations derived from European maize landraces. Populations comprised either doubled haploid lines (DH) derived directly from landraces, or gamete capture lines (GC) derived from crosses of the landraces with an inbred line. For two different landraces, both types of populations were generated, genotyped with 600k SNPs and phenotyped as lines per se for five traits. Our study explores three prediction scenarios: (i) within each of the four populations, (ii) across DH and GC populations from the same landrace, and (iii) across landraces using either DH or GC populations. Three haplotype construction methods were evaluated: 1. fixed-window blocks (FixedHB), 2. LD-based blocks (HaploView), and 3. IBD-based blocks (HaploBlocker). In within population predictions, FixedHB and HaploView methods performed as well as or slightly better than SNPs for all traits. HaploBlocker improved accuracy for certain traits but exhibited inferior performance for others. In prediction across populations, the parameter setting from HaploBlocker which controls the construction of shared haplotypes between populations played a crucial role for obtaining optimal results. When predicting across landraces, accuracies were low for both, SNP and haplotype approaches, but for specific traits substantial improvement was observed with HaploBlocker. This study provides recommendations for optimal haplotype construction and identifies relevant parameters for constructing haplotypes in the context of genomic prediction.
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Affiliation(s)
- Yan-Cheng Lin
- Chair of Plant Breeding, TUM School of Life Sciences, Technical University of Munich, Freising, Germany
| | - Manfred Mayer
- Chair of Plant Breeding, TUM School of Life Sciences, Technical University of Munich, Freising, Germany
- Bayer CropScience Deutschland GmbH, Borken, Germany
| | - Daniel Valle Torres
- Chair of Plant Breeding, TUM School of Life Sciences, Technical University of Munich, Freising, Germany
- Sugar Beet Breeding, Strube Research GmbH & Co. KG, Söllingen, Germany
| | - Torsten Pook
- Animal Breeding and Genomics, Wageningen University & Research, Wageningen, Netherlands
| | - Armin C. Hölker
- Product Development Maize and Oil Crops, KWS SAAT SE & Co. KGaA, Einbeck, Germany
| | - Thomas Presterl
- Product Development Maize and Oil Crops, KWS SAAT SE & Co. KGaA, Einbeck, Germany
| | - Milena Ouzunova
- Product Development Maize and Oil Crops, KWS SAAT SE & Co. KGaA, Einbeck, Germany
| | - Chris-Carolin Schön
- Chair of Plant Breeding, TUM School of Life Sciences, Technical University of Munich, Freising, Germany
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Biová J, Kaňovská I, Chan YO, Immadi MS, Joshi T, Bilyeu K, Škrabišová M. Natural and artificial selection of multiple alleles revealed through genomic analyses. Front Genet 2024; 14:1320652. [PMID: 38259621 PMCID: PMC10801239 DOI: 10.3389/fgene.2023.1320652] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2023] [Accepted: 11/17/2023] [Indexed: 01/24/2024] Open
Abstract
Genome-to-phenome research in agriculture aims to improve crops through in silico predictions. Genome-wide association study (GWAS) is potent in identifying genomic loci that underlie important traits. As a statistical method, increasing the sample quantity, data quality, or diversity of the GWAS dataset positively impacts GWAS power. For more precise breeding, concrete candidate genes with exact functional variants must be discovered. Many post-GWAS methods have been developed to narrow down the associated genomic regions and, ideally, to predict candidate genes and causative mutations (CMs). Historical natural selection and breeding-related artificial selection both act to change the frequencies of different alleles of genes that control phenotypes. With higher diversity and more extensive GWAS datasets, there is an increased chance of multiple alleles with independent CMs in a single causal gene. This can be caused by the presence of samples from geographically isolated regions that arose during natural or artificial selection. This simple fact is a complicating factor in GWAS-driven discoveries. Currently, none of the existing association methods address this issue and need to identify multiple alleles and, more specifically, the actual CMs. Therefore, we developed a tool that computes a score for a combination of variant positions in a single candidate gene and, based on the highest score, identifies the best number and combination of CMs. The tool is publicly available as a Python package on GitHub, and we further created a web-based Multiple Alleles discovery (MADis) tool that supports soybean and is hosted in SoyKB (https://soykb.org/SoybeanMADisTool/). We tested and validated the algorithm and presented the utilization of MADis in a pod pigmentation L1 gene case study with multiple CMs from natural or artificial selection. Finally, we identified a candidate gene for the pod color L2 locus and predicted the existence of multiple alleles that potentially cause loss of pod pigmentation. In this work, we show how a genomic analysis can be employed to explore the natural and artificial selection of multiple alleles and, thus, improve and accelerate crop breeding in agriculture.
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Affiliation(s)
- Jana Biová
- Department of Biochemistry, Faculty of Science, Palacký University in Olomouc, Olomouc, Czechia
| | - Ivana Kaňovská
- Department of Biochemistry, Faculty of Science, Palacký University in Olomouc, Olomouc, Czechia
| | - Yen On Chan
- MU Institute for Data Science and Informatics, University of Missouri-Columbia, Columbia, MO, United States
- Christopher S. Bond Life Sciences Center, University of Missouri-Columbia, Columbia, MO, United States
| | - Manish Sridhar Immadi
- Department of Electrical Engineering and Computer Science, University of Missouri-Columbia, Columbia, MO, United States
| | - Trupti Joshi
- MU Institute for Data Science and Informatics, University of Missouri-Columbia, Columbia, MO, United States
- Christopher S. Bond Life Sciences Center, University of Missouri-Columbia, Columbia, MO, United States
- Department of Electrical Engineering and Computer Science, University of Missouri-Columbia, Columbia, MO, United States
- Department of Biomedical Informatics, Biostatistics and Medical Epidemiology, University of Missouri-Columbia, Columbia, MO, United States
| | - Kristin Bilyeu
- United States Department of Agriculture-Agricultural Research Service, Plant Genetics Research Unit, Columbia, MO, United States
| | - Mária Škrabišová
- Department of Biochemistry, Faculty of Science, Palacký University in Olomouc, Olomouc, Czechia
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Difabachew YF, Frisch M, Langstroff AL, Stahl A, Wittkop B, Snowdon RJ, Koch M, Kirchhoff M, Cselényi L, Wolf M, Förster J, Weber S, Okoye UJ, Zenke-Philippi C. Genomic prediction with haplotype blocks in wheat. FRONTIERS IN PLANT SCIENCE 2023; 14:1168547. [PMID: 37229104 PMCID: PMC10203549 DOI: 10.3389/fpls.2023.1168547] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Accepted: 04/17/2023] [Indexed: 05/27/2023]
Abstract
Haplotype blocks might carry additional information compared to single SNPs and have therefore been suggested for use as independent variables in genomic prediction. Studies in different species resulted in more accurate predictions than with single SNPs in some traits but not in others. In addition, it remains unclear how the blocks should be built to obtain the greatest prediction accuracies. Our objective was to compare the results of genomic prediction with different types of haplotype blocks to prediction with single SNPs in 11 traits in winter wheat. We built haplotype blocks from marker data from 361 winter wheat lines based on linkage disequilibrium, fixed SNP numbers, fixed lengths in cM and with the R package HaploBlocker. We used these blocks together with data from single-year field trials in a cross-validation study for predictions with RR-BLUP, an alternative method (RMLA) that allows for heterogeneous marker variances, and GBLUP performed with the software GVCHAP. The greatest prediction accuracies for resistance scores for B. graminis, P. triticina, and F. graminearum were obtained with LD-based haplotype blocks while blocks with fixed marker numbers and fixed lengths in cM resulted in the greatest prediction accuracies for plant height. Prediction accuracies of haplotype blocks built with HaploBlocker were greater than those of the other methods for protein concentration and resistances scores for S. tritici, B. graminis, and P. striiformis. We hypothesize that the trait-dependence is caused by properties of the haplotype blocks that have overlapping and contrasting effects on the prediction accuracy. While they might be able to capture local epistatic effects and to detect ancestral relationships better than single SNPs, prediction accuracy might be reduced by unfavorable characteristics of the design matrices in the models that are due to their multi-allelic nature.
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Affiliation(s)
| | - Matthias Frisch
- Institute of Agronomy and Plant Breeding II, Justus Liebig University, Gießen, Germany
| | - Anna Luise Langstroff
- Institute of Agronomy and Plant Breeding I, Justus Liebig University, Gießen, Germany
| | - Andreas Stahl
- Institute for Resistance Research and Stress Tolerance, Julius Kühn Institute, Quedlinburg, Germany
| | - Benjamin Wittkop
- Institute of Agronomy and Plant Breeding I, Justus Liebig University, Gießen, Germany
| | - Rod J. Snowdon
- Institute of Agronomy and Plant Breeding I, Justus Liebig University, Gießen, Germany
| | | | | | - László Cselényi
- Department of Cereal Breeding, W. von Borries-Eckendorf GmbH & Co. KG, Leopoldshöhe, Germany
| | - Markus Wolf
- German Seed Alliance GmbH, Holtsee, Germany
- Saaten-Union Biotec GmbH, Leopoldshöhe, Germany
| | | | - Sven Weber
- Institute of Agronomy and Plant Breeding I, Justus Liebig University, Gießen, Germany
| | - Uche Joshua Okoye
- Institute of Agronomy and Plant Breeding II, Justus Liebig University, Gießen, Germany
| | - Carola Zenke-Philippi
- Institute of Agronomy and Plant Breeding II, Justus Liebig University, Gießen, Germany
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Alemu A, Batista L, Singh PK, Ceplitis A, Chawade A. Haplotype-tagged SNPs improve genomic prediction accuracy for Fusarium head blight resistance and yield-related traits in wheat. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2023; 136:92. [PMID: 37009920 PMCID: PMC10068637 DOI: 10.1007/s00122-023-04352-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Accepted: 03/21/2023] [Indexed: 06/19/2023]
Abstract
Linkage disequilibrium (LD)-based haplotyping with subsequent SNP tagging improved the genomic prediction accuracy up to 0.07 and 0.092 for Fusarium head blight resistance and spike width, respectively, across six different models. Genomic prediction is a powerful tool to enhance genetic gain in plant breeding. However, the method is accompanied by various complications leading to low prediction accuracy. One of the major challenges arises from the complex dimensionality of marker data. To overcome this issue, we applied two pre-selection methods for SNP markers viz. LD-based haplotype-tagging and GWAS-based trait-linked marker identification. Six different models were tested with preselected SNPs to predict the genomic estimated breeding values (GEBVs) of four traits measured in 419 winter wheat genotypes. Ten different sets of haplotype-tagged SNPs were selected by adjusting the level of LD thresholds. In addition, various sets of trait-linked SNPs were identified with different scenarios from the training-test combined and only from the training populations. The BRR and RR-BLUP models developed from haplotype-tagged SNPs had a higher prediction accuracy for FHB and SPW by 0.07 and 0.092, respectively, compared to the corresponding models developed without marker pre-selection. The highest prediction accuracy for SPW and FHB was achieved with tagged SNPs pruned at weak LD thresholds (r2 < 0.5), while stringent LD was required for spike length (SPL) and flag leaf area (FLA). Trait-linked SNPs identified only from training populations failed to improve the prediction accuracy of the four studied traits. Pre-selection of SNPs via LD-based haplotype-tagging could play a vital role in optimizing genomic selection and reducing genotyping costs. Furthermore, the method could pave the way for developing low-cost genotyping methods through customized genotyping platforms targeting key SNP markers tagged to essential haplotype blocks.
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Affiliation(s)
- Admas Alemu
- Department of Plant Breeding, Swedish University of Agricultural Sciences, Alnarp, Sweden
| | | | - Pawan K Singh
- International Maize and Wheat Improvement Center, Texcoco, Mexico
| | | | - Aakash Chawade
- Department of Plant Breeding, Swedish University of Agricultural Sciences, Alnarp, Sweden.
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Ishikawa G, Sakai H, Mizuno N, Solovieva E, Tanaka T, Matsubara K. Developing core marker sets for effective genomic-assisted selection in wheat and barley breeding programs. BREEDING SCIENCE 2022; 72:257-266. [PMID: 36408318 PMCID: PMC9653188 DOI: 10.1270/jsbbs.22004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/14/2022] [Accepted: 04/07/2022] [Indexed: 06/16/2023]
Abstract
Wheat (Triticum aestivum L.) and barley (Hordeum vulgare L.) are widely cultivated temperate crops. In breeding programs with these crops in Japan, effective genomic-assisted selection was performed by selecting core marker sets from thousands of genome-wide amplicon sequencing markers. The core sets consist of 768 and 960 markers for barley and wheat, respectively. These markers are distributed evenly across the genomes and effectively detect widely distributed polymorphisms in the chromosomes. The core set utility was assessed using 1,032 barley and 1,798 wheat accessions across the country. Minor allele frequency and chromosomal distributions showed that the core sets could effectively capture polymorphisms across the entire genome, indicating that the core sets are applicable to highly-related advanced breeding materials. Using the core sets, we also assessed the trait value predictability. As observed via fivefold cross-validation, the prediction accuracies of six barley traits ranged from 0.56-0.74 and 0.62 on average, and the corresponding values for eight wheat traits ranged from 0.44-0.83 and 0.65 on average. These data indicate that the established core marker sets enable breeding processes to be accelerated in a cost-effective manner and provide a strong foundation for further research on genomic selection in crops.
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Affiliation(s)
- Goro Ishikawa
- Institute of Crop Science, National Agriculture and Food Research Organization, 2-1-2 Kannondai, Tsukuba, Ibaraki 305-8518, Japan
| | - Hiroaki Sakai
- Research Center for Advanced Analysis, Core Technology Research Headquarters, National Agriculture and Food Research Organization, 3-1-1 Kannondai, Tsukuba, Ibaraki 305-8517, Japan
| | - Nobuyuki Mizuno
- Institute of Crop Science, National Agriculture and Food Research Organization, 2-1-2 Kannondai, Tsukuba, Ibaraki 305-8518, Japan
| | - Elena Solovieva
- Research Center for Advanced Analysis, Core Technology Research Headquarters, National Agriculture and Food Research Organization, 3-1-1 Kannondai, Tsukuba, Ibaraki 305-8517, Japan
| | - Tsuyoshi Tanaka
- Research Center for Advanced Analysis, Core Technology Research Headquarters, National Agriculture and Food Research Organization, 3-1-1 Kannondai, Tsukuba, Ibaraki 305-8517, Japan
| | - Kazuki Matsubara
- Institute of Crop Science, National Agriculture and Food Research Organization, 2-1-2 Kannondai, Tsukuba, Ibaraki 305-8518, Japan
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Lauer E, Holland J, Isik F. Prediction ability of genome-wide markers in Pinus taeda L. within and between population is affected by relatedness to the training population and trait genetic architecture. G3 (BETHESDA, MD.) 2022; 12:6440053. [PMID: 34849838 PMCID: PMC9210318 DOI: 10.1093/g3journal/jkab405] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Accepted: 11/08/2021] [Indexed: 11/26/2022]
Abstract
Genomic prediction has the potential to significantly increase the rate of genetic gain in tree breeding programs. In this study, a clonally replicated population (n = 2063) was used to train a genomic prediction model. The model was validated both within the training population and in a separate population (n = 451). The prediction abilities from random (20% vs 80%) cross validation within the training population were 0.56 and 0.78 for height and stem form, respectively. Removal of all full-sib relatives within the training population resulted in ∼50% reduction in their genomic prediction ability for both traits. The average prediction ability for all 451 individual trees was 0.29 for height and 0.57 for stem form. The degree of genetic linkage (full-sib family, half sib family, unrelated) between the training and validation sets had a strong impact on prediction ability for stem form but not for height. A dominant dwarfing allele, the first to be reported in a conifer species, was discovered via genome-wide association studies on linkage Group 5 that conferred a 0.33-m mean height reduction. However, the QTL was family specific. The rapid decay of linkage disequilibrium, large genome size, and inconsistencies in marker-QTL linkage phase suggest that large, diverse training populations are needed for genomic selection in Pinus taeda L.
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Affiliation(s)
- Edwin Lauer
- Department of Forestry and Environmental Resources, North Carolina State University, Raleigh, NC 27695, USA
| | - James Holland
- USDA-ARS Plant Science Research Unit, North Carolina State University, Raleigh, NC 27695, USA
| | - Fikret Isik
- Department of Forestry and Environmental Resources, North Carolina State University, Raleigh, NC 27695, USA
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Torres LG, de Oliveira EJ, Ogbonna AC, Bauchet GJ, Mueller LA, Azevedo CF, Fonseca e Silva F, Simiqueli GF, de Resende MDV. Can Cross-Country Genomic Predictions Be a Reasonable Strategy to Support Germplasm Exchange? - A Case Study With Hydrogen Cyanide in Cassava. FRONTIERS IN PLANT SCIENCE 2021; 12:742638. [PMID: 34956254 PMCID: PMC8692580 DOI: 10.3389/fpls.2021.742638] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/16/2021] [Accepted: 11/08/2021] [Indexed: 06/14/2023]
Abstract
Genomic prediction (GP) offers great opportunities for accelerated genetic gains by optimizing the breeding pipeline. One of the key factors to be considered is how the training populations (TP) are composed in terms of genetic improvement, kinship/origin, and their impacts on GP. Hydrogen cyanide content (HCN) is a determinant trait to guide cassava's products usage and processing. This work aimed to achieve the following objectives: (i) evaluate the feasibility of using cross-country (CC) GP between germplasm's of Embrapa Mandioca e Fruticultura (Embrapa, Brazil) and The International Institute of Tropical Agriculture (IITA, Nigeria) for HCN; (ii) provide an assessment of population structure for the joint dataset; (iii) estimate the genetic parameters based on single nucleotide polymorphisms (SNPs) and a haplotype-approach. Datasets of HCN from Embrapa and IITA breeding programs were analyzed, separately and jointly, with 1,230, 590, and 1,820 clones, respectively. After quality control, ∼14K SNPs were used for GP. The genomic estimated breeding values (GEBVs) were predicted based on SNP effects from analyses with TP composed of the following: (i) Embrapa genotypic and phenotypic data, (ii) IITA genotypic and phenotypic data, and (iii) the joint datasets. Comparisons on GEBVs' estimation were made considering the hypothetical situation of not having the phenotypic characterization for a set of clones for a certain research institute/country and might need to use the markers' effects that were trained with data from other research institutes/country's germplasm to estimate their clones' GEBV. Fixation index (FST) among the genetic groups identified within the joint dataset ranged from 0.002 to 0.091. The joint dataset provided an improved accuracy (0.8-0.85) compared to the prediction accuracy of either germplasm's sources individually (0.51-0.67). CC GP proved to have potential use under the present study's scenario, the correlation between GEBVs predicted with TP from Embrapa and IITA was 0.55 for Embrapa's germplasm, whereas for IITA's it was 0.1. This seems to be among the first attempts to evaluate the CC GP in plants. As such, a lot of useful new information was provided on the subject, which can guide new research on this very important and emerging field.
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Affiliation(s)
- Lívia Gomes Torres
- Department of Plant Science, Universidade Federal de Viçosa, Viçosa, Brazil
| | | | - Alex C. Ogbonna
- Department of Plant Breeding and Genetics, Cornell University, Ithaca, NY, United States
- Boyce Thompson Institute, Ithaca, NY, United States
| | | | - Lukas A. Mueller
- Department of Plant Breeding and Genetics, Cornell University, Ithaca, NY, United States
- Boyce Thompson Institute, Ithaca, NY, United States
| | | | | | | | - Marcos Deon Vilela de Resende
- Department of Forestry Engineering, Universidade Federal de Viçosa, Viçosa, Brazil
- Embrapa Café, Universidade Federal de Viçosa, Viçosa, Brazil
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Bhat JA, Yu D, Bohra A, Ganie SA, Varshney RK. Features and applications of haplotypes in crop breeding. Commun Biol 2021; 4:1266. [PMID: 34737387 PMCID: PMC8568931 DOI: 10.1038/s42003-021-02782-y] [Citation(s) in RCA: 39] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2021] [Accepted: 10/09/2021] [Indexed: 12/17/2022] Open
Abstract
Climate change with altered pest-disease dynamics and rising abiotic stresses threatens resource-constrained agricultural production systems worldwide. Genomics-assisted breeding (GAB) approaches have greatly contributed to enhancing crop breeding efficiency and delivering better varieties. Fast-growing capacity and affordability of DNA sequencing has motivated large-scale germplasm sequencing projects, thus opening exciting avenues for mining haplotypes for breeding applications. This review article highlights ways to mine haplotypes and apply them for complex trait dissection and in GAB approaches including haplotype-GWAS, haplotype-based breeding, haplotype-assisted genomic selection. Improvement strategies that efficiently deploy superior haplotypes to hasten breeding progress will be key to safeguarding global food security.
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Affiliation(s)
- Javaid Akhter Bhat
- National Center for Soybean Improvement, State Key Laboratory of Crop Genetics and Germplasm Enhancement, Nanjing Agricultural University, Nanjing, China
| | - Deyue Yu
- National Center for Soybean Improvement, State Key Laboratory of Crop Genetics and Germplasm Enhancement, Nanjing Agricultural University, Nanjing, China
| | - Abhishek Bohra
- Crop Improvement Division, ICAR- Indian Institute of Pulses Research (ICAR- IIPR), Kanpur, India
| | - Showkat Ahmad Ganie
- Department of Biotechnology, Visva-Bharati, Santiniketan, 731235, WB, India.
| | - Rajeev K Varshney
- Center of Excellence in Genomics & Systems Biology, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad, 502324, India.
- State Agricultural Biotechnology Centre, Centre for Crop & Food Research Innovation, Food Futures Institute, Murdoch University, Murdoch, WA, Australia.
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Bian C, Prakapenka D, Tan C, Yang R, Zhu D, Guo X, Liu D, Cai G, Li Y, Liang Z, Wu Z, Da Y, Hu X. Haplotype genomic prediction of phenotypic values based on chromosome distance and gene boundaries using low-coverage sequencing in Duroc pigs. Genet Sel Evol 2021; 53:78. [PMID: 34620094 PMCID: PMC8496108 DOI: 10.1186/s12711-021-00661-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2021] [Accepted: 08/20/2021] [Indexed: 11/22/2022] Open
Abstract
Background Genomic selection using single nucleotide polymorphism (SNP) markers has been widely used for genetic improvement of livestock, but most current methods of genomic selection are based on SNP models. In this study, we investigated the prediction accuracies of haplotype models based on fixed chromosome distances and gene boundaries compared to those of SNP models for genomic prediction of phenotypic values. We also examined the reasons for the successes and failures of haplotype genomic prediction. Methods We analyzed a swine population of 3195 Duroc boars with records on eight traits: body judging score (BJS), teat number (TN), age (AGW), loin muscle area (LMA), loin muscle depth (LMD) and back fat thickness (BF) at 100 kg live weight, and average daily gain (ADG) and feed conversion rate (FCR) from 30 to100 kg live weight. Ten-fold validation was used to evaluate the prediction accuracy of each SNP model and each multi-allelic haplotype model based on 488,124 autosomal SNPs from low-coverage sequencing. Haplotype blocks were defined using fixed chromosome distances or gene boundaries. Results Compared to the best SNP model, the accuracy of predicting phenotypic values using a haplotype model was greater by 7.4% for BJS, 7.1% for AGW, 6.6% for ADG, 4.9% for FCR, 2.7% for LMA, 1.9% for LMD, 1.4% for BF, and 0.3% for TN. The use of gene-based haplotype blocks resulted in the best prediction accuracy for LMA, LMD, and TN. Compared to estimates of SNP additive heritability, estimates of haplotype epistasis heritability were strongly correlated with the increase in prediction accuracy by haplotype models. The increase in prediction accuracy was largest for BJS, AGW, ADG, and FCR, which also had the largest estimates of haplotype epistasis heritability, 24.4% for BJS, 14.3% for AGW, 14.5% for ADG, and 17.7% for FCR. SNP and haplotype heritability profiles across the genome identified several genes with large genetic contributions to phenotypes: NUDT3 for LMA, LMD and BF, VRTN for TN, COL5A2 for BJS, BSND for ADG, and CARTPT for FCR. Conclusions Haplotype prediction models improved the accuracy for genomic prediction of phenotypes in Duroc pigs. For some traits, the best prediction accuracy was obtained with haplotypes defined using gene regions, which provides evidence that functional genomic information can improve the accuracy of haplotype genomic prediction for certain traits. Supplementary Information The online version contains supplementary material available at 10.1186/s12711-021-00661-y.
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Affiliation(s)
- Cheng Bian
- State Key Laboratory for Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing, 100193, China
| | - Dzianis Prakapenka
- Department of Animal Science, University of Minnesota, Saint Paul, MN, 55108, USA
| | - Cheng Tan
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangzhou, 510642, China.,National Engineering Research Center for Breeding Swine Industry, Wens Foodstuff Group Co., Ltd., Yunfu, 527400, China
| | - Ruifei Yang
- State Key Laboratory for Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing, 100193, China
| | - Di Zhu
- State Key Laboratory for Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing, 100193, China
| | - Xiaoli Guo
- State Key Laboratory for Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing, 100193, China
| | - Dewu Liu
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangzhou, 510642, China
| | - Gengyuan Cai
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangzhou, 510642, China.,National Engineering Research Center for Breeding Swine Industry, Wens Foodstuff Group Co., Ltd., Yunfu, 527400, China
| | - Yalan Li
- National Engineering Research Center for Breeding Swine Industry, Wens Foodstuff Group Co., Ltd., Yunfu, 527400, China
| | - Zuoxiang Liang
- Department of Animal Science, University of Minnesota, Saint Paul, MN, 55108, USA
| | - Zhenfang Wu
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangzhou, 510642, China. .,National Engineering Research Center for Breeding Swine Industry, Wens Foodstuff Group Co., Ltd., Yunfu, 527400, China.
| | - Yang Da
- Department of Animal Science, University of Minnesota, Saint Paul, MN, 55108, USA.
| | - Xiaoxiang Hu
- State Key Laboratory for Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing, 100193, China.
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Ahmar S, Ballesta P, Ali M, Mora-Poblete F. Achievements and Challenges of Genomics-Assisted Breeding in Forest Trees: From Marker-Assisted Selection to Genome Editing. Int J Mol Sci 2021; 22:10583. [PMID: 34638922 PMCID: PMC8508745 DOI: 10.3390/ijms221910583] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2021] [Revised: 09/26/2021] [Accepted: 09/27/2021] [Indexed: 12/23/2022] Open
Abstract
Forest tree breeding efforts have focused mainly on improving traits of economic importance, selecting trees suited to new environments or generating trees that are more resilient to biotic and abiotic stressors. This review describes various methods of forest tree selection assisted by genomics and the main technological challenges and achievements in research at the genomic level. Due to the long rotation time of a forest plantation and the resulting long generation times necessary to complete a breeding cycle, the use of advanced techniques with traditional breeding have been necessary, allowing the use of more precise methods for determining the genetic architecture of traits of interest, such as genome-wide association studies (GWASs) and genomic selection (GS). In this sense, main factors that determine the accuracy of genomic prediction models are also addressed. In turn, the introduction of genome editing opens the door to new possibilities in forest trees and especially clustered regularly interspaced short palindromic repeats and CRISPR-associated protein 9 (CRISPR/Cas9). It is a highly efficient and effective genome editing technique that has been used to effectively implement targetable changes at specific places in the genome of a forest tree. In this sense, forest trees still lack a transformation method and an inefficient number of genotypes for CRISPR/Cas9. This challenge could be addressed with the use of the newly developing technique GRF-GIF with speed breeding.
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Affiliation(s)
- Sunny Ahmar
- Institute of Biological Sciences, University of Talca, 1 Poniente 1141, Talca 3460000, Chile;
| | - Paulina Ballesta
- The National Fund for Scientific and Technological Development, Av. del Agua 3895, Talca 3460000, Chile
| | - Mohsin Ali
- Department of Forestry and Range Management, University of Agriculture Faisalabad, Faisalabad 38000, Pakistan;
| | - Freddy Mora-Poblete
- Institute of Biological Sciences, University of Talca, 1 Poniente 1141, Talca 3460000, Chile;
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Haplotype- and SNP-Based GWAS for Growth and Wood Quality Traits in Eucalyptus cladocalyx Trees under Arid Conditions. PLANTS 2021; 10:plants10010148. [PMID: 33450896 PMCID: PMC7828368 DOI: 10.3390/plants10010148] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/19/2020] [Revised: 01/06/2021] [Accepted: 01/11/2021] [Indexed: 12/14/2022]
Abstract
The agricultural and forestry productivity of Mediterranean ecosystems is strongly threatened by the adverse effects of climate change, including an increase in severe droughts and changes in rainfall distribution. In the present study, we performed a genome-wide association study (GWAS) to identify single-nucleotide polymorphisms (SNPs) and haplotype blocks associated with the growth and wood quality of Eucalyptus cladocalyx, a tree species suitable for low-rainfall sites. The study was conducted in a progeny-provenance trial established in an arid site with Mediterranean patterns located in the southern Atacama Desert, Chile. A total of 87 SNPs and 3 haplotype blocks were significantly associated with the 6 traits under study (tree height, diameter at breast height, slenderness coefficient, first bifurcation height, stem straightness, and pilodyn penetration). In addition, 11 loci were identified as pleiotropic through Bayesian multivariate regression and were mainly associated with wood hardness, height, and diameter. In general, the GWAS revealed associations with genes related to primary metabolism and biosynthesis of cell wall components. Additionally, associations coinciding with stress response genes, such as GEM-related 5 and prohibitin-3, were detected. The findings of this study provide valuable information regarding genetic control of morphological traits related to adaptation to arid environments.
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Liang Z, Tan C, Prakapenka D, Ma L, Da Y. Haplotype Analysis of Genomic Prediction Using Structural and Functional Genomic Information for Seven Human Phenotypes. Front Genet 2020; 11:588907. [PMID: 33324447 PMCID: PMC7726221 DOI: 10.3389/fgene.2020.588907] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2020] [Accepted: 10/28/2020] [Indexed: 11/19/2022] Open
Abstract
Genomic prediction using multi-allelic haplotype models improved the prediction accuracy for all seven human phenotypes, the normality transformed high density lipoproteins, low density lipoproteins, total cholesterol, triglycerides, weight, and the original height and body mass index without normality transformation. Eight SNP sets with 40,941-380,705 SNPs were evaluated. The increase in prediction accuracy due to haplotypes was 1.86-8.12%. Haplotypes using fixed chromosome distances had the best prediction accuracy for four phenotypes, fixed number of SNPs for two phenotypes, and gene-based haplotypes for high density lipoproteins and height (tied for best). Haplotypes of coding genes were more accurate than haplotypes of all autosome genes that included both coding and noncoding genes for triglycerides and weight, and nearly the same as haplotypes of all autosome genes for the other phenotypes. Haplotypes of noncoding genes (mostly lncRNAs) only improved the prediction accuracy over the SNP models for high density lipoproteins, total cholesterol, and height. ChIP-seq haplotypes had better prediction accuracy than gene-based haplotypes for total cholesterol, body mass index and low density lipoproteins. The accuracy of ChIP-seq haplotypes was most striking for low density lipoproteins, where all four haplotype models with ChIP-seq haplotypes had similarly high prediction accuracy over the best prediction model with gene-based haplotypes. Haplotype epistasis was shown to be the reason for the increased accuracy due to haplotypes. Low density lipoproteins had the largest haplotype epistasis heritability that explained 14.70% of the phenotypic variance and was 31.27% of the SNP additive heritability, and the largest increase in prediction accuracy relative to the best SNP model (8.12%). Relative to the SNP additive heritability of the same regions, noncoding genes had the highest haplotype epistasis heritability, followed by coding genes and ChIP-seq for the seven phenotypes. SNP and haplotype heritability profiles showed that the integration of SNP and haplotype additive values compensated the weakness of haplotypes in estimating SNP heritabilities for four phenotypes, whereas models with haplotype additive values fully accounted for SNP additive values for three phenotypes. These results showed that haplotype analysis can be a method to utilize functional and structural genomic information to improve the accuracy of genomic prediction.
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Affiliation(s)
- Zuoxiang Liang
- Department of Animal Science, University of Minnesota, Saint Paul, MN, United States
| | - Cheng Tan
- Department of Animal Science, University of Minnesota, Saint Paul, MN, United States
- National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangzhou, China
| | - Dzianis Prakapenka
- Department of Animal Science, University of Minnesota, Saint Paul, MN, United States
| | - Li Ma
- Department of Animal and Avian Sciences, University of Maryland, College Park, MD, United States
| | - Yang Da
- Department of Animal Science, University of Minnesota, Saint Paul, MN, United States
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