Background selection and the statistics of population differentiation: consequences for detecting local adaptation

Mapping Intimacies ◽

10.1101/326256 ◽

2018 ◽

Cited By ~ 1

Author(s):

Remi Matthey-Doret ◽

Michael C. Whitlock

Keyword(s):

Local Adaptation ◽

Dna Sequences ◽

Population Differentiation ◽

False Positive Rate ◽

Parameter Estimates ◽

Background Selection ◽

Deleterious Mutations ◽

Effective Population ◽

Genome Scans ◽

Positive Rate

AbstractBackground selection is a process whereby recurrent deleterious mutations cause a decrease in the effective population size and genetic diversity at linked loci. Several authors have suggested that variation in the intensity of background selection could cause variation in FST across the genome, which could confound signals of local adaptation in genome scans. We performed realistic simulations of DNA sequences, using parameter estimates from humans and sticklebacks, to investigate how variation in the intensity of background selection affects different statistics of population differentiation. We show that, in populations connected by gene flow, Weir & Cockerham’s (1984) estimator of FST is largely insensitive to locus-to-locus variation in the intensity of background selection. Unlike FST, however, dXY is negatively correlated with background selection. We also show that background selection does not greatly affect the false positive rate in FST outlier studies. Overall, our study indicates that background selection will not greatly interfere with finding the variants responsible for local adaptation.

CRISPRIdentify: Identification of CRISPR arrays using machine learning approach

10.1101/2020.11.05.369512 ◽

2020 ◽

Cited By ~ 1

Author(s):

Alexander Mitrofanov ◽

Omer S. Alkhnbashi ◽

Sergey A. Shmakov ◽

Kira S. Makarova ◽

Eugene V. Koonin ◽

...

Keyword(s):

Machine Learning ◽

Dna Sequences ◽

False Positive Rate ◽

Scoring Function ◽

Protein S ◽

Genomic Region ◽

Immune Functions ◽

Crispr Array ◽

Positive Rate ◽

Data Driven Approach

CRISPR-Cas are adaptive immune systems that degrade foreign genetic elements in archaea and bacteria. In carrying out their immune functions, CRISPR-Cas systems heavily rely on RNA components. These CRISPR (cr) RNAs are repeat-spacer units that are produced by processing of pre-crRNA, the transcript of CRISPR arrays, and guide Cas protein(s) to the cognate invading nucleic acids, enabling their destruction. Several bioinformatics tools have been developed to detect CRISPR arrays based solely on DNA sequences, but all these tools employ the same strategy of looking for repetitive patterns, which might correspond to CRISPR array repeats. The identified patterns are evaluated using a fixed, built-in scoring function, and arrays exceeding a cut-off value are reported. Here, we instead introduce a data-driven approach that uses machine learning to detect and differentiate true CRISPR arrays from false ones based on several features. Our CRISPR detection tool, CRISPRIdentify, performs three steps: detection, feature extraction and classification based on manually curated sets of positive and negative examples of CRISPR arrays. The identified CRISPR arrays are then reported to the user accompanied by detailed annotation. We demonstrate that our approach identifies not only previously detected CRISPR arrays, but also CRISPR array candidates not detected by other tools. Compared to other methods, our tool has a drastically reduced false-positive rate. In contrast to the existing tools, our approach not only provides the user with the basic statistics on the identified CRISPR arrays but also produces a certainty score as a practical measure of the likelihood that a given genomic region is a CRISPR array.

DETERMINATION OF VIRAL TROPISM BY GENOTYPING AND PHENOTYPING ASSAYS IN BRAZILIAN HIV-1-INFECTED PATIENTS

Revista do Instituto de Medicina Tropical de São Paulo ◽

10.1590/s0036-46652014000400003 ◽

2014 ◽

Vol 56 (4) ◽

pp. 287-290

Author(s):

Liã Bárbara Arruda ◽

Marilia Ladeira de Araújo ◽

Maira Luccia Martinez ◽

Claudio Roberto Gonsalez ◽

Alberto José da Silva Duarte ◽

...

Keyword(s):

Clinical Practice ◽

Dna Sequences ◽

False Positive Rate ◽

Coreceptor Usage ◽

Viral Strain ◽

Positive Rate ◽

Ccr5 Antagonists ◽

Virus Strains ◽

Hiv 1

The clinical application of CCR5 antagonists involves first determining the coreceptor usage by the infecting viral strain. Bioinformatics programs that predict coreceptor usage could provide an alternative method to screen candidates for treatment with CCR5 antagonists, particularly in countries with limited financial resources. Thus, the present study aims to identify the best approach using bioinformatics tools for determining HIV-1 coreceptor usage in clinical practice. Proviral DNA sequences and Trofile results from 99 HIV-1-infected subjects under clinical monitoring were analyzed in this study. Based on the Trofile results, the viral variants present were 81.1% R5, 21.4% R5X4 and 1.8% X4. Determination of tropism using a Geno2pheno[coreceptor] analysis with a false positive rate of 10% gave the most suitable performance in this sampling: the R5 and X4 strains were found at frequencies of 78.5% and 28.4%, respectively, and there was 78.6% concordance between the phenotypic and genotypic results. Further studies are needed to clarify how genetic diversity amongst virus strains affects bioinformatics-driven approaches for determining tropism. Although this strategy could be useful for screening patients in developing countries, some limitations remain that restrict the wider application of coreceptor usage tests in clinical practice.

Controlling the False-Positive Rate in Multilocus Genome Scans for Selection

Genetics ◽

10.1534/genetics.106.064642 ◽

2006 ◽

Vol 175 (2) ◽

pp. 737-750 ◽

Cited By ~ 121

Author(s):

Kevin R. Thornton ◽

Jeffrey D. Jensen

Keyword(s):

False Positive ◽

False Positive Rate ◽

Genome Scans ◽

Positive Rate

The Effects of Deleterious Mutations on Linked, Neutral Variation in Small Populations

Genetics ◽

10.1093/genetics/153.1.475 ◽

1999 ◽

Vol 153 (1) ◽

pp. 475-483 ◽

Cited By ~ 2

Author(s):

Snæbjörn Pálsson ◽

Pekka Pamilo

Keyword(s):

Small Populations ◽

Background Selection ◽

Deleterious Mutations ◽

Selection Coefficient ◽

Effective Population ◽

Strong Reduction ◽

Tight Linkage ◽

Neutral Marker ◽

Population Sizes ◽

Clear Increase

Abstract The effects of recessive, deleterious mutations on genetic variation at linked neutral loci can be heterozygosity-decreasing because of reduced effective population sizes or heterozygosity-increasing because of associative overdominance. Here we examine the balance between these effects by simulating individual diploid genotypes in small panmictic populations. The haploid genome consists of one linkage group with 1000 loci that can have deleterious mutations and a neutral marker. Combinations of the following parameters are studied: gametic mutation rate to harmful alleles (U), population size (N), recombination rate (r), selection coefficient (s), and dominance (h). Tight linkage (r ≤ 10–4) gives significant associative effects, leading either to strong reduction of heterozygosity when the product Nhs is large or to a clear increase when the product Nhs is small, the boundary between these effects being 1 < Nhs < 4 in our simulations. Associative overdominance can lead to heterozygosities that are larger than predicted by the background selection models and even larger than the neutral expectation.

CRISPRidentify: identification of CRISPR arrays using machine learning approach

Nucleic Acids Research ◽

10.1093/nar/gkaa1158 ◽

2020 ◽

Author(s):

Alexander Mitrofanov ◽

Omer S Alkhnbashi ◽

Sergey A Shmakov ◽

Kira S Makarova ◽

Eugene V Koonin ◽

...

Keyword(s):

Machine Learning ◽

Dna Sequences ◽

False Positive Rate ◽

Scoring Function ◽

Protein S ◽

Genomic Region ◽

Immune Functions ◽

Crispr Array ◽

Positive Rate ◽

Data Driven Approach

Abstract CRISPR–Cas are adaptive immune systems that degrade foreign genetic elements in archaea and bacteria. In carrying out their immune functions, CRISPR–Cas systems heavily rely on RNA components. These CRISPR (cr) RNAs are repeat-spacer units that are produced by processing of pre-crRNA, the transcript of CRISPR arrays, and guide Cas protein(s) to the cognate invading nucleic acids, enabling their destruction. Several bioinformatics tools have been developed to detect CRISPR arrays based solely on DNA sequences, but all these tools employ the same strategy of looking for repetitive patterns, which might correspond to CRISPR array repeats. The identified patterns are evaluated using a fixed, built-in scoring function, and arrays exceeding a cut-off value are reported. Here, we instead introduce a data-driven approach that uses machine learning to detect and differentiate true CRISPR arrays from false ones based on several features. Our CRISPR detection tool, CRISPRidentify, performs three steps: detection, feature extraction and classification based on manually curated sets of positive and negative examples of CRISPR arrays. The identified CRISPR arrays are then reported to the user accompanied by detailed annotation. We demonstrate that our approach identifies not only previously detected CRISPR arrays, but also CRISPR array candidates not detected by other tools. Compared to other methods, our tool has a drastically reduced false positive rate. In contrast to the existing tools, our approach not only provides the user with the basic statistics on the identified CRISPR arrays but also produces a certainty score as a practical measure of the likelihood that a given genomic region is a CRISPR array.

The speed of Muller's ratchet with background selection, and the degeneration of Y chromosomes

Genetics Research ◽

10.1017/s0016672301005213 ◽

2001 ◽

Vol 78 (2) ◽

pp. 149-161 ◽

Cited By ~ 41

Author(s):

ISABEL GORDO ◽

BRIAN CHARLESWORTH

Keyword(s):

Deleterious Mutation ◽

Selection Effect ◽

Background Selection ◽

Deleterious Mutations ◽

Effective Population ◽

Muller's Ratchet ◽

Analytical Approximations ◽

Y Chromosomes ◽

Muller’S Ratchet ◽

Rate Of Accumulation

The rate of accumulation of deleterious mutations by Muller's ratchet is investigated in large asexual haploid populations, for a range of parameters with potential biological relevance. The rate of this process is studied by considering a very simple model in which mutations can have two types of effect: either strongly deleterious or mildly deleterious. It is shown that the rate of accumulation of mildly deleterious mutations can be greatly increased by the presence of strongly deleterious mutations, and that this can be predicted from the associated reduction in effective population size (the background selection effect). We also examine the rate of the ratchet when there are two classes of mutation of similar but unequal effects on fitness. The accuracy of analytical approximations for the rate of this process is analysed. Its possible role in causing the degeneration of Y and neo-Y chromosomes is discussed in the light of our present knowledge of deleterious mutation rates and selection coefficients.

Unpacking conditional neutrality: genomic signatures of selection on conditionally beneficial and conditionally deleterious mutations

10.1101/549329 ◽

2019 ◽

Author(s):

Jonathan A. Mee ◽

Samuel Yeaman

Keyword(s):

Allele Frequency ◽

Local Adaptation ◽

Home Environment ◽

Cumulative Effect ◽

Antagonistic Pleiotropy ◽

Deleterious Mutations ◽

Genome Scans ◽

Beneficial Mutations ◽

Local Environments ◽

Non Local

AbstractIt is common to look for signatures of local adaptation in genomes by identifying loci with extreme levels of allele frequency divergence among populations. This approach to finding genes associated with local adaptation often assumes antagonistic pleiotropy, wherein alternative alleles are strongly favoured in alternative environments. Conditional neutrality has been proposed as an alternative to antagonistic pleiotropy, but conditionally neutral polymorphisms are transient and it is unclear how much outlier signal would be maintained under different forms of conditional neutrality. Here, we use individual-based simulations and a simple analytical heuristic to show that a pattern that mimics local adaptation at the phenotypic level, where each genotype has the highest fitness in its home environment, can be produced by the accumulation of mutations that are neutral in their home environment and deleterious in non-local environments. Because conditionally deleterious mutations likely arise at a rate many times higher than conditionally beneficial mutations, they can have a significant cumulative effect on fitness even when individual effect sizes are small. We show that conditionally deleterious mutations driving non-local maladaptation may be undetectable by even the most powerful genome scans, as differences in allele frequency between populations are typically small. We also explore the evolutionary effects of conditionally-beneficial mutations and find that they can maintain significant signals of local adaptation, and they would be more readily detectable than conditionally deleterious mutations using conventional genome scan approaches. We discuss implications for interpreting outcomes of transplant experiments and genome scans that are used to study the genetic basis of local adaptation.

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%.