Improved Confidence Intervals in Quantitative Trait Loci Mapping by Permutation Bootstrapping

Abstract The nonparametric bootstrap approach is known to be suitable for calculating central confidence intervals for the locations of quantitative trait loci (QTL). However, the distribution of the bootstrap QTL position estimates along the chromosome is peaked at the positions of the markers and is not tailed equally. This results in conservativeness and large width of the confidence intervals. In this study three modified methods are proposed to calculate nonparametric bootstrap confidence intervals for QTL locations, which compute noncentral confidence intervals (uncorrected method I), correct for the impact of the markers (weighted method I), or both (weighted method II). Noncentral confidence intervals were computed with an analog of the highest posterior density method. The correction for the markers is based on the distribution of QTL estimates along the chromosome when the QTL is not linked with any marker, and it can be obtained with a permutation approach. In a simulation study the three methods were compared with the original bootstrap method. The results showed that it is useful, first, to compute noncentral confidence intervals and, second, to correct the bootstrap distribution of the QTL estimates for the impact of the markers. The weighted method II, combining these two properties, produced the shortest and less biased confidence intervals in a large number of simulated configurations.

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Empirical Nonparametric Bootstrap Strategies in Quantitative Trait Loci Mapping: Conditioning on the Genetic Model

Genetics ◽

10.1093/genetics/148.1.525 ◽

1998 ◽

Vol 148 (1) ◽

pp. 525-535

Author(s):

Claude M Lebreton ◽

Peter M Visscher

Keyword(s):

Quantitative Trait Loci ◽

Confidence Interval ◽

Confidence Intervals ◽

Quantitative Trait ◽

Genetic Factors ◽

Genetic Model ◽

Original Data ◽

Nonparametric Bootstrap ◽

Test Protocol ◽

Trait Loci

AbstractSeveral nonparametric bootstrap methods are tested to obtain better confidence intervals for the quantitative trait loci (QTL) positions, i.e., with minimal width and unbiased coverage probability. Two selective resampling schemes are proposed as a means of conditioning the bootstrap on the number of genetic factors in our model inferred from the original data. The selection is based on criteria related to the estimated number of genetic factors, and only the retained bootstrapped samples will contribute a value to the empirically estimated distribution of the QTL position estimate. These schemes are compared with a nonselective scheme across a range of simple configurations of one QTL on a one-chromosome genome. In particular, the effect of the chromosome length and the relative position of the QTL are examined for a given experimental power, which determines the confidence interval size. With the test protocol used, it appears that the selective resampling schemes are either unbiased or least biased when the QTL is situated near the middle of the chromosome. When the QTL is closer to one end, the likelihood curve of its position along the chromosome becomes truncated, and the nonselective scheme then performs better inasmuch as the percentage of estimated confidence intervals that actually contain the real QTL's position is closer to expectation. The nonselective method, however, produces larger confidence intervals. Hence, we advocate use of the selective methods, regardless of the QTL position along the chromosome (to reduce confidence interval sizes), but we leave the problem open as to how the method should be altered to take into account the bias of the original estimate of the QTL's position.

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Identification of quantitative trait loci associated with canopy temperature in soybean

Scientific Reports ◽

10.1038/s41598-020-74614-8 ◽

2020 ◽

Vol 10 (1) ◽

Author(s):

Sumandeep K. Bazzer ◽

Larry C. Purcell

Keyword(s):

Quantitative Trait Loci ◽

Phenotypic Variation ◽

Quantitative Trait ◽

Recombinant Inbred Lines ◽

Canopy Temperature ◽

Growth And Yield ◽

Indirect Measure ◽

Trait Loci ◽

Genomic Regions ◽

The Impact

Abstract A consistent risk for soybean (Glycine max L.) production is the impact of drought on growth and yield. Canopy temperature (CT) is an indirect measure of transpiration rate and stomatal conductance and may be valuable in distinguishing differences among genotypes in response to drought. The objective of this study was to map quantitative trait loci (QTLs) associated with CT using thermal infrared imaging in a population of recombinant inbred lines developed from a cross between KS4895 and Jackson. Heritability of CT was 35% when estimated across environments. QTL analysis identified 11 loci for CT distributed on eight chromosomes that individually explained between 4.6 and 12.3% of the phenotypic variation. The locus on Gm11 was identified in two individual environments and across environments and explained the highest proportion of phenotypic variation (9.3% to 11.5%) in CT. Several of these CT loci coincided with the genomic regions from previous studies associated with canopy wilting, canopy temperature, water use efficiency, and other morpho-physiological traits related with drought tolerance. Candidate genes with biological function related to transpiration, root development, and signal transduction underlie these putative CT loci. These genomic regions may be important resources in soybean breeding programs to improve tolerance to drought.

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The impact of methylation quantitative trait loci (mQTLs) on active smoking-related DNA methylation changes

Clinical Epigenetics ◽

10.1186/s13148-017-0387-6 ◽

2017 ◽

Vol 9 (1) ◽

Cited By ~ 12

Author(s):

Xu Gao ◽

Hauke Thomsen ◽

Yan Zhang ◽

Lutz Philipp Breitling ◽

Hermann Brenner

Keyword(s):

Dna Methylation ◽

Quantitative Trait Loci ◽

Quantitative Trait ◽

Active Smoking ◽

Trait Loci ◽

The Impact ◽

Methylation Quantitative Trait Loci

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Predicting the impact of non-coding variants on DNA methylation

10.1101/073809 ◽

2016 ◽

Author(s):

Haoyang Zeng ◽

David K. Gifford

Keyword(s):

Dna Methylation ◽

Quantitative Trait Loci ◽

Quantitative Trait ◽

Sequence Variation ◽

Specific Gene ◽

Single Nucleotide ◽

Trait Loci ◽

Allele Specific ◽

Coding Variants ◽

The Impact

AbstractDNA methylation plays a crucial role in the establishment of tissue-specific gene expression and the regulation of key biological processes. However, our present inability to predict the effect of genome sequence variation on DNA methylation precludes a comprehensive assessment of the consequences of non-coding variation. We introduce CpGenie, a sequence-based framework that learns a regulatory code of DNA methylation using a deep convolutional neural network and uses this network to predict the impact of sequence variation on proximal CpG site DNA methylation. CpGenie produces allele-specific DNA methylation prediction with single-nucleotide sensitivity that enables accurate prediction of methylation quantitative trait loci (meQTL). We demonstrate that CpGenie prioritizes validated GWAS SNPs, and contributes to the prediction of functional non-coding variants, including expression quantitative trait loci (eQTL) and disease-associated mutations. CpGenie is publicly available to assist in identifying and interpreting regulatory non-coding variants.

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Genetic variants influence on the placenta regulatory landscape

10.1101/432211 ◽

2018 ◽

Author(s):

F. Delahaye ◽

C. Do ◽

Y. Kong ◽

R. Ashkar ◽

M. Sala ◽

...

Keyword(s):

Dna Methylation ◽

Quantitative Trait Loci ◽

High Resolution ◽

Quantitative Trait ◽

Association Studies ◽

Expression Data ◽

Healthy Women ◽

Trait Loci ◽

The Impact ◽

Regulatory Landscape

AbstractBackgroundFrom genomic association studies, quantitative trait loci analysis, and epigenomic mapping, it is evident that significant efforts are necessary to define genetic-epigenetic interactions and understand their role in disease susceptibility and progression. For this reason, an analysis of the effects of genetic variation on gene expression and DNA methylation in human placentas at high resolution and whole-genome coverage will have multiple mechanistic and practical implications.ResultsBy producing and analyzing DNA sequence variation (n=303), DNA methylation (n=303) and mRNA expression data (n=80) from placentas from healthy women, we investigate the regulatory landscape of the human placenta and offer analytical approaches to integrate different types of genomic data and address some potential limitations of current platforms. We distinguish two profiles of interaction between expression and DNA methylation, revealing linear or bimodal effects, reflecting differences in genomic context, transcription factor recruitment, and possibly cell subpopulations.ConclusionsThese findings help to clarify the interactions of genetic, epigenetic, and transcriptional regulatory mechanisms in normal human placentas. They also provide strong evidence for genotype-driven modifications of transcription and DNA methylation in normal placentas. In addition to these mechanistic implications, the data and analytical methods presented here will improve the interpretability of genome-wide and epigenome-wide association studies for human traits and diseases that involve placental functions.Author summaryThe placenta is a critical organ playing multiple roles including oxygen and metabolite transfer from mother to fetus, hormone production, and vascular perfusion. With this study, we aimed to deliver a placenta-specific regulatory map based on a combination of publicly available and newly generated data. To complete this reference, we obtained genotype information (n=303), DNA methylation (n=303) and expression data (n=80) for placentas from healthy women. Our analysis of methylation and expression quantitative trait loci (QTLs) and correlations between methylation and expression data were designed to identify fundamental associations between genome, transcriptome, and epigenome in this key fetal organ. The results provide high-resolution genetic and epigenetic maps specific to the placenta based on a representative ethnically diverse cohort. As interest and efforts are growing to better understand the etiology of placental disease and the impact of the environment on placental function these data will provide a reference and enhance future investigations.

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Classical and Bayesian Inference of an Exponentiated Half-Logistic Distribution under Adaptive Type II Progressive Censoring

Entropy ◽

10.3390/e23121558 ◽

2021 ◽

Vol 23 (12) ◽

pp. 1558

Author(s):

Ziyu Xiong ◽

Wenhao Gui

Keyword(s):

Confidence Intervals ◽

Bootstrap Method ◽

Information Matrix ◽

Logistic Distribution ◽

Type Ii ◽

Progressive Censoring ◽

Posterior Density ◽

Unknown Parameters ◽

Data Set ◽

Highest Posterior Density

The point and interval estimations for the unknown parameters of an exponentiated half-logistic distribution based on adaptive type II progressive censoring are obtained in this article. At the beginning, the maximum likelihood estimators are derived. Afterward, the observed and expected Fisher’s information matrix are obtained to construct the asymptotic confidence intervals. Meanwhile, the percentile bootstrap method and the bootstrap-t method are put forward for the establishment of confidence intervals. With respect to Bayesian estimation, the Lindley method is used under three different loss functions. The importance sampling method is also applied to calculate Bayesian estimates and construct corresponding highest posterior density (HPD) credible intervals. Finally, numerous simulation studies are conducted on the basis of Markov Chain Monte Carlo (MCMC) samples to contrast the performance of the estimations, and an authentic data set is analyzed for exemplifying intention.

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On the mapping of quantitative trait loci at marker and non-marker locations

Genetics Research ◽

10.1017/s0016672301005420 ◽

2002 ◽

Vol 79 (1) ◽

pp. 97-106 ◽

Cited By ~ 11

Author(s):

GRANT A. WALLING ◽

CHRIS S. HALEY ◽

MIGUEL PEREZ-ENCISO ◽

ROBIN THOMPSON ◽

PETER M. VISSCHER

Keyword(s):

Quantitative Trait Loci ◽

Quantitative Trait ◽

Null Hypothesis ◽

Simulated Data ◽

Estimation Methods ◽

Trait Loci ◽

Single Interval ◽

Regression Mapping ◽

The Impact ◽

Qtl Effects

Previous studies have noted that the estimated positions of a large proportion of mapped quantitative trait loci (QTLs) coincide with marker locations and have suggested that this indicates a bias in the mapping methodology. In this study we predict the expected proportion of QTLs with positions estimated to be at the location of a marker and further examine the problem using simulated data. The results show that the higher proportion of putative QTLs estimated to be at marker positions compared with non-marker positions is an expected consequence of the estimation methods. The study initially focused on a single interval with no QTLs and was extended to include multiple intervals and QTLs of large effect. Further, the study demonstrated that the larger proportion of estimated QTL positions at the location of markers was not unique to linear regression mapping. Maximum likelihood produced similar results, although the accumulation of positional estimates at outermost markers was reduced when regions outside the linkage group were also considered. The bias towards marker positions is greatest under the null hypothesis of no QTLs or when QTL effects are small. This study discusses the impact the findings could have on the calculation of thresholds and confidence intervals produced by bootstrap methods.

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