dQTG-seq

dQTG-seq maps quantitative trait loci (QTLs) in bi-parental segregation populations by integrating bulked segregant analysis (BSA) with whole-genome sequencing to detect extremely over-dominant and small-effect quantitative trait genes (QTGs).

Key Features:

• Integrated strategy: Combines BSA and whole-genome sequencing to use allele read counts from extreme-phenotype pools as the basis for QTL detection.
• G_w statistic: Implements a novel statistical measure G_w based on predicted and observed numbers of marker alleles and genotypes to identify QTGs.
• dQTG-seq1: Constructs G_w using predicted and observed numbers of marker alleles and genotypes for QTG identification.
• dQTG-seq2: Sequences reserved DNA/RNA from each extreme-phenotype F2 plant and uses observed allele/genotype counts to calculate G_w, improving detection of extremely over-dominant and small-effect genes.
• Multiple analytical methods: Includes dQTG-seq1, dQTG-seq2, G', deltaSNP, Euclidean distance (ED), and SmoothLOD methods.
• Input formats: Accepts a single data file encompassing two BSA and three QTL-mapping formats.
• Output formats: Produces two *.csv files and a figure for downstream analysis.
• Smoothing techniques: Applies smoothing methods AIC, Window size, and Block to refine results for each method.
• LOD significance via permutation: Determines the significance threshold of LOD scores through permutation experiments.
• Performance optimization: Uses the vroom function for efficient data reading and parallel operations for parameter estimation.

Scientific Applications:

• Complex-trait QTL mapping: Identifies QTLs associated with complex traits, particularly those governed by extremely over-dominant and small-effect genes.
• Rice trait analysis: In analyses of rice grain number per panicle, dQTG-seq2 demonstrated superior detection compared to composite interval mapping (CIM) and inclusive CIM.
• Cross-species QTL discovery: Applicable to uncovering the genetic basis of quantitative traits in various species using BSA and sequencing data.

Methodology:

Sequencing of extreme-phenotype individuals from F2 populations (and reserved DNA/RNA sequencing per plant in dQTG-seq2); using read counts of marker alleles to predict marker genotype read counts and calculating G_w from predicted and observed allele/genotype counts; applying smoothing methods (AIC, Window size, Block); determining LOD significance via permutation experiments; and using vroom for data reading with parallel operations for parameter estimation.