False Associations Research Articles

GLASS3: A Standalone Multiscale Seismic Detection Associator

Abstract The automated global real‐time association of phase picks into seismic sources comes with unique challenges when simultaneously monitoring at local, regional, and global scales. High spatial variability in seismic station density, transitory seismic data availability, and time‐varying noise characteristics of individual stations must be considered in the design of an associator that is fast and accurate with a low false association rate. These challenges are particularly apparent at the U.S. Geological Survey National Earthquake Information Center (NEIC), which monitors seismicity in near‐real time on local, regional, and global scales using seismic data from roughly 2100 real‐time seismic stations. To fully leverage this large dataset, NEIC developed a standalone self‐configuring seismic phase associator, GLobal ASSociator 3 (GLASS3) that simultaneously processes variably scaled 3D association webs, each with a unique set of nucleation criteria (e.g., nucleation stack threshold). GLASS3 has many useful features for real‐time monitoring including its computational efficiency, instantaneous pick processing, and on‐the‐fly configurability such as the creation and removal of targeted association webs and updates to supporting station metadata. GLASS3 runs both as part of a real‐time event processing system and as a configurable standalone associator that can be applied to a large variety of seismic problems. Here, we describe the GLASS3 algorithm and demonstrate (including input data and configuration files) its use in associating phase‐ambiguous picks on multiple scales.

Protecting the Privacy of Cancer Patients Using Fuzzy Association Rule Hiding

Objective:Privacy protection in the medical field means the protection of individuals from being associated with undesirable conditions, diagnoses or treatments (Sensitive Attributes). The problem of knowledge discovery from health care data by applying data mining algorithms is inversely related to the privacy of individuals. Due to the tremendous growth of data in a large scale, there is a demand to protect the sensitive data accessible from medical datasets.Methods:This paper considers the problem of building privacy preserving association rule mining algorithm using the notion of TF * IDF derived from the information retrieval domain. The highly sensitive transaction is chosen using the product of Relative Item Frequency and Condensed Frequency. Finally, sensitive fuzzy data is perturbed to hide these refined rules.Results:It has been found that the number of non-sensitive rules lost as a side effect of hiding sensitive rule is 20% less and number of ghost rules is 30% less in proposed work than in previous work using Transactional Impact factor method. The execution time of hiding a rule is 26% lesser on an average in the proposed technique for various values of minimum confidence threshold. It has been observed that the number of modifications to the original dataset after hiding three rules were reduced by 66% in proposed method than in previous work. As the number of modifications to original data is less the chances of generating false association is also reduced.Conclusion:In this paper, a novel method was presented to hide the sensitive rule in quantitative data by decreasing the support of the RHS of the rule. Experimental results demonstrate that the proposed approach is more efficient as it facilitates better rule hiding and minimizes the number of lost rules and ghost rules. Also, this approach makes minimum modifications to the dataset.

Hearing Loss and Depression in the Hispanic/Latino Population

Hearing Loss and Depression in the Hispanic/Latino Population

Robust aerial image mosaicing algorithm based on fuzzy outliers rejection

The use of unmanned aerial vehicles (UAVs) imagery for acquiring data is constantly evolving, due to the ease of use and low data acquisition costs. All of these made UAVs very popular with end customers and data acquisition companies. For most cases, the acquired UAV images need further more processing before being analyzed, and that because of changing of illumination condition in aerial environment and fog generated because of UAV flying speed. Image mosaicing is a practical solution for these problems; in which overlapped views of the same scene are combined to form a large image with high quality. The common problem associated with image mosaicing algorithms is the false associations (outliers) produced when defining the overlapping region between every two successive views. This article presents an image mosaicing algorithm based on efficient fuzzy technique for outliers rejection. Our proposed technique is based on using RANdom SAmpling Consensus (RANSAC) and bidirectional approaches with a fuzzy inference system in order to separate between inliers and outliers. The experimental results prove that the proposed method has good performance for aerial images and gives better results when compared with other techniques. Thus our approach is insensitive to the ordering, orientation, scale and illumination of the images.

Constructing the Microbial Association Network from Large-Scale Time Series Data Using Granger Causality.

The increasing availability of large-scale time series data allows the inference of microbial community dynamics by association network analysis. However, correlation-based association network analyses are noninformative of causal, mediating and time-dependent relationships between microbial community functional factors. To address this insufficiency, we introduced the Granger causality model to the analysis of a recent marine microbial time series dataset. We systematically constructed a directed acyclic network, representing both internal and external causal relationships among the microbial and environmental factors. We further optimized the network by removing false causal associations using the conditional Granger causality. The final network was visualized as a Granger graph, which was analyzed to identify causal relationships driven by key functional operators in the environment, such as Gammaproteobacteria, which was Granger caused by total organic nitrogen and primary production (p < 0.05 and Q < 0.05).

Spinning convincing stories for both true and false association signals.

When interpreting genome‐wide association peaks, it is common to annotate each peak by searching for genes with plausible relationships to the trait. However, “all that glitters is not gold”—one might interpret apparent patterns in the data as plausible even when the peak is a false positive. Accordingly, we sought to see how human annotators interpreted association results containing a mixture of peaks from both the original trait and a genetically uncorrelated “synthetic” trait. Two of us prepared a mix of original and synthetic peaks of three significance categories from five different scans along with relevant literature search results and then we all annotated these regions. Three annotators also scored the strength of evidence connecting each peak to the scanned trait and the likelihood of further studying that region. While annotators found original peaks to have stronger evidence (p Bonferroni = 0.017) and higher likelihood of further study ( p Bonferroni = 0.006) than synthetic peaks, annotators often made convincing connections between the synthetic peaks and the original trait, finding these connections 55% of the time. These results show that it is not difficult for annotators to make convincing connections between synthetic association signals and genes found in those regions.

Assessing Likelihood of Having False Positives Caused by Population Stratification

Population stratification is always a concern in association analysis. There is a debate on the extent of the problem in less extreme situations (Thomas and Witte [1], Wacholder et al. [2]). Wacholder et al. [3] and Ardlie et al. [4] showed that hidden population structure is not a serious threat to case-control designs. We propose a method of assessing the seriousness of the population stratification before designing association studies. If population stratification is not a serious problem, one may consider using case-control study instead of family-based design to get more power. In a case-control design, we compare chi-square statistics from a structured population (a union of two subpopulations) and a homogeneous population with the same prevalence and allele frequencies. We provide an explicit formula to calculate the chi-square statistics from 17 parameters, such as proportions of subpopulation, allele frequencies in subpopulations, etc. We choose these factors because they have potential to cause false associations. Each parameter takes a random value in a chosen range. We then calculate the likelihood of getting opposite conclusions in the structured and the homogeneous populations. This is the likelihood of having false positives caused by population stratification. The advantage of this method is to provide a cost effective way to choose between using case-control data and using family data before actually collecting those data. We conclude that sample sizes have a significant effect on the likelihood of false positive caused by population stratification. The larger the sample size is, the more likely to have false positive if the population structure is ignored. If the sample size will be smaller than 200 by budget constraints, then case-control study may be a better choice because of its power.

Pregnancy lipidomic profiles and DNA methylation in newborns from the CHAMACOS cohort.

Lipids play a role in many biological functions and the newly emerging field of lipidomics aims to characterize the varying classes of lipid molecules present in biological specimens. Animal models have shown associations between maternal dietary supplementation with fatty acids during pregnancy and epigenetic changes in their offspring, demonstrating a mechanism through which prenatal environment can affect outcomes in children; however, data on maternal lipid metabolite levels during pregnancy and newborn DNA methylation in humans are sparse. In this study, we assessed the relationship of maternal lipid metabolites measured in the blood from pregnant women with newborn DNA methylation profiles in the Center for the Health Assessment of Mothers and Children of Salinas cohort. Targeted metabolomics was performed by selected reaction monitoring liquid chromatography and triple quadrupole mass spectrometry to measure 92 metabolites in plasma samples of pregnant women at ∼26 weeks gestation. DNA methylation was assessed using the Infinium HumanMethylation 450K BeadChip adjusting for cord blood cell composition. We uncovered numerous false discovery rate significant associations between maternal metabolite levels, particularly phospholipid and lysolipid metabolites, and newborn methylation. The majority of the observed relationships were negative, suggesting that higher lipid metabolites during pregnancy are associated with lower methylation levels at genes related to fetal development. These results further elucidate the complex relationship between early life exposures, maternal lipid metabolites, and infant epigenetic status.

2 - MITOCHONDRIAL DNA SNPS ASSOCIATED WITH SCHIZOPHRENIA EXHIBIT HIGHLY VARIABLE INTER-ALLELIC HAPLOGROUP AFFILIATION AND NUCLEAR GENOGEOGRAPHIC AFFINITY: BI-GENOMIC LINKAGE DISEQUILIBRIUM RAISES MAJOR CONCERN FOR LINK TO DISEASE

Background Mitochondria play a significant role in human diseases. Genetic variants in mitochondrial DNA (mtDNA) – and in nuclear genes coding for mitochondrial function - have been associated with disease. Mitochondrial disease is clinically characterised by complex metabolic, neurological, muscular and psychiatric symptoms. SNPs in mtDNA have been proposed as potential disease modifiers. This has been reported in neurological degenerative diseases such as Alzheimer's disease and Parkinson's disease, metabolic diseases and cancers, as well as psychiatric diseases, notably schizophrenia (SZ) and bipolar disease. However, disease associations with mitochondrial DNA (mtDNA) SNPs have proven difficult to replicate. We identified eight mtDNA SNPs that had previously been associated with SZ and re-analysed the association with SZ in the Danish iPSYCH cohort, and examined the effect of mtDNA variation and variation in nuclear genogeographic affinity, or ancestry, on the association. Methods Persons were recruited through the iPSYCH program, which is a case-cohort study. DNA was isolated from dried blood spots obtained through the Danish Neonatal Screening program. Genotyping was performed using the PsychChip array, comprising 418 SNPs in mtDNA. Supervised ancestry estimation of the nuclear genome was performed using a reference population of 2,248 persons assigned to one of nine population Groups. The estimation was done with 103,268 nuclear SNPs using ADMIXTURE 1.3.050. Results A reanalysis of eight schizophrenia-associated mtDNA SNPs, in 23,743 normal Danes and 2,538 schizophrenia patients, revealed marked inter-allelic differences both in haplogroup affiliation and nuclear ancestry, genogeophraphic affinity (GGA). Only two mitochondrial SNPs, m.15043 A and m.15218 G, were significantly associated with schizophrenia. However, these associations disappeared when corrected for haplogroup affiliation. Discussion The combined variation in mtDNA affiliation and nuclear genomic ancestry, bi-genomic linkage disequilibrium (2GLD) could entail population stratification. This might result in both false positive and negative associations between mtDNA SNPs and disease. The extensive 2GLD documented for the eight SNPs examined here is a major concern when interpreting historic as well as designing future mtDNA association studies. The fact that careful control, as here, of mtDNA affiliation and the 2GLD, results in none of eight previously SZ associated mtDNA SNPs being associated with SZ in the very large Danish iPSYCH cohort, suggests that previously reported associations could indeed be spurious findings due to cryptic population stratification.

Population structure in genetic studies: Confounding factors and mixed models.

A genome-wide association study (GWAS) seeks to identify genetic variants that contribute to the development and progression of a specific disease. Over the past 10 years, new approaches using mixed models have emerged to mitigate the deleterious effects of population structure and relatedness in association studies. However, developing GWAS techniques to accurately test for association while correcting for population structure is a computational and statistical challenge. Using laboratory mouse strains as an example, our review characterizes the problem of population structure in association studies and describes how it can cause false positive associations. We then motivate mixed models in the context of unmodeled factors.

Allele balance bias identifies systematic genotyping errors and false disease associations.

In recent years, next‐generation sequencing (NGS) has become a cornerstone of clinical genetics and diagnostics. Many clinical applications require high precision, especially if rare events such as somatic mutations in cancer or genetic variants causing rare diseases need to be identified. Although random sequencing errors can be modeled statistically and deep sequencing minimizes their impact, systematic errors remain a problem even at high depth of coverage. Understanding their source is crucial to increase precision of clinical NGS applications. In this work, we studied the relation between recurrent biases in allele balance (AB), systematic errors, and false positive variant calls across a large cohort of human samples analyzed by whole exome sequencing (WES). We have modeled the AB distribution for biallelic genotypes in 987 WES samples in order to identify positions recurrently deviating significantly from the expectation, a phenomenon we termed allele balance bias (ABB). Furthermore, we have developed a genotype callability score based on ABB for all positions of the human exome, which detects false positive variant calls that passed state‐of‐the‐art filters. Finally, we demonstrate the use of ABB for detection of false associations proposed by rare variant association studies. Availability: https://github.com/Francesc-Muyas/ABB.

BiFET: sequencing Bias-free transcription factor Footprint Enrichment Test.

Transcription factor (TF) footprinting uncovers putative protein–DNA binding via combined analyses of chromatin accessibility patterns and their underlying TF sequence motifs. TF footprints are frequently used to identify TFs that regulate activities of cell/condition-specific genomic regions (target loci) in comparison to control regions (background loci) using standard enrichment tests. However, there is a strong association between the chromatin accessibility level and the GC content of a locus and the number and types of TF footprints that can be detected at this site. Traditional enrichment tests (e.g. hypergeometric) do not account for this bias and inflate false positive associations. Therefore, we developed a novel post-processing method, Bias-free Footprint Enrichment Test (BiFET), that corrects for the biases arising from the differences in chromatin accessibility levels and GC contents between target and background loci in footprint enrichment analyses. We applied BiFET on TF footprint calls obtained from EndoC-βH1 ATAC-seq samples using three different algorithms (CENTIPEDE, HINT-BC and PIQ) and showed BiFET’s ability to increase power and reduce false positive rate when compared to hypergeometric test. Furthermore, we used BiFET to study TF footprints from human PBMC and pancreatic islet ATAC-seq samples to show its utility to identify putative TFs associated with cell-type-specific loci.

Genome-Wide Association Analyses in the Model Rhizobium Ensifer meliloti.

Genome-wide association studies (GWAS) can identify genetic variants responsible for naturally occurring and quantitative phenotypic variation. Association studies therefore provide a powerful complement to approaches that rely on de novo mutations for characterizing gene function. Although bacteria should be amenable to GWAS, few GWAS have been conducted on bacteria, and the extent to which nonindependence among genomic variants (e.g., linkage disequilibrium [LD]) and the genetic architecture of phenotypic traits will affect GWAS performance is unclear. We apply association analyses to identify candidate genes underlying variation in 20 biochemical, growth, and symbiotic phenotypes among 153 strains of Ensifer meliloti For 11 traits, we find genotype-phenotype associations that are stronger than expected by chance, with the candidates in relatively small linkage groups, indicating that LD does not preclude resolving association candidates to relatively small genomic regions. The significant candidates show an enrichment for nucleotide polymorphisms (SNPs) over gene presence-absence variation (PAV), and for five traits, candidates are enriched in large linkage groups, a possible signature of epistasis. Many of the variants most strongly associated with symbiosis phenotypes were in genes previously identified as being involved in nitrogen fixation or nodulation. For other traits, apparently strong associations were not stronger than the range of associations detected in permuted data. In sum, our data show that GWAS in bacteria may be a powerful tool for characterizing genetic architecture and identifying genes responsible for phenotypic variation. However, careful evaluation of candidates is necessary to avoid false signals of association.IMPORTANCE Genome-wide association analyses are a powerful approach for identifying gene function. These analyses are becoming commonplace in studies of humans, domesticated animals, and crop plants but have rarely been conducted in bacteria. We applied association analyses to 20 traits measured in Ensifer meliloti, an agriculturally and ecologically important bacterium because it fixes nitrogen when in symbiosis with leguminous plants. We identified candidate alleles and gene presence-absence variants underlying variation in symbiosis traits, antibiotic resistance, and use of various carbon sources; some of these candidates are in genes previously known to affect these traits whereas others were in genes that have not been well characterized. Our results point to the potential power of association analyses in bacteria, but also to the need to carefully evaluate the potential for false associations.

A narrative case study of Indonesian EFL learners’ identities

Much research on identity (re)construction in periphery countries (e.g., Pakistan, Thailand) continues to shed light on how English learners negotiate and construct their identities. This narrative case study explores how two Indonesian learners of English who have been learning English for many years reflect on their journey of identity construction as English learners in their local contexts. Nested in a narrative inquiry methodology, this study examined the participants’ personal stories that were unique, rich and nuanced with the interplay of social, cultural and political dimensions (re)shaping their experiences. The initial phases of identity construction revealed from the participants’ early years indicated that the emergence of identity as English learners was largely marked by a false or negative association of the English language in relation to various aspects of being a school subject leading to a false sense of positive identity. Pedagogical implications of the study are also presented in this article.

A Bayesian measure of association that utilizes the underlying distributions of noise and information.

We propose a new approach, Bayesian Probability of Association (BPA) which takes into account the probability distributions of information and noise in the variables and uses Bayesian statistics to predict associations better than existing approaches. Our approach overcomes the limitations of linearity of the relationship and normality of the data, assumed by the Pearson correlation coefficient. It is different from the current measures of association because considering information separately from noise helps identify the association in information more accurately, makes the approach less sensitive to noise and also helps identify causal directions. We tested the approach on 15 datasets with no underlying association and on 75 datasets with known causal relationships and compared the results with other measures of association. No false associations were detected and true associations were predicted in more than 90% cases whereas the Pearson correlation coefficient and mutual information content predicted associations for less than half of the datasets.

P75. A visual paired associative recognition task for the diagnosis of Alzheimer’s disease

Objective Visual recognition tasks (VRTs) have shown promise for the early detection of AD ( Didic et al., 2013 ). Especially increased numbers of false positive answers seem to be indicative of AD ( Hildebrandt et al., 2009 ). As associative memory also has been proposed to be affected early in AD progression ( Fowler et al., 2002 ), we investigated whether the introduction of an associative memory aspect to an existing VRT ( Hildebrandt et al., 2009 ) could improve its sensitivity and specificity in AD detection. Methods We developed a visual paired associative recognition task (VPART, see Fig. 1 ), in which subjects had to name pictures presented in pairs and were then faced with different recognition trials concerning pairs of pictures: In one task, they were asked to decide whether none or one of the pictures had been presented before and in another task whether both presented pictures had been shown before and if so, whether the combination was the same or had changed. We compared the results of VPART with those of the VRT and of the CERAD-NAB (the gold standard for AD screening) of AD patients (verified by cerebrospinal fluid analysis), cognitively matched memory-impaired patients (CMMI) and healthy controls (HC). Recognition trials in the VPART were divided into those provoking false positives and those provoking false associations to assess the importance of both mistake types for AD detection. Results VPART showed a sensitivity of 100% for AD patients and a HC specificity of 100% and a CMMI specificity of 80%, while the VRT had a sensitivity of 73%, a HC specificity of 100% and a CMMI specificity of 53%. The number of false positives in VPART was sufficient to separate AD patients from other subjects, whereas there was no group difference in judging whether items were presented as pairs or not. Conclusions Although false associations themselves did not improve AD detection, false positive answers in VPART provided clear cut-off values. Therefore, VPART was better able to differentiate the three groups than was the VRT.

DNA methylation of imprinted genes in Mexican-American newborn children with prenatal phthalate exposure.

Imprinted genes exhibit expression in a parent-of-origin-dependent manner and are critical for child development. Recent limited evidence suggests that prenatal exposure to phthalates, ubiquitous endocrine disruptors, can affect their epigenetic dysregulation. We quantified DNA methylation of nine imprinted gene differentially methylated regions by pyrosequencing in 296 cord blood DNA samples in a Mexican-American cohort. Fetal exposure was estimated by phthalate metabolite concentrations in maternal urine samples during pregnancy. Several differentially methylated regions of imprinted genes were associated with high molecular weight phthalates. The most consistent, positive, and false discovery rate significant associations were observed for MEG3. Phthalate exposure in utero may affect methylation status of imprinted genes in newborn children.

Project MinE: study design and pilot analyses of a large-scale whole-genome sequencing study in amyotrophic lateral sclerosis

The most recent genome-wide association study in amyotrophic lateral sclerosis (ALS) demonstrates a disproportionate contribution from low-frequency variants to genetic susceptibility to disease. We have therefore begun Project MinE, an international collaboration that seeks to analyze whole-genome sequence data of at least 15 000 ALS patients and 7500 controls. Here, we report on the design of Project MinE and pilot analyses of successfully sequenced 1169 ALS patients and 608 controls drawn from the Netherlands. As has become characteristic of sequencing studies, we find an abundance of rare genetic variation (minor allele frequency < 0.1%), the vast majority of which is absent in public datasets. Principal component analysis reveals local geographical clustering of these variants within The Netherlands. We use the whole-genome sequence data to explore the implications of poor geographical matching of cases and controls in a sequence-based disease study and to investigate how ancestry-matched, externally sequenced controls can induce false positive associations. Also, we have publicly released genome-wide minor allele counts in cases and controls, as well as results from genic burden tests.

Prognostic Significance of Infarct Size and Location: The Case of Insular Stroke

The prognostic relevance of strokes in different locations is debated. For example, insular strokes have been associated with increased mortality, but this association could reflect their greater severity. In two independent cohorts of patients with supratentorial ischemic stroke (n = 90 and 105), we studied the prognostic consequences of lesion size and location using voxel-based lesion-symptom mapping before and after volume control, which better accounts for total lesion volume. Strokes affecting the insula were larger than non-insular strokes (28 vs 2cc and 25 vs 3cc, p < 0.001 in both cohorts). A number of supratentorial areas (mainly in the left hemisphere), including the insula, were associated with poor functional outcome in both cohorts before (4014 voxels) and after volume control (1378 voxels), while the associations with death were greatly reduced after volume control (from 8716 to 325 voxels). Exploratory analyses suggested that the method of lesion volume quantification, the National Institutes of Health Stroke Scale hemispheric bias and patient selection can result in false associations between specific brain lesions and outcomes. In conclusion, death in the first months after stroke is mainly explained by large infarct volumes, whereas lesions of specific supratentorial structures, mostly in the left hemisphere, also contribute to poor functional outcomes.

High plasma 25-hydroxyvitamin D and high risk of nonmelanoma skin cancer: a Mendelian randomization study of 97 849 individuals

In this study from Denmark, we investigate whether vitamin D status, usually measured by 25‐hydroxyvitamin D in blood samples, is associated with development of non‐melanoma skin cancer (NMSC). Previously, it has proven difficult to answer this particular question. If you simply divide everyone into two groups, i.e. those with a high and those with low measured 25‐hydroxyvitamin D, a higher incidence rate of NMSC would be observed in the group with high 25‐hydroxyvitamin D. However, this simple observation is susceptible to misinterpretation, e.g. sunlight's UV radiation causes damage to the skin that starts the development of skin cancer, as well as inducing vitamin D synthesis in the skin (i.e. helping the body to create vitamin D). On average, people who have higher high 25‐hydroxyvitamin D levels will therefore also have been exposed to more sunlight and UV radiation and be at higher risk of developing skin cancer. For this reason, we could observe a potentially false association between 25‐hydroxyvitamin D and cancer if we base our conclusions on just raw data alone. In this study, we used a method called Mendelian randomisation that is not susceptible to these potential errors, and we found that genetic variants affecting vitamin D neither increase nor decrease the risk of getting skin cancer. The implications of this study are that the association of 25‐hydroxyvitamin D with skin cancer cannot be taken at face value and similar studies are required for other types of cancer.