Genetic variants contribute to gene expression variability in humans

Detecting genetic effects on phenotype variability to capture gene-by-environment interactions: a systematic method comparison

Genetically associated phenotypic variability has been widely observed across organisms and traits, including in humans. Both gene-gene and gene-environment interactions can lead to an increase in genetically associated phenotypic variability. Therefore, detecting the underlying genetic variants, or variance Quantitative Trait Loci (vQTLs), can provide novel insights into complex traits. Established approaches to detect vQTLs apply different methodologies from variance-only approaches to mean-variance joint tests, but a comprehensive comparison of these methods is lacking. Here, we review available methods to detect vQTLs in humans, carry out a simulation study to assess their performance under different biological scenarios of gene-environment interactions, and apply the optimal approaches for vQTL identification to gene expression data. Overall, with a minor allele frequency (MAF) of less than 0.2, the squared residual value linear model (SVLM) and the deviation regression model (DRM) are optimal when the data follow normal and non-normal distributions, respectively. In addition, the Brown-Forsythe (BF) test is one of the optimal methods when the MAF is 0.2 or larger, irrespective of phenotype distribution. Additionally, a larger sample size and more balanced sample distribution in different exposure categories increase the power of BF, SVLM, and DRM. Our results highlight vQTL detection methods that perform optimally under realistic simulation settings and show that their relative performance depends on the phenotype distribution, allele frequency, sample size, and the type of exposure in the interaction model underlying the vQTL.

Pleiotropy, epistasis and the genetic architecture of quantitative traits

Pleiotropy (whereby one genetic polymorphism affects multiple traits) and epistasis (whereby non-linear interactions between genetic polymorphisms affect the same trait) are fundamental aspects of the genetic architecture of quantitative traits. Recent advances in the ability to characterize the effects of polymorphic variants on molecular and organismal phenotypes in human and model organism populations have revealed the prevalence of pleiotropy and unexpected shared molecular genetic bases among quantitative traits, including diseases. By contrast, epistasis is common between polymorphic loci associated with quantitative traits in model organisms, such that alleles at one locus have different effects in different genetic backgrounds, but is rarely observed for human quantitative traits and common diseases. Here, we review the concepts and recent inferences about pleiotropy and epistasis, and discuss factors that contribute to similarities and differences between the genetic architecture of quantitative traits in model organisms and humans.

eQTL studies: from bulk tissues to single cells

An expression quantitative trait locus (eQTL) is a chromosomal region where genetic variants are associated with the expression levels of specific genes that can be both nearby or distant. The identifications of eQTLs for different tissues, cell types, and contexts have led to a better understanding of the dynamic regulations of gene expressions and implications of functional genes and variants for complex traits and diseases. Although most eQTL studies have been performed on data collected from bulk tissues, recent studies have demonstrated the importance of cell-type-specific and context-dependent gene regulations in biological processes and disease mechanisms. In this review, we discuss statistical methods that have been developed to enable the detection of cell-type-specific and context-dependent eQTLs from bulk tissues, purified cell types, and single cells. We also discuss the limitations of the current methods and future research opportunities.

Altered Extracellular Vesicle miRNA Profile in Prodromal Alzheimer's Disease

Extracellular vesicles (EVs) are nanosized vesicles released by almost all body tissues, representing important mediators of cellular communication, and are thus promising candidate biomarkers for neurodegenerative diseases like Alzheimer's disease (AD). The aim of the present study was to isolate total EVs from plasma and characterize their microRNA (miRNA) contents in AD patients. We isolated total EVs from the plasma of all recruited subjects using ExoQuickULTRA exosome precipitation solution (SBI). Subsequently, circulating total EVs were characterized using Nanosight nanoparticle tracking analysis (NTA), transmission electron microscopy (TEM), and Western blotting. A panel of 754 miRNAs was determined with RT-qPCR using TaqMan OpenArray technology in a QuantStudio 12K System (Thermo Fisher Scientific). The results demonstrated that plasma EVs showed widespread deregulation of specific miRNAs (miR-106a-5p, miR-16-5p, miR-17-5p, miR-195-5p, miR-19b-3p, miR-20a-5p, miR-223-3p, miR-25-3p, miR-296-5p, miR-30b-5p, miR-532-3p, miR-92a-3p, and miR-451a), some of which were already known to be associated with neurological pathologies. A further validation analysis also confirmed a significant upregulation of miR-16-5p, miR-25-3p, miR-92a-3p, and miR-451a in prodromal AD patients, suggesting these dysregulated miRNAs are involved in the early progression of AD.

Gene expression underlying variation in the survival skills of red drum larvae (Sciaenops ocellatus)

Mortality rates of marine fish larvae are incredibly high and can determine year-class strength. The major causes of larval mortality are predation and starvation, and the performance of larvae in survival skills that can mitigate this mortality (predator evasion, foraging) varies among individuals and cohorts, but the causes of the variation are not known. Transcriptomics can link gene expression variation to phenotypic variation at the whole-system level to investigate the molecular basis of behavioural variation. We used tag-based RNA-sequencing to examine the molecular basis of variation in predator evasion and routine swimming (trait related to foraging efficiency) in the larval red drum, Sciaenops ocellatus. We looked for functional gene networks in which interindividual variation would explain variation in larval behavioural performance. We identified co-expressed gene groups ("modules") associated with predator evasion traits and found enrichment of motor, neural and energy metabolism pathways. These functional associations and pattern of correlations between modules and traits suggest that energy availability and allocation were responsible for the magnitude of startle responses, while differential neural and motor activation were associated with differences in response latency.

Comparison of cancer subtype identification methods combined with feature selection methods in omics data analysis

BACKGROUND

Cancer subtype identification is important for the early diagnosis of cancer and the provision of adequate treatment. Prior to identifying the subtype of cancer in a patient, feature selection is also crucial for reducing the dimensionality of the data by detecting genes that contain important information about the cancer subtype. Numerous cancer subtyping methods have been developed, and their performance has been compared. However, combinations of feature selection and subtype identification methods have rarely been considered. This study aimed to identify the best combination of variable selection and subtype identification methods in single omics data analysis.

RESULTS

Combinations of six filter-based methods and six unsupervised subtype identification methods were investigated using The Cancer Genome Atlas (TCGA) datasets for four cancers. The number of features selected varied, and several evaluation metrics were used. Although no single combination was found to have a distinctively good performance, Consensus Clustering (CC) and Neighborhood-Based Multi-omics Clustering (NEMO) used with variance-based feature selection had a tendency to show lower p-values, and nonnegative matrix factorization (NMF) stably showed good performance in many cases unless the Dip test was used for feature selection. In terms of accuracy, the combination of NMF and similarity network fusion (SNF) with Monte Carlo Feature Selection (MCFS) and Minimum-Redundancy Maximum Relevance (mRMR) showed good overall performance. NMF always showed among the worst performances without feature selection in all datasets, but performed much better when used with various feature selection methods. iClusterBayes (ICB) had decent performance when used without feature selection.

CONCLUSIONS

Rather than a single method clearly emerging as optimal, the best methodology was different depending on the data used, the number of features selected, and the evaluation method. A guideline for choosing the best combination method under various situations is provided.

eQTL Studies: from Bulk Tissues to Single Cells

An expression quantitative trait locus (eQTL) is a chromosomal region where genetic variants are associated with the expression levels of certain genes that can be both nearby or distant. The identifications of eQTLs for different tissues, cell types, and contexts have led to better understanding of the dynamic regulations of gene expressions and implications of functional genes and variants for complex traits and diseases. Although most eQTL studies to date have been performed on data collected from bulk tissues, recent studies have demonstrated the importance of cell-type-specific and context-dependent gene regulations in biological processes and disease mechanisms. In this review, we discuss statistical methods that have been developed to enable the detections of cell-type-specific and context-dependent eQTLs from bulk tissues, purified cell types, and single cells. We also discuss the limitations of the current methods and future research opportunities.

Endogenous miRNA-Based Innate-Immunity against SARS-CoV-2 Invasion of the Brain

The severe acute respiratory syndrome Coronavirus-2 (SARS-CoV-2), the causative agent of COVID-19, possesses an unusually large positive-sense, single-stranded viral RNA (ssvRNA) genome of about ~29,903 nucleotides (nt). In many respects, this ssvRNA resembles a very large, polycistronic messenger RNA (mRNA) possessing a 5'-methyl cap (m7GpppN), a 3'- and 5'-untranslated region (3'-UTR, 5'-UTR), and a poly-adenylated (poly-A+) tail. As such, the SARS-CoV-2 ssvRNA is susceptible to targeting by small non-coding RNA (sncRNA) and/or microRNA (miRNA), as well as neutralization and/or inhibition of its infectivity via the human body's natural complement of about ~2650 miRNA species. Depending on host cell and tissue type, in silico analysis, RNA sequencing, and molecular-genetic investigations indicate that, remarkably, almost every single human miRNA has the potential to interact with the primary sequence of SARS-CoV-2 ssvRNA. Individual human variation in host miRNA abundance, speciation, and complexity among different human populations and additional variability in the cell and tissue distribution of the SARS-CoV-2 angiotensin converting enzyme-2 (ACE2) receptor (ACE2R) appear to further contribute to the molecular-genetic basis for the wide variation in individual host cell and tissue susceptibility to COVID-19 infection. In this paper, we review recently described aspects of the miRNA and ssvRNA ribonucleotide sequence structure in this highly evolved miRNA-ssvRNA recognition and signaling system and, for the first time, report the most abundant miRNAs in the control superior temporal lobe neocortex (STLN), an anatomical area involved in cognition and targeted by both SARS-CoV-2 invasion and Alzheimer's disease (AD). We further evaluate important factors involving the neurotropic nature of SARS-CoV-2 and miRNAs and ACE2R distribution in the STLN that modulate significant functional deficits in the brain and CNS associated with SARS-CoV-2 infection and COVID-19's long-term neurological effects.

Mathematical model for the relationship between single-cell and bulk gene expression to clarify the interpretation of bulk gene expression data

BACKGROUND

Differential expression analysis is a standard approach in molecular biology. For example, genes whose expression levels differ between diseased and non-diseased samples are considered to be associated with that disease. On the other hand, differential variability analysis focuses on the differences of the variances of gene expression between sample groups. Although differential variability is also known to capture biological information, its interpretation remains unclear and controversial. Recent single-cell analyses have revealed that differences between sample groups can affect gene expression in a cellular subset-specific manner or by altering the proportion of a particular cellular subset. The aim of this study is to clarify the interpretation of mean and variance of bulk gene expression data.

METHOD

We developed a mathematical model in which the bulk gene expression value is proportional to the mean value of the single-cell gene expression profile. Based on this model, we performed theoretical, simulated and real single-cell RNA-seq data analyses.

RESULT AND CONCLUSION

We identified how differences in single-cell gene expression profiles affect the differences in the mean and the variance of bulk gene expression. It is shown that differential expression analysis of bulk expression data can overlook significant changes in gene expression at the single-cell level. Further, differential variability analysis capture the complex feature affected by different gene expression shifts for each subset, changes in the proportions of cellular subsets, and variation in single-cell distribution parameters among samples.

GWAS of longitudinal trajectories at biobank scale

Biobanks linked to massive, longitudinal electronic health record (EHR) data make numerous new genetic research questions feasible. One among these is the study of biomarker trajectories. For example, high blood pressure measurements over visits strongly predict stroke onset, and consistently high fasting glucose and Hb1Ac levels define diabetes. Recent research reveals that not only the mean level of biomarker trajectories but also their fluctuations, or within-subject (WS) variability, are risk factors for many diseases. Glycemic variation, for instance, is recently considered an important clinical metric in diabetes management. It is crucial to identify the genetic factors that shift the mean or alter the WS variability of a biomarker trajectory. Compared to traditional cross-sectional studies, trajectory analysis utilizes more data points and captures a complete picture of the impact of time-varying factors, including medication history and lifestyle. Currently, there are no efficient tools for genome-wide association studies (GWASs) of biomarker trajectories at the biobank scale, even for just mean effects. We propose TrajGWAS, a linear mixed effect model-based method for testing genetic effects that shift the mean or alter the WS variability of a biomarker trajectory. It is scalable to biobank data with 100,000 to 1,000,000 individuals and many longitudinal measurements and robust to distributional assumptions. Simulation studies corroborate that TrajGWAS controls the type I error rate and is powerful. Analysis of eleven biomarkers measured longitudinally and extracted from UK Biobank primary care data for more than 150,000 participants with 1,800,000 observations reveals loci that significantly alter the mean or WS variability.

Transcriptome analysis method based on differential distribution evaluation

Identifying differential genes over conditions provides insights into the mechanisms of biological processes and disease progression. Here we present an approach, the Kullback-Leibler divergence-based differential distribution (klDD), which provides a flexible framework for quantifying changes in higher-order statistical information of genes including mean and variance/covariation. The method can well detect subtle differences in gene expression distributions in contrast to mean or variance shifts of the existing methods. In addition to effectively identifying informational genes in terms of differential distribution, klDD can be directly applied to cancer subtyping, single-cell clustering and disease early-warning detection, which were all validated by various benchmark datasets.

Heterogeneity is not always a source of noise: Stochastic gene expression in regulatory heterozygote

Zygosity of diploid genome (i.e., degree to which two parental alleles of a gene have varied genetic sequences) adds another dimension to stochastic gene expression. The allelic imbalance in chromatin accessibility or divergence in regulatory sequences leads to fitness effects but the quantitative aspects thereof are largely left unexplored. We investigate diploid gene expression systems with homozygous (the same) and heterozygous (varied) combination of alleles in cis-regulatory sequences, not in structural gene loci, and characterize the zygosity-associated stochastic fluctuations in protein abundance. An emerging feat of heterozygosity is its counterintuitive capacity for genetic noise control. Especially when the noise is dominantly contributed to by the fluctuations in duty cycle ("reliability") rather than in process speed ("productivity") of gene expression machinery, its interallelic discrepancy acts to reduce the gene expression noise. These findings offer a novel insight into the rich repertoire of balancing selection enriched in the regulatory elements of immune response genes.

PRICKLE1 × FOCAD Interaction Revealed by Genome-Wide vQTL Analysis of Human Facial Traits

The human face is a highly complex and variable structure resulting from the intricate coordination of numerous genetic and non-genetic factors. Hundreds of genomic loci impacting quantitative facial features have been identified. While these associations have been shown to influence morphology by altering the mean size and shape of facial measures, their effect on trait variance remains unclear. We conducted a genome-wide association analysis for the variance of 20 quantitative facial measurements in 2,447 European individuals and identified several suggestive variance quantitative trait loci (vQTLs). These vQTLs guided us to conduct an efficient search for gene-by-gene (G × G) interactions, which uncovered an interaction between PRICKLE1 and FOCAD affecting cranial base width. We replicated this G × G interaction signal at the locus level in an additional 5,128 Korean individuals. We used the hypomorphic Prickle1 Beetlejuice (Prickle1 Bj ) mouse line to directly test the function of Prickle1 on the cranial base and observed wider cranial bases in Prickle1 Bj/Bj . Importantly, we observed that the Prickle1 and Focadhesin proteins co-localize in murine cranial base chondrocytes, and this co-localization is abnormal in the Prickle1 Bj/Bj mutants. Taken together, our findings uncovered a novel G × G interaction effect in humans with strong support from both epidemiological and molecular studies. These results highlight the potential of studying measures of phenotypic variability in gene mapping studies of facial morphology.

How Machine Learning and Statistical Models Advance Molecular Diagnostics of Rare Disorders Via Analysis of RNA Sequencing Data

Rare diseases, although individually rare, collectively affect approximately 350 million people worldwide. Currently, nearly 6,000 distinct rare disorders with a known molecular basis have been described, yet establishing a specific diagnosis based on the clinical phenotype is challenging. Increasing integration of whole exome sequencing into routine diagnostics of rare diseases is improving diagnostic rates. Nevertheless, about half of the patients do not receive a genetic diagnosis due to the challenges of variant detection and interpretation. During the last years, RNA sequencing is increasingly used as a complementary diagnostic tool providing functional data. Initially, arbitrary thresholds have been applied to call aberrant expression, aberrant splicing, and mono-allelic expression. With the application of RNA sequencing to search for the molecular diagnosis, the implementation of robust statistical models on normalized read counts allowed for the detection of significant outliers corrected for multiple testing. More recently, machine learning methods have been developed to improve the normalization of RNA sequencing read count data by taking confounders into account. Together the methods have increased the power and sensitivity of detection and interpretation of pathogenic variants, leading to diagnostic rates of 10-35% in rare diseases. In this review, we provide an overview of the methods used for RNA sequencing and illustrate how these can improve the diagnostic yield of rare diseases.

Variable expression quantitative trait loci analysis of breast cancer risk variants

Genome wide association studies (GWAS) have identified more than 180 variants associated with breast cancer risk, however the underlying functional mechanisms and biological pathways which confer disease susceptibility remain largely unknown. As gene expression traits are under genetic regulation we hypothesise that differences in gene expression variability may identify causal breast cancer susceptibility genes. We performed variable expression quantitative trait loci (veQTL) analysis using tissue-specific expression data from the Genotype-Tissue Expression (GTEx) Common Fund Project. veQTL analysis identified 70 associations (p < 5 × 10^–8) consisting of 60 genes and 27 breast cancer risk variants, including 55 veQTL that were observed in breast tissue only. Pathway analysis of genes associated with breast-specific veQTL revealed an enrichment of four genes (CYP11B1, CYP17A1 HSD3B2 and STAR) involved in the C21-steroidal biosynthesis pathway that converts cholesterol to breast-related hormones (e.g. oestrogen). Each of these four genes were significantly more variable in individuals homozygous for rs11075995 (A/A) breast cancer risk allele located in the FTO gene, which encodes an RNA demethylase. The A/A allele was also found associated with reduced expression of FTO, suggesting an epi-transcriptomic mechanism may underlie the dysregulation of genes involved in hormonal biosynthesis leading to an increased risk of breast cancer. These findings provide evidence that genetic variants govern high levels of expression variance in breast tissue, thus building a more comprehensive insight into the underlying biology of breast cancer risk loci.

Natural variation of the cardiac transcriptome in humans

We investigated the gene-expression variation among humans by analysing previously published mRNA-seq and ribosome footprint profiling of heart left-ventricles from healthy donors. We ranked the genes according to their coefficient of variation values and found that the top 5% most variable genes had special features compared to the rest of the genome, such as lower mRNA levels and shorter half-lives coupled to increased translation efficiency. We observed that these genes are mostly involved with immune response and have a pleiotropic effect on disease phenotypes, indicating that asymptomatic conditions contribute to the gene expression diversity of healthy individuals.

Schizophrenia: Developmental Variability Interacts with Risk Factors to Cause the Disorder: Nonspecific Variability-Enhancing Factors Combine with Specific Risk Factors to Cause Schizophrenia

A new etiological model is proposed for schizophrenia that combines variability-enhancing nonspecific factors acting during development with more specific risk factors. This model is better suited than the current etiological models of schizophrenia, based on the risk factors paradigm, for predicting and/or explaining several important findings about schizophrenia: high co-morbidity rates, low specificity of many risk factors, and persistence in the population of the associated genetic polymorphisms. Compared with similar models, e.g., de-canalization, common psychopathology factor, sexual-selection, or differential sensitivity to the environment, this proposal is more general and integrative. Recently developed research methods have proven the existence of genetic and environmental factors that enhance developmental variability. Applying such methods to newly collected or already available data can allow for testing the hypotheses upon which this model is built. If validated, this model may change the understanding of the etiology of schizophrenia, the research models, and preventionbrk paradigms.

Canalization and Robustness in Human Genetics and Disease

Canalization refers to the evolution of populations such that the number of individuals who deviate from the optimum trait, or experience disease, is minimized. In the presence of rapid cultural, environmental, or genetic change, the reverse process of decanalization may contribute to observed increases in disease prevalence. This review starts by defining relevant concepts, drawing distinctions between the canalization of populations and robustness of individuals. It then considers evidence pertaining to three continuous traits and six domains of disease. In each case, existing genetic evidence for genotype-by-environment interactions is insufficient to support a strong inference of decanalization, but we argue that the advent of genome-wide polygenic risk assessment now makes an empirical evaluation of the role of canalization in preventing disease possible. Finally, the contributions of both rare and common variants to congenital abnormality and adult onset disease are considered in light of a new kerplunk model of genetic effects.

Genetic variants and underlying mechanisms influencing variance heterogeneity in maize

Traditional genetic studies focus on identifying genetic variants associated with the mean difference in a quantitative trait. Because genetic variants also influence phenotypic variation via heterogeneity, we conducted a variance-heterogeneity genome-wide association study to examine the contribution of variance heterogeneity to oil-related quantitative traits. We identified 79 unique variance-controlling single nucleotide polymorphisms (vSNPs) from the sequences of 77 candidate variance-heterogeneity genes for 21 oil-related traits using the Levene test (P < 1.0 × 10^-5 ). About 30% of the candidate genes encode enzymes that work in lipid metabolic pathways, most of which define clear expression variance quantitative trait loci. Of the vSNPs specifically associated with the genetic variance heterogeneity of oil concentration, 89% can be explained by additional linked mean-effects genetic variants. Furthermore, we demonstrated that gene × gene interactions play important roles in the formation of variance heterogeneity for fatty acid compositional traits. The interaction pattern was validated for one gene pair (GRMZM2G035341 and GRMZM2G152328) using yeast two-hybrid and bimolecular fluorescent complementation analyses. Our findings have implications for uncovering the genetic basis of hidden additive genetic effects and epistatic interaction effects, and we indicate opportunities to stabilize efficient breeding and selection of high-oil maize (Zea mays L.).

Seq-ing answers: Current data integration approaches to uncover mechanisms of transcriptional regulation

Advancements in the field of next generation sequencing lead to the generation of ever-more data, with the challenge often being how to combine and reconcile results from different OMICs studies such as genome, epigenome and transcriptome. Here we provide an overview of the standard processing pipelines for ChIP-seq and RNA-seq as well as common downstream analyses. We describe popular multi-omics data integration approaches used to identify target genes and co-factors, and we discuss how machine learning techniques may predict transcriptional regulators and gene expression.

Developmental plasticity shapes social traits and selection in a facultatively eusocial bee

Developmental processes are an important source of phenotypic variation, but the extent to which this variation contributes to evolutionary change is unknown. We used integrative genomic analyses to explore the relationship between developmental and social plasticity in a bee species that can adopt either a social or solitary lifestyle. We find genes regulating this social flexibility also regulate development, and positive selection on these genes is influenced by their function during development. This suggests that developmental plasticity may influence the evolution of sociality. Our additional finding of genetic variants linked to differences in social behavior sheds light on how phenotypic variation derived from development may become encoded into the genome, and thus contribute to evolutionary change.

Developmental plasticity generates phenotypic variation, but how it contributes to evolutionary change is unclear. Phenotypes of individuals in caste-based (eusocial) societies are particularly sensitive to developmental processes, and the evolutionary origins of eusociality may be rooted in developmental plasticity of ancestral forms. We used an integrative genomics approach to evaluate the relationships among developmental plasticity, molecular evolution, and social behavior in a bee species (Megalopta genalis) that expresses flexible sociality, and thus provides a window into the factors that may have been important at the evolutionary origins of eusociality. We find that differences in social behavior are derived from genes that also regulate sex differentiation and metamorphosis. Positive selection on social traits is influenced by the function of these genes in development. We further identify evidence that social polyphenisms may become encoded in the genome via genetic changes in regulatory regions, specifically in transcription factor binding sites. Taken together, our results provide evidence that developmental plasticity provides the substrate for evolutionary novelty and shapes the selective landscape for molecular evolution in a major evolutionary innovation: Eusociality.

Overdispersed gene expression in schizophrenia

Schizophrenia (SCZ) is a severe, highly heterogeneous psychiatric disorder with varied clinical presentations. The polygenic genetic architecture of SCZ makes identification of causal variants a daunting task. Gene expression analyses hold the promise of revealing connections between dysregulated transcription and underlying variants in SCZ. However, the most commonly used differential expression analysis often assumes grouped samples are from homogeneous populations and thus cannot be used to detect expression variance differences between samples. Here, we applied the test for equality of variances to normalized expression data, generated by the CommonMind Consortium (CMC), from brains of 212 SCZ and 214 unaffected control (CTL) samples. We identified 87 genes, including VEGFA (vascular endothelial growth factor) and BDNF (brain-derived neurotrophic factor), that showed a significantly higher expression variance among SCZ samples than CTL samples. In contrast, only one gene showed the opposite pattern. To extend our analysis to gene sets, we proposed a Mahalanobis distance-based test for multivariate homogeneity of group dispersions, with which we identified 110 gene sets with a significantly higher expression variability in SCZ, including sets of genes encoding phosphatidylinositol 3-kinase (PI3K) complex and several others involved in cerebellar cortex morphogenesis, neuromuscular junction development, and cerebellar Purkinje cell layer development. Taken together, our results suggest that SCZ brains are characterized by overdispersed gene expression-overall gene expression variability among SCZ samples is significantly higher than that among CTL samples. Our study showcases the application of variability-centric analyses in SCZ research.

Variability in Gene Expression is Associated with Incomplete Penetrance in Inherited Eye Disorders

Inherited eye disorders (IED) are a heterogeneous group of Mendelian conditions that are associated with visual impairment. Although these disorders often exhibit incomplete penetrance and variable expressivity, the scale and mechanisms of these phenomena remain largely unknown. Here, we utilize publicly-available genomic and transcriptomic datasets to gain insights into variable penetrance in IED. Variants in a curated set of 340 IED-implicated genes were extracted from the Human Gene Mutation Database (HGMD) 2019.1 and cross-checked with the Genome Aggregation Database (gnomAD) 2.1 control-only dataset. Genes for which >1 variants were encountered in both HGMD and gnomAD were considered to be associated with variable penetrance (n = 56). Variability in gene expression levels was then estimated for the subset of these genes that was found to be adequately expressed in two relevant resources: the Genotype-Tissue Expression (GTEx) and Eye Genotype Expression (EyeGEx) datasets. We found that genes suspected to be associated with variable penetrance tended to have significantly more variability in gene expression levels in the general population (p = 0.0000015); this finding was consistent across tissue types. The results of this study point to the possible influence of cis and/or trans-acting elements on the expressivity of variants causing Mendelian disorders. They also highlight the potential utility of quantifying gene expression as part of the investigation of families showing evidence of variable penetrance.

High-Order Association Mapping for Expression Quantitative Trait Loci

Mapping expression quantitative trait loci (eQTLs) is an important avenue to identify putative genetic variants in regulatory regions. Famed eQTL mapping methods exploit the mean effects of locus-wise genetic variants on expression quantitative traits. Despite their successes, such methods are suboptimal because they neglect high-order heterogeneity inherent in genetic variants and covariates. High-order effects of observed loci are common due to their connections to various latent factors, i.e., latent interactions among genes and environmental factors. In this chapter, we introduce a new scheme to harmoniously integrate mean and high-order effects of genetic variants on expression quantitative trait. We rigorously evaluate its validity and utility of signal augmentation.

Are ENT1/ENT1, NOTCH3, and miR-21 Reliable Prognostic Biomarkers in Patients with Resected Pancreatic Adenocarcinoma Treated with Adjuvant Gemcitabine Monotherapy?

Evidence on equilibrative nucleoside transporter 1 (ENT1) and microRNA-21 (miR‑21) is not yet sufficiently convincing to consider them as prognostic biomarkers for patients with pancreatic ductal adenocarcinoma (PDAC). Here, we investigated the prognostic value of ENT1/ENT1, miR-21, and neurogenic locus homolog protein 3 gene (NOTCH3) in a well-defined cohort of resected patients treated with adjuvant gemcitabine chemotherapy (n = 69). Using a combination of gene expression quantification in microdissected tissue, immunohistochemistry, and univariate/multivariate statistical analyses we did not confirm association of ENT1/ENT1 and NOTCH3 with improved disease-specific survival (DSS). Low miR-21 was associated with longer DSS in patients with negative regional lymph nodes or primary tumor at stage 1 and 2. In addition, downregulation of ENT1 was observed in PDAC of patients with high ENT1 expression in normal pancreas, whereas NOTCH3 was upregulated in PDAC of patients with low NOTCH3 levels in normal pancreas. Tumor miR‑21 was upregulated irrespective of its expression in normal pancreas. Our data confirmed that patient stratification based on expression of ENT1/ENT1 or miR‑21 is not ready to be implemented into clinical decision-making processes. We also conclude that occurrence of ENT1 and NOTCH3 deregulation in PDAC is dependent on their expression in normal pancreas.

Genetic association of molecular traits: A help to identify causative variants in complex diseases

In the past 15 years, major progresses have been made in the understanding of the genetic basis of regulation of gene expression. These new insights have revolutionized our approach to resolve the genetic variation underlying complex diseases. Gene transcript levels were the first expression phenotypes that were studied. They are heritable and therefore amenable to genome-wide association studies. The genetic variants that modulate them are called expression quantitative trait loci. Their study has been extended to other molecular quantitative trait loci (molQTLs) that regulate gene expression at the various levels, from chromatin state to cellular responses. Altogether, these studies have generated a wealth of basic information on the genome-wide patterns of gene expression and their inter-individual variation. Most importantly, molQTLs have become an invaluable asset in the genetic study of complex diseases. Although the identification of the disease-causing variants on the basis of their overlap with molQTLs requires caution, molQTLs can help to prioritize the relevant candidate gene(s) in the disease-associated regions and bring a functional interpretation of the associated variants, therefore, bridging the gap between genotypes and clinical phenotypes.

Analytical strategies to include the X-chromosome in variance heterogeneity analyses: Evidence for trait-specific polygenic variance structure

Genotype-stratified variance of a quantitative trait could differ in the presence of gene-gene or gene-environment interactions. Genetic markers associated with phenotypic variance are thus considered promising candidates for follow-up interaction or joint location-scale analyses. However, as in studies of main effects, the X-chromosome is routinely excluded from "whole-genome" scans due to analytical challenges. Specifically, as males carry only one copy of the X-chromosome, the inherent sex-genotype dependency could bias the trait-genotype association, through sexual dimorphism in quantitative traits with sex-specific means or variances. Here we investigate phenotypic variance heterogeneity associated with X-chromosome single nucleotide polymorphisms (SNPs) and propose valid and powerful strategies. Among those, a generalized Levene's test has adequate power and remains robust to sexual dimorphism. An alternative approach is a sex-stratified analysis but at the cost of slightly reduced power and modeling flexibility. We applied both methods to an Estonian study of gene expression quantitative trait loci (eQTL; n = 841), and two complex trait studies of height, hip, and waist circumferences, and body mass index from Multi-Ethnic Study of Atherosclerosis (MESA; n = 2,073) and UK Biobank (UKB; n = 327,393). Consistent with previous eQTL findings on mean, we found some but no conclusive evidence for cis regulators being enriched for variance association. SNP rs2681646 is associated with variance of waist circumference (p = 9.5E-07) at X-chromosome-wide significance in UKB, with a suggestive female-specific effect in MESA (p = 0.048). Collectively, an enrichment analysis using permutated UKB (p < 0.1) and MESA (p < 0.01) datasets, suggests a possible polygenic structure for the variance of human height.

Genetic Effects on Dispersion in Urinary Albumin and Creatinine in Three House Mouse (Mus musculus) Cohorts

Conventionally, quantitative genetics concerns the heredity of trait means, but there is growing evidence for the existence of architectures in which certain alleles cause random variance in phenotype, termed ‘phenotypic dispersion’ (PD) or ‘variance QTL’ (vQTL), including in physiological traits like disease signs. However, the structure of this phenomenon is still poorly known. PD for urinary albumin (PD_UAlb) and creatinine (PD_UCrea) was mapped using curated data from two nearly genetically identical F₂ mouse (Mus musculus) cohorts (383 male F₂ C57BL/6J×A/J (97 SNP) and 207 male F₂ C57BL/6J×A/J ApoE knockout mice (144 SNP)) and a related mapping cohort (340 male F₂ DBA/2J×C57BL/6J (83 SNP, 8 microsatellites)). PD_UAlb was associated with markers in regions of Chr 1 (5-64 megabases (MB); 141-158 MB), 3 (∼113 MB), 8 (37-68 MB), 14 (92-117 MB) and 17 (14-24 MB) with several positions and quantitative architectures in common between the two C57BL/6J×A/J cohorts, most of which had a negative dominant construction. One locus for PD_UCrea was detected on Chr 19 (57 MB) in the C57BL/6J×A/J ApoE^−/− cohort. The large number of negative dominant loci for albuminuria dispersion relative to conventional quantitative trait loci suggests that the development of albuminuria may be largely genetically dynamic and that randomization in this development is detrimental.

Gene expression variation and parental allele inheritance in a Xiphophorus interspecies hybridization model

Understanding the genetic mechanisms underlying segregation of phenotypic variation through successive generations is important for understanding physiological changes and disease risk. Tracing the etiology of variation in gene expression enables identification of genetic interactions, and may uncover molecular mechanisms leading to the phenotypic expression of a trait, especially when utilizing model organisms that have well-defined genetic lineages. There are a plethora of studies that describe relationships between gene expression and genotype, however, the idea that global variations in gene expression are also controlled by genotype remains novel. Despite the identification of loci that control gene expression variation, the global understanding of how genome constitution affects trait variability is unknown. To study this question, we utilized Xiphophorus fish of different, but tractable genetic backgrounds (inbred, F₁ interspecies hybrids, and backcross hybrid progeny), and measured each individual’s gene expression concurrent with the degrees of inter-individual expression variation. We found, (a) F₁ interspecies hybrids exhibited less variability than inbred animals, indicting gene expression variation is not affected by the fraction of heterozygous loci within an individual genome, and (b), that mixing genotypes in backcross populations led to higher levels of gene expression variability, supporting the idea that expression variability is caused by heterogeneity of genotypes of cis or trans loci. In conclusion, heterogeneity of genotype, introduced by inheritance of different alleles, accounts for the largest effects on global phenotypical variability.

Phenotypical variability is a multi-factorial phenomenon. Although it has been shown that inheriting certain gene is associated with lower phenotypical variability, how genome complexity affect phenotypical variability is still unclear. To study this question, we used inbred Xiphophorus fish, backcross interspecies hybrids, and F₁ interspecies hybrids between select Xiphophorus species to model genetic composition with minimum, medium, and maximum heterozygosity respectively, and measured their global gene expression variability. We found gene expression variation is not affected by the percentage of heterozygous loci in individual genome, but instead related to heterogeneity of genotype at local or remote loci.

vqtl: An R Package for Mean-Variance QTL Mapping

We present vqtl, an R package for mean-variance QTL mapping. This QTL mapping approach tests for genetic loci that influence the mean of the phenotype, termed mean QTL, the variance of the phenotype, termed variance QTL, or some combination of the two, termed mean-variance QTL. It is unique in its ability to correct for variance heterogeneity arising not only from the QTL itself but also from nuisance factors, such as sex, batch, or housing. This package provides functions to conduct genome scans, run permutations to assess the statistical significance, and make informative plots to communicate results. Because it is inter-operable with the popular qtl package and uses many of the same data structures and input patterns, it will be straightforward for geneticists to analyze future experiments with vqtl as well as re-analyze past experiments, possibly discovering new QTL.

QTL Mapping on a Background of Variance Heterogeneity

Standard QTL mapping procedures seek to identify genetic loci affecting the phenotypic mean while assuming that all individuals have the same residual variance. But when the residual variance differs systematically between groups, perhaps due to a genetic or environmental factor, such standard procedures can falter: in testing for QTL associations, they attribute too much weight to observations that are noisy and too little to those that are precise, resulting in reduced power and and increased susceptibility to false positives. The negative effects of such "background variance heterogeneity" (BVH) on standard QTL mapping have received little attention until now, although the subject is closely related to work on the detection of variance-controlling genes. Here we use simulation to examine how BVH affects power and false positive rate for detecting QTL affecting the mean (mQTL), the variance (vQTL), or both (mvQTL). We compare linear regression for mQTL and Levene's test for vQTL, with tests more recently developed, including tests based on the double generalized linear model (DGLM), which can model BVH explicitly. We show that, when used in conjunction with a suitable permutation procedure, the DGLM-based tests accurately control false positive rate and are more powerful than the other tests. We also find that some adverse effects of BVH can be mitigated by applying a rank inverse normal transform. We apply our novel approach, which we term "mean-variance QTL mapping", to publicly available data on a mouse backcross and, after accommodating BVH driven by sire, detect a new mQTL for bodyweight.

QTL Mapping on a Background of Variance Heterogeneity

Standard QTL mapping procedures seek to identify genetic loci affecting the phenotypic mean while assuming that all individuals have the same residual variance. But when the residual variance differs systematically between groups, perhaps due to a genetic or environmental factor, such standard procedures can falter: in testing for QTL associations, they attribute too much weight to observations that are noisy and too little to those that are precise, resulting in reduced power and and increased susceptibility to false positives. The negative effects of such "background variance heterogeneity" (BVH) on standard QTL mapping have received little attention until now, although the subject is closely related to work on the detection of variance-controlling genes. Here we use simulation to examine how BVH affects power and false positive rate for detecting QTL affecting the mean (mQTL), the variance (vQTL), or both (mvQTL). We compare linear regression for mQTL and Levene's test for vQTL, with tests more recently developed, including tests based on the double generalized linear model (DGLM), which can model BVH explicitly. We show that, when used in conjunction with a suitable permutation procedure, the DGLM-based tests accurately control false positive rate and are more powerful than the other tests. We also find that some adverse effects of BVH can be mitigated by applying a rank inverse normal transform. We apply our novel approach, which we term "mean-variance QTL mapping", to publicly available data on a mouse backcross and, after accommodating BVH driven by sire, detect a new mQTL for bodyweight.

Selected RANKL/RANK/OPG system genetic variants in diabetic foot patients

Purpose

Diabetic foot is a complication of long-lasting diabetes mellitus affecting up to 15% of patients, both in type 1 and type 2 diabetes. Osteoprotegerin is involved in osteogenesis and calcification. The aim of the study was to assess the role of selected osteoprotegerin gene variants in diabetes patients with diabetic foot.

Methods

The study involved 300 patients with diabetes and diabetic foot and 968 healthy controls. The study group was formed by 243 patients with diabetic foot of neuropathic origin, 102 with diabetic foot of neuroischemic origin and 77 with Charcot neuroarthropathy.

Results

Compared to controls, rs1872426 and rs1485286 showed correlation with diabetic foot in diabetes subjects. Significant associations between rs2073618, rs1872426, rs7464496 and rs1485286 in men were reported. The aforementioned correlations were also present in type 2 diabetes patient subgroup. Variant rs1485286 was associated to diabetic foot of neuropathic origin. Sex-specificity for females was present for rs6993813 in patients with diabetic foot of neuropathic origin and type 1 diabetes. Variants rs1872426, rs2073617 and rs1485286 were correlated with CN. We found that age, body weight, body mass index, waist circumference, hip circumference and waist-hip ratio were among the basic risk factors of diabetic foot.

Conclusions

The following variants TNFRSF11B (rs2073618, rs2073617, rs1872426, rs1032128, rs7464496, rs11573829 and rs1485286), COLEC10 (rs6993813, rs3134069) and TNFSF11 (rs9533156) present differences in allele frequencies in diabetic foot patients and show correlation with gender, diabetes type and diabetic foot etiology.

The role of APETALA1 in petal number robustness

Invariant floral forms are important for reproductive success and robust to natural perturbations. Petal number, for example, is invariant in Arabidopsis thaliana flowers. However, petal number varies in the closely related species Cardamine hirsuta, and the genetic basis for this difference between species is unknown. Here we show that divergence in the pleiotropic floral regulator APETALA1 (AP1) can account for the species-specific difference in petal number robustness. This large effect of AP1 is explained by epistatic interactions: A. thaliana AP1 confers robustness by masking the phenotypic expression of quantitative trait loci controlling petal number in C. hirsuta. We show that C. hirsuta AP1 fails to complement this function of A. thaliana AP1, conferring variable petal number, and that upstream regulatory regions of AP1 contribute to this divergence. Moreover, variable petal number is maintained in C. hirsuta despite sufficient standing genetic variation in natural accessions to produce plants with four-petalled flowers.

Colon-specific eQTL analysis to inform on functional SNPs

Background

Genome-wide association studies on colorectal cancer have identified more than 60 susceptibility loci, but for most of them there is no clear knowledge of functionality or the underlying gene responsible for the risk modification. Expression quantitative trail loci (eQTL) may provide functional information for such single nucleotide polymorphisms (SNPs).

Methods

We have performed detailed eQTL analysis specific for colon tissue on a series of 97 colon tumours, their paired adjacent normal mucosa and 47 colon mucosa samples donated by healthy individuals. R package MatrixEQTL was used to search for genome-wide cis-eQTL and trans-eQTL fitting linear models adjusted for age, gender and tissue type to rank transformed expression data.

Results

The cis-eQTL analyses has revealed 29,073 SNP-gene associations with permutation-adjusted P-values < 0.01. These correspond to 363 unique genes. The trans-eQTL analysis identified 10,665 significant SNP-gene associations, most of them in the same chromosome, further than 1 Mb of the gene. We provide a web tool to search for specific SNPs or genes. The tool calculates Pearson or Spearman correlation, and allows to select tissue type for analysis. Data and plots can be exported.

Conclusions

This resource should be useful to prioritise SNPs for further functional studies and to identify relevant genes behind identified loci.

A monocyte gene expression signature in the early clinical course of Parkinson's disease

Microglia are the main immune cells of the brain and express a large genetic pattern of genes linked to Parkinson’s disease risk alleles. Monocytes like microglia are myeloid-lineage cells, raising the questions of the extent to which they share gene expression with microglia and whether they are already altered early in the clinical course of the disease. To decipher a monocytic gene expression signature in Parkinson’s disease, we performed RNA-seq and applied the two-sample Kolmogorov-Smirnov test to identify differentially expressed genes between controls and patients with Parkinson's disease and changes in gene expression variability and dysregulation. The gene expression profiles of normal human monocytes and microglia showed a plethora of differentially expressed genes. Additionally, we identified a distinct gene expression pattern of monocytes isolated from Parkinson’s disease patients at an early disease stage compared to controls using the Kolmogorov-Smirnov test. Differentially expressed genes included genes involved in immune activation such as HLA-DQB1, MYD88, REL, and TNF-α. Our data suggest that future studies of distinct leukocyte subsets are warranted to identify possible surrogate biomarkers and may lead to the identification of novel interventions early in the disease course.

A sibling method for identifying vQTLs

The propensity of a trait to vary within a population may have evolutionary, ecological, or clinical significance. In the present study we deploy sibling models to offer a novel and unbiased way to ascertain loci associated with the extent to which phenotypes vary (variance-controlling quantitative trait loci, or vQTLs). Previous methods for vQTL-mapping either exclude genetically related individuals or treat genetic relatedness among individuals as a complicating factor addressed by adjusting estimates for non-independence in phenotypes. The present method uses genetic relatedness as a tool to obtain unbiased estimates of variance effects rather than as a nuisance. The family-based approach, which utilizes random variation between siblings in minor allele counts at a locus, also allows controls for parental genotype, mean effects, and non-linear (dominance) effects that may spuriously appear to generate variation. Simulations show that the approach performs equally well as two existing methods (squared Z-score and DGLM) in controlling type I error rates when there is no unobserved confounding, and performs significantly better than these methods in the presence of small degrees of confounding. Using height and BMI as empirical applications, we investigate SNPs that alter within-family variation in height and BMI, as well as pathways that appear to be enriched. One significant SNP for BMI variability, in the MAST4 gene, replicated. Pathway analysis revealed one gene set, encoding members of several signaling pathways related to gap junction function, which appears significantly enriched for associations with within-family height variation in both datasets (while not enriched in analysis of mean levels). We recommend approximating laboratory random assignment of genotype using family data and more careful attention to the possible conflation of mean and variance effects.

veqtl-mapper: variance association mapping for molecular phenotypes

Motivation

Genetic loci associated with the variance of phenotypic traits have been of recent interest as they can be signatures of genetic interactions, gene by environment interactions, parent of origin effects and canalization. We present a fast efficient tool to map loci affecting variance of gene expression and other molecular phenotypes in cis. Results: Applied to the publicly available Geuvadis gene expression dataset, we identify 816 loci associated with variance of gene expression using an additive model, and 32 showing differences in variance between homozygous and heterozygous alleles, signatures of parent of origin effects.

Availability and implementation

Documentation and links to source code and binaries for linux can be found at https://funpopgen.github.io/veqm/ .

Contact

andrew.brown@unige.ch.

Supplementary information

Supplementary data are available at Bioinformatics online.

Simulating variance heterogeneity in quantitative genome wide association studies

BACKGROUND

Analyzing Variance heterogeneity in genome wide association studies (vGWAS) is an emerging approach for detecting genetic loci involved in gene-gene and gene-environment interactions. vGWAS analysis detects variability in phenotype values across genotypes, as opposed to typical GWAS analysis, which detects variations in the mean phenotype value.

RESULTS

A handful of vGWAS analysis methods have been recently introduced in the literature. However, very little work has been done for evaluating these methods. To enable the development of better vGWAS analysis methods, this work presents the first quantitative vGWAS simulation procedure. To that end, we describe the mathematical framework and algorithm for generating quantitative vGWAS phenotype data from genotype profiles. Our simulation model accounts for both haploid and diploid genotypes under different modes of dominance. Our model is also able to simulate any number of genetic loci causing mean and variance heterogeneity.

CONCLUSIONS

We demonstrate the utility of our simulation procedure through generating a variety of genetic loci types to evaluate common GWAS and vGWAS analysis methods. The results of this evaluation highlight the challenges current tools face in detecting GWAS and vGWAS loci.

Epistasis and destabilizing mutations shape gene expression variability in humans via distinct modes of action

Increasing evidence shows that phenotypic variance is genetically determined, but the underlying mechanisms of genetic control over the variance remain obscure. Here, we conducted variance-association mapping analyses to identify expression variability QTLs (evQTLs), i.e. genomic loci associated with gene expression variance, in humans. We discovered that common genetic variants may contribute to increasing gene expression variance via two distinct modes of action—epistasis and destabilization. Specifically, epistasis explains a quarter of the identified evQTLs, of which the formation is attributed to the presence of ‘third-party’ eQTLs that influence the gene expression mean in a fraction, rather than the entire set, of sampled individuals. On the other hand, the destabilization model explains the other three-quarters of evQTLs, caused by mutations that disrupt the stability of the transcription process of genes. To show the destabilizing effect, we measured discordant gene expression between monozygotic twins, and estimated the stability of gene expression in single samples using repetitive qRT-PCR assays. The mutations that cause destabilizing evQTLs were found to be associated with more pronounced expression discordance between twin pairs and less stable gene expression in single samples. Together, our results suggest that common genetic variants work either interactively or independently to shape the variability of gene expression in humans. Our findings contribute to the understanding of the mechanisms of genetic control over phenotypic variance and may have implications for the development of variance-centred analytic methods for quantitative trait mapping.

Epigenetic and Transcriptional Variability Shape Phenotypic Plasticity

Epigenetic and transcriptional variability contribute to the vast diversity of cellular and organismal phenotypes and are key in human health and disease. In this review, we describe different types, sources, and determinants of epigenetic and transcriptional variability, enabling cells and organisms to adapt and evolve to a changing environment. We highlight the latest research and hypotheses on how chromatin structure and the epigenome influence gene expression variability. Further, we provide an overview of challenges in the analysis of biological variability. An improved understanding of the molecular mechanisms underlying epigenetic and transcriptional variability, at both the intra- and inter-individual level, provides great opportunity for disease prevention, better therapeutic approaches, and personalized medicine.

Risk alleles of genes with monoallelic expression are enriched in gain-of-function variants and depleted in loss-of-function variants for neurodevelopmental disorders

Over 3000 human genes can be expressed from a single allele in one cell, and from the other allele-or both-in neighboring cells. Little is known about the consequences of this epigenetic phenomenon, monoallelic expression (MAE). We hypothesized that MAE increases expression variability, with a potential impact on human disease. Here, we use a chromatin signature to infer MAE for genes in lymphoblastoid cell lines and human fetal brain tissue. We confirm that across clones MAE status correlates with expression level, and that in human tissue data sets, MAE genes show increased expression variability. We then compare mono- and biallelic genes at three distinct scales. In the human population, we observe that genes with polymorphisms influencing expression variance are more likely to be MAE (P<1.1 × 10-6). At the trans-species level, we find gene expression differences and directional selection between humans and chimpanzees more common among MAE genes (P<0.05). Extending to human disease, we show that MAE genes are under-represented in neurodevelopmental copy number variants (CNVs) (P<2.2 × 10-10), suggesting that pathogenic variants acting via expression level are less likely to involve MAE genes. Using neuropsychiatric single-nucleotide polymorphism (SNP) and single-nucleotide variant (SNV) data, we see that genes with pathogenic expression-altering or loss-of-function variants are less likely MAE (P<7.5 × 10-11) and genes with only missense or gain-of-function variants are more likely MAE (P<1.4 × 10-6). Together, our results suggest that MAE genes tolerate a greater range of expression level than biallelic expression (BAE) genes, and this information may be useful in prediction of pathogenicity.

Systematic Analysis of Cell-to-Cell Expression Variation of T Lymphocytes in a Human Cohort Identifies Aging and Genetic Associations

Cell-to-cell expression variation (CEV) is a prevalent feature of even well-defined cell populations, but its functions, particularly at the organismal level, are not well understood. Using single-cell data obtained via high-dimensional flow cytometry of T cells as a model, we introduce an analysis framework for quantifying CEV in primary cell populations and studying its functional associations in human cohorts. Analyses of 840 CEV phenotypes spanning multiple baseline measurements of 14 proteins in 28 T cell subpopulations suggest that the quantitative extent of CEV can exhibit substantial subject-to-subject differences and yet remain stable within healthy individuals over months. We linked CEV to age and disease-associated genetic polymorphisms, thus implicating CEV as a biomarker of aging and disease susceptibility and suggesting that it might play an important role in health and disease. Our dataset, interactive figures, and software for computing CEV with flow cytometry data provide a resource for exploring CEV functions.

Genetics of Genome-Wide Recombination Rate Evolution in Mice from an Isolated Island

Recombination rate is a heritable quantitative trait that evolves despite the fundamentally conserved role that recombination plays in meiosis. Differences in recombination rate can alter the landscape of the genome and the genetic diversity of populations. Yet our understanding of the genetic basis of recombination rate evolution in nature remains limited. We used wild house mice (Mus musculus domesticus) from Gough Island (GI), which diverged recently from their mainland counterparts, to characterize the genetics of recombination rate evolution. We quantified genome-wide autosomal recombination rates by immunofluorescence cytology in spermatocytes from 240 F2 males generated from intercrosses between GI-derived mice and the wild-derived inbred strain WSB/EiJ. We identified four quantitative trait loci (QTL) responsible for inter-F2 variation in this trait, the strongest of which had effects that opposed the direction of the parental trait differences. Candidate genes and mutations for these QTL were identified by overlapping the detected intervals with whole-genome sequencing data and publicly available transcriptomic profiles from spermatocytes. Combined with existing studies, our findings suggest that genome-wide recombination rate divergence is not directional and its evolution within and between subspecies proceeds from distinct genetic loci.

Gene expression variability and the analysis of large-scale RNA-seq studies with the MDSeq

Rapidly decreasing cost of next-generation sequencing has led to the recent availability of large-scale RNA-seq data, that empowers the analysis of gene expression variability, in addition to gene expression means. In this paper, we present the MDSeq, based on the coefficient of dispersion, to provide robust and computationally efficient analysis of both gene expression means and variability on RNA-seq counts. The MDSeq utilizes a novel reparametrization of the negative binomial to provide flexible generalized linear models (GLMs) on both the mean and dispersion. We address challenges of analyzing large-scale RNA-seq data via several new developments to provide a comprehensive toolset that models technical excess zeros, identifies outliers efficiently, and evaluates differential expressions at biologically interesting levels. We evaluated performances of the MDSeq using simulated data when the ground truths are known. Results suggest that the MDSeq often outperforms current methods for the analysis of gene expression mean and variability. Moreover, the MDSeq is applied in two real RNA-seq studies, in which we identified functionally relevant genes and gene pathways. Specifically, the analysis of gene expression variability with the MDSeq on the GTEx human brain tissue data has identified pathways associated with common neurodegenerative disorders when gene expression means were conserved.

Genotypic variability based association identifies novel non-additive loci DHCR7 and IRF4 in sero-negative rheumatoid arthritis

Sero-negative rheumatoid arthritis (RA) is a highly heterogeneous disorder with only a few additive loci identified to date. We report a genotypic variability-based genome-wide association study (vGWAS) of six cohorts of sero-negative RA recruited in Europe and the US that were genotyped with the Immunochip. A two-stage approach was used: (1) a mixed model to partition dichotomous phenotypes into an additive component and non-additive residuals on the liability scale and (2) the Levene’s test to assess equality of the residual variances across genotype groups. The vGWAS identified rs2852853 (P = 1.3e-08, DHCR7) and rs62389423 (P = 1.8e-05, near IRF4) in addition to two previously identified loci (HLA-DQB1 and ANKRD55), which were all statistically validated using cross validation. DHCR7 encodes an enzyme important in cutaneous synthesis of vitamin D and DHCR7 mutations are believed to be important for early humans to adapt to Northern Europe where residents have reduced ultraviolet-B exposure and tend to have light skin color. IRF4 is a key locus responsible for skin color, with a vitamin D receptor-binding interval. These vGWAS results together suggest that vitamin D deficiency is potentially causal of sero-negative RA and provide new insights into the pathogenesis of the disorder.