Characterization of a major QTL and Genome-Wide Epistatic Interactions for the Transformation of Single Spikelet in Teosinte Ears into Paired Spikelets in Maize Ears During Maize Domestication

Maize ear carries paired spikelets, whereas the ear of its wild ancestor, teosinte, bears single spikelets. However, little is known about the genetic basis of the processes of transformation of single spikelets in teosinte ear to paired spikelets in maize ear. In this study, a two-ranked, paired-spikelets primitive maize and a two-ranked, single-spikelet teosinte were utilized to develop an F2 population, and QTL mapping for single vs. paired spikelets (PEDS) was performed. Two QTL (qPEDS1.1 and qPEDS3.1) for PEDS located on chromosomes 1L and 3S were identified in the 162 F2 plants using the inclusive composite interval mapping of additive (ICIM-ADD) module, explaining 1.93% and 23.79% of the phenotypic variance, respectively. Out of the 409 F2 plants, 43 plants with PEDS = 0% and 43 plants with PEDS > 20% were selected for selective genotyping; the QTL (qPEDS3.1) accounting for 64.01% of the phenotypic variance for PEDS was also detected. Moreover, the QTL (qPEDS3.1) was validated in three environments, which explained 31.05%, 38.94% and 23.16% of the phenotypic variance, respectively. In addition, 50 epistatic QTLs were detected in 162 F2 plants using the two-locus epistatic QTL (ICIM-EPI) module; they were distributed on all 10 chromosomes and explained 94.40% of the total phenotypic variance. The results contribute to a better understanding of the genetic basis of domestication of paired spikelets and provide a genetic resource for future map-based cloning; in addition, the systematic dissection of epistatic interactions underlies a theoretical framework for overcoming epistatic effects on QTL fine mapping.

A scalable unified framework of total and allele-specific counts for cis-QTL, fine-mapping, and prediction

Genetic studies of the transcriptome help bridge the gap between genetic variation and phenotypes. To maximize the potential of such studies, efficient methods to identify expression quantitative trait loci (eQTLs) and perform fine-mapping and genetic prediction of gene expression traits are needed. Current methods that leverage both total read counts and allele-specific expression to identify eQTLs are generally computationally intractable for large transcriptomic studies. Here, we describe a unified framework that addresses these needs and is scalable to thousands of samples. Using simulations and data from GTEx, we demonstrate its calibration and performance. For example, mixQTL shows a power gain equivalent to a 29% increase in sample size for genes with sufficient allele-specific read coverage. To showcase the potential of mixQTL, we apply it to 49 GTEx tissues and find 20% additional eQTLs (FDR < 0.05, per tissue) that are significantly more enriched among trait associated variants and candidate cis-regulatory elements comparing to the standard approach.

From Genetic Maps to QTL Cloning: An Overview for Durum Wheat

Durum wheat is one of the most important cultivated cereal crops, providing nutrients to humans and domestic animals. Durum breeding programs prioritize the improvement of its main agronomic traits; however, the majority of these traits involve complex characteristics with a quantitative inheritance (quantitative trait loci, QTL). This can be solved with the use of genetic maps, new molecular markers, phenotyping data of segregating populations, and increased accessibility to sequences from next-generation sequencing (NGS) technologies. This allows for high-density genetic maps to be developed for localizing candidate loci within a few Kb in a complex genome, such as durum wheat. Here, we review the identified QTL, fine mapping, and cloning of QTL or candidate genes involved in the main traits regarding the quality and biotic and abiotic stresses of durum wheat. The current knowledge on the used molecular markers, sequence data, and how they changed the development of genetic maps and the characterization of QTL is summarized. A deeper understanding of the trait architecture useful in accelerating durum wheat breeding programs is envisioned.

Construction of a Genetic Map by Resequencing and Specific Locus Amplified Fragment sequencing (SLAF-seq) for QTL fine mapping of plant height in upland cotton (Gossypium hirsutum L.)

Background: Gossypium hirsutum (upland cotton), the most widely cultivated cotton species in the world, is an important raw material for the textile industry. Using high-throughput sequencing to construct high-density genetic maps can be widely used in quantitative trait locus (QTL) mapping and molecular marker-assisted breeding.Results: In this study, an F2 population was used to construct a genetic map by parental resequencing and progeny SLAF-seq. The F2 population consisted of Gossypium hirsutum L. cultivars: CCRI 49 and 396289. The genetic map contained 4,607 single nucleotide polymorphisms markers, which overlapped a total length of 3,063.4 cM with an average genetic distance of 0.898 cM between adjacent markers. A high-density genetic map was used to map the QTLs of plant height in four environments. 16 QTLs were obtained, which could explain 2.07%–19.04% of the phenotypic variation. A total of 1,028 candidate genes were identified in the confidence interval and were categorized according to function through cluster of orthologous groups analysis, gene ntology analysis, and Kyoto Encyclopedia of Genes and Genomes analysis. Within the QTLs confidence interval, the D05 chromosome(ChrD05) was finely mapped using Mutmap-like strategy, and the reliability was validated by qRT-PCR of 18 candidate genes . Finally, we obtained 14 candidate genes that are most likely to be related to plant height.Conclusions: This study provides a successful application of parental sequencing and progeny SLAF-seq strategy in the genetic map of upland cotton in an F2 population. This study provides theoretical support for molecular marker-assisted breeding and plant height-heterosis in upland cotton and provides a fine mapping scheme for QTLs. In addition, the combination of multiple analytical methods also provided a solution for QTL fine mapping.