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

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.

Skim exome capture genotyping in wheat

“…However, it should be noted that in this experiment and the previous wheat PHG experiment, both are using cultivars and recombinant inbred lines that are expected to be homozygous, although they may have some internal heterogeneity. In the case of mapping populations that are still in the F 2 ‐F 4 generations, or are expected to retain high residual heterozygosity, higher depth of coverage will be needed to be able to detect heterozygous sites and properly phase haplotypes (Long et al., 2022).…”

Pangenomes as a Resource to Accelerate Breeding of Under-Utilised Crop Species

“…Another type of pangenome graph is the practical haplotype graph (PHG) [ 92 , 93 ], which is a trellis graph representing genic and intergenic regions. PHGs avoid challenges in aligning repetitive and highly divergent regions through the use of a reference genome coordinate system that uses genes to anchor sequences [ 92 , 94 ], minimising errors due to reference bias, poor alignment and miscalled variants [ 95 ]. A common use of PHGs is to determine which haplotypes or genotypes of parental haplotypes that have been sequenced at high coverage are present in progeny that have been sequenced at low coverage.…”

“…A common use of PHGs is to determine which haplotypes or genotypes of parental haplotypes that have been sequenced at high coverage are present in progeny that have been sequenced at low coverage. These graphs have been used in sorghum [ 92 ], maize [ 96 ] and cassava ( Manihot esculenta ) [ 95 ] to impute SNPs from low-coverage DNA sequence data. PHGs can support plant breeding as they can accurately capture the position of genomic variations among individuals.…”

Expanding Gene-Editing Potential in Crop Improvement with Pangenomes

“…Due to falling sequencing costs and the increased acknowledgment of significant gene presence/absence variation in some species, pangenomes have expanded beyond bacteria to higher organisms such as chicken [44] and human [45] as well as many plant species, allowing the analysis of the large-scale PAV observed in plants [46,47]. Pangenomics in plants was first proposed by Morgante et al in 2007 [48] and since then, pangenomes have been assembled for many crop plant species including soybean (Glycine max) [49,50], maize (Zea mays) [51], tomato (Solanum lycopersicum) [35], Brassica oleracea [39], Brassica napus [27,52], Brachypodium distachyon [53], barley (Hordeum vulgare) [54], rice [55], pigeon pea (Cajanus cajan) [29,56], apple (Malus domestica) [57], capsicum [25], sesame (Sesamum indicum) [58], sunflower (Helianthus annuus) [59], yuca (Manihot esculenta) [60], sorghum (Sorghum bicolor) [36,61], and bread wheat (Triticum aestivum) [62]. Pangenomes for non-food plant species such as Arabidopsis thaliana [63], Amborella trichopoda [64], cotton (Gossypium) [65], and barrel clover (Medicago truncatula) [66] have also been published (Table 1).…”