Control procedures and estimators of the false discovery rate and their application in low-dimensional settings: an empirical investigation

Broken Rotor Bar Detection Based on Steady-State Stray Flux Signals Using Triaxial Sensor with Random Positioning

This paper investigates the detection of broken rotor bar in squirrel cage induction motors using a novel approach of randomly positioning a triaxial sensor over the motor surface. This study is conducted on two motors under laboratory conditions, where one motor is kept in a healthy state, and the other is subjected to a broken rotor bar (BRB) fault. The induced electromotive force of the triaxial coils, recorded over ten days with 100 measurements per day, is statistically analyzed. Normality tests and graphical interpretation methods are used to evaluate the data distribution. Parametric and non-parametric approaches are used to analyze the data. Both approaches show that the measurement method is valid and consistent over time and statistically distinguishes healthy motors from those with BRB defects when a reference or threshold value is specified. While the comparison between healthy motors shows a discrepancy, the quantitative analysis shows a smaller estimated difference in mean values between healthy motors than comparing healthy and BRB motors.

Longitudinal study investigating the influence of COMT gene polymorphism on cortical thickness changes in Parkinson's disease over four years

Parkinson's disease (PD) is a progressive neurodegenerative disorder affecting over 3% of those over 65. It's caused by reduced dopaminergic neurons and Lewy bodies, leading to motor and non-motor symptoms. The relationship between COMT gene polymorphisms and PD is complex and not fully elucidated. Some studies have reported associations between certain COMT gene variants and PD risk, while others have not found significant associations. This study investigates how COMT gene variations impact cortical thickness changes in PD patients over time, aiming to link genetic factors, especially COMT gene variations, with PD progression. This study analyzed data from 44 PD patients with complete 4-year imaging follow-up from the Parkinson Progression Marker Initiative (PPMI) database. Magnetic resonance imaging (MRI) scans were acquired using consistent methods across 9 different MRI scanners. COMT single-nucleotide polymorphisms (SNPs) were assessed based on whole genome sequencing data. Longitudinal image analysis was conducted using FreeSurfer's processing pipeline. Linear mixed-effect models were employed to examine the interaction effect of genetic variations and time on cortical thickness, while controlling for covariates and subject-specific variations. The rs165599 SNP stands out as a potential contributor to alterations in cortical thickness, showing a significant reduction in overall mean cortical thickness in both hemispheres in homozygotes (Left: P = 0.023, Right: P = 0.028). The supramarginal, precentral, and superior frontal regions demonstrated significant bilateral alterations linked to rs165599. Our findings suggest that the rs165599 variant leads to earlier manifestation of cortical thinning during the course of the disease. However, it does not result in more severe cortical thinning outcomes over time. There is a need for larger cohorts and control groups to validate these findings and consider genetic variant interactions and clinical features to elucidate the specific mechanisms underlying COMT-related neurodegenerative processes in PD.

Estimating gene-level false discovery probability improves eQTL statistical fine-mapping precision

Statistical fine-mapping prioritizes putative causal variants from a large number of candidate variants, and is widely used in expression quantitative loci (eQTLs) studies. In eQTL fine-mapping, the existence of causal variants for gene expression is not guaranteed, since the genetic heritability of gene expression explained by nearby (cis-) variants is limited. Here we introduce a refined fine-mapping algorithm, named Knockoff-Finemap combination (KFc). KFc estimates the probability that the causal variant(s) exist in the cis-window of a gene through construction of knockoff genotypes (i.e. a set of synthetic genotypes that resembles the original genotypes), and uses it to adjust the posterior inclusion probabilities (PIPs). Utilizing simulated gene expression data, we show that KFc results in calibrated PIP distribution with improved precision. When applied to gene expression data of 465 genotyped samples from the Japan COVID-19 Task Force (JCTF), KFc resulted in significant enrichment of a functional score as well as reporter assay hits in the top PIP bins. When combined with functional priors derived from an external fine-mapping study (GTEx), KFc resulted in a significantly higher proportion of hematopoietic trait putative causal variants in the top PIP bins. Our work presents improvements in the precision of a major fine-mapping algorithm.

Maternal adverse childhood experiences (ACEs) and DNA methylation of newborns in cord blood

BACKGROUND

Adverse childhood experiences (ACEs) increase the risk of poor health outcomes later in life. Psychosocial stressors may also have intergenerational health effects by which parental ACEs are associated with mental and physical health of children. Epigenetic programming may be one mechanism linking parental ACEs to child health. This study aimed to investigate epigenome-wide associations of maternal preconception ACEs with DNA methylation patterns of children. In the Center for the Health Assessment of Mothers and Children of Salinas study, cord blood DNA methylation was measured using the Illumina HumanMethylation450 BeadChip. Preconception ACEs, which occurred during the mothers' childhoods, were collected using a standard ACE questionnaire including 10 ACE indicators. Maternal ACE exposures were defined in this study as (1) the total number of ACEs; (2) the total number of ACEs categorized as 0, 1-3, and > 4; and (3) individual ACEs. Associations of ACE exposures with differential methylated positions, regions, and CpG modules determined using weighted gene co-expression network analysis were evaluated adjusting for covariates.

RESULTS

Data on maternal ACEs and cord blood DNA methylation were available for 196 mother/newborn pairs. One differential methylated position was associated with maternal experience of emotional abuse (cg05486260/FAM135B gene; q value < 0.05). Five differential methylated regions were significantly associated with the total number of ACEs, and 36 unique differential methylated regions were associated with individual ACEs (Šidák p value < 0.05). Fifteen CpG modules were significantly correlated with the total number of ACEs or individual ACEs, of which 8 remained significant in fully adjusted models (p value < 0.05). Significant modules were enriched for pathways related to neurological and immune development and function.

CONCLUSIONS

Maternal ACEs prior to conception were associated with cord blood DNA methylation of offspring at birth. Although there was limited overlap between differential methylated regions and CpGs in modules associated with ACE exposures, statistically significant regions and networks were related to genes involved in neurological and immune function. Findings may provide insights to pathways linking psychosocial stressors to health. Further research is needed to understand the relationship between changes in DNA methylation and child health.

Brain structure and allelic associations in Alzheimer's disease

BACKGROUND

Alzheimer's disease (AD), the most prevalent form of dementia, affects 6.5 million Americans and over 50 million people globally. Clinical, genetic, and phenotypic studies of dementia provide some insights of the observed progressive neurodegenerative processes, however, the mechanisms underlying AD onset remain enigmatic.

AIMS

This paper examines late-onset dementia-related cognitive impairment utilizing neuroimaging-genetics biomarker associations.

MATERIALS AND METHODS

The participants, ages 65-85, included 266 healthy controls (HC), 572 volunteers with mild cognitive impairment (MCI), and 188 Alzheimer's disease (AD) patients. Genotype dosage data for AD-associated single nucleotide polymorphisms (SNPs) were extracted from the imputed ADNI genetics archive using sample-major additive coding. Such 29 SNPs were selected, representing a subset of independent SNPs reported to be highly associated with AD in a recent AD meta-GWAS study by Jansen and colleagues.

RESULTS

We identified the significant correlations between the 29 genomic markers (GMs) and the 200 neuroimaging markers (NIMs). The odds ratios and relative risks for AD and MCI (relative to HC) were predicted using multinomial linear models.

DISCUSSION

In the HC and MCI cohorts, mainly cortical thickness measures were associated with GMs, whereas the AD cohort exhibited different GM-NIM relations. Network patterns within the HC and AD groups were distinct in cortical thickness, volume, and proportion of White to Gray Matter (pct), but not in the MCI cohort. Multinomial linear models of clinical diagnosis showed precisely the specific NIMs and GMs that were most impactful in discriminating between AD and HC, and between MCI and HC.

CONCLUSION

This study suggests that advanced analytics provide mechanisms for exploring the interrelations between morphometric indicators and GMs. The findings may facilitate further clinical investigations of phenotypic associations that support deep systematic understanding of AD pathogenesis.

Costs and Benefits of Popular P-Value Correction Methods in Three Models of Quantitative Omic Experiments

The multiple hypothesis testing problem is inherent in large-scale quantitative "omic" experiments such as mass spectrometry-based proteomics. Yet, tools for comparing the costs and benefits of different p-value correction methods under different experimental conditions are lacking. We performed thousands of simulations of omic experiments under a range of experimental conditions and applied correction using the Benjamini-Hochberg (BH), Bonferroni, and permutation-based false discovery proportion (FDP) estimation methods. The tremendous false discovery rate (FDR) benefit of correction was confirmed in a range of different contexts. No correction method can guarantee a low FDP in a single experiment, but the probability of a high FDP is small when a high number and proportion of corrected p-values are significant. On average, correction decreased sensitivity, but the sensitivity costs of BH and permutation were generally modest compared to the FDR benefits. In a given experiment, observed sensitivity was always maintained or decreased by BH and Bonferroni, whereas it was often increased by permutation. Overall, permutation had better FDR and sensitivity than BH. We show how increasing sample size, decreasing variability, or increasing effect size can enable the detection of all true changes while still correcting p-values, and we present basic guidelines for omic experimental design. Analysis of an experimental proteomic data set with defined changes corroborated these trends. We developed an R Shiny web application for further exploration and visualization of these models, which we call the Simulator of P-value Multiple Hypothesis Correction (SIMPLYCORRECT) and a high-performance R package, permFDP, for easy use of the permutation-based FDP estimation method.

Cytokine adsorption in patients with acute-on-chronic liver failure (CYTOHEP)-a single center, open-label, three-arm, randomized, controlled intervention trial

Background

Liver cirrhosis is a major healthcare problem and the mortality rate is high. During recent years, systemic inflammation has been recognized as a major driver of hepatic decompensation and progression of liver cirrhosis to acute-on-chronic liver failure (ACLF). The aim of the CYTOHEP study is to assess the impact of extracorporeal hemoadsorption with the CytoSorb adsorber on serum bilirubin concentrations, humoral inflammation parameters, liver function parameters, and patient survival in patients with ACLF and acute kidney injury (AKI).

Methods

The CYTOHEP study is a prospective, single-center, open-label, three-arm, randomized, controlled intervention trial. Patients with ACLF and AKI stage 3 according to Kidney Disease: Improving Global Outcome (KDIGO) criteria will be randomized into three groups to be treated with (1) continuous renal replacement therapy (CRRT) and CytoSorb, (2) CRRT without CytoSorb, and (3) without both, CRRT and CytoSorb. In the hemoadsorption group, CytoSorb will be used for 72 h. The other groups receive standard of care with early or late initiation of CRRT, respectively. Primary endpoint of the study is serum bilirubin concentration after 72 h, important secondary endpoints are 30-day survival and a panel of inflammatory parameters.

Discussion

The CYTOHEP study is designed to evaluate the benefit of extracorporeal hemoadsorption in patients with ACLF. The results of this study will help to better understand the potential role of hemoadsorption for the treatment of ACLF and its impact on bilirubin levels, inflammatory parameters, and survival.

Trial registration

ClinicalTrials.gov NCT05019352. Registered on August 24, 2021. Deutsches Register Klinischer Studien (DRKS) DRKS00026082.

Multi-Omic Approaches to Identify Genetic Factors in Metabolic Syndrome

Metabolic syndrome (MetS) is a highly heritable disease and a major public health burden worldwide. MetS diagnosis criteria are met by the simultaneous presence of any three of the following: high triglycerides, low HDL/high LDL cholesterol, insulin resistance, hypertension, and central obesity. These diseases act synergistically in people suffering from MetS and dramatically increase risk of morbidity and mortality due to stroke and cardiovascular disease, as well as certain cancers. Each of these component features is itself a complex disease, as is MetS. As a genetically complex disease, genetic risk factors for MetS are numerous, but not very powerful individually, often requiring specific environmental stressors for the disease to manifest. When taken together, all sequence variants that contribute to MetS disease risk explain only a fraction of the heritable variance, suggesting additional, novel loci have yet to be discovered. In this article, we will give a brief overview on the genetic concepts needed to interpret genome-wide association studies (GWAS) and quantitative trait locus (QTL) data, summarize the state of the field of MetS physiological genomics, and to introduce tools and resources that can be used by the physiologist to integrate genomics into their own research on MetS and any of its component features. There is a wealth of phenotypic and molecular data in animal models and humans that can be leveraged as outlined in this article. Integrating these multi-omic QTL data for complex diseases such as MetS provides a means to unravel the pathways and mechanisms leading to complex disease and promise for novel treatments. © 2022 American Physiological Society. Compr Physiol 12:1-40, 2022.

Using knockoffs for controlled predictive biomarker identification

One of the key challenges of personalized medicine is to identify which patients will respond positively to a given treatment. The area of subgroup identification focuses on this challenge, that is, identifying groups of patients that experience desirable characteristics, such as an enhanced treatment effect. A crucial first step towards the subgroup identification is to identify the baseline variables (eg, biomarkers) that influence the treatment effect, which are known as predictive variables. Many subgroup discovery algorithms return importance scores that capture the variables' predictive strength. However, a major limitation of these scores is that they do not answer the core question: "Which variables are actually predictive?" With our work we answer this question by using the knockoff framework, which is a general framework for controlling the false discovery rate when performing prognostic variable selection. In contrast, our work is the first that uses knockoffs for predictive variable selection. We introduce two novel knockoff filters: one parametric, building on variable importance scores derived from a penalized linear regression model, and one non-parametric, building on causal forest variable importance scores. We conduct extensive simulations to validate performance of the proposed methodology and we also apply the proposed methods to data from a randomized clinical trial.

In-Depth Analysis of Genetic Variation Associated with Severe West Nile Viral Disease

West Nile virus (WNV) is a mosquito-borne virus which causes symptomatic disease in a minority of infected humans. To identify novel genetic variants associated with severe disease, we utilized data from an existing case-control study of WNV and included population controls for an expanded analysis. We conducted imputation and gene-gene interaction analysis in the largest and most comprehensive genetic study conducted to date for West Nile neuroinvasive disease (WNND). Within the imputed West Nile virus dataset (severe cases n = 381 and asymptomatic/mild controls = 441), we found novel loci within the MCF.2 Cell Line Derived Transforming Sequence Like (MCF2L) gene (rs9549655 and rs2297192) through the individual loci analyses, although none reached statistical significance. Incorporating population controls from the Wisconsin Longitudinal Study on Aging (n = 9012) did not identify additional novel variants, a possible reflection of the cohort's inclusion of individuals who could develop mild or severe WNV disease upon infection. Many of the top gene-gene interaction results were intergenic, with currently undefined biological roles, highlighting the need for further investigation into these regions and other identified gene targets in severe WNND. Further studies including larger sample sizes and more diverse populations reflective of those at risk are needed to fully understand the genetic architecture of severe WNDD and provide guidance on viable targets for therapeutic and vaccine development.

Comparing multiple comparisons: practical guidance for choosing the best multiple comparisons test

Multiple comparisons tests (MCTs) include the statistical tests used to compare groups (treatments) often following a significant effect reported in one of many types of linear models. Due to a variety of data and statistical considerations, several dozen MCTs have been developed over the decades, with tests ranging from very similar to each other to very different from each other. Many scientific disciplines use MCTs, including >40,000 reports of their use in ecological journals in the last 60 years. Despite the ubiquity and utility of MCTs, several issues remain in terms of their correct use and reporting. In this study, we evaluated 17 different MCTs. We first reviewed the published literature for recommendations on their correct use. Second, we created a simulation that evaluated the performance of nine common MCTs. The tests examined in the simulation were those that often overlapped in usage, meaning the selection of the test based on fit to the data is not unique and that the simulations could inform the selection of one or more tests when a researcher has choices. Based on the literature review and recommendations: planned comparisons are overwhelmingly recommended over unplanned comparisons, for planned non-parametric comparisons the Mann-Whitney-Wilcoxon U test is recommended, Scheffé’s S test is recommended for any linear combination of (unplanned) means, Tukey’s HSD and the Bonferroni or the Dunn-Sidak tests are recommended for pairwise comparisons of groups, and that many other tests exist for particular types of data. All code and data used to generate this paper are available at: https://github.com/stevemidway/MultipleComparisons.

Genome-wide association study identifies markers associated with carcass and meat quality traits in Italian Large White pigs

A GWAS was performed using the genotypes obtained by PorcineSNP60 v2 BeadChip and 11 phenotypic traits (carcass lean meat percentage; backfat thickness; Longissimus thoracis muscle thickness; lightness; backfat thickness measured with caliper at the midline; meat pH measured at about 1 h post mortem and 24 h post mortem; CIE L*, a* and b* color parameters; and water-holding capacity). Three markers were associated with three of the phenotypic traits considered: M1GA0009592 (SSC7) with backfat thickness and lean meat content, DIAS0002910 (SSC6) and ALGA0109856 (SSC6) with water-holding capacity. The marker M1GA0009592, associated with backfat thickness, lies in a QTL region near the gene JARID2, which is a transcription factor also involved in the regulation of adipose-derived stem cell pluripotency. The results seem to indicate a possible role of these genomic regions in the regulation of pig carcass fatness (i.e. backfat at last rib) and water-holding capacity.

Discovery and validation of candidate SNP markers associated to heat stress response in pregnant ewes managed inside a climate-controlled chamber

Sheep production in desert environments during summer is challenging due to heat stress which reduces feed intake, growth, and fertility. Despite warm conditions, some ewes are able to maintain a normal performance suggesting the existence of genetic bases underlying heat tolerance. Our objective was to discover and validate genetic markers associated with thermo-tolerance in pregnant ewes exposed to warm environmental conditions. Using a well-defined model laboratory of heat stress in sheep, pregnant Columbia-Rambouillet crossbred ewes (n = 100) were examined. Following acclimation to the laboratory at thermo-neutral conditions, heat stress was induced in ewes by increasing the temperature-humidity index in a control environmental chamber during mid-gestation. Feed intake, water consumption, and rectal temperature were recorded daily and used to establish the heat stress tolerance index (HSTI) for each ewe. Rectal temperature was a predictor (P < 0.05) of feed intake, and the regression coefficient was used to classify the HSTI. In a subset of 24 ewes, a genome-wide association study (GWAS) was performed using the Illumina OvineSNP50 BeadChip. Single-marker analysis detected 3 intragenic SNPs associated with HSTI (P value = 10^-5). Bayesian multi-marker approach discovered 26 chromosomal regions across the genome which accounted for 9.8% of the variation associated with HSTI. In an independent sheep population (n = 42), the three discovered SNPs were validated as molecular markers associated with thermo-tolerance phenotypic traits. These SNPs were located within the genes F13A1, PAM, and PRELID2. In conclusion, three SNPs appear to be novel molecular markers associated with heat stress tolerance in pregnant ewes providing new knowledge about genetic foundations of thermo-tolerance.

Genome-Wide Association Study Reveals Candidate Genes for Litter Size Traits in Pelibuey Sheep

The Pelibuey sheep has adaptability to climatic variations, resistance to parasites, and good maternal ability, whereas some ewes present multiple births, which increases the litter size in farm sheep. The litter size in some wool sheep breeds is associated with the presence of mutations, mainly in the family of the transforming growth factor β (TGF-β) genes. To explore genetic mechanisms underlying the variation in litter size, we conducted a genome-wide association study in two groups of Pelibuey sheep (multiparous sheep with two lambs per birth vs. uniparous sheep with a single lamb at birth) using the OvineSNP50 BeadChip. We identified a total of 57 putative SNPs markers (p < 3.0 × 10^-3, Bonferroni correction). The candidate genes that may be associated with litter size in Pelibuey sheep are CLSTN2, MTMR2, DLG1, CGA, ABCG5, TRPM6, and HTR1E. Genomic regions were also identified that contain three quantitative trait loci (QTLs) for aseasonal reproduction (ASREP), milk yield (MY), and body weight (BW). These results allowed us to identify SNPs associated with genes that could be involved in the reproductive process related to prolificacy.

Dietary Intake of β-Glucans Can Prolong Lifespan and Exert an Antioxidant Action on Aged Fish Nothobranchius guentheri

One of the widely accepted conjectures regarding mechanisms of aging is probably the oxidative stress hypothesis. β-1,3-Glucans, well-known immunostimulants, have been shown to increase nonspecific immunity and resistance against infections or pathogenic bacteria in several fish species, but its antiaging function remains poorly understood. By feeding of β-1,3-glucans to the annual fish, Nothobranchius guentheri, we detected the survivorship of the fish and estimated the development of age-related biomarkers at different stages. We first showed that administration of β-1,3-glucans was able to prolong the lifespan of the fish (p < 0.05). We then showed that β-1,3-glucans clearly reduced the accumulation of lipofuscin in the gills and the senescence-associated β-galactosidase in the caudal fins. Moreover, β-1,3-glucans were able to lower the levels of protein oxidation, lipid peroxidation, and reactive oxygen species (ROS) in the muscles. Finally, β-1,3-glucans could promote the activities of the antioxidant enzymes, including catalase, superoxide dismutase, and glutathione peroxidase in the fish, and slow down the increase of P66shc, a critical factor involved in the regulation of intracellular ROS contents. These data together suggest for the first time that β-1,3-glucans can extend the lifespan, delay the onset of age-related biomarkers and exert an antioxidant action of the aged fish, N. guentheri. It also implies that β-1,3-glucans may be potentially useful for health care in the elderly, including extension of the lifespan.

Stepwise approach to SNP-set analysis illustrated with the Metabochip and colorectal cancer in Japanese Americans of the Multiethnic Cohort

BACKGROUND

Common variants have explained less than the amount of heritability expected for complex diseases, which has led to interest in less-common variants and more powerful approaches to the analysis of whole-genome scans. Because of low frequency (low statistical power), less-common variants are best analyzed using SNP-set methods such as gene-set or pathway-based analyses. However, there is as yet no clear consensus regarding how to focus in on potential risk variants following set-based analyses. We used a stepwise, telescoping approach to analyze common- and rare-variant data from the Illumina Metabochip array to assess genomic association with colorectal cancer (CRC) in the Japanese sub-population of the Multiethnic Cohort (676 cases, 7180 controls). We started with pathway analysis of SNPs that are in genes and pathways having known mechanistic roles in colorectal cancer, then focused on genes within the pathways that evidenced association with CRC, and finally assessed individual SNPs within the genes that evidenced association. Pathway SNPs downloaded from the dbSNP database were cross-matched with Metabochip SNPs and analyzed using the logistic kernel machine regression approach (logistic SNP-set kernel-machine association test, or sequence kernel association test; SKAT) and related methods.

RESULTS

The TGF-β and WNT pathways were associated with all CRC, and the WNT pathway was associated with colon cancer. Individual genes demonstrating the strongest associations were TGFBR2 in the TGF-β pathway and SMAD7 (which is involved in both the TGF-β and WNT pathways). As partial validation of our approach, a known CRC risk variant in SMAD7 (in both the TGF-β and WNT pathways: rs11874392) was associated with CRC risk in our data. We also detected two novel candidate CRC risk variants (rs13075948 and rs17025857) in TGFBR2, a gene known to be associated with CRC risk.

CONCLUSIONS

A stepwise, telescoping approach identified some potentially novel risk variants associated with colorectal cancer, so it may be a useful method for following up on results of set-based SNP analyses. Further work is required to assess the statistical characteristics of the approach, and additional applications should aid in better clarifying its utility.