bioRxivGeneticsPreprint
Leveraging mutational burden for complex trait prediction in sorghum
Ravi ValluruiD, Élodie GazaveiD, Samuel B. FernandesiD, John N. FergusoniD, Roberto Daniel LozanoiD, Pradeepa HirannaiahiD, Tao Zuo, Patrick J. BrowniD, Andrew D. B. LeakeyiD, Michael A. GoreiD, Edward S. BuckleriD, Nonoy B. BandilloiD
Abstract
Sorghum (Sorghum bicolor (L.) Moench) is a major staple food cereal for millions of people worldwide. The sorghum genome, like other species, accumulates deleterious mutations, likely impacting its fitness. Though selection keeps deleterious mutations rare, their complete removal from the genome is impeded due to lack of recombination, drift, and their coupling with favorable loci. To study how deleterious mutations impact agronomic phenotypes, we identified putative deleterious mutations among ~5.5M segregating variants of 229 diverse sorghum lines. We provide the whole-genome estimate of the deleterious burden in sorghum, showing that about 33% of nonsynonymous substitutions are putatively deleterious. The pattern of mutation burden varies appreciably among racial groups; the caudatum shows higher mutation burden while the guinea has lower burden. Across racial groups, the mutation burden correlated negatively with biomass, plant height, Specific Leaf Area (SLA), and tissue starch content, suggesting deleterious burden decreases trait fitness. Putatively deleterious variants explain roughly half of the genetic variance. However, there is only moderate improvement in total heritable variance explained for biomass (7.6%) and plant height (5.2%). There is no advantage in total heritable variance for SLA and starch. The contribution of putatively deleterious variants to phenotypic diversity therefore appears to be dependent on the genetic architecture of traits. Overall, our results suggest that including putatively deleterious variants in models do not significantly improve breeding accuracy because of extensive linkage. However, knowledge of deleterious variants could be leveraged for sorghum breeding through genome editing.
- organism: plant (other)
- assay: whole-genome sequencing
- research focus: variant effect prediction
- approach: statistical model
- Citations
- 4
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