Abstract
Quantitative trait locus (QTL) analysis allows to identify regions responsible for a trait and to associate alleles with their effect on phenotypes. When using biallelic markers to find these QTL regions, two alleles per QTL are modelled. This assumption might be close to reality in specific biparental crosses but is unrealistic in situations where broader genetic diversity is studied. Diversity panels used in genome-wide association studies or multi-parental populations can easily harbour multiple QTL alleles at each locus, more so in the case of polyploids that carry more than two alleles per individual. In such situations a multiallelic model would be closer to reality, allowing for different genetic effects for each potential allele in the population. To obtain such multiallelic markers we propose the usage of haplotypes, concatenations of nearby SNPs. We developed “mpQTL” an R package that can perform a QTL analysis at any ploidy level under biallelic and multiallelic models, depending on the marker type given. We tested the effect of genetic diversity on the power and accuracy difference between bi-allelic and multiallelic models using a set of simulated multiparental autotetraploid, outbreeding populations. Multiallelic models had higher detection power and were more precise than biallelic, SNP-based models, particularly when genetic diversity was higher. This confirms that moving to multi-allelic QTL models can lead to improved detection and characterization of QTLs.Key messageQTL detection in populations with more than two functional QTL alleles (which is likely in multiparental and/or polyploid populations) is more powerful when using multiallelic models, rather than biallelic models.
Highlights
Quantitative trait locus (QTL) analyses are those experiments in which a population is genotyped with many markers that cover the whole genome, and phenotyped for traits of interest
When we move to scenarios where more than two alleles per QTL are expected to segregate at a single locus, for instance when heterozygosity is expected to be high or in multiparental populations, single Single Nucleotide Polymorphisms (SNPs) no longer tag QTL alleles uniquely
Each SNP allele might tag more than one functional QTL allele, creating a situation where the regression test is being performed between groups that do not represent a unique effect
Summary
Quantitative trait locus (QTL) analyses are those experiments in which a population is genotyped with many markers that cover the whole genome, and phenotyped for traits of interest. QTL studies have been extremely useful in unravelling genomic regions that control or contribute to important plant traits such as disease resistance, yield, crop quality or tolerance to abiotic stresses. The precision of these studies has been improved by the advent of high-throughput technologies, that facilitated genotyping of thousands. Thérèse Navarro et al BMC Bioinformatics (2022) 23:67 to millions of Single Nucleotide Polymorphisms (SNPs) in a single analysis This is nowadays possible in polyploid organisms, thanks to statistical and computational developments in the areas of genotyping, linkage map construction and QTL analysis [3, 4].
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