Top PDF Quantitative Trait Loci Analysis Using the False Discovery Rate

Quantitative Trait Loci Analysis Using the False Discovery Rate

Quantitative Trait Loci Analysis Using the False Discovery Rate

Replacing the suggestive linkage criterion with FDR control: Let us carry the above discussion into the case of suggestive linkage. The threshold is chosen so that there will be one false linkage per genome scan on the average. Using the Poisson approximation, such a threshold is equivalent to controlling the FWE at 0.6. Now when Z aykin et al. (2000, p. 1918) claim that the control of FWE is better than the FDR, they argue: ‘‘ . . . using an FWER controlling method, one may claim that all significances obtained in the study are real, gambling upon the occurrence that the given study was not one of the 25% (or whatever FWE level that is used) that will produce a false positive.’’ Consider the above argument applied to the criterion for suggestive linkages: gam- bling that the given study is not one of the 12 out of 20 that will produce a false positive, is difficult to justify. It is therefore our view that controlling the FWE at 0.6 cannot by itself be trusted to indicate suggestive results. If one reads carefully L ander and K ruglyak (1995) similar skepticism can be sensed, as, for example, they do not see a way to confirm suggestive linkages in a second study. We therefore suggest that this criterion be abandoned and be replaced by FDR control at lower level. A good choice is q ¼ 0.1, as done by L ee et al. (2002). We certainly do not recommend going higher than q ¼ 0.2 in published reports.
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Controlling for false positive findings of trans hubs in expression quantitative trait loci mapping

Controlling for false positive findings of trans hubs in expression quantitative trait loci mapping

In this paper, we perform linkage analysis for GAW15 data using robust score statistics, which enjoy excellent compu- tational efficiency (20 seconds for computing the score statistics for 3554 expressions on 1197 markers in R on a Thinkpad X40 laptop), and enable us to carry out large- scale permutation studies. Using the original phenotypes, we identify two candidate trans-hubs, one at 9p13.3 and the other at 14q32. However, after accounting for the expression correlations in the linkage analysis, both trans- hubs disappear. This suggests that conclusions with regard to regulation hot spots should be interpreted with great caution.
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Survival Analysis of Life Span Quantitative Trait Loci in Drosophila melanogaster

Survival Analysis of Life Span Quantitative Trait Loci in Drosophila melanogaster

We used quantitative trait loci (QTL) mapping to evaluate the age specificity of naturally segregating alleles affecting life span. Estimates of age-specific mortality rates were obtained from observing 51,778 mated males and females from a panel of 144 recombinant inbred lines (RILs). Twenty-five QTL were found, having 80 significant effects on life span and weekly mortality rates. Generation of RILs from heterozygous parents enabled us to contrast effects of QTL alleles with the means of RIL populations. Most of the low-frequency alleles increased mortality, especially at younger ages. Two QTL had negatively correlated effects on mortality at different ages, while the remainder were positively correlated. Chromo- somal positions of QTL were roughly concordant with estimates from other mapping populations. Our findings are broadly consistent with a mix of transient deleterious mutations and a few polymorphisms maintained by balancing selection, which together contribute to standing genetic variation in life span.
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Multiple trait analysis of genetic mapping for quantitative trait loci.

Multiple trait analysis of genetic mapping for quantitative trait loci.

We examined, in detail, various advantages and disadvantages of the joint analysis as compared to sepa- rate analyses for mapping QTL and also for testing a number of hyp[r]

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Genomic interrogation of familial short stature contributes to the discovery of the pathophysiological mechanisms and pharmaceutical drug repositioning

Genomic interrogation of familial short stature contributes to the discovery of the pathophysiological mechanisms and pharmaceutical drug repositioning

ASN: Asian; BH: Benjamini-Hochberg; BMP: Bone morphogenetic protein; BP: Biological process; EAS: East Asian; eQTL: Expression quantitative trait locus; FDR: False discovery rate; FR: Fruchterman-Reingold; FSS: Familial short stature; GAD: Gene-Disease Associations; GO: Gene ontology; GSEA: Gene set enrichment analysis; GTEx: Genotype-Tissue Expression; GWAS: Genome-wide association study; HPO: Human Phenotype Ontology; HPRD: Human Protein Reference Database; KEGG: Kyoto Encyclopedia of Genes and Genomes; LD: Linkage disequilibrium; MAF: Minor allele frequency; NET: Norepinephrine transporter; ORA: Over-representation analysis; PCA: Principal component analysis; PPI: Protein-protein interaction; QC: Quality control;
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A Whole Genome Scan for Quantitative Trait Loci Affecting Milk Protein Percentage in Israeli-Holstein Cattle, by Means of Selective Milk DNA Pooling in a Daughter Design, Using an Adjusted False Discovery Rate Criterion

A Whole Genome Scan for Quantitative Trait Loci Affecting Milk Protein Percentage in Israeli-Holstein Cattle, by Means of Selective Milk DNA Pooling in a Daughter Design, Using an Adjusted False Discovery Rate Criterion

Genotyping individual semen samples and individual and each of the above two series of tests (sire-by-marker combina- pooled milk samples was as described (Lipkin et al. 1998). tions and markers), a comparisonwise error rate (CWER), or Microsatellites: A total of 138 dinucleotide microsatellite type I error P-value, was calculated for each test using standard markers distributed over all 29 bovine autosomes were used statistical procedures, as detailed above. In the usual experi- in this study (web sites: U.S. Department of Agriculture, mental situation, where only a small number of treatments http://bos.cvm.tamu.edu/bovgbase.html; and IBRP Cattle are compared, a CWER P-value of 0.05 or 0.01 would lead to Genome Database, http://spinal.tag.csiro.au). The distance rejection of the null hypothesis (H 0 represents absence of between the markers or between the chromosome ends (cen- treatment effect), with type I error likelihood P ⬍ 0.05 or P ⬍ tromeres and telomeres) and the closest marker averaged 0.01. In the present case of linkage testing, however, multiple
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Multiple Quantitative Trait Loci Mapping With Cofactors and Application of Alternative Variants of the False Discovery Rate in an Enlarged Granddaughter Design

Multiple Quantitative Trait Loci Mapping With Cofactors and Application of Alternative Variants of the False Discovery Rate in an Enlarged Granddaughter Design

as defined above for sire j in the grandsire family i at chromo- and the QTL transition probability, and the remaining vari- somal position k. The null hypothesis was that no QTL segre- ables are as defined above. Note that this was no systematic gates on this chromosome for the trait under consideration, search for the presence of a statistical QTL-by-environment the alternative hypothesis was that one QTL segregates on interaction.

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Identification of expression quantitative trait loci by the interaction analysis using genetic algorithm

Identification of expression quantitative trait loci by the interaction analysis using genetic algorithm

We focused our search on eQTLs for three cancer related- genes. We tested the marginal effects of 2692 SNPs by fit- ting the first type of ANOVA model with individual SNPs and the expression values of the three cancer-related genes. The SNPs showing p-values of less than 0.001 were considered to have marginal effects and were classified as major SNPs in the genetic algorithm. All remaining SNPs were classified as minor SNPs in the genetic algorithm. Using this set of major and minor SNPs, we applied the GA for interaction analysis. We used a major mutation rate of 0.4, a minor mutation rate of 0.1, a cross-over rate of 0.6, and a population size of 1000.
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Identification of recombination hotspots and quantitative trait loci for recombination rate in layer chickens

Identification of recombination hotspots and quantitative trait loci for recombination rate in layer chickens

The number of recombination events was treated as a quantitative trait to map genetic variants that influence recombination rates. The proportion of genetic variance explained by each 1-Mb region across the genome in WL and BL is presented in Additional file 6: Figure S6 and Additional file 7: Figure S7. Tables 2 and 3 show the proportion of genetic variance explained, PPA of QTL regions, MAF, physical position, significance of the SNPs with highest effect, and the list of nearby candidate genes. Since it has been shown that the location of the causal mutation could be extended to 1 Mb on either side of the informative 1 Mb QTL regions, neighboring regions were combined for analysis [9, 37, 43]. In gen- eral, 14 QTL on eight chromosomes for recombination rate were identified in WL. Only 6 QTL on four chro- mosomes were identified in BL. The GV% explained by significant QTL regions ranged from 1.0% to 8.4% in WL, and from 0.8% to 19.7% in BL. No common QTL regions were identified across the two lines.
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Association of human XPA rs1800975 polymorphism and cancer susceptibility: an integrative analysis of 71 case–control studies

Association of human XPA rs1800975 polymorphism and cancer susceptibility: an integrative analysis of 71 case–control studies

XPA: Xeroderma pigmentosum group A; OR: Odd ratio; FPRP: False-positive report probability; TSA: Trial sequential analysis; eQTL: Expression quantitative trait loci; sQTL: Splicing quantitative trait loci; BCC: Basal cell carcinoma; NER: Nucleotide excision repair; SNP: Single nucleotide polymorphism; PRISMA: Preferred reporting items for systematic reviews and meta-analyses; HWE: Hardy–Weinberg equilibrium; EMBASE: Excerpta Medica Database; CNKI: China National Knowledge Infrastructure; NOS: Newcastle–Ottawa quality assessment Scale; CI: Confidence interval; GTEx: Genotype-Tissue Expression; TPM: Transcripts Per Million; TIMER: Tumor Immune Estimation Resource; TCGA : The Cancer Genome Atlas; NES: Normalized Effect Size; CHOL: Cholangiocar- cinoma; LIHC: Liver hepatocellular carcinoma; BLCA: Bladder Urothelial Car- cinoma; BRCA : Breast invasive carcinoma; KICH: Kidney Chromophobe; KIRC: Kidney renal clear cell carcinoma; KIRP: Kidney renal papillary cell carcinoma; LUAD: Lung adenocarcinoma; LUSC: Lung squamous cell carcinoma; READ: Rectum adenocarcinoma; THCA: Thyroid carcinoma; UCEC: Uterine Corpus Endometrial Carcinoma; XPD: Xeroderma pigmentosum group D; BC: Bladder cancer; RCC : Renal cell carcinoma; SCC: Squamous cell carcinoma; LSCC: Lung squamous cell carcinoma; GCA : Gastric cardiac adenocarcinoma; LA: Lung adenocarcinoma; NSCLC: Non-small cell lung cancer; ESCC: Esophageal squamous cell carcinoma; OSCC: Oral squamous cell carcinoma; ALL: Acute lymphoblastic leukemia; HCC: Hepatocellular carcinoma; PB: Population-based control; HB: Hospital-based control; PCR: Polymerase chain reaction; PCR-RFLP: PCR-restriction fragment length polymorphism; PCR-LDR: PCR-ligase detec- tion reaction; MALDI-TOF-MS: Matrix-assisted laser desorption/Ionization time of flight mass spectrometry.
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Application of the False Discovery Rate to Quantitative Trait Loci Interval Mapping With Multiple Traits

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

LK attempt to extend this principle to multiple testing nificant tests for cholesterol and marbling increased the in a QTL scan by taking the null hypothesis of no QTL stringency of thresholds for the other three traits when as valid for all tests conducted across the genome. This considered in a multiple-trait scenario. As a result, in null hypothesis is, however, by definition false for traits the multiple-trait test, the number of QTL detected for that have been shown by prior biometrical analyses to carcass weight and last rib back fat at the 10% level have nonzero heritabilities. Instead, the statistical prob- was reduced from 14 to 8 (Table 5). Paradoxically, as lem is to identify regions that harbor QTL vs. those that pointed out by Spelman (1998), when grouped with do not. The FDR approach deals directly and quantita- traits having many detectable QTL, tests for the traits tively with this challenge by controlling the proportion having few or no QTL will be pushed down to a high of false positives among all significant results. The rank number. This will tend to produce less stringent GWER approaches deal with this only qualitatively, by CWER thresholds for given FDR level and hence more controlling the probability that significant results in- QTL detected for these traits than when analyzed alone. clude no more than one false positive. This is seen in Table 5 for marbling at the FDR 0.10
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Integrative analysis of vascular endothelial cell genomic features identifies AIDA as a coronary artery disease candidate gene

Integrative analysis of vascular endothelial cell genomic features identifies AIDA as a coronary artery disease candidate gene

Pairs of guide RNAs (sgRNAs) were designed for each targeted genomic deletion and cloned into the pHKO9 vector under control of the same U6 promoter (Add- itional file 16). HEK 293 T cells were seeded at 5 × 10 5 cells/well in 6-well plates for 24 h. Lentivirus were pro- duced by co-transfecting the envelope and packaging plasmids pMD2G and psPAX2 respectively with the dual sgRNA expressing pHKO9 vector in HEK 293 T cells using Lipofectamine 2000 (ThermoFisher, 11,668,027) for 4 h then switched to virus-producing media contain- ing 10 μg/mL of BSA. Viral supernatant was harvested 48 h and 72 h following transfection and filtered through 0.45 μm filters. TeloHAEC cells stably expressing an ac- tive Cas9 protein were seeded at 2 × 10 5 cells/well in 6- well plates and later infected with the virus preparation and media containing 0.7 μg/mL of polybrene (Sigma, H9268). Selection with 200 μg/mL of G418 (Fisher, MT30234CR) was started 48 h post-infection. Antibiotic selective pressure was maintained for 5–6 days or until non-infected cells were dead. Sub-populations of 50 cells were derived and screened via PCR using primers sur- rounding the expected deletion (out-out PCR) (Add- itional file 16). Clonal cell lines were then derived from a PCR-positive deletion sub-population. Another round of out-out PCR was performed on these select clonal cell lines and PCR products were purified and cloned into
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A Statistical Framework for Quantitative Trait Mapping

A Statistical Framework for Quantitative Trait Mapping

Our data analysis consists of a model scanning step Prior distributions: All Bayesian analyses depend on followed by model selection. It represents a departure prior distributions. The influence of the prior distribu- from the Bayesian approach to model selection. We tion decays with increasing sample size and, for most carry out single and pairwise genome scans and select problems, vanishes asymptotically. For sample sizes that only those regions (or pairs) that exceed stringent per- are typical in most QTL studies (50–250 individuals), mutation testing thresholds (Churchill and Doerge the prior distribution on the model parameters is not 1994). We then fit multiple gene models that include likely to have a large effect on the posterior distributions. the regions identified as being significant in the genome However, it has a more tangible impact on the Bayes scans. This approach is consistent with the idea that one factors used for model selection. For example, the Bayes should report only highly significant QTL to minimize factors are not well defined if (improper) reference false positive results (Lander and Kruglyak 1995). priors are used for the genetic model parameters. There is often some fine tuning required to determine In our analyses we used proper priors whose weight which interaction effects to include and to resolve linked is approximately equal to that of one observation. This QTL. These model comparisons may be carried out assumption leads to the penalty term of n ⫺ v/2 in the
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The Association of Flowering Time Quantitative Trait Loci with Duplicated Regions and Candidate Loci in Brassica oleracea

The Association of Flowering Time Quantitative Trait Loci with Duplicated Regions and Candidate Loci in Brassica oleracea

var. alboglabra 3 var. italica cross, was scored for flowering time in two trials. Using information on 82 mapped molecular markers, spread evenly across the nine linkage groups, QTL were identified at six locations; one each on linkage groups O2 and O3 and two each on linkage groups O5 and O9. In total, these QTL explained 58 and 93% of the genetical variation in the two trials. Three of these QTL, on linkage groups O2, O3, and O9, were situated in regions showing considerable homology both with each other and with chromosome regions of B. nigra that have been shown to affect flowering time. These same regions are all homologous to a single tract of Arabidopsis chromosome 5, which contains a number of the flowering-related genes, one or more of which may be candidates for the QTL found in Brassica.
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Multitrait Fine Mapping of Quantitative Trait Loci Using Combined Linkage Disequilibria and Linkage Analysis

Multitrait Fine Mapping of Quantitative Trait Loci Using Combined Linkage Disequilibria and Linkage Analysis

is that founder individuals with no parents are unrelated First, many of the traits are environmentally and ge- and noninbred. It follows from this assumption that netically correlated. To use all information optimally the probability of genes in founders being identical by the correlation structure between traits should be taken descent (IBD) at marker loci or QTL is zero. However, into account in the analysis. This can increase the statisti- similarities in haplotypes of closely linked markers cal power of detection. However, analyzing many traits around a given position provide information on the jointly will not necessarily lead to higher power of detec- probability of founder genes being identical by descent tion because an increased number of parameters must in this position. Because individuals that carry a mutant be inferred. At least, when only two traits are considered gene will also be IBD in a chromosomal region sur- it has been shown, in the framework of linkage analysis, rounding the gene, linkage disequilibrium between that the statistical power to detect a QTL can be in- haplotypes and QTL loci can be utilized to map QTL creased by utilizing information from correlated traits. (Meuwissen and Goddard 2000). Meuwissen et al. Such increase in power was demonstrated using regres-
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Multiple Interval Mapping for Quantitative Trait Loci

Multiple Interval Mapping for Quantitative Trait Loci

rows in Q according to its flanking marker genotype. groups to cover most of the genome. A QTL is poten- Given the matrices D and Q, the MLEs and the asymp- tially located in any position of each interval. To detect totic variance-covariance matrix can be readily obtained QTL using the MIM model, model selection procedures by the general formulas. are considered because all possible subset selection is Note that, at the tested positions p 1 , p 2 , · · ·, and p m , not feasible. There are at least three basic model selec-

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Fast genome wide pedigree quantitative trait loci analysis using MENDEL

Fast genome wide pedigree quantitative trait loci analysis using MENDEL

an ultra-fast score test when pedigree structure is explicitly given. Score tests require no additional iteration under the alternative model.All that is needed is evaluation of a quadratic form combining the score vector and the expected information matrix at the maximum likelihood estimates under the null model. Fast pedigree GWAS is now implemented in our software package MENDEL [3] for easy use by the genetics community. In this paper, we demonstrate the capabilities of MENDEL on the Genetic Analysis Workshop 18 (GAW18) sequencing data.

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Bayesian Methods for Quantitative Trait Loci Mapping Based on Model Selection: Approximate Analysis Using the Bayesian Information Criterion

Bayesian Methods for Quantitative Trait Loci Mapping Based on Model Selection: Approximate Analysis Using the Bayesian Information Criterion

maximum likelihood. In a single model, estimates and Our approach is to relate trait values directly to confidence intervals from maximum likelihood will be marker genotypes, using multiple linear regression. similar to their Bayesian counterparts provided the sam- Since there are many markers in a typical cross, most ple size is large enough. More significant differences of these will not be near to a QTL. Therefore it is arise, however, when testing “precise hypotheses” (Ber- necessary to choose a model or models with subsets of ger and Berry 1988). Existence or nonexistence of a markers selected. Broman (1997) advocated a stepwise QTL linked to a particular marker is one example. This regression approach for choosing the “best” model. difference occurs because Bayesian inference considers However, we shall see that particularly with small sample the probability of the data under each of the two possible sizes there can be a multiplicity of models that are com- models, e.g., H 0 :␪ ⫽ 0 and H 1 :␪ ⬆ 0. The non-Bayesian patible with the data, and these alternative models need
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Precision mapping of quantitative trait loci.

Precision mapping of quantitative trait loci.

There will be, however, some interference on testing and estimation between those QTLs which are located in adjacent marker intervals when using this composite interval mapping met[r]

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Modeling Epistasis of Quantitative Trait Loci Using Cockerham's Model

Modeling Epistasis of Quantitative Trait Loci Using Cockerham's Model

Again, the partial regression coefficients of the additive by the genetic parameters of dominance effect of and dominance effects are confounded by epistatic ef- locus B (d 2 ). Both the F ∞ -metric and mixed-metric fects, and they are biased estimates of the additive and models do not follow the definition in the one-locus dominance effects. If r AB ⫽ 0.5, the four coefficients in analysis.

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