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ARS Home » Northeast Area » Ithaca, New York » Robert W. Holley Center for Agriculture & Health » Plant, Soil and Nutrition Research » Research » Publications at this Location » Publication #390776

Research Project: Improving Crop Efficiency Using Genomic Diversity and Computational Modeling

Location: Plant, Soil and Nutrition Research

Title: Genome-wide imputation using the practical haplotype graph in the heterozygous crop cassava

Author
item LONG, EVAN - Cornell University - New York
item Bradbury, Peter
item ROMAY, CINTA - Cornell University - New York
item Buckler, Edward - Ed
item ROBBINS, KELLY - Cornell University - New York

Submitted to: G3, Genes/Genomes/Genetics
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 10/14/2021
Publication Date: 11/9/2021
Citation: Long, E.M., Bradbury, P., Romay, C.M., Buckler IV, E.S., Robbins, K.R. 2021. Genome-wide imputation using the practical haplotype graph in the heterozygous crop cassava. G3, Genes/Genomes/Genetics. 12(1):jkab383. https://doi.org/10.1093/g3journal/jkab383.
DOI: https://doi.org/10.1093/g3journal/jkab383

Interpretive Summary: For outbred crops such as cassava, it is difficult to obtain accurate genetic information from the sparse genotyping generally performed in agronomic settings, especially without large financial resources. This genetic information is needed to efficiently implement methods such as genomic prediction or genome-wide association, which can help increase genetic gains in crop breeding. Accurate genome imputation can help produce this genetic information with limited resources, improving the breeders ability to leverage these genetic tools.

Technical Abstract: Genomic applications such as genomic selection and genome-wide association have become increasingly common since the advent of genome sequencing. The cost of sequencing has decreased in the past two decades; however, genotyping costs are still prohibitive to gathering large datasets for these genomic applications, especially in nonmodel species where resources are less abundant. Genotype imputation makes it possible to infer whole-genome information from limited input data, making large sampling for genomic applications more feasible. Imputation becomes increasingly difficult in heterozygous species where haplotypes must be phased. The practical haplotype graph (PHG) is a recently developed tool that can accurately impute genotypes, using a reference panel of haplotypes. We showcase the ability of the PHG to impute genomic information in the highly heterozygous crop cassava (Manihot esculenta). Accurately phased haplotypes were sampled from runs of homozygosity across a diverse panel of individuals to populate PHG, which proved more accurate than relying on computational phasing methods. The PHG achieved high imputation accuracy, using sparse skim-sequencing input, which translated to substantial genomic prediction accuracy in cross-validation testing. The PHG showed improved imputation accuracy, compared to a standard imputation tool Beagle, especially in predicting rare alleles.