Quantitative trait locus (QTL) detection is commonly performed by analysis of designed segregating populations derived from two inbred parental lines, where absence of selection, mutation and genetic drift is assumed. Even for designed populations, selection cannot always be avoided, with as consequence varying correlation between genotypes instead of uniform correlation. Akin to linkage disequilibrium mapping, ignoring this type of genetic relatedness will increase the rate of false-positives. In this paper, we advocate using mixed models including genetic relatedness, or 'kinship' information for QTL detection in populations where selection forces operated. We demonstrate our case with a three-way barley cross, designed to segregate for dwarfing, vernalization and spike morphology genes, in which selection occurred. The population of 161 inbred lines was screened with 1,536 single nucleotide polymorphisms (SNPs), and used for gene and QTL detection. The coefficient of coancestry matrix was estimated based on the SNPs and imposed to structure the distribution of random genotypic effects. The model incorporating kinship, coancestry, information was consistently superior to the one without kinship (according to the Akaike information criterion). We show, for three traits, that ignoring the coancestry information results in an unrealistically high number of marker-trait associations, without providing clear conclusions about QTL locations. We used a number of widely recognized dwarfing and vernalization genes known to segregate in the studied population as landmarks or references to assess the agreement of the mapping results with a priori candidate gene expectations. Additional QTLs to the major genes were detected for all traits as well.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3082036PMC
http://dx.doi.org/10.1007/s00122-011-1558-zDOI Listing

Publication Analysis

Top Keywords

qtl detection
16
gene qtl
8
three-way barley
8
barley cross
8
populations selection
8
genetic relatedness
8
dwarfing vernalization
8
selection
5
detection
4
detection three-way
4

Similar Publications

Genetic dissection of foxtail millet bristles using combined QTL mapping and RNA-seq.

Theor Appl Genet

January 2025

College of Agriculture, State Key Laboratory of Crop Stress Biology in Arid Areas, Northwest A&F University, Yangling, 712100, China.

QTL mapping of two RIL populations in multiple environments revealed a consistent QTL for bristle length, and combined with RNA-seq, a potential candidate gene influencing bristle length was identified. Foxtail millet bristles play a vital role in increasing yields and preventing bird damage. However, there is currently limited research on the molecular regulatory mechanisms underlying foxtail millet bristle formation, which constrains the genetic improvement and breeding of new foxtail millet varieties.

View Article and Find Full Text PDF

In this first QTL mapping study of embryo size in barley, novel and stable QTL were identified and candidate genes underlying a significant locus independent of kernel size were identified based on orthologous analysis and comparison of the whole-genome assemblies for both parental genotypes of the mapping population. Embryo, also known as germ, in cereal grains plays a crucial role in plant development. The embryo accounts for only a small portion of grain weight but it is rich in nutrients.

View Article and Find Full Text PDF

Carotenoids are a diverse group of pigments imparting red, orange, and yellow hues to many horticultural plants, also enhancing their nutritional properties and health benefits. In strawberry, the genetic and molecular mechanisms regulating the natural variation of fruit carotenoid composition remain largely unexplored. In this study, we use a population segregating in yellow/white flesh to detect a major quantitative trait locus (QTL), qYellow Flesh-4B, located on chromosome 4B and accounting for 82% of total phenotypic variation.

View Article and Find Full Text PDF

Large case-control genome-wide association studies (GWASs) have detected loci associated with insomnia, but how these risk loci confer disease risk remains largely unknown. By integrating brain protein quantitative trait loci (pQTL) (N = 376, N = 152) and expression QTL (eQTL) (N = 452) datasets, with the latest insomnia GWAS summary statistics (N = 109,548, N = 277440), we conducted proteome/transcriptome-wide association study (PWAS/TWAS) and Mendelian randomization (MR) analysis, aiming to identify causal proteins involving in the pathogenesis of insomnia. We also explored the bi-directional causality between insomnia and several common diseases.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!