scholarly journals WASP: allele-specific software for robust discovery of molecular quantitative trait loci

2014 ◽  
Author(s):  
Bryce van de Geijn ◽  
Graham McVicker ◽  
Yoav Gilad ◽  
Jonathan Pritchard
Keyword(s):  
Quantitative Trait Loci ◽  
Qtl Mapping ◽  
Error Rate ◽  
Quantitative Trait ◽  
Rna Seq ◽  
Read Mapping ◽  
Trait Loci ◽  
Allele Specific ◽  
Allele Specific Sequencing

Allele-specific sequencing reads provide a powerful signal for identifying molecular quantitative trait loci (QTLs), however they are challenging to analyze and prone to technical artefacts. Here we describe WASP, a suite of tools for unbiased allele-specific read mapping and discovery of molecular QTLs. Using simulated reads, RNA-seq reads and ChIP-seq reads, we demonstrate that our approach has a low error rate and is far more powerful than existing QTL mapping approaches.

scholarly journals On the Differences Between Maximum Likelihood and Regression Interval Mapping in the Analysis of Quantitative Trait Loci

Genetics ◽  
2000 ◽  
Vol 156 (2) ◽  
pp. 855-865 ◽  
Author(s):  
Chen-Hung Kao
Keyword(s):  
Quantitative Trait Loci ◽  
Maximum Likelihood ◽  
Qtl Mapping ◽  
Quantitative Trait ◽  
Mean Squared Error ◽  
Interval Mapping ◽  
Size Difference ◽  
Ratio Test ◽  
Trait Loci ◽  
Variance Explained

AbstractThe differences between maximum-likelihood (ML) and regression (REG) interval mapping in the analysis of quantitative trait loci (QTL) are investigated analytically and numerically by simulation. The analytical investigation is based on the comparison of the solution sets of the ML and REG methods in the estimation of QTL parameters. Their differences are found to relate to the similarity between the conditional posterior and conditional probabilities of QTL genotypes and depend on several factors, such as the proportion of variance explained by QTL, relative QTL position in an interval, interval size, difference between the sizes of QTL, epistasis, and linkage between QTL. The differences in mean squared error (MSE) of the estimates, likelihood-ratio test (LRT) statistics in testing parameters, and power of QTL detection between the two methods become larger as (1) the proportion of variance explained by QTL becomes higher, (2) the QTL locations are positioned toward the middle of intervals, (3) the QTL are located in wider marker intervals, (4) epistasis between QTL is stronger, (5) the difference between QTL effects becomes larger, and (6) the positions of QTL get closer in QTL mapping. The REG method is biased in the estimation of the proportion of variance explained by QTL, and it may have a serious problem in detecting closely linked QTL when compared to the ML method. In general, the differences between the two methods may be minor, but can be significant when QTL interact or are closely linked. The ML method tends to be more powerful and to give estimates with smaller MSEs and larger LRT statistics. This implies that ML interval mapping can be more accurate, precise, and powerful than REG interval mapping. The REG method is faster in computation, especially when the number of QTL considered in the model is large. Recognizing the factors affecting the differences between REG and ML interval mapping can help an efficient strategy, using both methods in QTL mapping to be outlined.

scholarly journals Comparison of quantitative trait loci methods: Total expression and allelic imbalance method in brain RNA-seq

PLoS ONE ◽  
2019 ◽  
Vol 14 (6) ◽  
pp. e0217765
Author(s):  
Jesper R. Gådin ◽  
Alfonso Buil ◽  
Carlo Colantuoni ◽  
Andrew E. Jaffe ◽  
Jacob Nielsen ◽  
...  
Keyword(s):  
Quantitative Trait Loci ◽  
Quantitative Trait ◽  
Allelic Imbalance ◽  
Rna Seq ◽  
Trait Loci

Application of the False Discovery Rate to Quantitative Trait Loci Interval Mapping With Multiple Traits

Genetics ◽  
2002 ◽  
Vol 161 (2) ◽  
pp. 905-914 ◽  
Author(s):  
Hakkyo Lee ◽  
Jack C M Dekkers ◽  
M Soller ◽  
Massoud Malek ◽  
Rohan L Fernando ◽  
...  
Keyword(s):  
Quantitative Trait Loci ◽  
False Discovery Rate ◽  
Error Rate ◽  
Quantitative Trait ◽  
Interval Mapping ◽  
Error Rates ◽  
Multiple Traits ◽  
Test Statistic ◽  
False Discovery ◽  
Trait Loci

Abstract Controlling the false discovery rate (FDR) has been proposed as an alternative to controlling the genomewise error rate (GWER) for detecting quantitative trait loci (QTL) in genome scans. The objective here was to implement FDR in the context of regression interval mapping for multiple traits. Data on five traits from an F2 swine breed cross were used. FDR was implemented using tests at every 1 cM (FDR1) and using tests with the highest test statistic for each marker interval (FDRm). For the latter, a method was developed to predict comparison-wise error rates. At low error rates, FDR1 behaved erratically; FDRm was more stable but gave similar significance thresholds and number of QTL detected. At the same error rate, methods to control FDR gave less stringent significance thresholds and more QTL detected than methods to control GWER. Although testing across traits had limited impact on FDR, single-trait testing was recommended because there is no theoretical reason to pool tests across traits for FDR. FDR based on FDRm was recommended for QTL detection in interval mapping because it provides significance tests that are meaningful, yet not overly stringent, such that a more complete picture of QTL is revealed.

Quantitative Trait Loci (QTL) Mapping

1999 ◽  
Author(s):  
Isabelle Le Roy
Keyword(s):  
Quantitative Trait Loci ◽  
Qtl Mapping ◽  
Quantitative Trait ◽  
Trait Loci

scholarly journals Mapping Splicing Quantitative Trait Loci in RNA-Seq

2014 ◽  
Vol 13s4 ◽  
pp. CIN.S13971 ◽  
Author(s):  
Cheng Jia ◽  
Yu Hu ◽  
Yichuan Liu ◽  
Mingyao Li
Keyword(s):  
Alternative Splicing ◽  
Quantitative Trait Loci ◽  
Statistical Methods ◽  
Random Effects ◽  
Quantitative Trait ◽  
Rna Seq ◽  
False Discovery Rates ◽  
Trait Loci ◽  
Meta Regression ◽  
Generalized Linear Mixed Effects

Background One of the major mechanisms of generating mRNA diversity is alternative splicing, a regulated process that allows for the flexibility of producing functionally different proteins from the same genomic sequences. This process is often altered in cancer cells to produce aberrant proteins that drive the progression of cancer. A better understanding of the misregulation of alternative splicing will shed light on the development of novel targets for pharmacological interventions of cancer. Methods In this study, we evaluated three statistical methods, random effects meta-regression, beta regression, and generalized linear mixed effects model, for the analysis of splicing quantitative trait loci (sQTL) using RNA-Seq data. All the three methods use exon-inclusion levels estimated by the PennSeq algorithm, a statistical method that utilizes paired-end reads and accounts for non-uniform sequencing coverage. Results Using both simulated and real RNA-Seq datasets, we compared these three methods with GLiMMPS, a recently developed method for sQTL analysis. Our results indicate that the most reliable and powerful method was the random effects meta-regression approach, which identified sQTLs at low false discovery rates but higher power when compared to GLiMMPS. Conclusions We have evaluated three statistical methods for the analysis of sQTLs in RNA-Seq. Results from our study will be instructive for researchers in selecting the appropriate statistical methods for sQTL analysis.

Letter to the Editor: Methods for mapping quantitative trait loci in autotetraploid species

2020 ◽  
Author(s):  
Jing Chen ◽  
Lindsey J Leach ◽  
Zewei Luo
Keyword(s):  
Quantitative Trait Loci ◽  
Qtl Mapping ◽  
Statistical Methods ◽  
Quantitative Trait ◽  
Important Species ◽  
Letter To The Editor ◽  
Trait Loci ◽  
Points Of View

Abstract Mapping quantitative trait loci (QTL) in autotetraploid species represents a timely and challenging task. Two papers published by Wu and his colleagues proposed statistical methods for QTL mapping in these evolutionarily and economically important species. In this Letter to the Editor, we present critical comments on the fundamental conceptual errors involved, from both statistical and genetic points of view.

scholarly journals A method for computing identity by descent probabilities and quantitative trait loci mapping with dominant (AFLP) markers

2002 ◽  
Vol 79 (3) ◽  
pp. 247-258 ◽  
Author(s):  
MIGUEL PÉREZ-ENCISO ◽  
ODILE ROUSSOT
Keyword(s):  
Quantitative Trait Loci ◽  
Qtl Mapping ◽  
Quantitative Trait ◽  
Cost Effective ◽  
Significant Loss ◽  
Outbred Population ◽  
Identity By Descent ◽  
Fluorescent Band ◽  
Length Polymorphisms ◽  
Trait Loci

Amplified fragment length polymorphisms (AFLPs) are a widely used marker system: the technique is very cost-effective, easy and rapid, and reproducibly generates hundreds of markers. Unfortunately, AFLP alleles are typically scored as the presence or absence of a band and, thus, heterozygous and dominant homozygous genotypes cannot be distinguished. This results in a significant loss of information, especially as regards mapping of quantitative trait loci (QTLs). We present a Monte Carlo Markov Chain method that allows us to compute the identity by descent probabilities (IBD) in a general pedigree whose individuals have been typed for dominant markers. The method allows us to include the information provided by the fluorescent band intensities of the markers, the rationale being that homozygous individuals have on average higher band intensities than heterozygous individuals, as well as information from linked markers in each individual and its relatives. Once IBD probabilities are obtained, they can be combined into the QTL mapping strategy of choice. We illustrate the method with two simulated populations: an outbred population consisting of full sib families, and an F2 cross between inbred lines. Two marker spacings were considered, 5 or 20 cM, in the outbred population. There was almost no difference, for the practical purpose of QTL estimation, between AFLPs and biallelic codominant markers when the band density is taken into account, especially at the 5 cM spacing. The performance of AFLPs every 5 cM was also comparable to that of highly polymorphic markers (microsatellites) spaced every 20 cM. In economic terms, QTL mapping with a dense map of AFLPs is clearly better than microsatellite QTL mapping and little is lost in terms of accuracy of position. Nevertheless, at low marker densities, AFLPs or other biallelic markers result in very inaccurate estimates of QTL position.

Molecular mapping of quantitative trait loci in japonica rice

Genome ◽  
1996 ◽  
Vol 39 (2) ◽  
pp. 395-403 ◽  
Author(s):  
Edilberto D. Redoña ◽  
David J. Mackill
Keyword(s):  
Oryza Sativa ◽  
Quantitative Trait Loci ◽  
Qtl Mapping ◽  
Phenotypic Variation ◽  
Quantitative Trait ◽  
Rflp Markers ◽  
Seedling Vigor ◽  
Trait Loci ◽  
Rice Chromosomes ◽  
Molecular Maps

Rice (Oryza sativa L.) molecular maps have previously been constructed using interspecific crosses or crosses between the two major subspecies: indica and japonica. For japonica breeding programs, however, it would be more suitable to use intrasubspecific crosses. A linkage map of 129 random amplified polymorphic DNA (RAPD) and 18 restriction fragment length polymorphism (RFLP) markers was developed using 118 F2 plants derived from a cross between two japonica cultivars with high and low seedling vigor, Italica Livorno (IL) and Labelle (LBL), respectively. The map spanned 980.5 cM (Kosambi function) with markers on all 12 rice chromosomes and an average distance of 7.6 cM between markers. Codominant (RFLP) and coupling phase linkages (among RAPDs) accounted for 79% of total map length and 71% of all intervals. This map contained a greater percentage of markers on chromosome 10, the least marked of the 12 rice chromosomes, than other rice molecular maps, but had relatively fewer markers on chromosomes 1 and 2. We used this map to detect quantitative trait loci (QTL) for four seedling vigor related traits scored on 113 F3 families in a growth chamber slantboard test at 18 °C. Two coleoptile, five root, and five mesocotyl length QTLs, each accounting for 9–50% of the phenotypic variation, were identified by interval analysis. Single-point analysis confirmed interval mapping results and detected additional markers significantly influencing each trait. About two-thirds of alleles positive for the putative QTLs were from the high-vigor parent, IL. One RAPD marker (OPAD13720) was associated with a IL allele that accounted for 18.5% of the phenotypic variation for shoot length, the most important determinant of seedling vigor in water-seeded rice. Results indicate that RAPDs are useful for map development and QTL mapping in rice populations with narrow genetic base, such as those derived from crosses among japonica cultivars. Other potential uses of the map are discussed. Key words : QTL mapping, RAPD, RFLP, seedling vigor, japonica, Oryza sativa.

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