A comprehensive benchmark of graph-based genetic variant genotyping algorithms on plant genomes for creating an accurate ensemble pipeline

• Published in: Genome Biology 2024-04, Vol.25 (1), p.91-24, Article 91
• Main Authors: Du, Ze-Zhen, He, Jia-Bao, Jiao, Wen-Biao
• Format: Article
• Language: English
• Subjects: Accuracy; Algorithms; Benchmarking; Genetic diversity; Genome graph; Genome, Plant; Genomes; Genomics; Genomics - methods; Genotype; Genotype & phenotype; Genotypes; Genotyping; Genotyping Techniques - methods; Graphs; Haplotypes; High-Throughput Nucleotide Sequencing - methods; Humans; Performance evaluation; Plant genomes; Rape plants; Sequence Analysis, DNA - methods; Software; Soybeans; Structural variation
• ISSN: 1474-760X; 1474-7596; 1474-760X
• DOI: 10.1186/s13059-024-03239-1

Although sequencing technologies have boosted the measurement of the genomic diversity of plant crops, it remains challenging to accurately genotype millions of genetic variants, especially structural variations, with only short reads. In recent years, many graph-based variation genotyping methods have been developed to address this issue and tested for human genomes. However, their performance in plant genomes remains largely elusive. Furthermore, pipelines integrating the advantages of current genotyping methods might be required, considering the different complexity of plant genomes. Here we comprehensively evaluate eight such genotypers in different scenarios in terms of variant type and size, sequencing parameters, genomic context, and complexity, as well as graph size, using both simulated and real data sets from representative plant genomes. Our evaluation reveals that there are still great challenges to applying existing methods to plants, such as excessive repeats and variants or high resource consumption. Therefore, we propose a pipeline called Ensemble Variant Genotyper (EVG) that can achieve better genotyping performance in almost all experimental scenarios and comparably higher genotyping recall and precision even using 5× reads. Furthermore, we demonstrate that EVG is more robust with an increasing number of graphed genomes, especially for insertions and deletions. Our study will provide new insights into the development and application of graph-based genotyping algorithms. We conclude that EVG provides an accurate, unbiased, and cost-effective way for genotyping both small and large variations and will be potentially used in population-scale genotyping for large, repetitive, and heterozygous plant genomes.