scholarly journals Analytical strategies to include the X-chromosome in variance heterogeneity analyses: evidence for trait-specific polygenic variance structure

2018 ◽  
Author(s):  
Wei Q. Deng ◽  
Shihong Mao ◽  
Anette Kalnapenkis ◽  
Tõnu Esko ◽  
Reedik Mägi ◽  
...  
Keyword(s):  
Sexual Dimorphism ◽  
X Chromosome ◽  
Quantitative Trait ◽  
Specific Effect ◽  
Enrichment Analysis ◽  
Complex Trait ◽  
Conclusive Evidence ◽  
Phenotypic Variance ◽  
Variance Heterogeneity ◽  
Analytical Strategies

AbstractGenotype-stratified variance of a quantitative trait could differ in the presence of gene-gene or gene-environment interactions. Genetic markers associated with phenotypic variance are thus considered promising candidates for follow-up interaction or joint location-scale analyses. However, as in studies of main effects, the X-chromosome is routinely excluded from ‘whole-genome’ scans due to analytical challenges. Specifically, as males carry only one copy of the X-chromosome, the inherent sex-genotype dependency could bias the trait-genotype association, through sexual dimorphism in quantitative traits with sex-specific means or variances. Here we investigate phenotypic variance heterogeneity associated with X-chromosome SNPs and propose valid and powerful strategies. Among those, a generalized Levene’s test has adequate power and remains robust to sexual dimorphism. An alternative approach is sex-stratified analysis but at the cost of slightly reduced power and modeling flexibility. We applied both methods to an Estonian study of gene expression quantitative trait loci (eQTL; n=841), and two complex trait studies of height, hip and waist circumferences, and body mass index from multi-ethnic study of atherosclerosis (MESA; n=2,073) and UK Biobank (UKB; n=327,393). Consistent with previous eQTL findings on mean, we found some but no conclusive evidence for cis regulators being enriched for variance association. SNP rs2681646 is associated with variance of waist circumference (p=9.5E-07) at X-chromosome-wide significance in UKB, with a suggestive female-specific effect in MESA (p=0.048). Collectively, an enrichment analysis using permutated UKB (p<1/10) and MESA (p<1/100) datasets, suggests a possible polygenic structure for the variance of human height.

scholarly journals MeSCoT: The tool for quantitative trait simulation through the mechanistic modelling of genes’ regulatory interactions

2021 ◽  
Author(s):  
Viktor Milkevych ◽  
Emre Karaman ◽  
Goutam Sahana ◽  
Luc Janss ◽  
Zexi Cai ◽  
...  
Keyword(s):  
Quantitative Trait ◽  
Molecular Mechanisms ◽  
Genetic Model ◽  
Complex Trait ◽  
Phenotypic Variance ◽  
Regulatory Interactions ◽  
Intermediate Phenotypes ◽  
Gene By Environment Interactions ◽  
Detailed Simulation

Abstract This work represents a novel mechanistic approach to simulate and study genomic networks with accompanying regulatory interactions and complex mechanisms of quantitative trait formation. The approach implemented in MeSCoT software is conceptually based on the omnigenic genetic model of quantitative (complex) trait, and closely imitates the basic in vivo mechanisms of quantitative trait realization. The software provides a framework to study molecular mechanisms of gene-by-gene and gene-by-environment interactions underlying quantitative trait’s realization and allows detailed mechanistic studies of impact of genetic and phenotypic variance on gene regulation. MeSCoT performs a detailed simulation of genes’ regulatory interactions for variable genomic architectures, and generates complete set of transcriptional and translational data together with simulated quantitative trait values. Such data provide opportunities to study, for example, verification of novel statistical methods aiming to integrate intermediate phenotypes together with final phenotype in quantitative genetic analyses, or to investigate novel approaches for exploiting gene-by-gene and gene-by-environment interactions.

scholarly journals Analytical strategies to include the X‐chromosome in variance heterogeneity analyses: Evidence for trait‐specific polygenic variance structure

2019 ◽  
Vol 43 (7) ◽  
pp. 815-830 ◽  
Author(s):  
Wei Q. Deng ◽  
Shihong Mao ◽  
Anette Kalnapenkis ◽  
Tõnu Esko ◽  
Reedik Mägi ◽  
...  
Keyword(s):  
X Chromosome ◽  
Variance Heterogeneity ◽  
Analytical Strategies ◽  
Polygenic Variance ◽  
Variance Structure

Quantitative Trait Loci for Susceptibility to Tapeworm Infection in the Red Flour Beetle

Genetics ◽  
2003 ◽  
Vol 165 (3) ◽  
pp. 1307-1315
Author(s):  
Daibin Zhong ◽  
Aditi Pai ◽  
Guiyun Yan
Keyword(s):  
Quantitative Trait Loci ◽  
Quantitative Trait ◽  
Genetic Basis ◽  
Life Cycles ◽  
Red Flour Beetle ◽  
Phenotypic Variance ◽  
Flour Beetle ◽  
Trait Loci ◽  
Host Ecology ◽  
Tapeworm Infection

Abstract Parasites have profound effects on host ecology and evolution, and the effects of parasites on host ecology are often influenced by the magnitude of host susceptibility to parasites. Many parasites have complex life cycles that require intermediate hosts for their transmission, but little is known about the genetic basis of the intermediate host's susceptibility to these parasites. This study examined the genetic basis of susceptibility to a tapeworm (Hymenolepis diminuta) in the red flour beetle (Tribolium castaneum) that serves as an intermediate host in its transmission. Quantitative trait loci (QTL) mapping experiments were conducted with two independent segregating populations using amplified fragment length polymorphism (AFLP) markers and randomly amplified polymorphic DNA (RAPD) markers. A total of five QTL that significantly affected beetle susceptibility were identified in the two reciprocal crosses. Two common QTL on linkage groups 3 and 6 were identified in both crosses with similar effects on the phenotype, and three QTL were unique to each cross. In one cross, the three main QTL accounted for 29% of the total phenotypic variance and digenic epistasis explained 39% of the variance. In the second cross, the four main QTL explained 62% of the variance and digenic epistasis accounted for only 5% of the variance. The actions of these QTL were either overdominance or underdominance. Our results suggest that the polygenic nature of beetle susceptibility to the parasites and epistasis are important genetic mechanisms for the maintenance of variation within or among beetle strains in susceptibility to tapeworm infection.

scholarly journals The Genetic Basis of the Interspecific Differences in Wing Size in Nasonia (Hymenoptera; Pteromalidae): Major Quantitative Trait Loci and Epistasis

Genetics ◽  
2002 ◽  
Vol 161 (2) ◽  
pp. 673-684
Author(s):  
J Gadau ◽  
R E Page ◽  
J H Werren
Keyword(s):  
Quantitative Trait Loci ◽  
Qtl Analysis ◽  
Quantitative Trait ◽  
Wing Length ◽  
Size Difference ◽  
Phenotypic Variance ◽  
Wing Size ◽  
Epistatic Interactions ◽  
Trait Loci ◽  
Wing Width

Abstract There is a 2.5-fold difference in male wing size between two haplodiploid insect species, Nasonia vitripennis and N. giraulti. The haploidy of males facilitated a full genomic screen for quantitative trait loci (QTL) affecting wing size and the detection of epistatic interactions. A QTL analysis of the interspecific wing-size difference revealed QTL with major effects and epistatic interactions among loci affecting the trait. We analyzed 178 hybrid males and initially found two major QTL for wing length, one for wing width, three for a normalized wing-size variable, and five for wing seta density. One QTL for wing width explains 38.1% of the phenotypic variance, and the same QTL explains 22% of the phenotypic variance in normalized wing size. This corresponds to a region previously introgressed from N. giraulti into N. vitripennis that accounts for 44% of the normalized wing-size difference between the species. Significant epistatic interactions were also found that affect wing size and density of setae on the wing. Screening for pairwise epistatic interactions between loci on different linkage groups revealed four additional loci for wing length and four loci for normalized wing size that were not detected in the original QTL analysis. We propose that the evolution of smaller wings in N. vitripennis males is primarily the result of major mutations at few genomic regions and involves epistatic interactions among some loci.

Quantitative Trait Loci Affecting Differences in Floral Morphology Between Two Species of Monkeyflower (Mimulus)

Genetics ◽  
1998 ◽  
Vol 149 (1) ◽  
pp. 367-382 ◽  
Author(s):  
H D Bradshaw ◽  
Kevin G Otto ◽  
Barbara E Frewen ◽  
John K McKay ◽  
Douglas W Schemske
Keyword(s):  
Quantitative Trait Loci ◽  
Reproductive Isolation ◽  
Quantitative Trait ◽  
Floral Morphology ◽  
Cumulative Effect ◽  
Sympatric Species ◽  
Accurate Estimate ◽  
Phenotypic Variance ◽  
Trait Loci ◽  
Morphological Differences

Abstract Conspicuous differences in floral morphology are partly responsible for reproductive isolation between two sympatric species of monkeyflower because of their effect on visitation of the flowers by different pollinators. Mimulus lewisii flowers are visited primarily by bumblebees, whereas M. cardinalis flowers are visited mostly by hummingbirds. The genetic control of 12 morphological differences between the flowers of M. lewisii and M. cardinalis was explored in a large linkage mapping population of F2 plants (n = 465) to provide an accurate estimate of the number and magnitude of effect of quantitative trait loci (QTLs) governing each character. Between one and six QTLs were identified for each trait. Most (9/12) traits appear to be controlled in part by at least one major QTL explaining ≥25% of the total phenotypic variance. This implies that either single genes of individually large effect or linked clusters of genes with a large cumulative effect can play a role in the evolution of reproductive isolation and speciation.

scholarly journals X chromosome escapee genes are involved in ischemic sexual dimorphism through epigenetic modification of inflammatory signals

2021 ◽  
Vol 18 (1) ◽  
Author(s):  
Shaohua Qi ◽  
Abdullah Al Mamun ◽  
Conelius Ngwa ◽  
Sharmeen Romana ◽  
Rodney Ritzel ◽  
...  
Keyword(s):  
Sexual Dimorphism ◽  
X Chromosome ◽  
Epigenetic Modification ◽  
Artery Occlusion ◽  
Sexually Dimorphic ◽  
Mrna Levels ◽  
Young Females ◽  
Human Stroke ◽  
Histone H3k4 ◽  
Cerebral Artery Occlusion

Abstract Background Stroke is a sexually dimorphic disease. Previous studies have found that young females are protected against ischemia compared to males, partially due to the protective effect of ovarian hormones, particularly estrogen (E2). However, there are also genetic and epigenetic effects of X chromosome dosage that contribute to stroke sensitivity and neuroinflammation after injury, especially in the aged. Genes that escape from X chromosome inactivation (XCI) contribute to sex-specific phenotypes in many disorders. Kdm5c and kdm6a are X escapee genes that demethylate H3K4me3 and H3K27me3, respectively. We hypothesized that the two demethylases play critical roles in mediating the stroke sensitivity. Methods To identify the X escapee genes involved in stroke, we performed RNA-seq in flow-sorted microglia from aged male and female wild type (WT) mice subjected to middle cerebral artery occlusion (MCAO). The expression of these genes (kdm5c/kdm6a) were confirmed in four core genotypes (FCG) mice and in post-mortem human stroke brains by immunohistochemistry (IHC), Western blot, and RT-PCR. Chromatin immunoprecipitation (ChIP) assays were conducted to detect DNA levels of inflammatory interferon regulatory factor (IRF) 4/5 precipitated by histone H3K4 and H3K27 antibodies. Manipulation of kdm5c/kdm6a expression with siRNA or lentivirus was performed in microglial culture, to determine downstream pathways and examine the regulatory roles in inflammatory cytokine production. Results Kdm5c and kdm6a mRNA levels were significantly higher in aged WT female vs. male microglia, and the sex difference also existed in ischemic brains from FCG mice and human stroke patients. The ChIP assay showed the IRF 4/5 had higher binding levels to demethylated H3K4 or H3K27, respectively, in female vs. male ischemic microglia. Knockdown or over expression of kdm5c/kdm6a with siRNA or lentivirus altered the methylation of H3K4 or H3K27 at the IRF4/5 genes, which in turn, impacted the production of inflammatory cytokines. Conclusions The KDM-Histone-IRF pathways are suggested to mediate sex differences in cerebral ischemia. Epigenetic modification of stroke-related genes constitutes an important mechanism underlying the ischemic sexual dimorphism.

scholarly journals Relationship between Yield Components and Partial Resistance to Lecanicillium fungicola in the Button Mushroom, Agaricus bisporus, Assessed by Quantitative Trait Locus Mapping

2012 ◽  
Vol 78 (7) ◽  
pp. 2435-2442 ◽  
Author(s):  
Marie Foulongne-Oriol ◽  
Anne Rodier ◽  
Jean-Michel Savoie
Keyword(s):  
Quantitative Trait ◽  
Wild Strain ◽  
Genomic Region ◽  
Yield Component ◽  
Phenotypic Variance ◽  
Button Mushroom ◽  
Content Type ◽  
Dry Bubble ◽  
Trait Locus ◽  
Genomic Regions

ABSTRACTDry bubble, caused byLecanicillium fungicola, is one of the most detrimental diseases affecting button mushroom cultivation. In a previous study, we demonstrated that breeding for resistance to this pathogen is quite challenging due to its quantitative inheritance. A second-generation hybrid progeny derived from an intervarietal cross between a wild strain and a commercial cultivar was characterized forL. fungicolaresistance under artificial inoculation in three independent experiments. Analysis of quantitative trait loci (QTL) was used to determine the locations, numbers, and effects of genomic regions associated with dry-bubble resistance. Four traits related to resistance were analyzed. Two to four QTL were detected per trait, depending on the experiment. Two genomic regions, on linkage group X (LGX) and LGVIII, were consistently detected in the three experiments. The genomic region on LGX was detected for three of the four variables studied. The total phenotypic variance accounted for by all QTL ranged from 19.3% to 42.1% over all traits in all experiments. For most of the QTL, the favorable allele for resistance came from the wild parent, but for some QTL, the allele that contributed to a higher level of resistance was carried by the cultivar. Comparative mapping with QTL for yield-related traits revealed five colocations between resistance and yield component loci, suggesting that the resistance results from both genetic factors and fitness expression. The consequences for mushroom breeding programs are discussed.

scholarly journals Opportunities and limits of combining microbiome and genome data for complex trait prediction

2020 ◽  
Author(s):  
Miguel Pérez-Enciso ◽  
Laura M. Zingaretti ◽  
Yuliaxis Ramayo-Caldas ◽  
Gustavo de los Campos
Keyword(s):  
Complex Traits ◽  
Variance Components ◽  
Complex Trait ◽  
Parameter Estimates ◽  
Phenotypic Variance ◽  
Genome Data ◽  
Individual Snps ◽  
Simulation Strategy ◽  
Microbiome Data ◽  
Trait Prediction

AbstractThe analysis and prediction of complex traits using microbiome data combined with host genomic information is a topic of utmost interest. However, numerous questions remain to be answered: How useful can the microbiome be for complex trait prediction? Are microbiability estimates reliable? Can the underlying biological links between the host’s genome, microbiome, and the phenome be recovered? Here, we address these issues by (i) developing a novel simulation strategy that uses real microbiome and genotype data as input, and (ii) proposing a variance-component approach which, in the spirit of mediation analyses, quantifies the proportion of phenotypic variance explained by genome and microbiome, and dissects it into direct and indirect effects. The proposed simulation approach can mimic a genetic link between the microbiome and SNP data via a permutation procedure that retains the distributional properties of the data. Results suggest that microbiome data could significantly improve phenotype prediction accuracy, irrespective of whether some abundances are under direct genetic control by the host or not. Overall, random-effects linear methods appear robust for variance components estimation, despite the highly leptokurtic distribution of microbiota abundances. Nevertheless, we observed that accuracy depends in part on the number of microorganisms’ taxa influencing the trait of interest. While we conclude that overall genome-microbiome-links can be characterized via variance components, we are less optimistic about the possibility of identifying the causative effects, i.e., individual SNPs affecting abundances; power at this level would require much larger sample sizes than the ones typically available for genome-microbiome-phenome data.Author summaryThe microbiome consists of the microorganisms that live in a particular environment, including those in our organism. There is consistent evidence that these communities play an important role in numerous traits of relevance, including disease susceptibility or feed efficiency. Moreover, it has been shown that the microbiome can be relatively stable throughout an individual’s life and that is affected by the host genome. These reasons have prompted numerous studies to determine whether and how the microbiome can be used for prediction of complex phenotypes, either using microbiome alone or in combination with host’s genome data. However, numerous questions remain to be answered such as the reliability of parameter estimates, or which is the underlying relationship between microbiome, genome, and phenotype. The few available empirical studies do not provide a clear answer to these problems. Here we address these issues by developing a novel simulation strategy and we show that, although the microbiome can significantly help in prediction, it will be difficult to retrieve the actual biological basis of interactions between the microbiome and the trait.

scholarly journals Quantitative Trait Loci Affecting Survival and Fertility-Related Traits in Caenorhabditis elegans Show Genotype-Environment Interactions, Pleiotropy and Epistasis

Genetics ◽  
1999 ◽  
Vol 153 (3) ◽  
pp. 1233-1243 ◽  
Author(s):  
David R Shook ◽  
Thomas E Johnson
Keyword(s):  
Caenorhabditis Elegans ◽  
Quantitative Trait Loci ◽  
Population Growth ◽  
Quantitative Trait ◽  
Age Of Onset ◽  
Inbred Strains ◽  
Interval Mapping ◽  
Phenotypic Variance ◽  
Epistatic Interactions ◽  
Trait Loci

Abstract We have identified, using composite interval mapping, quantitative trait loci (QTL) affecting a variety of life history traits (LHTs) in the nematode Caenorhabditis elegans. Using recombinant inbred strains assayed on the surface of agar plates, we found QTL for survival, early fertility, age of onset of sexual maturity, and population growth rate. There was no overall correlation between survival on solid media and previous measures of survival in liquid media. Of the four survival QTL found in these two environments, two have genotype-environment interactions (GEIs). Epistatic interactions between markers were detected for four traits. A multiple regression approach was used to determine which single markers and epistatic interactions best explained the phenotypic variance for each trait. The amount of phenotypic variance accounted for by genetic effects ranged from 13% (for internal hatching) to 46% (for population growth). Epistatic effects accounted for 9–11% of the phenotypic variance for three traits. Two regions containing QTL that affected more than one fertility-related trait were found. This study serves as an example of the power of QTL mapping for dissecting the genetic architecture of a suite of LHTs and indicates the potential importance of environment and GEIs in the evolution of this architecture.

Sign in / Sign up

Export Citation Format

Share Document