Should We Consider Gene×Environment Interaction in the Hunt for Quantitative Trait Loci?

2001 ◽  
Vol 21 (S1) ◽  
pp. S831-S836 ◽  
Author(s):  
W. James Gauderman ◽  
John L. Morrison ◽  
Kimberly D. Siegmund
Keyword(s):  
Quantitative Trait Loci ◽  
Quantitative Trait ◽  
Environment Interaction ◽  
Gene Environment Interaction ◽  
Gene Environment ◽  
Trait Loci

scholarly journals Quantitative Trait Loci Controlling Light and Hormone Response in Two Accessions ofArabidopsis thaliana

Genetics ◽  
2002 ◽  
Vol 160 (2) ◽  
pp. 683-696 ◽  
Author(s):  
Justin O Borevitz ◽  
Julin N Maloof ◽  
Jason Lutes ◽  
Tsegaye Dabi ◽  
Joanna L Redfern ◽  
...  
Keyword(s):  
Quantitative Trait Loci ◽  
Quantitative Trait ◽  
Natural Variation ◽  
Red Light ◽  
Light Response ◽  
Environment Interaction ◽  
Isogenic Line ◽  
Hormone Response ◽  
New Genes ◽  
Trait Loci

AbstractWe have mapped quantitative trait loci (QTL) responsible for natural variation in light and hormone response between the Cape Verde Islands (Cvi) and Landsberg erecta (Ler) accessions of Arabidopsis thaliana using recombinant inbred lines (RILs). Hypocotyl length was measured in four light environments: white, blue, red, and far-red light and in the dark. In addition, white light plus gibberellin (GA) and dark plus the brassinosteroid biosynthesis inhibitor brassinazole (BRZ) were used to detect hormone effects. Twelve QTL were identified that map to loci not previously known to affect light response, as well as loci where candidate genes have been identified from known mutations. Some QTL act in all environments while others show genotype-by-environment interaction. A global threshold was established to identify a significant epistatic interaction between two loci that have few main effects of their own. LIGHT1, a major QTL, has been confirmed in a near isogenic line (NIL) and maps to a new locus with effects in all light environments. The erecta mutation can explain the effect of the HYP2 QTL in the blue, BRZ, and dark environments, but not in far-red. LIGHT2, also confirmed in an NIL, has effects in white and red light and shows interaction with GA. The phenotype and map position of LIGHT2 suggest the photoreceptor PHYB as a candidate gene. Natural variation in light and hormone response thus defines both new genes and known genes that control light response in wild accessions.

scholarly journals Quantitative Trait Loci × Maternal Cytoplasmic Environment Interaction for Development Rate inOncorhynchus mykiss

Genetics ◽  
2006 ◽  
Vol 175 (1) ◽  
pp. 335-347 ◽  
Author(s):  
Krista M. Nichols ◽  
Karl W. Broman ◽  
Kyle Sundin ◽  
Jennifer M. Young ◽  
Paul A. Wheeler ◽  
...  
Keyword(s):  
Quantitative Trait Loci ◽  
Quantitative Trait ◽  
Development Rate ◽  
Environment Interaction ◽  
Trait Loci

Identification of quantitative trait loci underlying fatty acid content of soybean (Glycine max), including main, epistatic and QTL×environment effects across multiple environments

2017 ◽  
Vol 68 (9) ◽  
pp. 842 ◽  
Author(s):  
Ning Xia ◽  
Depeng Wu ◽  
Xia Li ◽  
Weili Teng ◽  
Xue Zhao ◽  
...  
Keyword(s):  
Glycine Max ◽  
Fatty Acid ◽  
Quantitative Trait Loci ◽  
Quantitative Trait ◽  
Acid Content ◽  
Recombinant Inbred Lines ◽  
Fatty Acid Content ◽  
Environment Interaction ◽  
Epistatic Qtls ◽  
Trait Loci

The uses and nutritional value of soybean (Glycine max (L.) Merrill) oil are largely influenced by the levels and relative proportions in the seed of the five major fatty acids: oleic (OA), palmitic (PA), stearic (SA), linoleic (LLA), linolenic (LNA). The present study was undertaken to identify quantitative trait loci (QTLs) that are associated with fatty acid content (particularly OA) and to determine the effects of epistasis and the environment. The mapping population included 134 recombinant inbred lines (RILs) derived from soybean varieties Suinong10 and L-9. Phenotypic data of the two parents and their RILs were obtained at Harbin in 2013, 2014 and 2015. Nineteen QTLs associated with individual fatty acid content (six for OA, four for LNA, three for PA, two for SA, four for LLA) were identified. Twelve of these QTLs (four for OA, three for LNA, two for PA, one for SA, two for LLA) were detected with an additive main effect and/or additive × environment interaction effect in certain environments. Epistatic QTLs were identified for contents of OA (two QTLs), LNA (one QTL) and LLA (one QTL) in different environments, and which exhibited significant epistatic effects. Our observation of these additive and epistatic QTLs suggested that soybean possesses a complex network for fatty acid accumulation, which is valuable for marker-assisted selection.

scholarly journals Mapping multiple quantitative trait loci under Bayes error control

2009 ◽  
Vol 91 (3) ◽  
pp. 147-159
Author(s):  
DANIEL SHRINER
Keyword(s):  
Quantitative Trait Loci ◽  
Quantitative Trait ◽  
Error Control ◽  
Positive Outcome ◽  
Entire Genome ◽  
Predictive Values ◽  
Significance Threshold ◽  
Simultaneous Control ◽  
Gene Environment ◽  
Trait Loci

SummaryIn mapping of quantitative trait loci (QTLs), performing hypothesis tests of linkage to a phenotype of interest across an entire genome involves multiple comparisons. Furthermore, linkage among loci induces correlation among tests. Under many multiple comparison frameworks, these problems are exacerbated when mapping multiple QTLs. Traditionally, significance thresholds have been subjectively set to control the probability of detecting at least one false positive outcome, although such thresholds are known to result in excessively low power to detect true positive outcomes. Recently, false discovery rate (FDR)-controlling procedures have been developed that yield more power both by relaxing the stringency of the significance threshold and by retaining more power for a given significance threshold. However, these procedures have been shown to perform poorly for mapping QTLs, principally because they ignore recombination fractions between markers. Here, I describe a procedure that accounts for recombination fractions and extends FDR control to include simultaneous control of the false non-discovery rate, i.e. the overall error rate is controlled. This procedure is developed in the Bayesian framework using a direct posterior probability approach. Data-driven significance thresholds are determined by minimizing the expected loss. The procedure is equivalent to jointly maximizing positive and negative predictive values. In the context of mapping QTLs for experimental crosses, the procedure is applicable to mapping main effects, gene–gene interactions and gene–environment interactions.

scholarly journals Genotype-Environment Interaction at Quantitative Trait Loci Affecting Sensory Bristle Number in Drosophila melanogaster

Genetics ◽  
1998 ◽  
Vol 149 (4) ◽  
pp. 1883-1898 ◽  
Author(s):  
Marjorie C Gurganus ◽  
James D Fry ◽  
Sergey V Nuzhdin ◽  
Elena G Pasyukova ◽  
Richard F Lyman ◽  
...  
Keyword(s):  
Drosophila Melanogaster ◽  
Quantitative Trait Loci ◽  
Quantitative Trait ◽  
Rank Order ◽  
Recombinant Inbred Lines ◽  
Environment Interaction ◽  
Genotype Environment Interaction ◽  
Bristle Number ◽  
A Genome ◽  
Trait Loci

AbstractThe magnitude of segregating variation for bristle number in Drosophila melanogaster exceeds that predicted from models of mutation-selection balance. To evaluate the hypothesis that genotype-environment interaction (GEI) maintains variation for bristle number in nature, we quantified the extent of GEI for abdominal and sternopleural bristles among 98 recombinant inbred lines, derived from two homozygous laboratory strains, in three temperature environments. There was considerable GEI for both bristle traits, which was mainly attributable to changes in rank order of line means. We conducted a genome-wide screen for quantitative trait loci (QTLs) affecting bristle number in each sex and temperature environment, using a dense (3.2-cM) marker map of polymorphic insertion sites of roo transposable elements. Nine sternopleural and 11 abdominal bristle number QTLs were detected. Significant GEI was exhibited by 14 QTLs, but there was heterogeneity among QTLs in their sensitivity to thermal and sexual environments. To further evaluate the hypothesis that GEI maintains variation for bristle number, we require estimates of allelic effects across environments at genetic loci affecting the traits. This level of resolution may be achievable for Drosophila bristle number because candidate loci affecting bristle development often map to the same location as bristle number QTLs.

scholarly journals Widespread methylation quantitative trait loci and their role in schizophrenia risk

2020 ◽  
Author(s):  
Kira A. Perzel Mandell ◽  
Nicholas J. Eagles ◽  
Richard Wilton ◽  
Amanda J. Price ◽  
Stephen A. Semick ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Genetic Variation ◽  
Quantitative Trait Loci ◽  
Human Brain ◽  
Quantitative Trait ◽  
Functional Regions ◽  
Gene Environment ◽  
Trait Loci ◽  
Genome Bisulfite Sequencing ◽  
Methylation Quantitative Trait Loci

AbstractDNA methylation (DNAm) regulates gene expression and may represent gene-environment interactions. Using whole genome bisulfite sequencing, we surveyed DNAm in a large sample (n=344) of human brain tissues. We identify widespread genetic influence on local methylation levels throughout the genome, with 76% of SNPs and 38% of CpGs being part of methylation quantitative trait loci (meQTLs). These associations can further be clustered into regions that are differentially methylated by a given SNP, highlighting putative functional regions that explain much of the heritability associated with risk loci. Furthermore, some CpH sites associated with genetic variation. We have established a comprehensive, single base resolution view of association between genetic variation and genomic methylation, and implicate schizophrenia GWAS-associated variants as influencing the epigenetic plasticity of the brain.One-sentence summaryMost genetic variants associated with DNA methylation levels, and implicated schizophrenia GWAS variants in the human brain.

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