Geographical genomics of human leukocyte gene expression variation in southern Morocco

imply: improving cell-type deconvolution accuracy using personalized reference profiles

Using computational tools, bulk transcriptomics can be deconvoluted to estimate the abundance of constituent cell types. However, existing deconvolution methods are conditioned on the assumption that the whole study population is served by a single reference panel, ignoring person-to-person heterogeneity. Here, we present imply, a novel algorithm to deconvolute cell type proportions using personalized reference panels. Simulation studies demonstrate reduced bias compared with existing methods. Real data analyses on longitudinal consortia show disparities in cell type proportions are associated with several disease phenotypes in Type 1 diabetes and Parkinson's disease. imply is available through the R/Bioconductor package ISLET at https://bioconductor.org/packages/ISLET/ .

Sex-biased gene regulation varies across human populations as a result of adaptive evolution

OBJECTIVES

Studies of human sexual dimorphism and gender disparities in health focus on ostensibly universal molecular sex differences, such as sex chromosomes and circulating hormone levels, while ignoring the extraordinary diversity in biology, behavior, and culture acquired by different human populations over their unique evolutionary histories.

MATERIALS AND METHODS

Using RNA-Seq data and whole genome sequences from 1000G and HGDP, we investigate variation in sex-biased gene expression across 11 human populations and test whether population-level variation in sex-biased expression may have resulted from adaptive evolution in regions containing sex-specific regulatory variants.

RESULTS

We find that sex-biased gene expression in humans is highly variable, mostly population-specific, and demonstrates between population reversals. Expression quantitative trait locus mapping reveals sex-specific regulatory regions with evidence of recent positive natural selection, suggesting that variation in sex-biased expression may have evolved as an adaptive response to ancestral environments experienced by human populations.

DISCUSSION

These results indicate that sex-biased gene expression is more flexible than previously thought and is not generally shared among human populations. Instead, molecular phenotypes associated with sex depend on complex interactions between population-specific molecular evolution and physiological responses to contemporary socioecologies.

A multi-cohort genome-wide association study in African ancestry individuals reveals risk loci for primary open-angle glaucoma

Primary open-angle glaucoma (POAG), the leading cause of irreversible blindness worldwide, disproportionately affects individuals of African ancestry. We conducted a genome-wide association study (GWAS) for POAG in 11,275 individuals of African ancestry (6,003 cases; 5,272 controls). We detected 46 risk loci associated with POAG at genome-wide significance. Replication and post-GWAS analyses, including functionally informed fine-mapping, multiple trait co-localization, and in silico validation, implicated two previously undescribed variants (rs1666698 mapping to DBF4P2; rs34957764 mapping to ROCK1P1) and one previously associated variant (rs11824032 mapping to ARHGEF12) as likely causal. For individuals of African ancestry, a polygenic risk score (PRS) for POAG from our mega-analysis (African ancestry individuals) outperformed a PRS from summary statistics of a much larger GWAS derived from European ancestry individuals. This study quantifies the genetic architecture similarities and differences between African and non-African ancestry populations for this blinding disease.

The Impact of Recent Demography on Functional Genetic Variation in North African Human Groups

The strategic location of North Africa has made the region the core of a wide range of human demographic events, including migrations, bottlenecks, and admixture processes. This has led to a complex and heterogeneous genetic and cultural landscape, which remains poorly studied compared to other world regions. Whole-exome sequencing is particularly relevant to determine the effects of these demographic events on current-day North Africans' genomes, since it allows to focus on those parts of the genome that are more likely to have direct biomedical consequences. Whole-exome sequencing can also be used to assess the effect of recent demography in functional genetic variation and the efficacy of natural selection, a long-lasting debate. In the present work, we use newly generated whole-exome sequencing and genome-wide array genotypes to investigate the effect of demography in functional variation in 7 North African populations, considering both cultural and demographic differences and with a special focus on Amazigh (plur. Imazighen) groups. We detect genetic differences among populations related to their degree of isolation and the presence of bottlenecks in their recent history. We find differences in the functional part of the genome that suggest a relaxation of purifying selection in the more isolated groups, allowing for an increase of putatively damaging variation. Our results also show a shift in mutational load coinciding with major demographic events in the region and reveal differences within and between cultural and geographic groups.

Increasing diversity of functional genetics studies to advance biological discovery and human health

In this perspective we discuss the current lack of genetic and environmental diversity in functional genomics datasets. There is a well-described Eurocentric bias in genetic and functional genomic research that has a clear impact on the benefit this research can bring to underrepresented populations. Current research focused on genetic variant-to-function experiments aims to identify molecular QTLs, but the lack of data from genetically diverse individuals has limited analyses to mostly populations of European ancestry. Although some efforts have been established to increase diversity in functional genomic studies, much remains to be done to consistently generate data for underrepresented populations from now on. We discuss the major barriers for this continuity and suggest actionable insights, aiming to empower research and researchers from underserved populations.

Ancestry-specific regulatory and disease architectures are likely due to cell-type-specific gene-by-environment interactions

Multi-ancestry genome-wide association studies (GWAS) have highlighted the existence of variants with ancestry-specific effect sizes. Understanding where and why these ancestry-specific effects occur is fundamental to understanding the genetic basis of human diseases and complex traits. Here, we characterized genes differentially expressed across ancestries (ancDE genes) at the cell-type level by leveraging single-cell RNA-seq data in peripheral blood mononuclear cells for 21 individuals with East Asian (EAS) ancestry and 23 individuals with European (EUR) ancestry (172K cells); then, we tested if variants surrounding those genes were enriched in disease variants with ancestry-specific effect sizes by leveraging ancestry-matched GWAS of 31 diseases and complex traits (average N = 90K and 267K in EAS and EUR, respectively). We observed that ancDE genes tend to be cell-type-specific, to be enriched in genes interacting with the environment, and in variants with ancestry-specific disease effect sizes, suggesting the impact of shared cell-type-specific gene-by-environment (GxE) interactions between regulatory and disease architectures. Finally, we illustrated how GxE interactions might have led to ancestry-specific MCL1 expression in B cells, and ancestry-specific allele effect sizes in lymphocyte count GWAS for variants surrounding MCL1. Our results imply that large single-cell and GWAS datasets in diverse populations are required to improve our understanding on the effect of genetic variants on human diseases.

imply: improving cell-type deconvolution accuracy using personalized reference profiles

Real-world clinical samples are often admixtures of signal mosaics from multiple pure cell types. Using computational tools, bulk transcriptomics can be deconvoluted to solve for the abundance of constituent cell types. However, existing deconvolution methods are conditioned on the assumption that the whole study population is served by a single reference panel, which ignores person-to-person heterogeneity. Here we present imply, a novel algorithm to deconvolute cell type proportions using personalized reference panels. imply can borrow information across repeatedly measured samples for each subject, and obtain precise cell type proportion estimations. Simulation studies demonstrate reduced bias in cell type abundance estimation compared with existing methods. Real data analyses on large longitudinal consortia show more realistic deconvolution results that align with biological facts. Our results suggest that disparities in cell type proportions are associated with several disease phenotypes in type 1 diabetes and Parkinson's disease. Our proposed tool imply is available through the R/Bioconductor package ISLET at https://bioconductor.org/packages/ISLET/.

Applying an evolutionary mismatch framework to understand disease susceptibility

Noncommunicable diseases (NCDs) are on the rise worldwide. Obesity, cardiovascular disease, and type 2 diabetes are among a long list of "lifestyle" diseases that were rare throughout human history but are now common. The evolutionary mismatch hypothesis posits that humans evolved in environments that radically differ from those we currently experience; consequently, traits that were once advantageous may now be "mismatched" and disease causing. At the genetic level, this hypothesis predicts that loci with a history of selection will exhibit "genotype by environment" (GxE) interactions, with different health effects in "ancestral" versus "modern" environments. To identify such loci, we advocate for combining genomic tools in partnership with subsistence-level groups experiencing rapid lifestyle change. In these populations, comparisons of individuals falling on opposite extremes of the "matched" to "mismatched" spectrum are uniquely possible. More broadly, the work we propose will inform our understanding of environmental and genetic risk factors for NCDs across diverse ancestries and cultures.

Integrative Post-Genome-Wide Association Study Analyses Relevant to Psychiatric Disorders: Imputing Transcriptome and Proteome Signals

Background

The genome-wide association study (GWAS) is a common tool to identify genetic variants associated with complex traits, including psychiatric disorders (PDs). However, post-GWAS analyses are needed to extend the statistical inference to biologically relevant entities, e.g., genes, proteins, and pathways. To achieve this goal, researchers developed methods that incorporate biologically relevant intermediate molecular phenotypes, such as gene expression and protein abundance, which are posited to mediate the variant-trait association. Transcriptome-wide association study (TWAS) and proteome-wide association study (PWAS) are commonly used methods to test the association between these molecular mediators and the trait.

Summary

In this review, we discuss the most recent developments in TWAS and PWAS. These methods integrate existing "omic" information with the GWAS summary statistics for trait(s) of interest. Specifically, they impute transcript/protein data and test the association between imputed gene expression/protein level with phenotype of interest by using (i) GWAS summary statistics and (ii) reference transcriptomic/proteomic/genomic datasets. TWAS and PWAS are suitable as analysis tools for (i) primary association scan and (ii) fine-mapping to identify potentially causal genes for PDs.

Key Messages

As post-GWAS analyses, TWAS and PWAS have the potential to highlight causal genes for PDs. These prioritized genes could indicate targets for the development of novel drug therapies. For researchers attempting such analyses, we recommend Mendelian randomization tools that use GWAS statistics for both trait and reference datasets, e.g., summary Mendelian randomization (SMR). We base our recommendation on (i) being able to use the same tool for both TWAS and PWAS, (ii) not requiring the pre-computed weights (and thus easier to update for larger reference datasets), and (iii) most larger transcriptome reference datasets are publicly available and easy to transform into a compatible format for SMR analysis.

The genomic analysis of current-day North African populations reveals the existence of trans-Saharan migrations with different origins and dates

The Sahara Desert has acted as a barrier to human gene-flow between the northern and central parts of Africa since its aridification. Nonetheless, some contacts between both sides of the desert have occurred throughout history, mainly driven by commercial activity. Part of this was the infamous trans-Saharan slave trade, which forcedly brought peoples from south of the Sahara to North Africa from Roman times until the nineteenth century. Although historical records exist, the genetic aspects of these trans-Saharan migrations have not been deeply studied. In the present study, we assess the genetic influence of trans-Saharan migrations in current-day North Africa and characterize its amount, geographical origin, and dates. We confirm the heterogeneous and generally low-frequency presence of genomic segments of sub-Saharan origin in present-day North Africans acquired in recent historical times, and we show evidence of at least two admixture events: one dated around the thirteenth-fourteenth centuries CE between North Africans and a Western-sub-Saharan-like source similar to current-day Senegambian populations, and another one dated around the seventeenth century CE involving Tunisians and an Eastern-sub-Saharan-like source related to current-day south-Sudan and Kenyan populations. Time and location coincide with the peak of trans-Saharan slave-trade activity between Western African empires and North African powers, and are also concordant with the possibility of continuous recent south-to-north gene-flow. These findings confirm the trans-Saharan human genetic contacts, providing new and precise evidence about its possible dates and geographical origins, which are pivotal to understanding the genomic composition of an underrepresented region such as North Africa.

Recombination affects allele-specific expression of deleterious variants in human populations

How the genetic composition of a population changes through stochastic processes, such as genetic drift, in combination with deterministic processes, such as selection, is critical to understanding how phenotypes vary in space and time. Here, we show how evolutionary forces affecting selection, including recombination and effective population size, drive genomic patterns of allele-specific expression (ASE). Integrating tissue-specific genotypic and transcriptomic data from 1500 individuals from two different cohorts, we demonstrate that ASE is less often observed in regions of low recombination, and loci in high or normal recombination regions are more efficient at using ASE to underexpress harmful mutations. By tracking genetic ancestry, we discriminate between ASE variability due to past demographic effects, including subsequent bottlenecks, versus local environment. We observe that ASE is not randomly distributed along the genome and that population parameters influencing the efficacy of natural selection alter ASE levels genome wide.

Integrative analysis reveals mouse strain-dependent responses to acute ozone exposure associated with airway macrophage transcriptional activity

Acute ozone (O3) exposure is associated with multiple adverse cardiorespiratory outcomes, the severity of which varies across individuals in human populations and inbred mouse strains. However, molecular determinants of response, including susceptibility biomarkers that distinguish who will develop severe injury and inflammation, are not well characterized. We and others have demonstrated that airway macrophages (AMs) are an important resident immune cell type that are functionally and transcriptionally responsive to O3 inhalation. Here, we sought to explore influences of strain, exposure, and strain-by-O3 exposure interactions on AM gene expression and identify transcriptional correlates of O3-induced inflammation and injury across six mouse strains, including five Collaborative Cross (CC) strains. We exposed adult mice of both sexes to filtered air (FA) or 2 ppm O3 for 3 h and measured inflammatory and injury parameters 21 h later. Mice exposed to O3 developed airway neutrophilia and lung injury with strain-dependent severity. In AMs, we identified a common core O3 transcriptional response signature across all strains, as well as a set of genes exhibiting strain-by-O3 exposure interactions. In particular, a prominent gene expression contrast emerged between a low- (CC017/Unc) and high-responding (CC003/Unc) strain, as reflected by cellular inflammation and injury. Further inspection indicated that differences in their baseline gene expression and chromatin accessibility profiles likely contribute to their divergent post-O3 exposure transcriptional responses. Together, these results suggest that aspects of O3-induced respiratory responses are mediated through altered AM transcriptional signatures and further confirm the importance of gene-environment interactions in mediating differential responsiveness to environmental agents.

Human immune diversity: from evolution to modernity

The extreme diversity of the human immune system, forged and maintained throughout evolutionary history, provides a potent defense against opportunistic pathogens. At the same time, this immune variation is the substrate upon which a plethora of immune-associated diseases develop. Genetic analysis suggests that thousands of individually weak loci together drive up to half of the observed immune variation. Intense selection maintains this genetic diversity, even selecting for the introgressed Neanderthal or Denisovan alleles that have reintroduced variation lost during the out-of-Africa migration. Variations in age, sex, diet, environmental exposure, and microbiome each potentially explain the residual variation, with proof-of-concept studies demonstrating both plausible mechanisms and correlative associations. The confounding interaction of many of these variables currently makes it difficult to assign definitive contributions. Here, we review the current state of play in the field, identify the key unknowns in the causality of immune variation, and identify the multidisciplinary pathways toward an improved understanding.

Loneliness: An Immunometabolic Syndrome

Loneliness has been defined as an agonizing encounter, experienced when the need for human intimacy is not met adequately, or when a person’s social network does not match their preference, either in number or attributes. This definition helps us realize that the cause of loneliness is not merely being alone, but rather not being in the company we desire. With loneliness being introduced as a measurable, distinct psychological experience, it has been found to be associated with poor health behaviors, heightened stress response, and inadequate physiological repairing activity. With these three major pathways of pathogenesis, loneliness can do much harm; as it impacts both immune and metabolic regulation, altering the levels of inflammatory cytokines, growth factors, acute-phase reactants, chemokines, immunoglobulins, antibody response against viruses and vaccines, and immune cell activity; and affecting stress circuitry, glycemic control, lipid metabolism, body composition, metabolic syndrome, cardiovascular function, cognitive function and mental health, respectively. Taken together, there are too many immunologic and metabolic manifestations associated with the construct of loneliness, and with previous literature showcasing loneliness as a distinct psychological experience and a health determinant, we propose that loneliness, in and of itself, is not just a psychosocial phenomenon. It is also an all-encompassing complex of systemic alterations that occur with it, expanding it into a syndrome of events, linked through a shared network of immunometabolic pathology. This review aims to portray a detailed picture of loneliness as an “immunometabolic syndrome”, with its multifaceted pathology.

Whole-exome analysis in Tunisian Imazighen and Arabs shows the impact of demography in functional variation

Human populations are genetically affected by their demographic history, which shapes the distribution of their functional genomic variation. However, the genetic impact of recent demography is debated. This issue has been studied in different populations, but never in North Africans, despite their relevant cultural and demographic diversity. In this study we address the question by analyzing new whole-exome sequences from two culturally different Tunisian populations, an isolated Amazigh population and a close non-isolated Arab-speaking population, focusing on the distribution of functional variation. Both populations present clear differences in their variant frequency distribution, in general and for putatively damaging variation. This suggests a relevant effect in the Amazigh population of genetic isolation, drift, and inbreeding, pointing to relaxed purifying selection. We also discover the enrichment in Imazighen of variation associated to specific diseases or phenotypic traits, but the scarce genetic and biomedical data in the region limits further interpretation. Our results show the genomic impact of recent demography and reveal a clear genetic differentiation probably related to culture. These findings highlight the importance of considering cultural and demographic heterogeneity within North Africa when defining population groups, and the need for more data to improve knowledge on the region's health and disease landscape.

Functional dynamic genetic effects on gene regulation are specific to particular cell types and environmental conditions

Genetic effects on gene expression and splicing can be modulated by cellular and environmental factors; yet interactions between genotypes, cell type, and treatment have not been comprehensively studied together. We used an induced pluripotent stem cell system to study multiple cell types derived from the same individuals and exposed them to a large panel of treatments. Cellular responses involved different genes and pathways for gene expression and splicing and were highly variable across contexts. For thousands of genes, we identified variable allelic expression across contexts and characterized different types of gene-environment interactions, many of which are associated with complex traits. Promoter functional and evolutionary features distinguished genes with elevated allelic imbalance mean and variance. On average, half of the genes with dynamic regulatory interactions were missed by large eQTL mapping studies, indicating the importance of exploring multiple treatments to reveal previously unrecognized regulatory loci that may be important for disease.

Altered immunoemotional regulatory system in COVID-19: From the origins to opportunities

The emergence of the novel coronavirus (SARS-CoV-2) and the worldwide spread of the coronavirus disease (COVID-19) have led to social regulations that caused substantial changes in manners of daily life. The subsequent loneliness and concerns of the pandemic during social distancing, quarantine, and lockdown are psychosocial stressors that negatively affect the immune system. These effects occur through mechanisms controlled by the sympathetic nervous system (SNS) and the hypothalamic-pituitary-adrenocortical (HPA) axis that alter immune regulation, namely the conserved transcriptional response to adversity (CTRA), which promotes inflammation and diminishes antiviral responses, leading to inadequate protection against viral disease. Unhealthy eating habits, physical inactivity, sleep disturbances, and mental health consequences of COVID-19 add on to the pathological effects of loneliness, making immunity against this ferocious virus an even tougher fight. Therefore, social isolation, with its unintended consequences, has inherently paradoxical effects on immunity in relation to viral disease. Though this paradox can present a challenge, its acknowledgment can serve as an opportunity to address the associated issues and find ways to mitigate the adverse effects. In this review, we aim to explore, in detail, the pathological effects of the new social norms on immunity and present suggested methods to improve our physical, psychological, and healthcare abilities to fight viral infection in the context of the COVID-19 pandemic.

Perspectives on studying molecular adaptations of amphibians in the genomic era

Understanding the genetic mechanisms underlying particular adaptations/phenotypes of organisms is one of the core issues of evolutionary biology. The use of genomic data has greatly advanced our understandings on this issue, as well as other aspects of evolutionary biology, including molecular adaptation, speciation, and even conservation of endangered species. Despite the well-recognized advantages, usages of genomic data are still limited to non-mammal vertebrate groups, partly due to the difficulties in assembling large or highly heterozygous genomes. Although this is particularly the case for amphibians, nonetheless, several comparative and population genomic analyses have shed lights into the speciation and adaptation processes of amphibians in a complex landscape, giving a promising hope for a wider application of genomics in the previously believed challenging groups of organisms. At the same time, these pioneer studies also allow us to realize numerous challenges in studying the molecular adaptations and/or phenotypic evolutionary mechanisms of amphibians. In this review, we first summarize the recent progresses in the study of adaptive evolution of amphibians based on genomic data, and then we give perspectives regarding how to effectively identify key pathways underlying the evolution of complex traits in the genomic era, as well as directions for future research.

Differential expression of nuclear genes encoding mitochondrial proteins from urban and rural populations in Morocco

Urbanization in low-income countries represents an important inflection point in the epidemiology of disease, with rural populations experiencing high rates of chronic and recurrent infections and urban populations displaying a profile of noncommunicable diseases. To investigate if urbanization alters the expression of genes encoding mitochondrial proteins, we queried gene microarray data from rural and urban populations living in Morocco (GSE17065). The R Bioconductor packages edgeR and limma were used to identify genes with different expression. The experimental design was modeled upon location and sex. Nuclear genes encoding mitochondrial proteins were identified from the MitoCarta2.0 database. Of the 1158 genes listed in the MitoCarta2.0 database, 847 genes (73%) were available for analysis in the Moroccan dataset. The urban-rural comparison with the greatest environmental differences showed that 76.5% of the MitoCarta2.0 genes were differentially expressed, with 97% of the genes having an increased expression in the urban area. Enrichment analysis revealed 367 significantly enriched pathways (adjusted p value < 0.05), with oxidative phosphorylation, insulin secretion and glucose regulations (adj.p values = 6.93E-16) being the top three. Four significantly perturbed KEGG disease pathways were associated with urbanization-three degenerative neurological diseases (Huntington's, Alzheimer's, and Parkinson's diseases) and herpes simplex infection (false discover rate corrected p value (PGFdr) < 0.2). Mitochondrial RNA metabolic processing and translational elongation were the biological processes that had the greatest enrichment (enrichment ratios 14.0 and 14.8, respectively, FDR < 0.5). Our study links urbanization in Morocco with changes in the expression of the nuclear genes encoding mitochondrial proteins.

The Potential Impact of Social Genomics on Wound Healing

Significance: Human skin wounds carry an immense epidemiologic and financial burden, and their impact will continue to grow with an aging population and rising incidence of comorbid conditions known to affect wound healing. To comprehensively address this growing clinical issue, physicians should also be aware of how conditions of the human social environment may affect wound healing. Here we provide a review of the emerging field of social genomics and its potential impact on the wound healing. Recent Advances: Multiple studies using human and animal models have correlated social influences and their contributing effects to acute and chronic stress with delays in wound healing. Furthermore, observations between nongenetic factors such as nutrition, socioeconomic, and educational status have also shown to have a direct or indirect impact on clinical outcomes of wound healing. Critical Issues: Nutrition, financial burden, socioeconomic and education status, and acute and chronic stress are variables that have either direct (epigenetic) or indirect impact on wound healing and patients' quality of life. Wound care is costly and remains a challenge placing economic burden on patients. Furthermore, poor clinical outcomes and complications including loss of mobility and disability may lead to job loss, further contributing to socioeconomic related stress. Thus, the economic burden and inadequate wound healing are intertwined, making each other worse. Future Directions: Although some evidence regarding the specific changes in genetic pathways imparted by conditions of the social environment exists, further studies are warranted to identify potential mechanisms, interventions, and prevention approaches.

Sex Differences in the Blood Transcriptome Identify Robust Changes in Immune Cell Proportions with Aging and Influenza Infection

Sex differences in autoimmunity and infection suggest that a better understanding of molecular sex differences will improve the diagnosis and treatment of immune-related disease. We identified 144 differentially expressed genes, referred to as immune sex expression signature (iSEXS), between human males and females using an integrated multi-cohort analysis of blood transcriptome profiles from six discovery cohorts from five continents with 458 healthy individuals. We validated iSEXS in 11 additional cohorts of 524 peripheral blood samples. When we separated iSEXS into genes located on sex chromosomes (XY-iSEXS) or autosomes (autosomal-iSEXS), both modules distinguished males and females. iSEXS reflects sex differences in immune cell proportions, with female-associated genes showing higher expression by CD4+ T cells and male-associated genes showing higher expression by myeloid cells. Autosomal-iSEXS detected an increase in monocytes with age in females, reflected sex-differential immune cell dynamics during influenza infection, and predicted antibody response in males, but not females.

Sociogenomic Approach to Wound Care: A New Patient-Centered Paradigm

Psychoneuroendocrinology studies provided first insight into social determinants of wound healing. Social stressors impede wound healing. In 2005, we first reported that the transcriptome of wound-site neutrophil is highly responsive to psychological stress in young men. Bioinformatics processing of transcriptome-wide data from neutrophils provided first insight into social transduction pathways relevant to wound healing. In 2010, Idaghdour et al. presented striking evidence demonstrating that genetic factors are responsible for only 5% of the variation in genomic expression. In contrast, the living environment of the individual, urban or rural, was responsible for as much as 50% of such variation. Genetic and environmental factors acted in a largely additive manner. This observation may be credited as the foundation stone of human social genomics. The environment of a patient, including social factors, influences gene expression relevant to wound healing. The nonhealing wound itself and its worsening outcome, including pain, are likely to cause stress. Conversely, positive social interactions may circumvent barriers to wound healing. Thus, interventions directed at the social environment of a wound care patient are likely to help manage wound chronicity. The genomic and related Big Data technology platforms have vastly improved during the past 5 years during which these technologies have also become widely accessible and affordable. Thus, this is the right time to revisit the choice of technologies for the study of social genomics of wound healing. Against the backdrop of our current understanding of the mechanisms of wound healing, such precision approach is likely to transform wound care and its outcomes making it patient-centered and, therefore, more effective.

Implication of Spiritual Network Support System in Epigenomic Modulation and Health Trajectory

With challenges in understanding the multifactorial etiologies of disease and individual treatment effect heterogeneities over the past four decades, much has been acquired on how physical, chemical and social environments affect human health, predisposing certain subpopulations to adverse health outcomes, especially the socio-environmentally disadvantaged (SED). Current translational data on gene and adverse environment interaction have revealed how adverse gene-environment interaction, termed aberrant epigenomic modulation, translates into impaired gene expression via messenger ribonucleic acid (mRNA) dysregulation, reflecting abnormal protein synthesis and hence dysfunctional cellular differentiation and maturation. The environmental influence on gene expression observed in most literature includes physical, chemical, physicochemical and recently social environment. However, data are limited on spiritual or religious environment network support systems, which reflect human psychosocial conditions and gene interaction. With this limited information, we aimed to examine the available data on spiritual activities characterized by prayers and meditation for a possible explanation of the nexus between the spiritual network support system (SNSS) as a component of psychosocial conditions, implicated in social signal transduction, and the gene expression correlate. With the intent to incorporate SNSS in human psychosocial conditions, we assessed the available data on bereavement, loss of spouse, loneliness, social isolation, low socio-economic status (SES), chronic stress, low social status, social adversity (SA) and early life stress (ELS), as surrogates for spiritual support network connectome. Adverse human psychosocial conditions have the tendency for impaired gene expression through an up-regulated conserved transcriptional response to adversity (CTRA) gene expression via social signal transduction, involving the sympathetic nervous system (SNS), beta-adrenergic receptors, the hypothalamus-pituitary-adrenal (HPA) axis and the glucocorticoid response. This review specifically explored CTRA gene expression and the nuclear receptor subfamily 3 group C member 1 (NR3C1) gene, a glucocorticoid receptor gene, in response to stress and the impaired negative feedback, given allostatic overload as a result of prolonged and sustained stress and social isolation as well as the implied social interaction associated with religiosity. While more remains to be investigated on psychosocial and immune cell response and gene expression, current data on human models do implicate appropriate gene expression via the CTRA and NR3C1 gene in the SNSS as observed in meditation, yoga and thai-chi, implicated in malignant neoplasm remission. However, prospective epigenomic studies in this context are required in the disease causal pathway, prognosis and survival, as well as cautious optimism in the application of these findings in clinical and public health settings, due to unmeasured and potential confoundings implicated in these correlations.

Comprehensive Multiple eQTL Detection and Its Application to GWAS Interpretation

Expression QTL (eQTL) detection has emerged as an important tool for unraveling the relationship between genetic risk factors and disease or clinical phenotypes. Most studies are predicated on the assumption that only a single causal variant explains the association signal in each interval. This greatly simplifies the statistical modeling, but is liable to biases in scenarios where multiple local causal-variants are responsible. Here, our primary goal was to address the prevalence of secondary cis-eQTL signals regulating peripheral blood gene expression locally, utilizing two large human cohort studies, each >2500 samples with accompanying whole genome genotypes. The CAGE (Consortium for the Architecture of Gene Expression) dataset is a compendium of Illumina microarray studies, and the Framingham Heart Study is a two-generation Affymetrix dataset. We also describe Bayesian colocalization analysis of the extent of sharing of cis-eQTL detected in both studies as well as with the BIOS RNAseq dataset. Stepwise conditional modeling demonstrates that multiple eQTL signals are present for ∼40% of over 3500 eGenes in both microarray datasets, and that the number of loci with additional signals reduces by approximately two-thirds with each conditioning step. Although <20% of the peak signals across platforms fine map to the same credible interval, the colocalization analysis finds that as many as 50-60% of the primary eQTL are actually shared. Subsequently, colocalization of eQTL signals with GWAS hits detected 1349 genes whose expression in peripheral blood is associated with 591 human phenotype traits or diseases, including enrichment for genes with regulatory functions. At least 10%, and possibly as many as 40%, of eQTL-trait colocalized signals are due to nonprimary cis-eQTL peaks, but just one-quarter of these colocalization signals replicated across the gene expression datasets. Our results are provided as a web-based resource for visualization of multi-site regulation of gene expression and its association with human complex traits and disease states.

Biology, biography, and the translational gap

Medicine-based evidence integrates a patient's biology and biography to improve individual medical care and to help eliminate the translational gap between research and the clinic.

Functional SNPs in the Human Autoimmunity-Associated Locus 17q12-21

Genome-wide association studies (GWASes) revealed several single-nucleotide polymorphisms (SNPs) in the human 17q12-21 locus associated with autoimmune diseases. However, follow-up studies are still needed to identify causative SNPs directly mediating autoimmune risk in the locus. We have chosen six SNPs in high linkage disequilibrium with the GWAS hits that showed the strongest evidence of causality according to association pattern and epigenetic data and assessed their functionality in a local genomic context using luciferase reporter system. We found that rs12946510, rs4795397, rs12709365, and rs8067378 influenced the reporter expression level in leukocytic cell lines. The strongest effect visible in three distinct cell types was observed for rs12946510 that is predicted to alter MEF2A/C and FOXO1 binding sites.

Discovering in vivo cytokine-eQTL interactions from a lupus clinical trial

BACKGROUND

Cytokines are critical to human disease and are attractive therapeutic targets given their widespread influence on gene regulation and transcription. Defining the downstream regulatory mechanisms influenced by cytokines is central to defining drug and disease mechanisms. One promising strategy is to use interactions between expression quantitative trait loci (eQTLs) and cytokine levels to define target genes and mechanisms.

RESULTS

In a clinical trial for anti-IL-6 in patients with systemic lupus erythematosus, we measure interferon (IFN) status, anti-IL-6 drug exposure, and whole blood genome-wide gene expression at three time points. We show that repeat transcriptomic measurements increases the number of cis eQTLs identified compared to using a single time point. We observe a statistically significant enrichment of in vivo eQTL interactions with IFN status and anti-IL-6 drug exposure and find many novel interactions that have not been previously described. Finally, we find transcription factor binding motifs interrupted by eQTL interaction SNPs, which point to key regulatory mediators of these environmental stimuli and therefore potential therapeutic targets for autoimmune diseases. In particular, genes with IFN interactions are enriched for ISRE binding site motifs, while those with anti-IL-6 interactions are enriched for IRF4 motifs.

CONCLUSIONS

This study highlights the potential to exploit clinical trial data to discover in vivo eQTL interactions with therapeutically relevant environmental variables.

Urbanization in Sub-Saharan Africa: Declining Rates of Chronic and Recurrent Infection and Their Possible Role in the Origins of Non-communicable Diseases

BACKGROUND

Non-communicable diseases (NCDs), such as atherosclerosis and cancers, are a leading cause of death worldwide. An important, yet poorly explained epidemiological feature of NCDs is their low incidence in under developed areas of low-income countries and rising rates in urban areas.

METHODS

With the goal of better understanding how urbanization increases the incidence of NCDs, we provide an overview of the urbanization process in sub-Saharan Africa, discuss gene expression differences between rural and urban populations, and review the current NCD determinant model. We conclude by identifying research priorities.

RESULTS

Declining rates of chronic and recurrent infection are the hallmark of urbanization in sub-Saharan Africa. Gene profiling studies show urbanization results in complex molecular changes, with almost one-third of the peripheral blood leukocyte transcriptome altered. The current NCD determinant model could be improved by including a possible effect from declining rates of infection and expanding the spectrum of diseases that increase with urbanization.

CONCLUSIONS

Urbanization in sub-Saharan Africa provides a unique opportunity to investigate the mechanism by which the environment influences disease epidemiology. Research priorities include: (1) studies to define the relationship between infection and risk factors for NCDs, (2) explaining the observed differences in the inflammatory response between rural and urban populations, and (3) identification of animal models that simulate the biological changes that occurs with urbanization. A better understanding of the biological changes that occur with urbanization could lead to new prevention and treatment strategies for some of the most common surgical diseases in high-income countries.

The ecology of immune state in a wild mammal, Mus musculus domesticus

The immune state of wild animals is largely unknown. Knowing this and what affects it is important in understanding how infection and disease affects wild animals. The immune state of wild animals is also important in understanding the biology of their pathogens, which is directly relevant to explaining pathogen spillover among species, including to humans. The paucity of knowledge about wild animals' immune state is in stark contrast to our exquisitely detailed understanding of the immunobiology of laboratory animals. Making an immune response is costly, and many factors (such as age, sex, infection status, and body condition) have individually been shown to constrain or promote immune responses. But, whether or not these factors affect immune responses and immune state in wild animals, their relative importance, and how they interact (or do not) are unknown. Here, we have investigated the immune ecology of wild house mice-the same species as the laboratory mouse-as an example of a wild mammal, characterising their adaptive humoral, adaptive cellular, and innate immune state. Firstly, we show how immune variation is structured among mouse populations, finding that there can be extensive immune discordance among neighbouring populations. Secondly, we identify the principal factors that underlie the immunological differences among mice, showing that body condition promotes and age constrains individuals' immune state, while factors such as microparasite infection and season are comparatively unimportant. By applying a multifactorial analysis to an immune system-wide analysis, our results bring a new and unified understanding of the immunobiology of a wild mammal.

Out of Africa: Insights from a prospective pediatric surgery database

In this year's Robert E. Gross lecture, I describe how experiences early in my career at a government referral hospital in Banjul, The Gambia, influenced my research. Collecting prospective data on all children presenting to the hospital with surgical problems allowed me to gain an understanding of the epidemiology of childhood surgical conditions in sub-Saharan Africa and an appreciation for the inherent challenges of delivering surgical care in settings of limited resources. Based on findings from this database, my research over the past 20years has focused on developing strategies for improving surgical care in low-income countries and better understanding the geographical variations that occur in some of the most common surgical conditions in high-income countries (e.g., appendicitis). Although this research continues to be a work-in-progress, it has the potential to improve the surgical care of children in both high- and low-income countries. Much of this research would not have been possible had I not ventured off the usual path for an academic surgeon.

Constraints on eQTL Fine Mapping in the Presence of Multisite Local Regulation of Gene Expression

Expression quantitative trait locus (eQTL) detection has emerged as an important tool for unraveling of the relationship between genetic risk factors and disease or clinical phenotypes. Most studies use single marker linear regression to discover primary signals, followed by sequential conditional modeling to detect secondary genetic variants affecting gene expression. However, this approach assumes that functional variants are sparsely distributed and that close linkage between them has little impact on estimation of their precise location and the magnitude of effects. We describe a series of simulation studies designed to evaluate the impact of linkage disequilibrium (LD) on the fine mapping of causal variants with typical eQTL effect sizes. In the presence of multisite regulation, even though between 80 and 90% of modeled eSNPs associate with normally distributed traits, up to 10% of all secondary signals could be statistical artifacts, and at least 5% but up to one-quarter of credible intervals of SNPs within r² > 0.8 of the peak may not even include a causal site. The Bayesian methods eCAVIAR and DAP (Deterministic Approximation of Posteriors) provide only modest improvement in resolution. Given the strong empirical evidence that gene expression is commonly regulated by more than one variant, we conclude that the fine mapping of causal variants needs to be adjusted for multisite influences, as conditional estimates can be highly biased by interference among linked sites, but ultimately experimental verification of individual effects is needed. Presumably similar conclusions apply not just to eQTL mapping, but to multisite influences on fine mapping of most types of quantitative trait.

Comprehensive assessment showed no associations of variants at the SLC10A1 locus with susceptibility to persistent HBV infection among Southern Chinese

The sodium taurocholate cotransporting polypeptide (NTCP) encoded by SLC10A1 was recently demonstrated to be a functional receptor for hepatitis B virus (HBV). The role of SLC10A1 polymorphisms, particularly the Ser267Phe variant (rs2296651) in exon 4, has been frequently investigated in regard to risk of persistent HBV infection. However, these investigations have generated conflicting results. To examine whether common genetic variation at the SLC10A1 locus is associated with risk of persistent HBV infection, haplotype-tagging and imputed single nucleotide polymorphisms (SNPs) were assessed in two case-control sample sets, totally including 2,550 cases (persistently HBV infected subjects, PIs) and 2,124 controls (spontaneously recovered subjects, SRs) of Southern Chinese ancestry. To test whether rare or subpolymorphic SLC10A1 variants are associated with disease risk, the gene's exons in 244 cases were sequenced. Overall, we found neither SNPs nor haplotypes of SLC10A1 showed significant association in the two sample sets. Furthermore, no significant associations of rare variants or copy number variation covering SLC10A1 were observed. Finally, expression quantitative trait locus analyses revealed that SNPs potentially affecting SLC10A1 expression also showed no significant associations. We conclude that genetic variation at the SLC10A1 locus is not likely a major risk factor of persistent HBV infection among Southern Chinese.

The Genetic Architecture of Gene Expression in Peripheral Blood

We analyzed the mRNA levels for 36,778 transcript expression traits (probes) from 2,765 individuals to comprehensively investigate the genetic architecture and degree of missing heritability for gene expression in peripheral blood. We identified 11,204 cis and 3,791 trans independent expression quantitative trait loci (eQTL) by using linear mixed models to perform genome-wide association analyses. Furthermore, using information on both closely and distantly related individuals, heritability was estimated for all expression traits. Of the set of expressed probes (15,966), 10,580 (66%) had an estimated narrow-sense heritability (h²) greater than zero with a mean (median) value of 0.192 (0.142). Across these probes, on average the proportion of genetic variance explained by all eQTL (h_COJO²) was 31% (0.060/0.192), meaning that 69% is missing, with the sentinel SNP of the largest eQTL explaining 87% (0.052/0.060) of the variance attributed to all identified cis- and trans-eQTL. For the same set of probes, the genetic variance attributed to genome-wide common (MAF > 0.01) HapMap 3 SNPs (h_g²) accounted for on average 48% (0.093/0.192) of h². Taken together, the evidence suggests that approximately half the genetic variance for gene expression is not tagged by common SNPs, and of the variance that is tagged by common SNPs, a large proportion can be attributed to identifiable eQTL of large effect, typically in cis. Finally, we present evidence that, compared with a meta-analysis, using individual-level data results in an increase of approximately 50% in power to detect eQTL.

Global variation in gene expression and the value of diverse sampling

The genomics era has accelerated our understanding of how genetic and epigenetic factors influence both normal variable traits and disease risk in humans. However, the majority of "omics" studies have focused on individuals living in urban centers, primarily from Europe and Asia, neglecting much of the genetic and environmental variation that exists across worldwide populations. Comparative studies of gene regulation in ethnically diverse populations are informing our understanding of how evolutionary forces have shaped the genetic and molecular mechanisms underlying complex traits, and studying gene expression in different environmental contexts is enabling the dissection of disease-related pathways such as immune response. Such approaches are vital to the equitable application of genomics and medicine.

Microenvironmental Gene Expression Plasticity Among Individual Drosophila melanogaster

Differences in phenotype among genetically identical individuals exposed to the same environmental condition are often noted in genetic studies. Despite this commonplace observation, little is known about the causes of this variability, which has been termed microenvironmental plasticity. One possibility is that stochastic or technical sources of variance produce these differences. A second possibility is that this variation has a genetic component. We have explored gene expression robustness in the transcriptomes of 730 individual Drosophila melanogaster of 16 fixed genotypes, nine of which are infected with Wolbachia. Three replicates of flies were grown, controlling for food, day/night cycles, humidity, temperature, sex, mating status, social exposure, and circadian timing of RNA extraction. Despite the use of inbred genotypes, and carefully controlled experimental conditions, thousands of genes were differentially expressed, revealing a unique and dynamic transcriptional signature for each individual fly. We found that 23% of the transcriptome was differentially expressed among individuals, and that the variability in gene expression among individuals is influenced by genotype. This transcriptional variation originated from specific gene pathways, suggesting a plastic response to the microenvironment; but there was also evidence of gene expression differences due to stochastic fluctuations. These observations reveal previously unappreciated genetic sources of variability in gene expression among individuals, which has implications for complex trait genetics and precision medicine.

Identification of context-dependent expression quantitative trait loci in whole blood

Genetic risk factors often localize to noncoding regions of the genome with unknown effects on disease etiology. Expression quantitative trait loci (eQTLs) help to explain the regulatory mechanisms underlying these genetic associations. Knowledge of the context that determines the nature and strength of eQTLs may help identify cell types relevant to pathophysiology and the regulatory networks underlying disease. Here we generated peripheral blood RNA-seq data from 2,116 unrelated individuals and systematically identified context-dependent eQTLs using a hypothesis-free strategy that does not require previous knowledge of the identity of the modifiers. Of the 23,060 significant cis-regulated genes (false discovery rate (FDR) ≤ 0.05), 2,743 (12%) showed context-dependent eQTL effects. The majority of these effects were influenced by cell type composition. A set of 145 cis-eQTLs depended on type I interferon signaling. Others were modulated by specific transcription factors binding to the eQTL SNPs.

The transition from a rural to an urban environment in Africa alters G protein-coupled receptor signaling

Urbanization in Africa is associated with an increased incidence of non-communicable diseases, yet the cause and the mechanism remain poorly understood. Here, we propose a role for G protein-coupled receptor (GPCR) signaling in the biological changes that occur with urbanization and suggest a critical area of convergence in GPCR signaling might provide a molecular signature for exposure to environmental factors. As a first step in investing this hypothesis, we examined the expression of the G protein α, β and γ subunit, G protein related kinase, and β-arrestin genes in a rural and urban population living in Morocco (NCBI GSE8847). Three genes associated with the phosphatidylinositol signaling pathway (GNAQ, GNA11 and GNA15), and one gene controlling the cyclic adenosine monophosphate pathway (GNAI2) was altered by urbanization. Of note, the expression of ARRB1 gene, which encodes the β-arrestin 1 protein and dampens the cellular responses to extracellular signals, was greater in the rural compared to the urban population (P<0.00002). These preliminary findings support our hypothesis that urbanization fundamentally alters GPCR signaling, resulting in both a qualitative and quantitative change in the signaling process. Because GPCR signaling is involved in a broad spectrum of cellular functions, further research is needed confirm these preliminary findings and to investigate what role GPCRs might have in the biological changes that occur with urbanization.

Four Susceptibility Loci for Gallstone Disease Identified in a Meta-analysis of Genome-Wide Association Studies

BACKGROUND & AIMS

A genome-wide association study (GWAS) of 280 cases identified the hepatic cholesterol transporter ABCG8 as a locus associated with risk for gallstone disease, but findings have not been reported from any other GWAS of this phenotype. We performed a large-scale, meta-analysis of GWASs of individuals of European ancestry with available prior genotype data, to identify additional genetic risk factors for gallstone disease.

METHODS

We obtained per-allele odds ratio (OR) and standard error estimates using age- and sex-adjusted logistic regression models within each of the 10 discovery studies (8720 cases and 55,152 controls). We performed an inverse variance weighted, fixed-effects meta-analysis of study-specific estimates to identify single-nucleotide polymorphisms that were associated independently with gallstone disease. Associations were replicated in 6489 cases and 62,797 controls.

RESULTS

We observed independent associations for 2 single-nucleotide polymorphisms at the ABCG8 locus: rs11887534 (OR, 1.69; 95% confidence interval [CI], 1.54-1.86; P = 2.44 × 10(-60)) and rs4245791 (OR, 1.27; P = 1.90 × 10(-34)). We also identified and/or replicated associations for rs9843304 in TM4SF4 (OR, 1.12; 95% CI, 1.08-1.16; P = 6.09 × 10(-11)), rs2547231 in SULT2A1 (encodes a sulfoconjugation enzyme that acts on hydroxysteroids and cholesterol-derived sterol bile acids) (OR, 1.17; 95% CI, 1.12-1.21; P = 2.24 × 10(-10)), rs1260326 in glucokinase regulatory protein (OR, 1.12; 95% CI, 1.07-1.17; P = 2.55 × 10(-10)), and rs6471717 near CYP7A1 (encodes an enzyme that catalyzes conversion of cholesterol to primary bile acids) (OR, 1.11; 95% CI, 1.08-1.15; P = 8.84 × 10(-9)). Among individuals of African American and Hispanic American ancestry, rs11887534 and rs4245791 were associated positively with gallstone disease risk, whereas the association for the rs1260326 variant was inverse.

CONCLUSIONS

In this large-scale GWAS of gallstone disease, we identified 4 loci in genes that have putative functions in cholesterol metabolism and transport, and sulfonylation of bile acids or hydroxysteroids.

Personalized medicine in thrombosis: back to the future

Most physicians believe they practiced personalized medicine prior to the genomics era that followed the sequencing of the human genome. The focus of personalized medicine has been primarily genomic medicine, wherein it is hoped that the nucleotide dissimilarities among different individuals would provide clinicians with more precise understanding of physiology, more refined diagnoses, better disease risk assessment, earlier detection and monitoring, and tailored treatments to the individual patient. However, to date, the "genomic bench" has not worked itself to the clinical thrombosis bedside. In fact, traditional plasma-based hemostasis-thrombosis laboratory testing, by assessing functional pathways of coagulation, may better help manage venous thrombotic disease than a single DNA variant with a small effect size. There are some new and exciting discoveries in the genetics of platelet reactivity pertaining to atherothrombotic disease. Despite a plethora of genetic/genomic data on platelet reactivity, there are relatively little actionable pharmacogenetic data with antiplatelet agents. Nevertheless, it is crucial for genome-wide DNA/RNA sequencing to continue in research settings for causal gene discovery, pharmacogenetic purposes, and gene-gene and gene-environment interactions. The potential of genomics to advance medicine will require integration of personal data that are obtained in the patient history: environmental exposures, diet, social data, etc. Furthermore, without the ritual of obtaining this information, we will have depersonalized medicine, which lacks the precision needed for the research required to eventually incorporate genomics into routine, optimal, and value-added clinical care.

Widespread and Adaptive Alterations in Genome-Wide Gene Expression Associated with Ecological Divergence of Two Oryza Species

Ecological speciation is a common mechanism by which new species arise. Despite great efforts, the role of gene expression in ecological divergence and speciation is poorly understood. Here, we conducted a genome-wide gene expression investigation of two Oryza species that are evolutionarily young and distinct in ecology and morphology. Using digital gene expression technology and the paired-end RNA sequencing method, we obtained 21,415 expressed genes across three reproduction-related tissues. Of them, approximately 8% (1,717) differed significantly in expression levels between the two species and these differentially expressed genes are randomly distributed across the genome. Moreover, 62% (1,064) of the differentially expressed genes exhibited a signature of directional selection in at least one species. Importantly, the genes with differential expression between species evolved more rapidly at the 5' flanking sequences than the genes without differential expression relative to coding sequences, suggesting that cis-regulatory changes are likely adaptive and play an important role in the ecological divergence of the two species. Finally, we showed evidence of significant differentiation between species in phenotype traits and observed that genes with differential expression were overrepresented with functional terms involving phenotypic and ecological differentiation between the two species, including reproduction- and stress-related characteristics. Our findings demonstrate that ecological speciation is associated with widespread and adaptive alterations in genome-wide gene expression and provide new insights into the importance of regulatory evolution in ecological speciation in plants.

Phylogenetic ANOVA: The Expression Variance and Evolution Model for Quantitative Trait Evolution

A number of methods have been developed for modeling the evolution of a quantitative trait on a phylogeny. These methods have received renewed interest in the context of genome-wide studies of gene expression, in which the expression levels of many genes can be modeled as quantitative traits. We here develop a new method for joint analyses of quantitative traits within- and between species, the Expression Variance and Evolution (EVE) model. The model parameterizes the ratio of population to evolutionary expression variance, facilitating a wide variety of analyses, including a test for lineage-specific shifts in expression level, and a phylogenetic ANOVA that can detect genes with increased or decreased ratios of expression divergence to diversity, analogous to the famous Hudson Kreitman Aguadé (HKA) test used to detect selection at the DNA level. We use simulations to explore the properties of these tests under a variety of circumstances and show that the phylogenetic ANOVA is more accurate than the standard ANOVA (no accounting for phylogeny) sometimes used in transcriptomics. We then apply the EVE model to a mammalian phylogeny of 15 species typed for expression levels in liver tissue. We identify genes with high expression divergence between species as candidates for expression level adaptation, and genes with high expression diversity within species as candidates for expression level conservation and/or plasticity. Using the test for lineage-specific expression shifts, we identify several candidate genes for expression level adaptation on the catarrhine and human lineages, including genes putatively related to dietary changes in humans. We compare these results to those reported previously using a model which ignores expression variance within species, uncovering important differences in performance. We demonstrate the necessity for a phylogenetic model in comparative expression studies and show the utility of the EVE model to detect expression divergence, diversity, and branch-specific shifts.

Expression quantitative trait locus analysis for translational medicine

Expression quantitative trait locus analysis has emerged as an important component of efforts to understand how genetic polymorphisms influence disease risk and is poised to make contributions to translational medicine. Here we review how expression quantitative trait locus analysis is aiding the identification of which gene(s) within regions of association are causal for a disease or phenotypic trait; the narrowing down of the cell types or regulators involved in the etiology of disease; the characterization of drivers and modifiers of cancer; and our understanding of how different environments and cellular contexts can modify gene expression. We also introduce the concept of transcriptional risk scores as a means of refining estimates of individual liability to disease based on targeted profiling of the transcripts that are regulated by polymorphisms jointly associated with disease and gene expression.

Seasonal effects on gene expression

Many health conditions, ranging from psychiatric disorders to cardiovascular disease, display notable seasonal variation in severity and onset. In order to understand the molecular processes underlying this phenomenon, we have examined seasonal variation in the transcriptome of 606 healthy individuals. We show that 74 transcripts associated with a 12-month seasonal cycle were enriched for processes involved in DNA repair and binding. An additional 94 transcripts demonstrated significant seasonal variability that was largely influenced by blood cell count levels. These transcripts were enriched for immune function, protein production, and specific cellular markers for lymphocytes. Accordingly, cell counts for erythrocytes, platelets, neutrophils, monocytes, and CD19 cells demonstrated significant association with a 12-month seasonal cycle. These results demonstrate that seasonal variation is an important environmental regulator of gene expression and blood cell composition. Notable changes in leukocyte counts and genes involved in immune function indicate that immune cell physiology varies throughout the year in healthy individuals.

Evaluating intra- and inter-individual variation in the human placental transcriptome

BACKGROUND

Gene expression variation is a phenotypic trait of particular interest as it represents the initial link between genotype and other phenotypes. Analyzing how such variation apportions among and within groups allows for the evaluation of how genetic and environmental factors influence such traits. It also provides opportunities to identify genes and pathways that may have been influenced by non-neutral processes. Here we use a population genetics framework and next generation sequencing to evaluate how gene expression variation is apportioned among four human groups in a natural biological tissue, the placenta.

RESULTS

We estimate that on average, 33.2%, 58.9%, and 7.8% of the placental transcriptome is explained by variation within individuals, among individuals, and among human groups, respectively. Additionally, when technical and biological traits are included in models of gene expression they each account for roughly 2% of total gene expression variation. Notably, the variation that is significantly different among groups is enriched in biological pathways associated with immune response, cell signaling, and metabolism. Many biological traits demonstrate correlated changes in expression in numerous pathways of potential interest to clinicians and evolutionary biologists. Finally, we estimate that the majority of the human placental transcriptome exhibits expression profiles consistent with neutrality; the remainder are consistent with stabilizing selection, directional selection, or diversifying selection.

CONCLUSIONS

We apportion placental gene expression variation into individual, population, and biological trait factors and identify how each influence the transcriptome. Additionally, we advance methods to associate expression profiles with different forms of selection.