Across-population genomic prediction in grapevine opens up promising prospects for breeding

Crop breeding involves two selection steps: choosing progenitors and selecting offspring within progenies. Genomic prediction, based on genome-wide marker estimation of genetic values, could facilitate these steps. However, its potential usefulness in grapevine (Vitis vinifera L.) has only been evaluated in non-breeding contexts mainly through cross-validation within a single population. We tested across-population genomic prediction in a more realistic breeding configuration, from a diversity panel to ten bi-parental crosses connected within a half-diallel mating design. Prediction quality was evaluated over 15 traits of interest (related to yield, berry composition, phenology and vigour), for both the average genetic value of each cross (cross mean) and the genetic values of individuals within each cross (individual values). Genomic prediction in these conditions was found useful: for cross mean, average per-trait predictive ability was 0.6, while per-cross predictive ability was halved on average, but reached a maximum of 0.7. Mean predictive ability for individual values within crosses was 0.26, about half the within-half-diallel value taken as a reference. For some traits and/or crosses, these across-population predictive ability values are promising for implementing genomic selection in grapevine breeding. This study also provided key insights on variables affecting predictive ability. Per-cross predictive ability was well predicted by genetic distance between parents and when this predictive ability was below 0.6, it was improved by training set optimization. For individual values, predictive ability mostly depended on trait-related variables (magnitude of the cross effect and heritability). These results will greatly help designing grapevine breeding programs assisted by genomic prediction.

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The utility of genomic prediction models in evolutionary genetics

Proceedings of The Royal Society B Biological Sciences ◽

10.1098/rspb.2021.0693 ◽

2021 ◽

Vol 288 (1956) ◽

pp. 20210693

Author(s):

Suzanne E. McGaugh ◽

Aaron J. Lorenz ◽

Lex E. Flagel

Keyword(s):

Best Practices ◽

Genomic Prediction ◽

Complex Traits ◽

Genetic Architecture ◽

Prediction Models ◽

Evolutionary Genetics ◽

Breeding Value ◽

Evolutionary Genomics ◽

Genome Wide ◽

Genetic Value

Variation in complex traits is the result of contributions from many loci of small effect. Based on this principle, genomic prediction methods are used to make predictions of breeding value for an individual using genome-wide molecular markers. In breeding, genomic prediction models have been used in plant and animal breeding for almost two decades to increase rates of genetic improvement and reduce the length of artificial selection experiments. However, evolutionary genomics studies have been slow to incorporate this technique to select individuals for breeding in a conservation context or to learn more about the genetic architecture of traits, the genetic value of missing individuals or microevolution of breeding values. Here, we outline the utility of genomic prediction and provide an overview of the methodology. We highlight opportunities to apply genomic prediction in evolutionary genetics of wild populations and the best practices when using these methods on field-collected phenotypes.

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Genome-wide association mapping and genomic prediction for adult stage sclerotinia stem rot resistance in Brassica napus (L) under field environments

Scientific Reports ◽

10.1038/s41598-021-01272-9 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Jayanta Roy ◽

T. M. Shaikh ◽

Luis del Río Mendoza ◽

Shakil Hosain ◽

Venkat Chapara ◽

...

Keyword(s):

Genomic Prediction ◽

Predictive Ability ◽

Significant Snps ◽

Genome Wide Association ◽

Stem Rot ◽

Lesion Length ◽

Sclerotinia Stem Rot ◽

Agar Plug ◽

Genome Wide ◽

Full Flowering

AbstractSclerotinia stem rot (SSR) is a fungal disease of rapeseed/canola that causes significant seed yield losses and reduces its oil content and quality. In the present study, the reaction of 187 diverse canola genotypes to SSR was characterized at full flowering stage using the agar plug to stem inoculation method in four environments. Genome-wide association study (GWAS) using three different algorithms identified 133 significant SNPs corresponding with 123 loci for disease traits like stem lesion length (LL), lesion width (LW), and plant mortality at 14 (PM_14D) and 21 (PM_21D) days. The explained phenotypic variation of these SNPs ranged from 3.6 to 12.1%. Nineteen significant SNPs were detected in two or more environments, disease traits with at least two GWAS algorithms. The strong correlations observed between LL and other three disease traits evaluated, suggest they could be used as proxies for SSR resistance phenotyping. Sixty-nine candidate genes associated with disease resistance mechanisms were identified. Genomic prediction (GP) analysis with all the four traits employing genome-wide markers resulted in 0.41–0.64 predictive ability depending on the model specifications. The highest predictive ability for PM_21D with three models was about 0.64. From our study, the identified resistant genotypes and stable significant SNP markers will serve as a valuable resource for future SSR resistance breeding. Our study also suggests that genomic selection holds promise for accelerating canola breeding progress by enabling breeders to select SSR resistance genotypes at the early stage by reducing the need to phenotype large numbers of genotypes.

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Genome-Wide Association and Genomic Prediction for Fry Color in Potato

Agronomy ◽

10.3390/agronomy10010090 ◽

2020 ◽

Vol 10 (1) ◽

pp. 90 ◽

Cited By ~ 1

Author(s):

Stephen Byrne ◽

Fergus Meade ◽

Francesca Mesiti ◽

Denis Griffin ◽

Colum Kennedy ◽

...

Keyword(s):

Genomic Prediction ◽

Marker Assisted Selection ◽

Potato Breeding ◽

Large Population ◽

Predictive Ability ◽

Small Subset ◽

Genome Wide ◽

Fry Color ◽

Color Data ◽

Major Qtl

Potatoes destined for crisping are normally stored above 8 degrees; below this glucose accumulates leading to very dark fry colors and potential acrylamide build up. Unfortunately, sprouting occurs above 4 degrees and impacts product quality, necessitating the use of sprout suppressant chemicals. Therefore, a goal of breeders is to develop potatoes with excellent fry color, which is maintained under storage below 8 degrees. Genomic or marker-assisted selection offers an opportunity to improve the efficiency of potato breeding and thereby assist breeders in achieving this goal. In this study, we have accumulated fry-color data on a large population of potato lines and combined this with genotypic data to carry out a GWAS and to evaluate accuracy of genomic prediction. We were able to identify a major QTL on chromosome 10 for fry color, and predict fry color with moderate accuracy using genome-wide markers. Furthermore, our results provide evidence that it is possible to identify a small subset of SNPs for processing characteristics that can give moderate predictive ability, albeit lower than that achieved with genome-wide markers.

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Interest of phenomic prediction as an alternative to genomic prediction in grapevine

10.1101/2021.12.16.472608 ◽

2021 ◽

Author(s):

Charlotte Brault ◽

Juliette Lazerges ◽

Agnès Doligez ◽

Miguel Thomas ◽

Martin Ecarnot ◽

...

Keyword(s):

Genomic Prediction ◽

Reflectance Spectrum ◽

Predictive Ability ◽

Relationship Matrix ◽

Diversity Panel ◽

Cereal Species ◽

Genetic Signal ◽

First Time ◽

Half Diallel ◽

Two Populations

Phenomic prediction has been defined as an alternative to genomic prediction by using spectra instead of molecular markers. A reflectance spectrum reflects the biochemical composition within a tissue, under genetic determinism. Thus, a relationship matrix built from spectra could potentially capture genetic signal. This new methodology has been successfully applied in several cereal species but little is known so far about its interest in perennial species. Besides, phenomic prediction has only been tested for a restricted set of traits, mainly related to yield or phenology. This study aims at applying phenomic prediction for the first time in grapevine, using spectra collected on two tissues and over two consecutive years, on two populations and for 15 traits. First, we characterized the genetic signal in spectra and under which condition it could be maximized, then phenomic predictive ability was compared to genomic predictive ability. We found that the co-inertia between spectra and genomic data was stable across tissues or years, but variable across populations, with co-inertia around 0.3 and 0.6 for diversity panel and half-diallel populations, respectively. Differences between populations were also observed for predictive ability of phenomic prediction, with an average of 0.27 for the diversity panel and 0.35 for the half-diallel. For both populations, there was a correlation across traits between predictive ability of genomic and phenomic prediction, with a slope around 1 and an intercept of -0.2, thus suggesting that phenomic prediction could be applied for any trait.

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Genetic Mapping and Genomic Prediction of Sclerotinia Stem Rot Resistance to Rapeseed/Canola (Brassica Napus L.) at Seedling Stage

10.21203/rs.3.rs-1135987/v1 ◽

2021 ◽

Author(s):

Jayanta Roy ◽

Luis E. del Río Mendoza ◽

Nonoy Bandillo ◽

Phillip E. McClean ◽

Mukhlesur Rahman

Keyword(s):

Genomic Prediction ◽

Predictive Ability ◽

Resistance Breeding ◽

Significant Snps ◽

Stem Rot ◽

Post Inoculation ◽

Phenotypic Variance ◽

Sclerotinia Stem Rot ◽

Brassica Napus L ◽

Genome Wide

Abstract The lack of complete host resistance and a complex resistance inheritance nature between rapeseed/canola and Sclerotinia sclerotiorum often limits the development of functional molecular markers that enable breeding for sclerotinia stem rot (SSR) resistance. However, genomics-assisted selection has the potential to accelerate the breeding for SSR resistance. Therefore, genome-wide association (GWA) mapping and genomic prediction (GP) was performed using a diverse panel of 337 rapeseed/canola genotypes. Three-weeks old seedlings were screened using the petiole inoculation technique (PIT). Days to wilt (DW) up to 2 weeks and lesion phenotypes (LP) at 3, 4, and 7 days post inoculation (dpi) were recorded. A strong correlation (r = -0.94) between DW and LP_4dpi implied that a single time point scoring at four days could be used as a proxy trait. GWA analyses using single-locus (SL) and multi-locus (ML) models identified a total of 35, and 219 significantly associated SNPs, respectively. Out of these, seventy-one SNPs were identified by a combination of the SL model and any of the ML models, at least two ML models, or two traits. These SNPs explained 1.4-13.3% of the phenotypic variance, and considered as significant, could be associated with SSR resistance. Eighty-one candidate genes with a function in disease resistance were associated with the significant SNPs. Six GP models resulted in moderate to high (0.45-0.68) predictive ability depending on SSR resistance traits. The resistant genotypes and significant SNPs will serve as valuable resources for future SSR resistance breeding. Our results also highlight the potential of genomic selection to improve rapeseed/canola breeding for SSR resistance.

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MegaLMM: Mega-scale linear mixed models for genomic predictions with thousands of traits

Genome Biology ◽

10.1186/s13059-021-02416-w ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Daniel E. Runcie ◽

Jiayi Qu ◽

Hao Cheng ◽

Lorin Crawford

Keyword(s):

Genomic Prediction ◽

Large Scale ◽

Mixed Model ◽

Human Genetics ◽

Linear Mixed Effect Model ◽

Mixed Effect ◽

Statistical Framework ◽

Effect Model ◽

Plant Data ◽

Genetic Value

AbstractLarge-scale phenotype data can enhance the power of genomic prediction in plant and animal breeding, as well as human genetics. However, the statistical foundation of multi-trait genomic prediction is based on the multivariate linear mixed effect model, a tool notorious for its fragility when applied to more than a handful of traits. We present , a statistical framework and associated software package for mixed model analyses of a virtually unlimited number of traits. Using three examples with real plant data, we show that can leverage thousands of traits at once to significantly improve genetic value prediction accuracy.

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Genome-wide fine-mapping identifies pleiotropic and functional variants that predict many traits across global cattle populations

Nature Communications ◽

10.1038/s41467-021-21001-0 ◽

2021 ◽

Vol 12 (1) ◽

Cited By ~ 1

Author(s):

Ruidong Xiang ◽

Iona M. MacLeod ◽

Hans D. Daetwyler ◽

Gerben de Jong ◽

Erin O’Connor ◽

...

Keyword(s):

Dairy Cattle ◽

Chromosome Segment ◽

Multiple Traits ◽

Functional Variants ◽

Genome Wide ◽

The Usa ◽

Pleiotropic Qtl ◽

Causal Variants ◽

Genetic Value ◽

Bayesian Mixture Models

AbstractThe difficulty in finding causative mutations has hampered their use in genomic prediction. Here, we present a methodology to fine-map potentially causal variants genome-wide by integrating the functional, evolutionary and pleiotropic information of variants using GWAS, variant clustering and Bayesian mixture models. Our analysis of 17 million sequence variants in 44,000+ Australian dairy cattle for 34 traits suggests, on average, one pleiotropic QTL existing in each 50 kb chromosome-segment. We selected a set of 80k variants representing potentially causal variants within each chromosome segment to develop a bovine XT-50K genotyping array. The custom array contains many pleiotropic variants with biological functions, including splicing QTLs and variants at conserved sites across 100 vertebrate species. This biology-informed custom array outperformed the standard array in predicting genetic value of multiple traits across populations in independent datasets of 90,000+ dairy cattle from the USA, Australia and New Zealand.

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