Estimation of QTL heritability based on pooled sequencing data

Harnessing the potential of bulk segregant analysis sequencing and its related approaches in crop breeding

Most plant traits are governed by polygenes including both major and minor genes. Linkage mapping and positional cloning have contributed greatly to mapping genomic loci controlling important traits in crop species. However, they are low-throughput, time-consuming, and have low resolution due to which their efficiency in crop breeding is reduced. In this regard, the bulk segregant analysis sequencing (BSA-seq) and its related approaches, viz., quantitative trait locus (QTL)-seq, bulk segregant RNA-Seq (BSR)-seq, and MutMap, have emerged as efficient methods to identify the genomic loci/QTLs controlling specific traits at high resolution, accuracy, reduced time span, and in a high-throughput manner. These approaches combine BSA with next-generation sequencing (NGS) and enable the rapid identification of genetic loci for qualitative and quantitative assessments. Many previous studies have shown the successful identification of the genetic loci for different plant traits using BSA-seq and its related approaches, as discussed in the text with details. However, the efficiency and accuracy of the BSA-seq depend upon factors like sequencing depth and coverage, which enhance the sequencing cost. Recently, the rapid reduction in the cost of NGS together with the expected cost reduction of third-generation sequencing in the future has further increased the accuracy and commercial applicability of these approaches in crop improvement programs. This review article provides an overview of BSA-seq and its related approaches in crop breeding together with their merits and challenges in trait mapping.

Estimating genetic variance contributed by a quantitative trait locus: A random model approach

Detecting quantitative trait loci (QTL) and estimating QTL variances (represented by the squared QTL effects) are two main goals of QTL mapping and genome-wide association studies (GWAS). However, there are issues associated with estimated QTL variances and such issues have not attracted much attention from the QTL mapping community. Estimated QTL variances are usually biased upwards due to estimation being associated with significance tests. The phenomenon is called the Beavis effect. However, estimated variances of QTL without significance tests can also be biased upwards, which cannot be explained by the Beavis effect; rather, this bias is due to the fact that QTL variances are often estimated as the squares of the estimated QTL effects. The parameters are the QTL effects and the estimated QTL variances are obtained by squaring the estimated QTL effects. This square transformation failed to incorporate the errors of estimated QTL effects into the transformation. The consequence is biases in estimated QTL variances. To correct the biases, we can either reformulate the QTL model by treating the QTL effect as random and directly estimate the QTL variance (as a variance component) or adjust the bias by taking into account the error of the estimated QTL effect. A moment method of estimation has been proposed to correct the bias. The method has been validated via Monte Carlo simulation studies. The method has been applied to QTL mapping for the 10-week-body-weight trait from an F₂ mouse population.

One of the goals of QTL mapping and GWAS is to quantify the size of a QTL, which is measured by the QTL variance or the proportion of trait variance explained by the QTL. The effect of a QTL appears in a linear or linear mixed model as a regression coefficient and defined as a fixed effect. The estimated QTL variance in conventional QTL mapping studies takes the square of the estimated QTL effect. This is a biased estimate of QTL variance. An unbiased estimate of the QTL variance should be obtained by (1) treating the QTL effect as random and estimating the variance of the random effect or (2) adjusting the squared estimated QTL effect by the squared estimation error. We proved that the two methods are identical. We further proved that the usual R² (goodness of fit) in regression analysis is equivalent to the biased QTL heritability while the adjusted R² is equivalent to the bias corrected QTL heritability.

Bulk segregation analysis in the NGS era: a review of its teenage years

Bulk segregation analysis (BSA) utilizes a strategy of pooling individuals with extreme phenotypes to conduct economical and rapidly linked marker screening or quantitative trait locus (QTL) mapping. With the development of next-generation sequencing (NGS) technology in the past 10 years, BSA methods and technical systems have been gradually developed and improved. At the same time, the ever-decreasing costs of sequencing accelerate NGS-based BSA application in different species, including eukaryotic yeast, grain crops, economic crops, horticultural crops, trees, aquatic animals, and insects. This paper provides a landscape of BSA methods and reviews the BSA development process in the past decade, including the sequencing method for BSA, different populations, different mapping algorithms, associated region threshold determination, and factors affecting BSA mapping. Finally, we summarize related strategies in QTL fine mapping combining BSA.

Optimization of BSA-seq experiment for QTL mapping

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.

Bulked Segregant Analysis Coupled with Whole-Genome Sequencing (BSA-Seq) Mapping Identifies a Novel pi21 Haplotype Conferring Basal Resistance to Rice Blast Disease

Basal or partial resistance has been considered race-non-specific and broad-spectrum. Therefore, the identification of genes or quantitative trait loci (QTLs) conferring basal resistance and germplasm containing them is of significance in breeding crops with durable resistance. In this study, we performed a bulked segregant analysis coupled with whole-genome sequencing (BSA-seq) to identify QTLs controlling basal resistance to blast disease in an F₂ population derived from two rice varieties, 02428 and LiXinGeng (LXG), which differ significantly in basal resistance to rice blast. Four candidate QTLs, qBBR-4, qBBR-7, qBBR-8, and qBBR-11, were mapped on chromosomes 4, 7, 8, and 11, respectively. Allelic and genotypic association analyses identified a novel haplotype of the durable blast resistance gene pi21 carrying double deletions of 30 bp and 33 bp in 02428 (pi21-2428) as a candidate gene of qBBR-4. We further assessed haplotypes of Pi21 in 325 rice accessions, and identified 11 haplotypes among the accessions, of which eight were novel types. While the resistant pi21 gene was found only in japonica before, three Chinese indica varieties, ShuHui881, Yong4, and ZhengDa4Hao, were detected carrying the resistant pi21-2428 allele. The pi21-2428 allele and pi21-2428-containing rice germplasm, thus, provide valuable resources for breeding rice varieties, especially indica rice varieties, with durable resistance to blast disease. Our results also lay the foundation for further identification and functional characterization of the other three QTLs to better understand the molecular mechanisms underlying rice basal resistance to blast disease.

BRM: a statistical method for QTL mapping based on bulked segregant analysis by deep sequencing

Motivation

Bulked segregant analysis by deep sequencing (BSA-seq) has been widely used for quantitative trait locus (QTL) mapping in recent years. A number of different statistical methods for BSA-seq have been proposed. However, determination of significance threshold, the key point for QTL identification, remains to be a problem that has not been well solved due to the difficulty of multiple testing correction. In addition, estimation of the confidence interval is also a problem to be solved.

Results

In this paper, we propose a new statistical method for BSA-seq, named Block Regression Mapping (BRM). BRM is robust to sequencing noise and is applicable to the case of low sequencing depth. Significance threshold can be reasonably determined by taking multiple testing correction into account. Meanwhile, the confidence interval of QTL position can also be estimated.

Availability and implementation

The R scripts of our method are open source under GPLv3 license at https://github.com/huanglikun/BRM.

Supplementary information

Supplementary data are available at Bioinformatics online.

Identification and fine mapping of Bph33, a new brown planthopper resistance gene in rice (Oryza sativa L.)

BACKGROUND

Host-plant resistance is the most desirable and economic way to overcome BPH damage to rice. As single-gene resistance is easily lost due to the evolution of new BPH biotypes, it is urgent to explore and identify new BPH resistance genes.

RESULTS

In this study, using F_2:3 populations and near-isogenic lines (NILs) derived from crosses between two BPH-resistant Sri Lankan rice cultivars (KOLAYAL and POLIYAL) and a BPH-susceptible cultivar 9311, a new resistance gene Bph33 was fine mapped to a 60-kb region ranging 0.91-0.97 Mb on the short arm of chromosome 4 (4S), which was at least 4 Mb distant from those genes/QTLs (Bph12, Bph15, Bph3, Bph20, QBph4 and QBph4.2) reported before. Seven genes were predicted in this region. Based on sequence and expression analyses, a Leucine Rich Repeat (LRR) family gene (LOC_Os04g02520) was identified as the most possible candidate of Bph33. The gene exhibited continuous and stable resistance from seedling stage to tillering stage, showing both antixenosis and antibiosis effects on BPH.

CONCLUSION

The results of this study will facilitate map-based cloning and marker-assisted selection of the gene.