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The Fate of Deleterious Variants in a Barley Genomic Prediction Population.

ABSTRACT: Targeted identification and purging of deleterious genetic variants has been proposed as a novel approach to animal and plant breeding. This strategy is motivated, in part, by the observation that demographic events and strong selection associated with cultivated species pose a "cost of domestication." This includes an increase in the proportion of genetic variants that are likely to reduce fitness. Recent advances in DNA resequencing and sequence constraint-based approaches to predict the functional impact of a mutation permit the identification of putatively deleterious SNPs (dSNPs) on a genome-wide scale. Using exome capture resequencing of 21 barley lines, we identified 3855 dSNPs among 497,754 total SNPs. We generated whole-genome resequencing data of Hordeum murinum ssp. glaucum as a phylogenetic outgroup to polarize SNPs as ancestral vs. derived. We also observed a higher proportion of dSNPs per synonymous SNPs (sSNPs) in low-recombination regions of the genome. Using 5215 progeny from a genomic prediction experiment, we examined the fate of dSNPs over three breeding cycles. Adjusting for initial frequency, derived alleles at dSNPs reduced in frequency or were lost more often than other classes of SNPs. The highest-yielding lines in the experiment, as chosen by standard genomic prediction approaches, carried fewer homozygous dSNPs than randomly sampled lines from the same progeny cycle. In the final cycle of the experiment, progeny selected by genomic prediction had a mean of 5.6% fewer homozygous dSNPs relative to randomly chosen progeny from the same cycle.

SUBMITTER: Kono TJY

PROVIDER: S-EPMC6893365 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature

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The Fate of Deleterious Variants in a Barley Genomic Prediction Population.

Kono Thomas J Y TJY Liu Chaochih C Vonderharr Emily E EE Koenig Daniel D Fay Justin C JC Smith Kevin P KP Morrell Peter L PL

Genetics 20191025 4

Targeted identification and purging of deleterious genetic variants has been proposed as a novel approach to animal and plant breeding. This strategy is motivated, in part, by the observation that demographic events and strong selection associated with cultivated species pose a "cost of domestication." This includes an increase in the proportion of genetic variants that are likely to reduce fitness. Recent advances in DNA resequencing and sequence constraint-based approaches to predict the funct ...[more]

PMID: 31653677

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Project description:BackgroundMyoclonus-Dystonia (M-D) is a pleiotropic neuropsychiatric disorder of variable penetrance. Pathogenic variants in SGCE, a maternally imprinted gene, are the most frequent known genetic cause of M-D. The population prevalence of SGCE-linked M-D is unknown, the pathogenicity of SGCE variants identified in patients with M-D may be indeterminant, and SGCE variants predicted to be deleterious by in silico analysis may appear in patients undergoing whole-exome or whole-genome sequencing for seemingly unrelated disorders. The Genome Aggregation Database (gnomAD) v2 provides variant data on 125,748 exomes and 15,708 genomes from unrelated individuals sequenced as part of various disease-specific and population genetic studies.MethodsSGCE variants included in the gnomAD v2 dataset were analyzed with Combined Annotation Dependent Depletion (CADD), and database for nonsynonymous single nucleotide polymorphisms' functional predictions (dbNSFP). We determined the frequency of annotated SGCE variants, ranked by scores of deleteriousness, within the gnomAD v2 dataset. Deleteriousness scores were compared to a subset of published disease associated SGCE pathogenic variants.ResultsWithin gnomAD v2, there were 56, 408, and 1250 alleles harboring SGCE variants with CADD scores greater than 30, 25, and 20, respectively. We estimate that approximately 1/348 individuals in the United States population harbors an SGCE variant with a CADD score ≥ 25.DiscussionSGCE M-D may be underdiagnosed due to pleiotropy, mild phenotypes, variable penetrance, and impaired access to genetic testing. Due to the high population prevalence of deleterious SGCE variants, caution should be used when asserting pathogenicity without co-segregation analyses and expert neurological examination of phenotypes within pedigrees.HighlightsIn silico analyses of a large population database of genetic variants revealed that over 0.2% of individuals in the United States harbor a highly deleterious SGCE variant. This finding suggests that M-D and minor phenotypic variants such as mild isolated myoclonus may be underdiagnosed.

Project description:Multi-parent Advanced Generation Inter-crosses (MAGIC) lines have mosaic genomes that are generated shuffling the genetic material of the founder parents following pre-defined crossing schemes. In cereal crops, these experimental populations have been extensively used to investigate the genetic bases of several traits and dissect the genetic bases of epistasis. In plants, genomic prediction models are usually fitted using either diverse panels of mostly unrelated accessions or individuals of biparental families and several empirical analyses have been conducted to evaluate the predictive ability of models fitted to these populations using different traits. In this paper, we constructed, genotyped and evaluated a barley MAGIC population of 352 individuals developed with a diverse set of eight founder parents showing contrasting phenotypes for grain yield. We combined phenotypic and genotypic information of this MAGIC population to fit several genomic prediction models which were cross-validated to conduct empirical analyses aimed at examining the predictive ability of these models varying the sizes of training populations. Moreover, several methods to optimize the composition of the training population were also applied to this MAGIC population and cross-validated to estimate the resulting predictive ability. Finally, extensive phenotypic data generated in field trials organized across an ample range of water regimes and climatic conditions in the Mediterranean were used to fit and cross-validate multi-environment genomic prediction models including G×E interaction, using both genomic best linear unbiased prediction and reproducing kernel Hilbert space along with a non-linear Gaussian Kernel. Overall, our empirical analyses showed that genomic prediction models trained with a limited number of MAGIC lines can be used to predict grain yield with values of predictive ability that vary from 0.25 to 0.60 and that beyond QTL mapping and analysis of epistatic effects, MAGIC population might be used to successfully fit genomic prediction models. We concluded that for grain yield, the single-environment genomic prediction models examined in this study are equivalent in terms of predictive ability while, in general, multi-environment models that explicitly split marker effects in main and environmental-specific effects outperform simpler multi-environment models.

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Dataset Information

The Fate of Deleterious Variants in a Barley Genomic Prediction Population.

Publications

The Fate of Deleterious Variants in a Barley Genomic Prediction Population.

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