Barstow Ashley C, McNellie James P, Smart Brian C, Keepers Kyle G, Prasifka Jarrad R, Kane Nolan C, Hulke Brent S
Department of Plant Sciences, North Dakota State University, Fargo, North Dakota, USA.
USDA-ARS Sunflower Improvement Research Unit, Edward T. Schafer Agricultural Research Center, Fargo, North Dakota, USA.
Plant Genome. 2025 Jun;18(2):e70042. doi: 10.1002/tpg2.70042.
Accurate variant calling is critical for identifying the genetic basis of complex traits, yet filters used in variant detection 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 Mendelian segregation. The resulting variant sets were evaluated in the context of nectar volume quantitative trait loci (QTL) mapping in sunflower (Helianthus annuus L.). Our previous research failed to detect an interval containing a strong candidate gene for nectar production (HaCWINV2). We removed hard filters and implemented a chi-square goodness-of-fit test to retain variants that segregate according to expected genetic ratios. 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 nectar production phenotypic variation. In finding nine putative homologs of Arabidopsis genes with nectary function within QTL regions, we demonstrate that this filtering strategy has a higher power of true variant detection in QTL mapping than the commonly used variant depth filtering strategy. Future research will adapt the technique to multiple population contexts, such as genomic selection.
准确的变异检测对于识别复杂性状的遗传基础至关重要，然而变异检测中使用的过滤方法可能会无意中排除有价值的遗传信息。在本研究中，我们将用于消除与重复区域和技术问题相关的易出错变异的常见测序深度过滤方法，与针对预期孟德尔分离的生物学相关过滤方法进行了比较。在向日葵（Helianthus annuus L.）花蜜量数量性状基因座（QTL）定位的背景下对所得变异集进行了评估。我们之前的研究未能检测到包含一个用于花蜜产生的强候选基因（HaCWINV2）的区间。我们去除了严格的过滤方法，并实施了卡方拟合优度检验以保留按照预期遗传比例分离的变异。我们证明，生物学相关过滤保留了更多显著的QTL和候选基因，包括HaCWINV2，同时更有效地去除了由于技术错误产生的变异，并解释了48.55%的花蜜产生表型变异。在QTL区域内发现了9个拟南芥具有蜜腺功能基因的推定同源物，我们证明这种过滤策略在QTL定位中比常用的变异深度过滤策略具有更高的真实变异检测能力。未来的研究将把该技术应用于多种群体背景，如基因组选择。
