scholarly journals Testing the correspondence between map positions of quantitative trait loci

1999 ◽  
Vol 74 (3) ◽  
pp. 323-328 ◽  
Author(s):  
PETER D. KEIGHTLEY ◽  
SARA A. KNOTT
Keyword(s):  
Qtl Mapping ◽  
Quantitative Trait ◽  
Permutation Test ◽  
Test Statistics ◽  
Test Statistic ◽  
Genome Wide ◽  
Trait Locus ◽  
Experimental Pair ◽  
Mouse Body Weight ◽  
Unequal Length

There are several instances in which quantitative trait locus (QTL) mapping experiments have been independently carried out for similar traits in different laboratories. We develop a permutation test of the correspondence between the test statistics obtained from genome-wide QTL scans in two such experiments to test whether the same QTLs are segregating in the experimental pair. In simulations, we show that the permutation test has the desired properties if chromosomes are of equal length, but bias can occur if chromosomes are of unequal length, a problem connected with autocorrelation of test statistic values. We apply the test to data from three recent mouse body weight QTL mapping experiments. The results from the test are non-significant, and imply a lack of overall concordance between the QTLs that were segregating in these experiments.

scholarly journals Identification and Functional Verification of Cold Tolerance Genes in Spring Maize Seedlings Based on a Genome-Wide Association Study and Quantitative Trait Locus Mapping

2021 ◽  
Vol 12 ◽  
Author(s):  
Yukun Jin ◽  
Zhongren Zhang ◽  
Yongjing Xi ◽  
Zhou Yang ◽  
Zhifeng Xiao ◽  
...  
Keyword(s):  
Quantitative Trait Locus ◽  
Food Security ◽  
Qtl Mapping ◽  
Cold Tolerance ◽  
Quantitative Trait ◽  
Genome Wide Association ◽  
Maize Growth ◽  
Genome Wide ◽  
A Genome ◽  
Trait Locus

Maize (Zea mays L.) is a tropical crop, and low temperature has become one of the main abiotic stresses for maize growth and development, affecting many maize growth processes. The main area of maize production in China, Jilin province, often suffers from varying degrees of cold damage in spring, which seriously affects the quality and yield of maize. In the face of global climate change and food security concerns, discovering cold tolerance genes, developing cold tolerance molecular markers, and creating cold-tolerant germplasm have become urgent for improving maize resilience against these conditions and obtaining an increase in overall yield. In this study, whole-genome sequencing and genotyping by sequencing were used to perform genome-wide association analysis (GWAS) and quantitative trait locus (QTL) mapping of the two populations, respectively. Overall, four single-nucleotide polymorphisms (SNPs) and 12 QTLs were found to be significantly associated with cold tolerance. Through joint analysis, an intersection of GWAS and QTL mapping was found on chromosome 3, on which the Zm00001d002729 gene was identified as a potential factor in cold tolerance. We verified the function of this target gene through overexpression, suppression of expression, and genetic transformation into maize. We found that Zm00001d002729 overexpression resulted in better cold tolerance in this crop. The identification of genes associated with cold tolerance contributes to the clarification of the underlying mechanism of this trait in maize and provides a foundation for the adaptation of maize to colder environments in the future, to ensure food security.

scholarly journals Epigenome-Wide Study Identified Methylation Sites Associated with the Risk of Obesity

Nutrients ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 1984
Author(s):  
Majid Nikpay ◽  
Sepehr Ravati ◽  
Robert Dent ◽  
Ruth McPherson
Keyword(s):  
Quantitative Trait Locus ◽  
Quantitative Trait ◽  
Rare Variants ◽  
Genome Wide ◽  
A Genome ◽  
Genome Wide Search ◽  
Risk Snps ◽  
Trait Locus ◽  
Genomic Regions ◽  
Eqtl Data

Here, we performed a genome-wide search for methylation sites that contribute to the risk of obesity. We integrated methylation quantitative trait locus (mQTL) data with BMI GWAS information through a SNP-based multiomics approach to identify genomic regions where mQTLs for a methylation site co-localize with obesity risk SNPs. We then tested whether the identified site contributed to BMI through Mendelian randomization. We identified multiple methylation sites causally contributing to the risk of obesity. We validated these findings through a replication stage. By integrating expression quantitative trait locus (eQTL) data, we noted that lower methylation at cg21178254 site upstream of CCNL1 contributes to obesity by increasing the expression of this gene. Higher methylation at cg02814054 increases the risk of obesity by lowering the expression of MAST3, whereas lower methylation at cg06028605 contributes to obesity by decreasing the expression of SLC5A11. Finally, we noted that rare variants within 2p23.3 impact obesity by making the cg01884057 site more susceptible to methylation, which consequently lowers the expression of POMC, ADCY3 and DNAJC27. In this study, we identify methylation sites associated with the risk of obesity and reveal the mechanism whereby a number of these sites exert their effects. This study provides a framework to perform an omics-wide association study for a phenotype and to understand the mechanism whereby a rare variant causes a disease.

scholarly journals Genome-wide Scan of IQ Finds Significant Linkage to a Quantitative Trait Locus on 2q

2005 ◽  
Vol 36 (1) ◽  
pp. 45-55 ◽  
Author(s):  
M. Luciano ◽  
M. J. Wright ◽  
D. L. Duffy ◽  
M. A. Wainwright ◽  
G. Zhu ◽  
...  
Keyword(s):  
Quantitative Trait Locus ◽  
Quantitative Trait ◽  
Genome Wide ◽  
Trait Locus ◽  
Significant Linkage ◽  
Genome Wide Scan

scholarly journals Consequences of Recombination Rate Variation on Quantitative Trait Locus Mapping Studies: Simulations Based on the Drosophila melanogaster Genome

Genetics ◽  
2001 ◽  
Vol 159 (2) ◽  
pp. 581-588
Author(s):  
Mohamed A F Noor ◽  
Aimee L Cunningham ◽  
John C Larkin
Keyword(s):  
Quantitative Trait Locus ◽  
Drosophila Melanogaster ◽  
Qtl Mapping ◽  
Quantitative Trait ◽  
Recombination Rate ◽  
Genome Project ◽  
Gene Density ◽  
Rate Variation ◽  
Trait Locus ◽  
Recombination Rate Variation

Abstract We examine the effect of variation in gene density per centimorgan on quantitative trait locus (QTL) mapping studies using data from the Drosophila melanogaster genome project and documented regional rates of recombination. There is tremendous variation in gene density per centimorgan across this genome, and we observe that this variation can cause systematic biases in QTL mapping studies. Specifically, in our simulated mapping experiments of 50 equal-effect QTL distributed randomly across the physical genome, very strong QTL are consistently detected near the centromeres of the two major autosomes, and few or no QTL are often detected on the X chromosome. This pattern persisted with varying heritability, marker density, QTL effect sizes, and transgressive segregation. Our results are consistent with empirical data collected from QTL mapping studies of this species and its close relatives, and they explain the “small X-effect” that has been documented in genetic studies of sexual isolation in the D. melanogaster group. Because of the biases resulting from recombination rate variation, results of QTL mapping studies should be taken as hypotheses to be tested by additional genetic methods, particularly in species for which detailed genetic and physical genome maps are not available.

scholarly journals Independent genome-wide scans identify a chromosome 18 quantitative-trait locus influencing dyslexia

10.1038/ng792 ◽  
2001 ◽  
Vol 30 (1) ◽  
pp. 86-91 ◽  
Author(s):  
Simon E. Fisher ◽  
Clyde Francks ◽  
Angela J. Marlow ◽  
I. Laurence MacPhie ◽  
Dianne F. Newbury ◽  
...  
Keyword(s):  
Quantitative Trait Locus ◽  
Quantitative Trait ◽  
Chromosome 18 ◽  
Genome Wide ◽  
Trait Locus

scholarly journals An approach to gene-based testing accounting for dependence of tests among nearby genes

2021 ◽  
Author(s):  
Ronald J Yurko ◽  
Kathryn Roeder ◽  
Bernie Devlin ◽  
Max G'Sell
Keyword(s):  
Multiple Testing ◽  
Association Studies ◽  
Autism Spectrum ◽  
P Value ◽  
Genome Wide Association Studies ◽  
Strongly Correlated ◽  
Test Statistics ◽  
Test Statistic ◽  
Genome Wide ◽  
Insight Into

In genome-wide association studies (GWAS), it has become commonplace to test millions of SNPs for phenotypic association. Gene-based testing can improve power to detect weak signal by reducing multiple testing and pooling signal strength. While such tests account for linkage disequilibrium (LD) structure of SNP alleles within each gene, current approaches do not capture LD of SNPs falling in different nearby genes, which can induce correlation of gene-based test statistics. We introduce an algorithm to account for this correlation. When a gene's test statistic is independent of others, it is assessed separately; when test statistics for nearby genes are strongly correlated, their SNPs are agglomerated and tested as a locus. To provide insight into SNPs and genes driving association within loci, we develop an interactive visualization tool to explore localized signal. We demonstrate our approach in the context of weakly powered GWAS for autism spectrum disorder, which is contrasted to more highly powered GWAS for schizophrenia and educational attainment. To increase power for these analyses, especially those for autism, we use adaptive p-value thresholding (AdaPT), guided by high-dimensional metadata modeled with gradient boosted trees, highlighting when and how it can be most useful. Notably our workflow is based on summary statistics.

Identification of RAPD markers for percent hull in oat

Genome ◽  
1997 ◽  
Vol 40 (6) ◽  
pp. 873-878 ◽  
Author(s):  
P. S. Ronald ◽  
G. A. Penner ◽  
P. D. Brown ◽  
A. Brûlé-Babel
Keyword(s):  
Quantitative Trait Locus ◽  
Quantitative Trait ◽  
Genetic Factors ◽  
Bulked Segregant Analysis ◽  
Multiple Time ◽  
Test Statistics ◽  
Marker Loci ◽  
Correct Determination ◽  
Trait Locus

Percent hull is an important physical parameter of oat grain quality, but it is affected by environment. Multiple time-consuming evaluations are required to obtain a correct determination of phenotype. The application of marker-assisted selection for the genes involved would greatly simplify the identification of desirable oat genotypes. Bulked segregant analysis, with selected progeny lines derived from a cross between Cascade and AC Marie (30 and 23% hull, respectively), was used to identify randomly amplified polymorphic DNA markers linked to genetic factors controlling primary kernel hull percentage in oat. Twelve polymorphisms, identified between bulks, were tested for linkage to genetic factors controlling hull percentage by genotyping 80 randomly selected F2-derived F8 lines from the progeny population. Three markers showed significant test statistics for quantitative trait locus effects, when tested with primary kernel percent hull data from two environments. Together, the unlinked marker loci OPC13800, OPD20600, and OPK71300 explained approximately 41% of the genetic variance in primary kernel percent hull, after accounting for the main effect of environment.Key words: Avena sativa, hull percentage, bulked segregant analysis, quantitative trait locus.

Genome-Wide Scans Reveal a Quantitative Trait Locus for Withers Height in Horses Near the ANKRD1 Gene

2018 ◽  
Vol 60 ◽  
pp. 67-73.e1 ◽  
Author(s):  
Mohammed A. Al Abri ◽  
Christian Posbergh ◽  
Katelyn Palermo ◽  
Nathan B. Sutter ◽  
John Eberth ◽  
...  
Keyword(s):  
Quantitative Trait Locus ◽  
Quantitative Trait ◽  
Genome Wide ◽  
Trait Locus

scholarly journals A compendium of uniformly processed human gene expression and splicing quantitative trait loci

2021 ◽  
Vol 53 (9) ◽  
pp. 1290-1299
Author(s):  
Nurlan Kerimov ◽  
James D. Hayhurst ◽  
Kateryna Peikova ◽  
Jonathan R. Manning ◽  
Peter Walter ◽  
...  
Keyword(s):  
Gene Expression ◽  
Quantitative Trait ◽  
Target Genes ◽  
Genome Wide Association Study ◽  
Cell Types ◽  
Summary Statistics ◽  
Genome Wide ◽  
Cell Type Specific ◽  
Trait Locus ◽  
Complex Human Traits

AbstractMany gene expression quantitative trait locus (eQTL) studies have published their summary statistics, which can be used to gain insight into complex human traits by downstream analyses, such as fine mapping and co-localization. However, technical differences between these datasets are a barrier to their widespread use. Consequently, target genes for most genome-wide association study (GWAS) signals have still not been identified. In the present study, we present the eQTL Catalogue (https://www.ebi.ac.uk/eqtl), a resource of quality-controlled, uniformly re-computed gene expression and splicing QTLs from 21 studies. We find that, for matching cell types and tissues, the eQTL effect sizes are highly reproducible between studies. Although most QTLs were shared between most bulk tissues, we identified a greater diversity of cell-type-specific QTLs from purified cell types, a subset of which also manifested as new disease co-localizations. Our summary statistics are freely available to enable the systematic interpretation of human GWAS associations across many cell types and tissues.

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