Two Powerful Tests for Parent-of-Origin Effects at Quantitative Trait Loci on the X Chromosome

2018 ◽  
Vol 83 (5) ◽  
pp. 250-273
Author(s):  
Pui Yin Lau ◽  
Kar Fu Yeung ◽  
Ji-Yuan Zhou ◽  
Wing Kam Fung
Keyword(s):  
Quantitative Trait Loci ◽  
X Chromosome ◽  
Quantitative Trait ◽  
Trait Loci ◽  
Parent Of Origin ◽  
Origin Effects

scholarly journals Quantitative trait loci with parent-of-origin effects in chicken

2004 ◽  
Vol 84 (1) ◽  
pp. 57-66 ◽  
Author(s):  
MARIA TUISKULA-HAAVISTO ◽  
DIRK-JAN DE KONING ◽  
MERVI HONKATUKIA ◽  
NINA F. SCHULMAN ◽  
ASKO MÄKI-TANILA ◽  
...  
Keyword(s):  
Quantitative Trait Loci ◽  
Quantitative Trait ◽  
Trait Loci ◽  
Parent Of Origin ◽  
Origin Effects

scholarly journals Mapping quantitative trait loci for body weight on the X chromosome in mice. I. Analysis of a reciprocal F2 population

1997 ◽  
Vol 70 (2) ◽  
pp. 117-124 ◽  
Author(s):  
KELLIE A. RANCE ◽  
WILLIAM G. HILL ◽  
PETER D. KEIGHTLEY
Keyword(s):  
Body Weight ◽  
Quantitative Trait Loci ◽  
X Chromosome ◽  
Quantitative Trait ◽  
Selection Line ◽  
Fat Percentage ◽  
Analytical Technique ◽  
F2 Population ◽  
Trait Loci ◽  
Males And Females

Evidence of a large sex-linked effect accounting for 25% of the divergence between mouse lines selected for body weight has been described previously. A marker-based study was undertaken to determine the number and map positions of the putative X-linked quantitative trait loci (QTLs). An F2 population was generated from a reciprocal F1 between an inbred low line derived from the low selection line and the high selection line. To enable inference of marker-associated QTL effects on the X chromosome, an analytical technique was developed based on the multiple regression method of Haley and Knott. The analysis of data on 10 week weight indicated a single QTL of large effect situated at about 23 cM from the proximal end of the chromosome, with a peak LOD score of 24·4. The likelihood curve showed a single well-defined peak, and gave a 95% confidence interval for the QTL location of 8 cM. The estimates for the additive genotypic effects in males and females (half the differences between hemizygous males and between homozygous females) were 2·6 g in both cases, or 17% and 20% of the 10 week body weight in males and females respectively. Dominance effects in the females were found to be non-significant. No significant X-linked effect on carcass fat percentage was detected, but a single X-linked QTL appears to explain almost the entire X-linked body weight effect.

The X Chromosome harbors quantitative trait loci for backfat thickness and intramuscular fat content in pigs

2000 ◽  
Vol 11 (9) ◽  
pp. 800-802 ◽  
Author(s):  
Barbara Harlizius ◽  
Annemieke P. Rattink ◽  
Dirk J. de Koning ◽  
Marilyne Faivre ◽  
Ruth G. Joosten ◽  
...  
Keyword(s):  
Quantitative Trait Loci ◽  
X Chromosome ◽  
Quantitative Trait ◽  
Intramuscular Fat ◽  
Fat Content ◽  
Backfat Thickness ◽  
Trait Loci ◽  
Intramuscular Fat Content

scholarly journals Analysis of expression Quantitative Trait Loci for NLGN4X in relation to language and neurodevelopmental function: an exploratory analysis using FUSION and GTEx workflows

2021 ◽  
Author(s):  
Nuala H Simpson ◽  
Dorothy V M Bishop ◽  
Dianne F Newbury
Keyword(s):  
Quantitative Trait Loci ◽  
X Chromosome ◽  
Quantitative Trait ◽  
Neurodevelopmental Disorder ◽  
High Rate ◽  
Eqtl Analysis ◽  
Expression Quantitative Trait Loci ◽  
True Negative ◽  
Two Samples ◽  
Trait Loci

Language disorders in children are highly heritable, but progress in identifying genetic variants that contribute to language phenotypes has been slow. Here we applied a novel approach by identifying SNPs that are associated with gene expression in the brain, taking as our focus a gene on the X chromosome, NLGN4X, which has been postulated to play a role in neurodevelopmental disorders affecting language and communication. We found no significant associations between expression quantitative trait loci (eQTLs) and phenotypes of nonword repetition, general language ability or neurodevelopmental disorder in two samples of twin children, who had been selected for a relatively high rate of language problems. We report here our experiences with two methods, FUSION and GTEx, for eQTL analysis. It is likely that our null result represents a true negative, but for the interest of others interested in using these methods, we note specific challenges encountered in applying this approach to our data: a) complications associated with studying a gene on the X chromosome; b) lack of agreement between expression estimates from FUSION and GTEx; c) software compatibility issues with different versions of the R programming language.

scholarly journals Treatment of the X chromosome in mapping multiple quantitative trait loci

2021 ◽  
Vol 11 (2) ◽  
Author(s):  
Quoc Tran ◽  
Karl W Broman
Keyword(s):  
Quantitative Trait Loci ◽  
X Chromosome ◽  
Quantitative Trait ◽  
Likelihood Method ◽  
Special Treatment ◽  
Data Set ◽  
Significance Threshold ◽  
Multiple Qtl ◽  
Trait Loci ◽  
Eqtl Data

Abstract Statistical methods to map quantitative trait loci (QTL) often neglect the X chromosome and may focus exclusively on autosomal loci. But the X chromosome often requires special treatment: sex and cross-direction covariates may need to be included to avoid spurious evidence of linkage, and the X chromosome may require a separate significance threshold. In multiple-QTL analyses, including the consideration of epistatic interactions, the X chromosome also requires special care and consideration. We extend a penalized likelihood method for multiple-QTL model selection, to appropriately handle the X chromosome. We examine its performance in simulation and by application to a large eQTL data set. The method has been implemented in the package R/qtl.

scholarly journals Mapping quantitative trait loci for body weight on the X chromosome in mice. II. Analysis of congenic backcrosses

1997 ◽  
Vol 70 (2) ◽  
pp. 125-133 ◽  
Author(s):  
KELLIE A. RANCE ◽  
SIMON C. HEATH ◽  
PETER D. KEIGHTLEY
Keyword(s):  
Body Weight ◽  
Quantitative Trait Loci ◽  
X Chromosome ◽  
Quantitative Trait ◽  
Phenotypic Information ◽  
Multipoint Linkage ◽  
Trait Loci ◽  
Males And Females ◽  
The Difference ◽  
Almost All

In a QTL mapping study with an F2 population of mice, we have shown that one or more sex-linked factors account for a large part of the divergence between mouse lines selected for high and low body weight. Here, we describe a study undertaken to map the putative X-linked quantitative trait loci (QTLs) by backcrossing segments of chromosome from the high line onto an inbred line derived from the low line, thereby removing possible contributions from the autosomes and linked segments of the X chromosome. Sublines containing a regional at the proximal end of the X chromosome were found to be associated with large differences in body weight, and to account for almost all the difference between the lines. A Markov chain Monte Carlo based multipoint linkage analysis incorporating the available marker and phenotypic information from the backcross pedigree was used to map the QTL to a region of about 6 cM. There was no evidence for QTLs elsewhere on the chromosome. The estimated QTL effect is approximately 20% of mean body weight in males and females at 10 weeks. From results obtained from this study and the accompanying F2 analysis, we conclude the presence of a single factor for body weight localizing to about position (±SE) 26·4±1·2 cM on the X chromosome, which increases body weight by approximately 18% at 10 weeks. A strategy to positionally clone the QTL is discussed.

scholarly journals Treatment of the X chromosome in mapping multiple quantitative trait loci

2020 ◽  
Author(s):  
Quoc Tran ◽  
Karl W. Broman
Keyword(s):  
Quantitative Trait Loci ◽  
X Chromosome ◽  
Quantitative Trait ◽  
Likelihood Method ◽  
Special Treatment ◽  
Data Set ◽  
Significance Threshold ◽  
Multiple Qtl ◽  
Trait Loci ◽  
Eqtl Data

ABSTRACTStatistical methods to map quantitative trait loci (QTL) often neglect the X chromosome and may focus exclusively on autosomal loci. But the X chromosome often requires special treatment: sex and cross-direction covariates may need to be included to avoid spurious evidence of linkage, and the X chromosome may require a separate significance threshold. In multiple-QTL analyses, including the consideration of epistatic interactions, the X chromosome also requires special care and consideration. We extend a penalized likelihood method for multiple-QTL model selection, to appropriately handle the X chromosome. We examine its performance in simulation and by application to a large eQTL data set. The method has been implemented in the package R/qtl.

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