Outlier Detection Test Research Articles

Genome scans for selection signatures identify candidate virulence genes for adaptation of the soybean cyst nematode to host resistance.

Plant pathogens are constantly under selection pressure for host resistance adaptation. Soybean cyst nematode (SCN, Heterodera glycines) is a major pest of soybean primarily managed through resistant cultivars; however, SCN populations have evolved virulence in response to selection pressures driven by repeated monoculture of the same genetic resistance. Resistance to SCN is mediated by multiple epistatic interactions between Rhg (for resistance to H. glycines) genes. However, the identity of SCN virulence genes that confer the ability to overcome resistance remains unknown. To identify candidate genomic regions showing signatures of selection for increased virulence, we conducted whole genome resequencing of pooled individuals (Pool-Seq) from two pairs of SCN populations adapted on soybeans with Peking-type (rhg1-a, rhg2, and Rhg4) resistance. Population differentiation and principal component analysis-based approaches identified approximately 0.72-0.79 million SNPs, the frequency of which showed potential selection signatures across multiple genomic regions. Chromosomes 3 and 6 between population pairs showed the greatest density of outlier SNPs with high population differentiation. Conducting multiple outlier detection tests to identify overlapping SNPs resulted in a total of 966 significantly differentiated SNPs, of which 285 exon SNPs were mapped to 97 genes. Of these, six genes encoded members of known stylet-secreted effector protein families potentially involved in host defence modulation including venom-allergen-like, annexin, glutathione synthetase, SPRYSEC, chitinase, and CLE effector proteins. Further functional analysis of identified candidate genes will provide new insights into the genetic mechanisms by which SCN overcomes soybean resistance and inform the development of molecular markers for rapidly screening the virulence profile of an SCN-infested field.

Crisis Detection in the Age of Digital Communication: The Power of Social Listening as a Method to Identify Corporate Events in Time Series Data

The increased usage of digital media to exchange information has increased the speed in which corporate crises become known. This has increased the necessity to react to a crisis as quickly as possible. As a result, social listening – i.e. listening to and analysing digital communication – is establishing itself as an instrument for companies to control their own representation in the media. Against this background, different methodological approaches in crisis detection (e.g. outlier detection, t-test and Chow test) were tested regarding their quality. For that, we used a data set created by an AI crawling online sources and analysing the results using a neural network. The findings of this study suggest that crises can be identified quite reliably using existing econometric methods. A simple outlier detection in a time series of the total number of fragments that uses a time frame of one month on each side of a crisis seems to be the best method so far with the method by Chen and Liu being a close second. The results of this study provide a foundational contribution to this field of research and can help companies detect crises as early as possible allowing the management to react appropriately.

Analysis the characterization of climate change and its impacts on smallholder farmers in Eastern Ethiopia

The impact of climate change on agriculture and food production is manifested through alterations in agro-ecological factors. The region of sub-Saharan Africa is particularly susceptible to the effects of climate change and variability, given its reliance on rainfall-dependent agriculture and inadequate adaptive capabilities. The objective of this study was to analysis of the characterization of climate change and its impacts on smallholder farmers in eastern Ethiopia. Historical daily rainfall, maximum and minimum temperature in 1991–2021 years of 12 weather station were collected from National Meteorological Institute of Ethiopia and NASA/POWER. Data quality control, trend test and outlier detection test were used. Climate characterization (onset, cessation, LGS and Dry/wet spell length) and precipitation concentration index were examined. The earliest, and latest start of rainy season in Kombolcha and Haramaya were 81DOY (March 21) and 225DOY (August 13) within mean 130DOY and 125DOY, respectively. In study area the minimum and maximum length of growing period of was recorded in Chinakesen and Haramaya by the values of 32DOY and 253DOY (Days of the year) respectively. In this arid and semi-arid areas, growing period was recorded under the short season crop maturity while long cropping season maturity does no satisfy crop water requirement due to moisture stress. The coefficient of variation of length of growing season from Mieso and Chinakesen were 36.2% and 47.9% which implies that the areas were highly vulnerable to climate extreme events of drought. Length of growing season (LGS) of Gemechis district was decreasing by a factor −2.533 shows highly significant at alpha value of 0.05 confidence level. Dry spell length probability occurrence of 5 days during the main cropping rainy season is significantly very high. The 30 years of data record suggests that a 29–48% precipitation concentration index of kiremt (JJAS) and belg (FMAM) seasonal rainfall which are demonstrating irregular precipitation. kiremt (JJAS) rainfall trend tests of Babile, Kurfa chale and Mieso districts were increasing by a factor of 2.016, 2.043 and 2.117, respectively and statistically significant at 95% confidence level, in the time period of 1991–2021 years. If furtherstudy will be examined climate extreme indices and adaptation strategies designed to simulate the impacts and variability of climate change in the study area.

Low genome-wide divergence between two lizard populations with high adaptive phenotypic differentiation.

Usually, adaptive phenotypic differentiation is paralleled by genetic divergence between locally adapted populations. However, adaptation can also happen in a scenario of nonsignificant genetic divergence due to intense gene flow and/or recent differentiation. While this phenomenon is rarely published, findings on incipient ecologically driven divergence or isolation by adaptation are relatively common, which could confound our understanding about the frequency at which they actually occur in nature. Here, we explore genome‐wide traces of divergence between two populations of the lacertid lizard Psammodromus algirus separated by a 600 m elevational gradient. These populations seem to be differentially adapted to their environments despite showing low levels of genetic differentiation (according to previously studies of mtDNA and microsatellite data). We performed a search for outliers (i.e., loci subject to selection) trying to identify specific loci with FST statistics significantly higher than those expected on the basis of overall, genome‐wide estimates of genetic divergence. We find that local phenotypic adaptation (in terms of a wide diversity of characters) was not accompanied by genome‐wide differentiation, even when we maximized the chances of unveiling such differentiation at particular loci with FST‐based outlier detection tests. Instead, our analyses confirmed the lack of genome‐wide differentiation on the basis of more than 70,000 SNPs, which is concordant with a scenario of local adaptation without isolation by environment. Our results add evidence to previous studies in which local adaptation does not lead to any kind of isolation (or early stages of ecological speciation), but maintains phenotypic divergence despite the lack of a differentiated genomic background.

Empirical influence functions and their non-standard applications

Abstract The main objective of the empirical influence function (EIF) is to describe how estimates behave when an observation set is affected by gross errors. Unlike the influence function, which represents the estimation method’s general properties, EIF can provide valuable information about applying different methods to a particular network. The chosen example allows us to compare different robust methods. The paper focuses on non-standard applications of EIF, for example, in assuming steering parameter of robust methods (usually related to the assumed interval for acceptable observation errors). The paper shows that commonly used values do not always work well, and EIFs might help choose appropriate values, guaranteeing the estimation process’s robustness. The most important new application of EIFs concerns the detection and assessment of a single gross error. The blinded experiments proved that such an approach is correct and can be an alternative to classic statistical tests for outlier detection.

Nonstationary analysis of the extreme temperatures in Turkey

Record-breaking extreme temperatures have been measured in the last two decades all over Turkey, with recent studies detecting positive trends in extreme temperature time series. In this study, nonstationary extreme value analysis was performed on extreme temperature time series obtained from fifty stations scattered over the seven geographical regions of Turkey. Basic characterization of the data set was defined through outlier detection, homogeneity, trend detection, and stationarity tests. Trend-including non-stationary extreme temperature time series were analyzed with non-stationary Generalized Extreme Value distribution. Three main physical drivers were considered as the leading causes that trigger the observed trends in extreme temperatures over Turkey: time, teleconnection patterns of the Arctic Oscillations, and those of the North Atlantic Oscillations. The results showed that most of the absolute annual minimum and maximum temperature time series are inhomogeneous while the possible breakpoints date back to the1970s and 1990s, respectively. More than half of the absolute annual maximum time series (26/50 and many of the absolute annual minimum time series (21/50) showed a positive trend. No negative trend was detected in the extreme temperature time series. Based on the frequency analysis of the 21 annual maximum time series, the non-stationary estimations of 50-year return levels were detected to be higher than in the stationary model (between 0.44 °C and 3.73 °C). The return levels in 15 of the 20 minimum temperature time series increased from 0.11 °C up to 12.28 °C. Elevation increases the nonstationarity impact on absolute minimum temperatures and decreases it on absolute maximums. The findings in this study indicate that the consideration of non-stationarity in extreme temperature time series is a necessity during return level estimations over the study area.

Multivariate and Confirmatory Factor Analysis of Vehicular Emissions in Some Parts of Benin City, Nigeria

Decline in air quality over the years has been linked to the growing rate of urbanization as well as an increase in the number of vehicles occasioned by the increased purchasing power of the people. Most of the pollutants emitted from vehicular activities have been observed to have adverse effects on individuals as well as the atmosphere. The focus of this study is to employ multivariate and confirmatory factor analysis (CFA) based on maximum likelihood estimation, to study the incidence of vehicular emissions in some parts of Benin city metropolis. Seven (7) georeferenced points (University of Benin main gate, Ekosodin junction, Agen Junction, Super D junction, Nitel junction, Okhunmwun junction and Oluku market junction) were used for data collection. Pollutants from vehicular emission, namely; dinitrogen oxide (NO2), carbon monoxide (CO) including the total radiation were monitored in the morning and evening for a period (7th July to 12th August, 2020) with the aid of portable toxic gas monitors and radiation alert meters. Other measured parameters of interest include maximum temperature and wind speed. To ascertain the quality of the data, selected preliminary analysis, namely; test of normality, test of homogeneity, outlier detection and reliability test were done. With a calculated partial Eta squared based on Pillai’s trace of 0.325, it was concluded that; there exist about 32.50 % variability among the dependent variables. Based on the CFA results, it was concluded that the hypothesis formulated for multivariate analysis of variance fits the sample data. Therefore, the alternate hypothesis was rejected and it was concluded that; the difference in the concentration of vehicular emission was as a result of change in temperature occasioned by time of measurement.

Geospatial analysis of vehicular emissions in some parts of Benin City, Nigeria

Decline in air quality over the years has been linked to the growing rate of urbanization and the increase in the number of vehicles plying the roads. The focus of this study is to monitor the incidence of vehicular emissions in some parts of Benin City and employ geostatistical techniques such as kriging interpolation to study the spatial distribution of some selected pollutants around the study area. Seven (7) georeferenced points, namely; University of Benin Main Gate, Ekosodin junction, Agen Junction, Super D junction, Nitel junction, Okhunmwun junction and Oluku Market junction were used for data collection. Pollutant from vehicular emission, namely; dinitrogen oxide (NO2), carbon monoxide (CO) including the total radiation were monitored in the morning and evening for a period of 35 days (7th July to 12th August 2020) with the aid of portable toxic gas monitors and radiation alert meters. Other parameters of interest which were also measured include; maximum temperature and wind speed using infra-red thermometers and portable anemometer. To ascertain the quality of the data, selected preliminary analysis, namely; test of normality, test of homogeneity, outlier detection and reliability test were done. Results of the study revealed a growing concentration of CO and NO2 around Ugbowo maingate and Okhunmwun community especially during the peak hours.

Emission dispersion modeling and geospatial analysis of vehicular emissions in some parts of Benin City, Nigeria

Over the years, decline in air quality has been connected to the growing rate of urbanization and increasing number of vehicles on the roads. Most of the pollutants emitted from vehicular activities have been observed to have adverse effects on individuals as well as the atmosphere. Although, the focus of this study is to develop an emission dispersion model to predict the concentration of specific air pollutants with distance, the application of geostatistical technique such as Kriging interpolation to study the spatial distribution of pollutants from vehicular emissions around the study area was also exemplified. Seven (7) georeferenced points, namely, Ugbowo main gate, Ekosodin junction, Agen junction, Super D junction, Nitel junction, Okhunmwun junction and Oluku market junction were used for data collection. Pollutants from vehicular emissions, namely, nitrogen dioxide (NO2), carbon monoxide (CO) including the total radiation were monitored in the morning and evening for a period of 35 days (7th July to 12th August 2020) with the aid of portable toxic gas monitors and radiation alert meters. Other parameters of interest, which were also measured include maximum temperature and wind speed using infra-red thermometers and portable anemometer respectively. To ascertain the quality of the data, selected preliminary analysis, namely, test of normality, test of homogeneity, outlier detection and reliability test were done. Result of the study showed a high concentration of NO2, CO and total radiation around Ugbowo main gate and Okhunmwun community and environs especially during the peak hours of evening (5.0 p.m. – 6.0 p.m.) when the traffic load is high.

A Comparative Analysis of Different Outlier Detection Techniques in Cognitive Radio Networks with Malicious Users

In a cognitive radio (CR), opportunistic secondary users (SUs) periodically sense the primary user’s (PU’s) existence in the network. Spectrum sensing of a single SU is not precise due to wireless channels and hidden terminal issues. One promising solution is cooperative spectrum sensing (CSS) that allows multiple SUs’ cooperation to sense the PU’s activity. In CSS, the misdetection of the PU signal by the SU causes system inefficiency that increases the interference to the system. This paper introduces a new category of a malicious user (MU), i.e., a lazy malicious user (LMU) with two operating modes such as an awakened mode and sleeping mode. In the awakened mode, the LMU reports accurately the PU activity like other normal cooperative users, while in the sleeping mode, it randomly reports abnormal sensing data similar to an always yes malicious user (AYMU) or always no malicious user (ANMU). In this paper, statistical analysis is carried out to detect the behavior of different abnormal users and mitigate their harmful effects. Results are collected for the different hard combination schemes in the presence of the LMU and opposite categories of malicious users (OMUs). Simulation results collected for the error probability, detection probability, and false alarm at different levels of the signal-to-noise ratios (SNRs) and various contributions of the LMUs and OMUs confirmed that out of the many outlier detection tests, the median test performs better in MU detection by producing minimum error probability results in the CSS. The results are further compared by keeping minimum SNR values with the mean test, quartile test, Grubbs test, and generalized extreme studentized deviate (GESD) test. Similarly, performance gain of the median test is examined further separately in the AND, OR, and voting schemes that show minimum error probability results of the proposed test as compared with all other outlier detection tests in discarding abnormal sensing reports.

Development of Intensity Duration Frequency (IDF) Curves for Rainfall Prediction within the Middle Niger River Basin

Rainfall Intensity-Duration-Frequency (IDF) relationship remains one of the mostly used tools in hydrology and water resources engineering, especially for planning, design and operations of water resource projects. IDF relationship can provide adequate information about the intensity of rainfall at different duration for various return periods. The focus of this research was to develop IDF curves for the prediction of rainfall intensity within the middle Niger River Basin (Lokoja and Ilorin) using annual maximum daily rainfall data. Forty (40) year’s annual maximum rainfall data ranging from 1974 to 2013 was employed for the study. To ascertain the data quality, selected preliminary analysis technique including; descriptive statistics, test of homogeneity and outlier detection test were employed. To compute the three hours rainfall intensity, the ratio of rainfall amount and duration was used while the popular Gumbel probability distribution model was employed to calculate the rainfall frequency factor. To assess the best fit model that can be employed to predict rainfall intensity for various return periods at ungauged locations, four empirical IDF equations, namely; Talbot, Bernard, Kimijima and Sherman equations were employed. The model with the least calculated sum of minimized root mean square error (RMSE) was adopted as the best fit empirical model. Results obtained revealed that the Talbot model was the best fit model for Ilorin and Lokoja with calculated sum of minimized error of 1.32170E-07 and 8.953636E-08. This model was thereafter employed to predict the rainfall intensity for different durations at 2, 5, 10, 25, 50 and 100yrs return periods respectively.

Use of statistical approaches to improve the quality control of the dose delivery in radiotherapy

The gamma index is a measure used routinely for the quality control of dose delivery in radiotherapy, implemented in commercial systems for the verification of treatment plans. It involves comparison of the difference between planned and delivered doses to a single reference. The same reference value is selected for all points in the plan that can potentially hide dose delivery errors, especially in medium and low dose areas. In this study, a receiver operating characteristic analysis is used to demonstrate the limits of the performance of the global gamma index as a method for detecting dose delivery errors. The performance of a global gamma index is compared with two approaches based on statistical tests for outlier detection. Two statistical approaches are considered: according to the first, the distribution of the delivered doses is estimated based on an appropriate calibration procedure. According to the second, the distribution of the delivered doses is estimated based on the detection of relatively homogeneous regions of a plan and analyzing the distributions of planned doses within these regions. The performance of the three approaches is compared based on analytical considerations and in simulations in which errors are intentionally introduced to the plan delivery and noise related to dose delivery is modeled. We have shown that a statistics-based approach to gamma analysis generally leads to better detection of true delivery errors. The results of analytical consideration coincide with the simulations. In simulations, we observe that both statistical approaches are better detectors of true delivery errors than the global method for the gamma-index passing rate in the range from 0.9–1.0. It is shown that the global gamma index is a weak detector of dose delivery errors, which in some circumstances behaves only slightly better than a purely random classifier.

Empirical estimation of the power of test in outlier detection problem

Classical outlier detection test methods such as Baarda test and Pope test are generally preferred in geodetic problems. They depend on the Least Square Estimation (LSE) and LSE is very sensitive to the variations of the model. The capacity of the LSE changes depending on the different significance level, different type of outlier, the number of outlier, magnitude of outlier, number of observations and the number of unknowns. In statistics, the power of test is the probability of rejecting the null hypothesis when the null hypothesis is false. It is a theoretical assumption and depends on the significance level α (Type I error) and β (Type II error). The different types of the outliers, such as random or non-random, affect the results of the test methods; but the power of test is the same for all different types of the outliers. In this study, empirical estimation of the power of test is presented as Mean Success Rate (MSR). The theoretical power of test and empirical MSR have been estimated for univariate model and linear model by using Baarda test; according to the obtained results, MSR can be used as empirical value of the power of test and capacity of the test models. Also, MSR reflects more realistic results than the theoretical power of test.

CLOUD: a non-parametric detection test for microbiome outliers

BackgroundDysbiosis of the human gut microbiome is defined as a maladaptive or clinically relevant deviation of the community profile from the healthy or normal state. Dysbiosis has been implicated in an extensive set of metabolic, auto-immune, and infectious diseases, and yet there is substantial inter-individual variation in microbiome composition even within body sites of healthy humans. An individual’s microbiome varies over time in a high-dimensional space to form their personal microbiome cloud. This cloud may or may not be similar to that of other people, both in terms of the average microbiome profile (conformity) and the diameter of the cloud (stability). However, there is currently no robust non-parametric test that determines whether a patient’s microbiome cloud is an outlier with respect to a reference group of healthy individuals with widely varying microbiome profiles.MethodsHere, we propose a test for outliers’ detection in the human gut microbiome that accounts for the wide range of microbiome phenotypes observed in a typical set of healthy individuals and for intra-individual temporal variation. Our robust nonparametric outlier detection test, the CLOUD test, performs two assessments of a patient’s microbiome health: conformity, the extent to which the patient’s microbiome cloud is ecologically similar to a subset of healthy subjects; and stability, which compares the cloud diameter of a patient to those of healthy subjects. The CLOUD test is based on locally linear embedded ecological distances, allowing it to account for widely varying microbiome compositions among reference individuals. It also leverages temporal variability within patients and reference individuals to increase the robustness of the test.ResultsWe describe the CLOUD test, and we apply it to one novel and two previously published cohorts of patients receiving fecal microbiota transplantation for recurrent Clostridium difficile colitis, as well as to two known healthy cohorts, demonstrating high concordance of the CLOUD conformity and stability indices with clinical outcomes.ConclusionsAlthough the CLOUD test is not, on its own, a test for clinical dysbiosis, it nonetheless provides a framework for outlier testing that could be incorporated into evaluation of suspected dysbiosis, which may play a role in diagnosis and prognosis of numerous pediatric and adult diseases.

Genome-wide characteristics of de novo mutations in autism.

De novo mutations (DNMs) are important in autism spectrum disorder (ASD), but so far analyses have mainly been on the ~1.5% of the genome encoding genes. Here, we performed whole-genome sequencing (WGS) of 200 ASD parent–child trios and characterised germline and somatic DNMs. We confirmed that the majority of germline DNMs (75.6%) originated from the father, and these increased significantly with paternal age only (P=4.2×10−10). However, when clustered DNMs (those within 20 kb) were found in ASD, not only did they mostly originate from the mother (P=7.7×10−13), but they could also be found adjacent to de novo copy number variations where the mutation rate was significantly elevated (P=2.4×10−24). By comparing with DNMs detected in controls, we found a significant enrichment of predicted damaging DNMs in ASD cases (P=8.0×10−9; odds ratio=1.84), of which 15.6% (P=4.3×10−3) and 22.5% (P=7.0×10−5) were non-coding or genic non-coding, respectively. The non-coding elements most enriched for DNM were untranslated regions of genes, regulatory sequences involved in exon-skipping and DNase I hypersensitive regions. Using microarrays and a novel outlier detection test, we also found aberrant methylation profiles in 2/185 (1.1%) of ASD cases. These same individuals carried independently identified DNMs in the ASD-risk and epigenetic genes DNMT3A and ADNP. Our data begins to characterize different genome-wide DNMs, and highlight the contribution of non-coding variants, to the aetiology of ASD.

Defective Sensor Identification for WSNs Involving Generic Local Outlier Detection Tests

The behavior of a wireless sensor network dedicated to distributed estimation tasks may be significantly altered by the presence of nodes whose sensors are defective and produce erroneous measurements. This paper proposes and analyzes the performance of two distributed algorithms to help each node in determining whether it is equipped with a defective sensor. A node first collects data from its neighborhood, processes them to decide, using some generic local outlier detection test, whether these data contain outliers and broadcasts the result. Then, it determines the status of its own sensor using its result and those received from neighboring nodes. A single-decision and an iterative algorithm for defective sensor detection are proposed. Bounds on the performance of the single-decision algorithm are derived. A theoretical analysis of the probability of error and of the equilibrium of the iterative algorithm is provided for a wide class of local outlier detection tests. The tradeoff between false alarm probability and detection probability is characterized theoretically and by simulation. MAC-layer issues, as well as the effect of packet losses are accounted for.

Human Blood Cell Level of 5-Hydroxymethylcytosine (5hmC) Declines Steadily during Aging and Is Multifactorial

Abstract BACKGROUND. The most frequently mutated genes documented in blood cells of aging individuals are known epigenetic regulator genes such as TET2 and DNMT3A, which suggests that alteration of epigenetic homeostasis could be a predisposing factor in the pathogenesis of several age-associated hematological malignancies. This study is aimed at determining if changes in hematopoietic 5-hydroxymethylcytosine (5hmC) and 5-methylcytosine (5mC) levels occur in normal individuals, and if they are independent of acquired mutations in epigenetic regulators. METHOD. The study population comprised 198 unrelated women randomly selected to form four age categories (neonates, 25-30 years, 70-75 years and >90 years) from the general community. Genomic DNA from total blood cells or cord blood (12.5 ug) was hydrolysed using DNA Degradase Plus and analysed by mass spectrometry (LC-ESI-MS/MS-MRM) to quantify global 5-methyl-2'-deoxycytidine and 5-hydroxymethyl-2'-deoxycytidine levels. Statistical analysis (normality test, outlier detection, descriptive statistic, non-parametric Kruskal-Wallis and Mann Whitney tests) were performed using NCSS 07.1.21 to correlate methylation (5mC and 5hmC) levels with i) X-chromosome inactivation (XCI) patterns (evaluated by HUMARA) in polymorphonuclear (PMN) and ii) telomeres length (measured by the method of Cawthon). All individuals over 70 were sequenced in search for mutations in epigenetic regulator genes including TET2, DNMT3A, ASXL1, IDH1, IDH2 and WT1. RESULTS. 5hmC and 5mC levels: Global 5hmC levels decline steadily with age in human blood cells (30% from birth to old age, P <0.000001). This reduction is progressive between 0 and 75 years of age and plateaued at a low level thereafter (Figure 1). A less severe reduction in 5mC level was also observed between newborn and elderly individuals (3%, P<0.000024). Correlation with biological characteristics: Low level of 5hmC was associated with more important XCI skewing in PMN (age-adjusted, P =0.0304) as well as a reduction of telomere length (age-adjusted, P =0.0354), both surrogate markers of clonal dominance. Correlation with mutational status: Of the 100 individuals over 70 years, 16 had a somatic mutation in TET2, 10 in DNMT3A and none in IDH1, IDH2 or WT1. Individuals with TET2 gene mutation (variant allele frequency (VAF) >10%) had a significant reduction in 5hmC but not the DNMT3A subjects. To evaluate if the TET2 mutated individuals were the sole drivers of the documented reduction in 5hmC, we removed these subjects from the statistical analysis and correlation was preserved. However, correlation with XCI skewing and telomere reduction was no longer significant. CONCLUSION. These results document a significant age-associated reduction in 5hmC during aging in the hematopoietic compartment. DNA hypohydroxymethylation should be considered as a new hallmark of aging hematopoiesis. We also demonstrate that acquired mutations in key epigenetic regulators such as TET2 are not the sole cause of this reduction. Epigenetic drift, which is defined as the cumulative selection of permissive stochastic epigenetic modifications, is responsible for most of the age-associated loss. These results also indicate that the impact on clonal dominance may be different according to the cause of 5hmC (mutation vs drift). Further studies are needed to evaluate the impact of 5hmC reduction on the risk of hematological cancer initiation. Figure 1. Figure 1. Disclosures Busque: Novartis: Consultancy; BMS: Consultancy; PFIZER: Consultancy.

Examination of the Spencer-McCuen Outlier-Detection Test for Log-Pearson Type 3 Distributed Data

AbstractIdentification of outliers in flood records can be an important step in a robust flood frequency analysis procedure. Bulletin 17B includes the Grubbs-Beck test (with α=10%) as an objective criterion of whether the smallest observations in a flood record are outliers. The Spencer-McCuen test extends the Grubbs-Beck test to consider explicitly whether the three smallest observations are outliers in log-Pearson Type 3 (or equivalently Pearson Type 3) distributed samples with log-space skew coefficients between [−1,1], and three significance levels [1, 5, and 10%]. Presented here are Monte Carlo experiments evaluating the performance of the Spencer-McCuen test. When that test relies on the sample skew coefficient as an estimate of the population skew, the test generally fails to achieve the nominal significance levels. The same is true when used with a generalized (weighted) skew coefficient. Thus, the test will often be inappropriate if used as originally proposed. More fundamentally, when a proposed...

A practical guide to environmental association analysis in landscape genomics.

Landscape genomics is an emerging research field that aims to identify the environmental factors that shape adaptive genetic variation and the gene variants that drive local adaptation. Its development has been facilitated by next-generation sequencing, which allows for screening thousands to millions of single nucleotide polymorphisms in many individuals and populations at reasonable costs. In parallel, data sets describing environmental factors have greatly improved and increasingly become publicly accessible. Accordingly, numerous analytical methods for environmental association studies have been developed. Environmental association analysis identifies genetic variants associated with particular environmental factors and has the potential to uncover adaptive patterns that are not discovered by traditional tests for the detection of outlier loci based on population genetic differentiation. We review methods for conducting environmental association analysis including categorical tests, logistic regressions, matrix correlations, general linear models and mixed effects models. We discuss the advantages and disadvantages of different approaches, provide a list of dedicated software packages and their specific properties, and stress the importance of incorporating neutral genetic structure in the analysis. We also touch on additional important aspects such as sampling design, environmental data preparation, pooled and reduced-representation sequencing, candidate-gene approaches, linearity of allele-environment associations and the combination of environmental association analyses with traditional outlier detection tests. We conclude by summarizing expected future directions in the field, such as the extension of statistical approaches, environmental association analysis for ecological gene annotation, and the need for replication and post hoc validation studies.

Tracking footprints of selection associated with soybean adaptation to Central-East Europe environments

The identification of genomic regions affected by phenotypic selection during breeding is important for elucidating the genetic basis of complex traits underlying adaptation to a specific target environment. This study investigated the soybean (Glycine max (L.) Merr.) adaptation in Central-East European environments by a hitchhiking mapping approach and pedigree analysis. Population genetic principles were applied to microsatellite markers using multiple outlier detection tests. The analyzed populations comprised ancestral and elite varieties. Genetic diversity parameters showed significant differences between the populations. Eight markers were considered as strong positive selection candidate loci, indicating regions involved in the adaptation. Bottleneck tests provided no evidence of population bottlenecks for the candidate positive selection loci, suggesting that selection might shaped the pattern of genetic diversity in these regions. Co-localisation of the candidate positive selection loci and previously mapped quantitative trait loci (QTLs) revealed a high level of agreement between the identified QTLs and the traits expected to be under selection during soybean breeding. The identification and differentiation of selectively important QTLs have practical importance for future breeding programs, aiming at developing varieties for a specific target environment.