Location: Hard Winter Wheat Genetics ResearchTitle: Development of the Wheat Practical Haplotype Graph Database as a Resource for Genotyping Data Storage and Genotype Imputation
|MILLER, ZACK - Cornell College - Iowa|
|NYINE, MOSES - Kansas State University|
|HE, FEI - Kansas State University|
|Buckler, Edward - Ed|
|AKHUNOV, EDUARD - Kansas State University|
|Bowden, Robert - Bob|
Submitted to: bioRxiv
Publication Type: Pre-print Publication
Publication Acceptance Date: 6/11/2021
Publication Date: 6/11/2021
Citation: Jordan, K., Bradbury, P., Miller, Z., Nyine, M., He, F., Guttieri, M.J., Brown Guedira, G.L., Buckler Iv, E.S., Jannink, J., Akhunov, E., Ward, B.P., Bai, G., Bowden, R.L., Fiedler, J.D., Faris, J.D. 2021. Development of the Wheat Practical Haplotype Graph Database as a Resource for Genotyping Data Storage and Genotype Imputation. bioRxiv. https://doi.org/10.1101/2021.06.10.447944.
Interpretive Summary: Developing and using large numbers of DNA markers is rather difficult and expensive in wheat. The Practical Haplotype Graph (PHG) is a new bioinformatic tool that leverages existing high coverage DNA sequencing data to accurately impute marker data on additional lines with inexpensive low coverage input data. We provide evidence that a custom-built database that represents the diversity in US wheat breeding programs accurately (93%) predicts over 1.4 million variants of the DNA sequence with as little as one-one hundredth coverage input data. The PHG had significantly higher accuracy than the currently popular marker imputation tool called Beagle. The PHG has the potential to become an accurate, expandable, flexible, inexpensive imputation tool for marker genotyping in wheat.
Technical Abstract: We developed a haplotype diversity database for bread wheat (Triticum aestivum L.) by applying the Practical Haplotype Graph (PHG) tool for effective diversity data storage and imputation. The wheat PHG database was built using whole exome capture sequencing data generated for a diverse set of 65 wheat accessions. Population haplotypes were inferred for the reference ranges, which are defined by the boundaries of the high-quality gene models in the wheat genome. Missing genotypes in the inference panels, composed of wheat cultivars or recombinant inbred lines genotyped by exome capture, genotyping-by-sequencing (GBS), and whole-genome skim-seq sequencing approaches, were imputed using the wheat PHG database. Though imputation accuracy varied depending on the method of sequencing and depth of read coverage, we found 93% accuracy of imputation with 0.01x sequence coverage, which was only slightly lower than the accuracy obtained using the 0.5x sequence coverage data (96.9%). By direct comparison, PHG imputation outperformed Beagle imputation by nearly 4% (p-value = 0.00027). The reduced accuracy of imputation with GBS data (90.4%), likely is associated with the small overlap between the GBS and exome capture datasets used for constructing PHG. The highest imputation accuracy was obtained with exome capture for the wheat D genome, which also showed the highest levels of linkage disequlibrium and proportion of identity-by-descent regions among accessions in our reference panel. We demonstrate that genetic mapping based on genotypes imputed using PHG identifies SNPs with a broader range of effect sizes that together explain a higher proportion of genetic variance for heading date and meiotic crossover rate compared to previous studies.