Inferring the genetic architecture of expression variation from replicated high throughput allele-specific expression experiments

False Positive Rate ◽

Regulatory Sequences ◽

Binomial Model ◽

Specific Expression ◽

Expression Variation ◽

Positive Rate ◽

AbstractGene expression variation between alleles in a diploid cell is mediated by variation in cis regulatory sequences, which usually refers to the differences in DNA sequence between two alleles near the gene of interest. Expression differences caused by cis variation has been estimated by the ratio of the expression level of the two alleles under a binomial model. However, the binomial model underestimates the variance among replicated experiments resulting in the exaggerated statistical significance of estimated cis effects and thus many false discoveries of cis-affected genes. Here we describe a beta-binomial model that estimates the cis-effect for each gene while permitting overdispersion of variance among replicates. We demonstrated with simulated null data (data without true cis-effect) that the new model fits the true distribution better, resulting in approximately 5% false positive rate under 5% significance level in all null datasets, considerably better than the 6%-40% false positive rate of the binomial model. Additional replicates increase the performance of the beta-binomial model but not of the binomial model. We also collected new allele-specific expression data from an experiment comprised of 20 replicates of a yeast hybrid (YPS128/RM11-1a). We eliminated the mapping bias problem with de novo assemblies of the two parental genomes. By applying the beta-binomial model to this dataset, we found that cis effects are ubiquitous, affecting around 70% of genes. However, most of these changes are small in magnitude. The high number of replicates enabled us a better approximation of cis landscape within species and also provides a resource for future exploration for better models.

Replicate sequencing libraries are important for quantification of allelic imbalance

Nature Communications ◽

10.1038/s41467-021-23544-8 ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Asia Mendelevich ◽

Svetlana Vinogradova ◽

Saumya Gupta ◽

Andrey A. Mironov ◽

Shamil R. Sunyaev ◽

...

Keyword(s):

Allelic Imbalance ◽

False Positive Rate ◽

Error Rates ◽

Differential Analysis ◽

Rna Seq ◽

Specific Expression ◽

Technical Noise ◽

Specific Analysis ◽

Positive Rate ◽

AbstractA sensitive approach to quantitative analysis of transcriptional regulation in diploid organisms is analysis of allelic imbalance (AI) in RNA sequencing (RNA-seq) data. A near-universal practice in such studies is to prepare and sequence only one library per RNA sample. We present theoretical and experimental evidence that data from a single RNA-seq library is insufficient for reliable quantification of the contribution of technical noise to the observed AI signal; consequently, reliance on one-replicate experimental design can lead to unaccounted-for variation in error rates in allele-specific analysis. We develop a computational approach, Qllelic, that accurately accounts for technical noise by making use of replicate RNA-seq libraries. Testing on new and existing datasets shows that application of Qllelic greatly decreases false positive rate in allele-specific analysis while conserving appropriate signal, and thus greatly improves reproducibility of AI estimates. We explore sources of technical overdispersion in observed AI signal and conclude by discussing design of RNA-seq studies addressing two biologically important questions: quantification of transcriptome-wide AI in one sample, and differential analysis of allele-specific expression between samples.

Allele-specific expression variation at different ploidy levels in Squalius alburnoides

Scientific Reports ◽

10.1038/s41598-019-40210-8 ◽

2019 ◽

Vol 9 (1) ◽

Cited By ~ 1

Author(s):

Isa Matos ◽

Miguel P. Machado ◽

Manfred Schartl ◽

Maria Manuela Coelho

Keyword(s):

Specific Expression ◽

Ploidy Levels ◽

Expression Variation ◽

CompMap: an allele-specific expression read-counter based on competitive mapping

10.1101/2021.02.12.431019 ◽

2021 ◽

Author(s):

S. Sánchez-Ramírez ◽

A. D. Cutter

Keyword(s):

Error Rates ◽

Supplementary Information ◽

Homologous Region ◽

Regulatory Sequences ◽

Specific Expression ◽

Phenotypic Differences ◽

Mapping Bias ◽

Individual Snps ◽

ABSTRACTSummaryChanges to regulatory sequences account for important phenotypic differences between species and populations. In heterozygote individuals, regulatory polymorphism typically manifests as allele-specific expression (ASE) of transcripts. ASE data from inter-species and inter-population hybrids, in conjunction with expression data from the parents, can be used to infer regulatory changes in cis and trans throughout the genome. Improper data handling, however, can create problems of mapping bias and excessive loss of information, which are prone to arise unintentionally from the cumbersome pipelines with multiple dependencies that are common among current methods. Here, we introduce a new, selfcontained method implemented in Python that generates allele-specific expression counts from genotype-specific map alignments. Rather than assessing individual SNPs, our approach sorts and counts reads within a given homologous region by comparing individual read-mapping statistics from each parental alignment. Reads that are aligned ambiguously to both references are resolved proportionally to the allele-specific matching read counts or statistically using a binomial distribution. Using simulations, we show CompMap has low error rates in assessing regulatory divergence.AvailabilityThe Python code with examples and installation instructions is available on the GitHub repository https://github.com/santiagosnchez/[email protected] information

Marginal Effects in Interaction Models: Determining and Controlling the False Positive Rate

Comparative Political Studies ◽

10.1177/0010414017730080 ◽

2017 ◽

Vol 51 (9) ◽

pp. 1144-1176 ◽

Cited By ~ 28

Author(s):

Justin Esarey ◽

Jane Lawrence Sumner

Keyword(s):

Political Parties ◽

False Positive ◽

Software Package ◽

False Positive Rate ◽

Marginal Effect ◽

Marginal Effects ◽

Political Studies ◽

Positive Rate ◽

The Relationship

When a researcher suspects that the marginal effect of [Formula: see text] on [Formula: see text] varies with [Formula: see text], a common approach is to plot [Formula: see text] at different values of [Formula: see text] along with a pointwise confidence interval generated using the procedure described in Brambor, Clark, and Golder to assess the magnitude and statistical significance of the relationship. Our article makes three contributions. First, we demonstrate that the Brambor, Clark, and Golder approach produces statistically significant findings when [Formula: see text] at a rate that can be many times larger or smaller than the nominal false positive rate of the test. Second, we introduce the interactionTest software package for R to implement procedures that allow easy control of the false positive rate. Finally, we illustrate our findings by replicating an empirical analysis of the relationship between ethnic heterogeneity and the number of political parties from Comparative Political Studies.

The impact of P-hacking on "Redefine statistical significance"

10.31234/osf.io/bp2z4 ◽

2017 ◽

Author(s):

Harry Crane

Keyword(s):

False Positive ◽

False Positive Rate ◽

Perceived Benefits ◽

Replication Rate ◽

Recent Proposal ◽

Positive Rate ◽

Replication Crisis ◽

The Impact ◽

Lower Cutoff

A recent proposal to "redefine statistical significance" (Benjamin, et al. Nature Human Behaviour, 2017) claims that false positive rates "would immediately improve" by factors greater than two and replication rates would double simply by changing the conventional cutoff for 'statistical significance' from P<0.05 to P<0.005. I analyze the veracity of these claims, focusing especially on how Benjamin, et al neglect the effects of P-hacking in assessing the impact of their proposal. My analysis shows that once P-hacking is accounted for the perceived benefits of the lower threshold all but disappear, prompting two main conclusions: (i) The claimed improvements to false positive rate and replication rate in Benjamin, et al (2017) are exaggerated and misleading. (ii) There are plausible scenarios under which the lower cutoff will make the replication crisis worse.

Structure and allele-specific expression variation of novel α/β hydrolase fold proteins in gentian plants

Molecular Genetics and Genomics ◽

10.1007/s00438-007-0230-4 ◽

2007 ◽

Vol 278 (1) ◽

pp. 95-104 ◽

Cited By ~ 14

Author(s):

Takashi Hikage ◽

Yasushi Saitoh ◽

Claire Tanaka-Saito ◽

Hisakazu Hagami ◽

Fumi Satou ◽

...

Keyword(s):

Specific Expression ◽

Expression Variation ◽

Hydrolase Fold ◽

Allele Specific Expression in Human – Genomic Makeup and Phenotypic Implications

10.1101/757997 ◽

2019 ◽

Author(s):

Kerem Wainer-Katsir ◽

Michal Linial

Keyword(s):

Healthy Human ◽

Specific Expression ◽

Distribution Width ◽

Allele Specific ◽

Mhc Locus ◽

Phenotype Diversity ◽

The Uk ◽

Gwas Catalog

AbstractThe allele-specific expression phenomenon refers to unbalanced expression from the two parental alleles in a tissue of a diploid organism. AlleleDB is a high-quality resource that reports on about 30,000 ASE variants (ASE-V) from hundreds of human samples. In this study, we present the genomic characteristics and phenotypic implications of ASE. We identified tens of segments with extreme density of ASE-V, many of them are located at the major histocompatibility complex (MHC) locus. Notably, at a resolution of 100 nucleotides, the likelihood of ASE-V increases with the density of polymorphic sites. Another dominant trend of ASE is a strong bias of the expression to the major allele. This observation relies on the known allele frequencies in the healthy human population. Overlap of ASE-V and GWAS associations was calculated for 48 phenotypes from the UK-Biobank. ASE-V were significantly associated with a risk for inflammation (e.g. asthma), autoimmunity (e.g., rheumatoid arthritis, multiple sclerosis, and type 1 diabetes) and several blood cell traits (e.g., red cell distribution width). At the level of the ASE-genes, we seek association with all traits and conditions reported in the GWAS catalog. The statistical significance of ASE-genes to GWAS catalog reveals association with the susceptibility to virus infection, autoimmunity, inflammation, allergies, blood cancer and more. We postulate that ASE determines phenotype diversity between individuals and the risk for a variety of immune-related conditions.

Improving diagnosis of acute appendicitis with atypical findings by Tc-99m HMPAO leukocyte scan

Nuklearmedizin ◽

10.1055/s-0038-1623994 ◽

2002 ◽

Vol 41 (01) ◽

pp. 37-41 ◽

Cited By ~ 3

Author(s):

S. Shung-Shung ◽

S. Yu-Chien ◽

Y. Mei-Due ◽

W. Hwei-Chung ◽

A. Kao

Keyword(s):

Acute Appendicitis ◽

False Positive ◽

False Positive Rate ◽

Accurate Method ◽

Clinical Findings ◽

Pathological Findings ◽

Lower Quadrant ◽

Predictive Values ◽

Positive Rate

Summary Aim: Even with careful observation, the overall false-positive rate of laparotomy remains 10-15% when acute appendicitis was suspected. Therefore, the clinical efficacy of Tc-99m HMPAO labeled leukocyte (TC-WBC) scan for the diagnosis of acute appendicitis in patients presenting with atypical clinical findings is assessed. Patients and Methods: Eighty patients presenting with acute abdominal pain and possible acute appendicitis but atypical findings were included in this study. After intravenous injection of TC-WBC, serial anterior abdominal/pelvic images at 30, 60, 120 and 240 min with 800k counts were obtained with a gamma camera. Any abnormal localization of radioactivity in the right lower quadrant of the abdomen, equal to or greater than bone marrow activity, was considered as a positive scan. Results: 36 out of 49 patients showing positive TC-WBC scans received appendectomy. They all proved to have positive pathological findings. Five positive TC-WBC were not related to acute appendicitis, because of other pathological lesions. Eight patients were not operated and clinical follow-up after one month revealed no acute abdominal condition. Three of 31 patients with negative TC-WBC scans received appendectomy. They also presented positive pathological findings. The remaining 28 patients did not receive operations and revealed no evidence of appendicitis after at least one month of follow-up. The overall sensitivity, specificity, accuracy, positive and negative predictive values for TC-WBC scan to diagnose acute appendicitis were 92, 78, 86, 82, and 90%, respectively. Conclusion: TC-WBC scan provides a rapid and highly accurate method for the diagnosis of acute appendicitis in patients with equivocal clinical examination. It proved useful in reducing the false-positive rate of laparotomy and shortens the time necessary for clinical observation.

Predicting Fetal Chromosome Anomalies in the First Trimester Using Pregnancy Associated Plasma Protein-A: A Comparison of Statistical Methods

Methods of Information in Medicine ◽

10.1055/s-0038-1634910 ◽

1993 ◽

Vol 32 (02) ◽

pp. 175-179 ◽

Cited By ~ 7

Author(s):

B. Brambati ◽

T. Chard ◽

J. G. Grudzinskas ◽

M. C. M. Macintosh

Keyword(s):

Logistic Regression ◽

General Population ◽

Likelihood Ratio ◽

False Positive ◽

False Positive Rate ◽

Ratio Method ◽

Detection Rates ◽

Gaussian Distributions ◽

Positive Rate ◽

Likelihood Ratio Method

Abstract:The analysis of the clinical efficiency of a biochemical parameter in the prediction of chromosome anomalies is described, using a database of 475 cases including 30 abnormalities. A comparison was made of two different approaches to the statistical analysis: the use of Gaussian frequency distributions and likelihood ratios, and logistic regression. Both methods computed that for a 5% false-positive rate approximately 60% of anomalies are detected on the basis of maternal age and serum PAPP-A. The logistic regression analysis is appropriate where the outcome variable (chromosome anomaly) is binary and the detection rates refer to the original data only. The likelihood ratio method is used to predict the outcome in the general population. The latter method depends on the data or some transformation of the data fitting a known frequency distribution (Gaussian in this case). The precision of the predicted detection rates is limited by the small sample of abnormals (30 cases). Varying the means and standard deviations (to the limits of their 95% confidence intervals) of the fitted log Gaussian distributions resulted in a detection rate varying between 42% and 79% for a 5% false-positive rate. Thus, although the likelihood ratio method is potentially the better method in determining the usefulness of a test in the general population, larger numbers of abnormal cases are required to stabilise the means and standard deviations of the fitted log Gaussian distributions.

Identification of and Correction for Publication Bias: Comment

10.31222/osf.io/dh87m ◽

2019 ◽

Author(s):

Amanda Kvarven ◽

Eirik Strømland ◽

Magnus Johannesson

Keyword(s):

Publication Bias ◽

False Positive ◽

Large Scale ◽

Meta Analysis ◽

False Positive Rate ◽

Effect Sizes ◽

Replication Studies ◽

Moderate Reduction ◽

Positive Rate ◽

Meta Analyses

Andrews & Kasy (2019) propose an approach for adjusting effect sizes in meta-analysis for publication bias. We use the Andrews-Kasy estimator to adjust the result of 15 meta-analyses and compare the adjusted results to 15 large-scale multiple labs replication studies estimating the same effects. The pre-registered replications provide precisely estimated effect sizes, which do not suffer from publication bias. The Andrews-Kasy approach leads to a moderate reduction of the inflated effect sizes in the meta-analyses. However, the approach still overestimates effect sizes by a factor of about two or more and has an estimated false positive rate of between 57% and 100%.