Variant filters using segregation information improve mapping of nectar-production genes in sunflower ( Helianthus annuus L.)

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ABSTRACT Accurate variant calling is critical for identifying the genetic basis of complex traits, yet filters used in variant detection and validation may inadvertently exclude valuable genetic information. In this study, we compare common sequencing depth filters, used to eliminate error-prone variants associated with repetitive regions and technical issues, with a biologically relevant filtering approach that targets expected population-level Mendelian segregation. The resulting variant sets were evaluated in the context of nectar volume QTL mapping in sunflower ( Helianthus annuus L.). Our previous research failed to detect a significant interval containing a strong candidate gene for nectar production ( HaCWINV2) . We removed certain hard filters and implemented a Chi-square goodness-of-fit test to retain variants that segregate according to expected genetic ratios. We hypothesized that this will enhance mapping resolution and capture key genetic regions previously missed. We demonstrate that biologically relevant filtering retains more significant QTL and candidate genes, including HaCWINV2 , while removing variants due to technical errors more effectively, and accounted for 48.55% of phenotypic variation. In finding nine putative homologs of Arabidopsis genes with nectary function within QTL regions, we demonstrate that this filtering strategy, which considers biological contexts, has a higher power of true variant detection than the commonly used variant depth filtering strategy. PLAIN LANGUAGE SUMMARY In genomic research, identifying genetic markers is key to understanding complex traits, but traditional methods for filtering genetic data can sometimes miss important information. In this study, we explored a new data filtering approach for mapping genes related to nectar production in sunflower. We applied a more flexible filtering method that considers how markers are expected to segregate in breeding populations. Our previous work failed to identify an important gene previously hypothesized to be involved in nectar production, likely due to overly strict filtering. Our improved approach identified nine sunflower genes related to nectar production genes in the model species Arabidopsis thaliana , as compared to zero genes identified from the previous filtering strategy. This study highlights the value of using flexible, biologically relevant filtering methods, which can lead to better results in plant genomic studies. CORE IDEAS Discovering biologically meaningful variants from sequence data requires a careful and critical view of bioinformatic workflows. The use of arbitrary filters can remove significant genomic variation that contributes to the phenotype of interest. Arbitrary filters can also fail to remove variant call errors. A Chi-square filtering strategy based on segregation ratio retained a larger number of valid variants. More candidate regions with putative nectar-related genes and better statistical support were discovered.

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