Predicting the Genomic Resolution of Bulk Segregant Analysis

Abstract

Bulk segregant analysis (BSA) is a technique for identifying the genetic loci that underlie phenotypic trait differences. The basic approach of this method is to compare two pools of individuals from the opposing tails of the phenotypic distribution, sampled from an interbred population. Each pool is sequenced and scanned for alleles that show divergent frequencies between the pools, indicating potential association with the observed trait differences. BSA has already been successfully applied to the mapping of various quantitative trait loci in organisms ranging from yeast to maize. However, these studies have typically suffered from rather low mapping resolution, and we still lack a detailed understanding of how this resolution is affected by experimental parameters. Here, we use coalescence theory to calculate the expected genomic resolution of BSA. We first show that in an idealized interbreeding population of infinite size, the expected length of the mapped region is inversely proportional to the recombination rate, the number of generations of interbreeding, and the number of genomes sampled, as intuitively expected. In a finite population, coalescence events in the genealogy of the sample reduce the number of potentially informative recombination events during interbreeding, thereby increasing the length of the mapped region. This is incorporated into our theory by an effective population size parameter that specifies the pairwise coalescence rate of the interbreeding population. The mapping resolution predicted by our theory closely matches numerical simulations. Furthermore, we show that the approach can easily be extended to modifications of the crossing scheme. Our framework enables researchers to predict the expected power of their mapping experiments, and to evaluate how their experimental design could be tuned to optimize mapping resolution.

DeepBSA: A deep-learning algorithm improves bulked segregant analysis for dissecting complex traits

“…We simulated a series of pool numbers to evaluate the absolute bias and signal-to-noise ratio across different numbers of pools; our results indicated that greater numbers of bulked pools improve the mapping resolution of BSA (Supplemental Figure 5). As previously reported, more bulked individuals increases recombination events in BSA (Shen and Messer, 2022). Our statistical results confirmed that better dissection could be obtained using multiple pools (Supplemental Figures 2-5).…”