Quantifying the Relative Importance of Genetics and Environment on the Comorbidity between Mental- and Cardiometabolic Disorders: A Comprehensive Analysis of National Register Data from 17 million Scandinavians

Mental disorders (MDs) are leading causes of disability and premature death worldwide, partly due to high comorbidity with cardiometabolic disorders (CMDs). Reasons for this comorbidity are still poorly understood. We leverage nation-wide health records and complete genealogies of Denmark and Sweden (n=17 million) to reveal the genetic and environmental contributions underlying the observed comorbidity between six MDs and 14 CMDs. Genetic factors contributed about 50% to the comorbidity of schizophrenia, affective disorders, and autism spectrum disorder with CMDs, whereas the comorbidity of attention-deficit/hyperactivity disorder and anorexia with CMDs was mainly or fully driven by environmental factors. These findings provide causal insight to guide clinical and scientific initiatives directed at achieving mechanistic understanding as well as preventing and alleviating the consequences of these disorders.

Infection Polygenic Factors Account for a Small Proportion of the Relationship Between Infections and Mental Disorders

BACKGROUND

Several recent studies have suggested a role for infections in the development of mental disorders; however, the genetic contribution to this association is understudied.

METHODS

We use the iPSYCH case-cohort genotyped sample (n = 65,534) and Danish health care registry data to study the genetic association between infections and mental disorders. To test the hypothesis that these associations are due to genetic pleiotropy, we estimated the genetic correlation between infection and mental disorders. Polygenic risk scores (PRSs) were used to assess whether genetic pleiotropy of infections and mental disorders was mediated by actual infection diagnoses.

RESULTS

We observed that schizophrenia, attention-deficit/hyperactivity disorder, major depressive disorder, bipolar disorder, and posttraumatic stress disorder (r_g ranging between 0.18 and 0.83), but not autism spectrum disorder and anorexia nervosa, were significantly genetically correlated with infection diagnoses. PRSs for infections were associated with modest increase in risk of attention-deficit/hyperactivity disorder, major depressive disorder, and schizophrenia in the iPSYCH case-cohort (hazard ratios = 1.04 to 1.10) but was not associated with risk of anorexia, autism, or bipolar disorder. Using mediation analysis, we show that infection diagnoses account for only a small proportion (6%-14%) of the risk for mental disorders conferred by infection PRSs.

CONCLUSIONS

Infections and mental disorders share a modest genetic architecture. Infection PRSs can predict risk of certain mental disorders; however, this effect is moderate. Finally, recorded infections partially explain the relationship between infection PRSs and mental disorders.

Content and Performance of the MiniMUGA Genotyping Array: A New Tool To Improve Rigor and Reproducibility in Mouse Research

The laboratory mouse is the most widely used animal model for biomedical research, due in part to its well-annotated genome, wealth of genetic resources, and the ability to precisely manipulate its genome. Despite the importance of genetics for mouse research, genetic quality control (QC) is not standardized, in part due to the lack of cost-effective, informative, and robust platforms. Genotyping arrays are standard tools for mouse research and remain an attractive alternative even in the era of high-throughput whole-genome sequencing. Here, we describe the content and performance of a new iteration of the Mouse Universal Genotyping Array (MUGA), MiniMUGA, an array-based genetic QC platform with over 11,000 probes. In addition to robust discrimination between most classical and wild-derived laboratory strains, MiniMUGA was designed to contain features not available in other platforms: (1) chromosomal sex determination, (2) discrimination between substrains from multiple commercial vendors, (3) diagnostic SNPs for popular laboratory strains, (4) detection of constructs used in genetically engineered mice, and (5) an easy-to-interpret report summarizing these results. In-depth annotation of all probes should facilitate custom analyses by individual researchers. To determine the performance of MiniMUGA, we genotyped 6899 samples from a wide variety of genetic backgrounds. The performance of MiniMUGA compares favorably with three previous iterations of the MUGA family of arrays, both in discrimination capabilities and robustness. We have generated publicly available consensus genotypes for 241 inbred strains including classical, wild-derived, and recombinant inbred lines. Here, we also report the detection of a substantial number of XO and XXY individuals across a variety of sample types, new markers that expand the utility of reduced complexity crosses to genetic backgrounds other than C57BL/6, and the robust detection of 17 genetic constructs. We provide preliminary evidence that the array can be used to identify both partial sex chromosome duplication and mosaicism, and that diagnostic SNPs can be used to determine how long inbred mice have been bred independently from the relevant main stock. We conclude that MiniMUGA is a valuable platform for genetic QC, and an important new tool to increase the rigor and reproducibility of mouse research.

Collaborative Cross mice reveal extreme epilepsy phenotypes and genetic loci for seizure susceptibility

OBJECTIVE

Animal studies remain essential for understanding mechanisms of epilepsy and identifying new therapeutic targets. However, existing animal models of epilepsy do not reflect the high level of genetic diversity found in the human population. The Collaborative Cross (CC) population is a genetically diverse recombinant inbred panel of mice. The CC offers large genotypic and phenotypic diversity, inbred strains with stable genomes that allow for repeated phenotypic measurements, and genomic tools including whole genome sequence to identify candidate genes and candidate variants.

METHODS

We evaluated multiple complex epileptic traits in a sampling of 35 CC inbred strains using the flurothyl-induced seizure and kindling paradigm. We created an F2 population of 297 mice with extreme seizure susceptibility and performed quantitative trait loci (QTL) mapping to identify genomic regions associated with seizure sensitivity. We used quantitative RNA sequencing from CC hippocampal tissue to identify candidate genes and whole genome sequence to identify genetic variants likely affecting gene expression.

RESULTS

We identified new mouse models with extreme seizure susceptibility, seizure propagation, epileptogenesis, and SUDEP (sudden unexpected death in epilepsy). We performed QTL mapping and identified one known and seven novel loci associated with seizure sensitivity. We combined whole genome sequencing and hippocampal gene expression to pinpoint biologically plausible candidate genes (eg, Gabra2) and variants associated with seizure sensitivity.

SIGNIFICANCE

New mouse models of epilepsy are needed to better understand the complex genetic architecture of seizures and to identify therapeutics. We performed a phenotypic screen utilizing a novel genetic reference population of CC mice. The data we provide enable the identification of protective/risk genes and novel molecular mechanisms linked to complex seizure traits that are currently challenging to study and treat.

Whole Genome Sequencing and Progress Toward Full Inbreeding of the Mouse Collaborative Cross Population

Two key features of recombinant inbred panels are well-characterized genomes and reproducibility. Here we report on the sequenced genomes of six additional Collaborative Cross (CC) strains and on inbreeding progress of 72 CC strains. We have previously reported on the sequences of 69 CC strains that were publicly available, bringing the total of CC strains with whole genome sequence up to 75. The sequencing of these six CC strains updates the efforts toward inbreeding undertaken by the UNC Systems Genetics Core. The timing reflects our competing mandates to release to the public as many CC strains as possible while achieving an acceptable level of inbreeding. The new six strains have a higher than average founder contribution from non-domesticus strains than the previously released CC strains. Five of the six strains also have high residual heterozygosity (>14%), which may be related to non-domesticus founder contributions. Finally, we report on updated estimates on residual heterozygosity across the entire CC population using a novel, simple and cost effective genotyping platform on three mice from each strain. We observe a reduction in residual heterozygosity across all previously released CC strains. We discuss the optimal use of different genetic resources available for the CC population.

Quantitative trait mapping in Diversity Outbred mice identifies two genomic regions associated with heart size

Heart size is an important factor in cardiac health and disease. In particular, increased heart weight is predictive of adverse cardiovascular outcomes in multiple large community-based studies. We use two cohorts of Diversity Outbred (DO) mice to investigate the role of genetics, sex, age, and diet on heart size. DO mice (n = 289) of both sexes from generation 10 were fed a standard chow diet, and analyzed at 12-15 weeks of age. Another cohort of female DO mice (n = 258) from generation 11 were fed either a high-fat, cholesterol-containing (HFC) diet or a low-fat, high-protein diet, and analyzed at 24-25 weeks. We did not observe an effect of diet on body or heart weight in generation 11 mice, although we previously reported an effect on other cardiovascular risk factors, including cholesterol, triglycerides, and insulin. We do observe a significant genetic effect on heart weight in this population. We identified two quantitative trait loci for heart weight, one (Hwtf1) at a genome-wide significance level of p ≤ 0.05 on MMU15 and one (Hwtf2) at a genome-wide suggestive level of p ≤ 0.1 on MMU10, that together explain 13.3% of the phenotypic variance. Hwtf1 contained collagen type XXII alpha 1 chain (Col22a1), and the NZO/HlLtJ and WSB/EiJ haplotypes were associated with larger hearts. This is consistent with heart tissue Col22a1 expression in DO founders and SNP patterns within Hwtf1 for Col22a1. Col22a1 has been previously associated with cardiac fibrosis in mice, suggesting that Col22a1 may be involved in pathological cardiac hypertrophy.

Obp56h Modulates Mating Behavior in Drosophila melanogaster

Social interactions in insects are driven by conspecific chemical signals that are detected via olfactory and gustatory neurons. Odorant binding proteins (Obps) transport volatile odorants to chemosensory receptors, but their effects on behaviors remain poorly characterized. Here, we report that RNAi knockdown of Obp56h gene expression in Drosophila melanogaster enhances mating behavior by reducing courtship latency. The change in mating behavior that results from inhibition of Obp56h expression is accompanied by significant alterations in cuticular hydrocarbon (CHC) composition, including reduction in 5-tricosene (5-T), an inhibitory sex pheromone produced by males that increases copulation latency during courtship. Whole genome RNA sequencing confirms that expression of Obp56h is virtually abolished in Drosophila heads. Inhibition of Obp56h expression also affects expression of other chemoreception genes, including upregulation of lush in both sexes and Obp83ef in females, and reduction in expression of Obp19b and Or19b in males. In addition, several genes associated with lipid metabolism, which underlies the production of cuticular hydrocarbons, show altered transcript abundances. Our data show that modulation of mating behavior through reduction of Obp56h is accompanied by altered cuticular hydrocarbon profiles and implicate 5-T as a possible ligand for Obp56h.

A genetic analysis of the stinging and guarding behaviors of the honey bee

In order to identify genes that are influencing defensive behaviors, we have taken a new approach by dissecting colony-level defensive behavior into individual behavioral measurements using two families containing backcross workers from matings involving European and Africanized bees. We removed the social context from stinging behavior by using a laboratory assay to measure the stinging response of individual bees. A mild shock was given to bees using a constant-current stimulator. The time it took bees to sting in response to this stimulus was recorded. In addition, bees that were seen performing guard behaviors at the hive entrance were collected. We performed QTL mapping in two backcross families with SNP probes within genes and identified two new QTL regions for stinging behavior and another QTL region for guarding behavior. We also identified several candidate genes involved in neural signaling, neural development and muscle development that may be influencing stinging and guarding behaviors. The lack of overlap between these regions and previous defensive behavior QTL underscores the complexity of this behavior and increases our understanding of its genetic architecture.