Optimization of BSA-seq experiment for QTL mapping

Abstract

Deep sequencing-based bulked segregant analysis (BSA-seq) has become a popular approach for quantitative trait loci (QTL) mapping in recent years. Effective statistical methods for BSA-seq have been developed, but how to design a suitable experiment for BSA-seq remains unclear. In this paper, we show in theory how the major experimental factors (including population size, pool proportion, pool balance, and generation) and the intrinsic factors of a QTL (including heritability and degree of dominance) affect the power of QTL detection and the precision of QTL mapping in BSA-seq. Increasing population size can improve the power and precision, depending on the QTL heritability. The best proportion of each pool in the population is around 0.25. So, 0.25 is generally applicable in BSA-seq. Small pool proportion can greatly reduce the power and precision. Imbalance of pool pair in size also causes decrease of the power and precision. Additive effect is more important than dominance effect for QTL mapping. Increasing the generation of filial population produced by selfing can significantly increase the power and precision, especially from F2 to F3. These findings enable researchers to optimize the experimental design for BSA-seq. A web-based program named BSA-seq Design Tool is available at http://124.71.74.135/BSA-seqDesignTool/ and https://github.com/huanglikun/BSA-seqDesignTool.

High‐resolution bulked segregant analysis enables candidate gene identification for bacterial wilt resistance in Italian ryegrass (Lolium multiflorum Lam.)

“…The high population size coupled with a wholegenome sequencing approach at high coverage was able to optimize most of the factors on which the success of BSA studies rely, (de la Fuente Cant ó & Vigouroux, 2022; Huang et al, 2022;Magwene et al, 2011). In BSA studies that have been performed so far, the sizes of the investigated populations were generally small, often around 200 individuals, though they can range from <100 to >10 000 individuals (reviewed in Huang et al, 2022). Pool sizes were also variable, ranging from only five individuals per pool to more than 400.…”

High-resolution bulked segregant analysis enables candidate gene identification for bacterial wilt resistance in Italian ryegrass

“…The high population size coupled with a whole-genome sequencing approach at high coverage was able to optimize most of the factors on which the success of BSA studies rely, (Magwene et al, 2011;de la Fuente Cantó and Vigouroux, 2022;Huang et al, 2022). In BSA studies that have been performed so far, the sizes of the investigated populations were generally small, often around 200 individuals, though they can range from <100 to >10,000 individuals (reviewed in Huang et al, 2022). Pool sizes were also variable, ranging from only five individuals per pool to more than 400.…”

Identification of single major QTL and candidate gene(s) governing hull-less seed trait in pumpkin

“…Moreover, the various mapping studies reported the bulk sizes in the range of 3–11.9% (Takagi et al, 2013 ; Singh et al, 2016 ; Wei et al, 2016 ; Ramos et al, 2020 ). The filial populations produced through selfing can significantly increase the power and precision, especially from F 2 to F 3 , and have been used in the present study (Huang et al, 2022 ).…”