Genomic prediction with allele dosage information in highly polyploid species

Several studies have shown how to leverage allele dosage information to improve the accuracy of genomic selection models in autotetraploids. In this study we expanded the methodology used for genomic selection in autotetraploids to higher (and mixed) ploidy levels. We adapted the models to build covariance matrices of both additive and digenic dominance effects that are subsequently used in genomic selection models. We applied these models using estimates of ploidy and allele dosage to sugarcane and sweet potato datasets and validated our results by also applying the models in simulated data. For the simulated datasets, including allele dosage information led up to 140% higher mean predictive abilities in comparison to using diploidized markers. Including dominance effects was highly advantageous when using diploidized markers, leading to mean predictive abilities which were up to 115% higher in comparison to only including additive effects. When the frequency of heterozygous genotypes in the population was low, such as in the sugarcane and sweet potato datasets, there was little advantage in including allele dosage information in the models. Overall, we show that including allele dosage can improve genomic selection in highly polyploid species under higher frequency of different heterozygous genotypic classes and high dominance degree levels.

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Relationships among blueberry species within the section Cyanococcus of the Vaccinium genus based on EST-PCR markers

Canadian Journal of Plant Science ◽

10.1139/cjps-2021-0221 ◽

2021 ◽

Author(s):

Lisa Jeannine Rowland ◽

Elizabeth L. Ogden ◽

James R. Ballington

Keyword(s):

Polymerase Chain Reaction ◽

Expressed Sequence Tag ◽

Clustering Methods ◽

Polyploid Species ◽

Pcr Markers ◽

Chain Reaction ◽

Ploidy Levels ◽

Commercial Species ◽

Polymerase Chain ◽

Expressed Sequence

Commercial blueberry species of North America belong to the Vaccinium genus, section Cyanococcus. Phylogenetic relationships of 50 accessions of different ploidy levels within Cyanococcus were investigated using 249 expressed sequence tag-polymerase chain reaction markers and standard clustering methods. Of the commercial species, tetraploid V. corymbosum grouped most closely with the diploids, V. fuscatum and V. caesariense, followed by the diploid V. elliottii. Tetraploid V. angustifolium grouped with the diploids, V. boreale and V. myrtilloides. Hexaploid V. virgatum grouped most closely with the diploid V. tenellum, thus shedding light on the origins of these polyploid species.

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330 A hybrid model for genomic selection using prioritized SNPs based on FST scores in the presence of non-genotyped animals

Journal of Animal Science ◽

10.1093/jas/skz258.102 ◽

2019 ◽

Vol 97 (Supplement_3) ◽

pp. 51-51

Author(s):

Sajjad Toghiani ◽

Ling-Yun Chang ◽

El H Hay ◽

Andrew J Roberts ◽

Samuel E Aggrey ◽

...

Keyword(s):

Genomic Selection ◽

Hybrid Approach ◽

Computational Cost ◽

Simulated Data ◽

Snp Markers ◽

Genomic Relationship Matrix ◽

Polygenic Effect ◽

Relationship Matrix ◽

Continuous Increase ◽

Missing Genotypes

Abstract The dramatic advancement in genotyping technology has greatly reduced the complexity and cost of genotyping. The continuous increase in the density of marker panels is resulting in little to no improvement in the accuracy of genomic selection. Direct inversion of the genomic relationship matrix is infeasible for some livestock populations due to the excessive computational cost. In addition, most animals in genetic evaluation programs are non-genotyped. Including these animals in a genomic evaluation requires the imputation of the missing genotypes when using regression methods. To overcome these challenges, a hybrid approach is proposed. This approach fits a subset of SNP markers selected based on FST scores and a classical polygenic effect. The method was first tested using only genotyped animals and then extended to accommodate non-genotyped animals. The proposed approach was evaluated using simulated data for a trait with heritability of 0.1 and 0.4 and weaning weight in a crossbred beef cattle population. When all animals were genotyped, the hybrid approach using only 2.5% of prioritized SNPs exceeded the prediction accuracies of BayesB, BayesC, and GBLUP by more than 7%. When non-genotyped animals were incorporated, the proposed approach significantly outperformed ss-GBLUP method in terms of prediction accuracy under both simulated heritability scenarios. Although the results seem to depend on the genetic complexity of the trait, the proposed approach resulted in higher prediction accuracies than current methods. Furthermore, its computational costs in terms of CPU time and peak memory are substantially lower than the current methods.

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Genotype × Environment Interaction in Psychopathology: Fact or Artifact?

Twin Research and Human Genetics ◽

10.1375/twin.9.1.1 ◽

2006 ◽

Vol 9 (1) ◽

pp. 1-8 ◽

Cited By ~ 96

Author(s):

Lindon J. Eaves

Keyword(s):

Candidate Genes ◽

Biological Significance ◽

Simulated Data ◽

Threshold Value ◽

Environment Interaction ◽

Additive Effects ◽

Mixture Of Distributions ◽

Genotype Environment Interaction ◽

Genes And Environment ◽

Main Effects

AbstractRecent studies have claimed to detect interaction between candidate genes and specific environmental factors (Genotype × Environment interaction, G × E) in susceptibility to psychiatric disorder. The objective of the present study was to examine possible artifacts that could explain widely publicized findings. The additive effects of candidate genes and measured environment on liability to disorder were simulated under a model that allowed for mixture of distributions in liability conditional on genotype and environment. Simulated liabilities were dichotomized at a threshold value to reflect diagnosis of disorder. Multiple blocks of simulated data were analyzed by standard statistical methods to test for the main effects and interactions of genes and environment on outcome. The main outcome of this study was simulated liabilities and diagnoses of major depression and antisocial behavior. Analysis of the dichotomized data by logistic regression frequently detected significant G × E interaction even though none was present for liability. There is therefore reason to question the biological significance of published findings.

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Historical Datasets Support Genomic Selection Models for the Prediction of Cotton Fiber Quality Phenotypes Across Multiple Environments

G3 Genes|Genome|Genetics ◽

10.1534/g3.118.200140 ◽

2018 ◽

Vol 8 (5) ◽

pp. 1721-1732 ◽

Cited By ~ 5

Author(s):

Washington Gapare ◽

Shiming Liu ◽

Warren Conaty ◽

Qian-Hao Zhu ◽

Vanessa Gillespie ◽

...

Keyword(s):

Genomic Selection ◽

Cotton Fiber ◽

Fiber Quality ◽

Selection Models ◽

Cotton Fiber Quality ◽

Multiple Environments

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Genomic Selection with Allele Dosage in Panicum maximum Jacq.

G3 Genes|Genome|Genetics ◽

10.1534/g3.118.200986 ◽

2019 ◽

Vol 9 (8) ◽

pp. 2463-2475 ◽

Cited By ~ 6

Author(s):

Letícia A. de C. Lara ◽

Mateus F. Santos ◽

Liana Jank ◽

Lucimara Chiari ◽

Mariane de M. Vilela ◽

...

Keyword(s):

Genomic Selection ◽

Panicum Maximum ◽

Allele Dosage

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Multienvironment and Multitrait Genomic Selection Models in Unbalanced Early-Generation Wheat Yield Trials

Crop Science ◽

10.2135/cropsci2018.03.0189 ◽

2019 ◽

Vol 59 (2) ◽

pp. 491-507 ◽

Cited By ~ 10

Author(s):

Brian P. Ward ◽

Gina Brown-Guedira ◽

Priyanka Tyagi ◽

Frederic L. Kolb ◽

David A. Van Sanford ◽

...

Keyword(s):

Genomic Selection ◽

Wheat Yield ◽

Early Generation ◽

Selection Models ◽

Yield Trials

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Genome-wide association study and genomic predictions for resistance against Piscirickettsia salmonis in coho salmon (Oncorhynchus kisutch) using ddRAD sequencing

10.1101/124099 ◽

2017 ◽

Cited By ~ 3

Author(s):

Agustín Barría ◽

Kris A. Christensen ◽

Katharina Correa ◽

Ana Jedlicki ◽

Jean P. Lhorente ◽

...

Keyword(s):

Genomic Selection ◽

Genetic Improvement ◽

Coho Salmon ◽

Association Studies ◽

Oncorhynchus Kisutch ◽

Genome Wide Association ◽

Genome Wide Association Studies ◽

Selection Models ◽

Genome Wide ◽

Piscirickettsia Salmonis

ABSTRACTPiscirickettsia salmonis is one of the main infectious diseases affecting coho salmon (Oncorhynchus kisutch) farming. Current treatments have been ineffective for the control of the disease. Genetic improvement for P. salmonis resistance has been proposed as a feasible alternative for the control of this infectious disease in farmed fish. Genotyping by sequencing (GBS) strategies allow genotyping hundreds of individuals with thousands of single nucleotide polymorphisms (SNPs), which can be used to perform genome wide association studies (GWAS) and predict genetic values using genome-wide information. We used double-digest restriction-site associated DNA (ddRAD) sequencing to dissect the genetic architecture of resistance against P. salmonis in a farmed coho salmon population and identify molecular markers associated with the trait. We also evaluated genomic selection (GS) models in order to determine the potential to accelerate the genetic improvement of this trait by means of using genome-wide molecular information. 764 individuals from 33 full-sib families (17 highly resistant and 16 highly susceptible) which were experimentally challenged against P. salmonis were sequenced using ddRAD sequencing. A total of 4,174 SNP markers were identified in the population. These markers were used to perform a GWAS and testing genomic selection models. One SNP related with iron availability was genome-wide significantly associated with resistance to P. salmonis defined as day of death. Genomic selection models showed similar accuracies and predictive abilities than traditional pedigree-based best linear unbiased prediction (PBLUP) method.

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Genomic prediction in an outcrossing and autotetraploid fruit crop: lessons from blueberry breeding

10.1101/2021.03.05.434007 ◽

2021 ◽

Author(s):

Luís Felipe V. Ferrão ◽

Rodrigo R. Amadeu ◽

Juliana Benevenuto ◽

Ivone de Bem Oliveira ◽

Patricio R. Munoz

Keyword(s):

Genomic Selection ◽

Genomic Prediction ◽

Recurrent Selection ◽

Vaccinium Corymbosum ◽

Breeding Program ◽

Polyploid Species ◽

Specialty Crop ◽

Validation Set ◽

Production Areas ◽

The University

AbstractBlueberry (Vaccinium corymbosum and hybrids) is a specialty crop, with expanding production and consumption worldwide. The blueberry breeding program at the University of Florida (UF) has greatly contributed to the expansion of production areas by developing low-chilling cultivars better adapted to subtropical and Mediterranean climates of the globe. The breeding program has historically focused on phenotypic recurrent selection. As an autopolyploid, outcrossing, perennial, long juvenile phase crop, blueberry’s breeding cycles are costly and time-consuming, which results in low genetic gains per unit of time. Motivated by the application of molecular markers for a more accurate selection in early stages of breeding, we performed pioneering genomic prediction studies and optimization for implementation in the blueberry breeding program. We have also addressed some complexities of sequence-based geno- typing and model parametrization for an autopolyploid crop, providing empirical contributions that can be extended to other polyploid species. We herein revisited some of our previous genomic prediction studies and described the current achievements in the crop. In this paper, our contribution for genomic prediction in an autotetraploid crop is three-fold: i) summarize previous results on the relevance of model parametrizations, such as diploid or polyploid methods, and inclusion of dominance effects; ii) assess the importance of sequence depth of coverage and genotype dosage calling steps; iii) demonstrate the real impact of genomic selection on leveraging breeding decisions by using an independent validation set. Altogether, we propose a strategy for the use of genomic selection in blueberry, with potential to be applied to other polyploid species of a similar background.

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Whole genome duplication potentiates inter-specific hybridisation and niche shifts in Australian burrowing frogs Neobatrachus

10.1101/593699 ◽

2019 ◽

Cited By ~ 2

Author(s):

Polina Yu. Novikova ◽

Ian G. Brennan ◽

William Booker ◽

Michael Mahony ◽

Paul Doughty ◽

...

Keyword(s):

Gene Flow ◽

Sequence Data ◽

Diploid Species ◽

Exome Capture ◽

Polyploid Species ◽

Ploidy Levels ◽

Population Genomic ◽

Niche Shifts ◽

Inheritance Mode ◽

The Impact

Polyploidy has played an important role in evolution across the tree of life but it is still unclear how polyploid lineages may persist after their initial formation. While both common and well-studied in plants, polyploidy is rare in animals and generally less well-understood. The Australian burrowing frog genus Neobatrachus is comprised of six diploid and three polyploid species and offers a powerful animal polyploid model system. We generated exome-capture sequence data from 87 individuals representing all nine species of Neobatrachus to investigate species-level relationships, the origin and inheritance mode of polyploid species, and the population genomic effects of polyploidy on genus-wide demography. We resolve the phylogenetic relationships among Neobatrachus species and provide further support that the three polyploid species have independent autotetraploid origins. We document higher genetic diversity in tetraploids, resulting from widespread gene flow specifically between the tetraploids, asymmetric inter-ploidy gene flow directed from sympatric diploids to tetraploids, and current isolation of diploid species from each other. We also constructed models of ecologically suitable areas for each species to investigate the impact of climate variation on frogs with differing ploidy levels. These models suggest substantial change in suitable areas compared to past climate, which in turn corresponds to population genomic estimates of demographic histories. We propose that Neobatrachus diploids may be suffering the early genomic impacts of climate-induced habitat loss, while tetraploids appear to be avoiding this fate, possibly due to widespread gene flow into tetraploid lineages specifically. Finally, we demonstrate that Neobatrachus is an attractive model to study the effects of ploidy on the evolution of adaptation in animals.

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