Lingo: an automated, web-based deep phenotyping platform for language ability

Background

Language and the ability to communicate effectively are key factors in mental health and well-being. Despite this critical importance, research on language is limited by the lack of a scalable phenotyping toolkit.

Methods

Here, we describe and showcase Lingo - a flexible online battery of language and nonverbal reasoning skills based on seven widely used tasks (COWAT, picture narration, vocal rhythm entrainment, rapid automatized naming, following directions, sentence repetition, and nonverbal reasoning). The current version of Lingo takes approximately 30 minutes to complete, is entirely open source, and allows for a wide variety of performance metrics to be extracted. We asked > 1,300 individuals from multiple samples to complete Lingo, then investigated the validity and utility of the resulting data.

Results

We conducted an exploratory factor analysis across 14 features derived from the seven assessments, identifying five factors. Four of the five factors showed acceptable test-retest reliability (Pearson's R > 0.7). Factor 2 showed the highest reliability (Pearson's R = 0.95) and loaded primarily on sentence repetition task performance. We validated Lingo with objective measures of language ability by comparing performance to gold-standard assessments: CELF-5 and the VABS-3. Factor 2 was significantly associated with the CELF-5 "core language ability" scale (Pearson's R = 0.77, p-value < 0.05) and the VABS-3 "communication" scale (Pearson's R = 0.74, p-value < 0.05). Factor 2 was positively associated with phenotypic and genetic measures of socieconomic status. Interestingly, we found the parents of children with language impairments had lower Factor 2 scores (p-value < 0.01). Finally, we found Lingo factor scores were significantly predictive of numerous psychiatric and neurodevelopmental conditions.

Conclusions

Together, these analyses support Lingo as a powerful platform for scalable deep phenotyping of language and other cognitive abilities. Additionally, exploratory analyses provide supporting evidence for the heritability of language ability and the complex relationship between mental health and language.

Polygenic Scores Clarify the Relationship Between Mental Health and Gender Diversity

Background

Gender-diverse individuals are at increased risk for mental health problems, but it is unclear whether this is due to shared environmental or genetic factors.

Methods

In two SPARK samples, we tested for associations of 16 polygenic scores (PGSs) with quantitative measures of gender diversity and mental health. In study 1, 639 independent adults (59% autistic) reported their mental health with the Adult Self-Report and their gender diversity with the Gender Self-Report (GSR). The GSR has 2 dimensions: binary (degree of identification with the gender opposite that implied by sex designated at birth) and nonbinary (degree of identification with a gender that is neither male nor female). In study 2 (N = 5165), we used a categorical measure of gender identity.

Results

In study 1, neuropsychiatric PGSs were positively associated with Adult Self-Report scores: externalizing was positively associated with the attention-deficit/hyperactivity disorder PGS (β = 0.10 [0.03-0.17]), and internalizing was positively associated with the PGSs for depression (β = 0.07 [0-0.14]) and neuroticism (β = 0.10 [0.03-0.17]). Interestingly, GSR scores were not significantly associated with any neuropsychiatric PGS. However, GSR nonbinary was positively associated with the cognitive performance PGS (β = 0.11 [0.05-0.18]), with the effect size comparable in magnitude to the associations of the neuropsychiatric PGSs with the Adult Self-Report. Additionally, GSR binary was positively associated with the nonheterosexual sexual behavior PGS (β = 0.07 [0-0.14]). In study 2, the cognitive performance PGS effect replicated; transgender and nonbinary individuals had higher PGSs (t316 = 4.16).

Conclusions

We showed that while gender diversity is phenotypically positively associated with mental health problems, the strongest PGS associations with gender diversity were with the cognitive performance PGS, not the neuropsychiatric PGSs.

The Iowa Health Data Resource (IHDR): an innovative framework for transforming the clinical health data ecosystem

IMPORTANCE

This manuscript will be of interest to most Clinical and Translational Science Awards (CTSA) as they retool for the increasing emphasis on translational science from translational research. This effort is an extension of the EDW4R work that most CTSAs have done to deploy infrastructure and tools for researchers to access clinical data.

OBJECTIVES

The Iowa Health Data Resource (IHDR) is a strategic investment made by the University of Iowa to improve access to real-world health data. The goals of IHDR are to improve the speed of translational health research, to boost interdisciplinary collaboration, and to improve literacy about health data. The first objective toward this larger goal was to address gaps in data access, data literacy, lack of computational environments for processing Personal Health Information (PHI) and the lack of processes and expertise for creating transformative datasets.

METHODS

A three-pronged approach was taken to address the objective. The approach involves integration of an intercollegiate team of non-informatics faculty and staff, a data enclave for secure patient data analyses, and novel comprehensive datasets.

RESULTS

To date, all five of the health science colleges (dentistry, medicine, nursing, pharmacy, and public health) have had at least one staff and one faculty member complete the two-month experiential learning curriculum. Over the first two years of this project, nine cohorts totaling 36 data liaisons have been trained, including 18 faculty and 18 staff. IHDR data enclave eliminated the need to duplicate computational infrastructure inside the hospital firewall which reduced infrastructure, hardware and human resource costs while leveraging the existing expertise embedded in the university research computing team. The creation of a process to develop and implement transformative datasets has resulted in the creation of seven domain specific datasets to date.

CONCLUSION

The combination of people, process, and technology facilitates collaboration and interdisciplinary research in a secure environment using curated data sets. While other organizations have implemented individual components to address EDW4R operational demands, the IHDR combines multiple resources into a novel, comprehensive ecosystem IHDR enables scientists to use analysis tools with electronic patient data to accelerate time to science.

Using deep learning to quantify neuronal activation from single-cell and spatial transcriptomic data

Neuronal activity-dependent transcription directs molecular processes that regulate synaptic plasticity, brain circuit development, behavioral adaptation, and long-term memory. Single cell RNA-sequencing technologies (scRNAseq) are rapidly developing and allow for the interrogation of activity-dependent transcription at cellular resolution. Here, we present NEUROeSTIMator, a deep learning model that integrates transcriptomic signals to estimate neuronal activation in a way that we demonstrate is associated with Patch-seq electrophysiological features and that is robust against differences in species, cell type, and brain region. We demonstrate this method's ability to accurately detect neuronal activity in previously published studies of single cell activity-induced gene expression. Further, we applied our model in a spatial transcriptomic study to identify unique patterns of learning-induced activity across different brain regions in male mice. Altogether, our findings establish NEUROeSTIMator as a powerful and broadly applicable tool for measuring neuronal activation, whether as a critical covariate or a primary readout of interest.

The Gender Self-Report: A multidimensional gender characterization tool for gender-diverse and cisgender youth and adults

Gender identity is a core component of human experience, critical to account for in broad health, development, psychosocial research, and clinical practice. Yet, the psychometric characterization of gender has been impeded due to challenges in modeling the myriad gender self-descriptors, statistical power limitations related to multigroup analyses, and equity-related concerns regarding the accessibility of complex gender terminology. Therefore, this initiative employed an iterative multi-community-driven process to develop the Gender Self-Report (GSR), a multidimensional gender characterization tool, accessible to youth and adults, nonautistic and autistic people, and gender-diverse and cisgender individuals. In Study 1, the GSR was administered to 1,654 individuals, sampled through seven diversified recruitments to be representative across age (10-77 years), gender and sexuality diversity (∼33% each gender diverse, cisgender sexual minority, cisgender heterosexual), and autism status (> 33% autistic). A random half-split subsample was subjected to exploratory factor analytics, followed by confirmatory analytics in the full sample. Two stable factors emerged: Nonbinary Gender Diversity and Female-Male Continuum (FMC). FMC was transformed to Binary Gender Diversity based on designated sex at birth to reduce collinearity with designated sex at birth. Differential item functioning by age and autism status was employed to reduce item-response bias. Factors were internally reliable. Study 2 demonstrated the construct, convergent, and ecological validity of GSR factors. Of the 30 hypothesized validation comparisons, 26 were confirmed. The GSR provides a community-developed gender advocacy tool with 30 self-report items that avoid complex gender-related "insider" language and characterize diverse populations across continuous multidimensional binary and nonbinary gender traits. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Mapping the spatial transcriptomic signature of the hippocampus during memory consolidation

Memory consolidation involves discrete patterns of transcriptional events in the hippocampus. Despite the emergence of single-cell transcriptomic profiling techniques, mapping the transcriptomic signature across subregions of the hippocampus has remained challenging. Here, we utilized unbiased spatial sequencing to delineate transcriptome-wide gene expression changes across subregions of the dorsal hippocampus of male mice following learning. We find that each subregion of the hippocampus exhibits distinct yet overlapping transcriptomic signatures. The CA1 region exhibited increased expression of genes related to transcriptional regulation, while the DG showed upregulation of genes associated with protein folding. Importantly, our approach enabled us to define the transcriptomic signature of learning within two less-defined hippocampal subregions, CA1 stratum radiatum, and oriens. We demonstrated that CA1 subregion-specific expression of a transcription factor subfamily has a critical functional role in the consolidation of long-term memory. This work demonstrates the power of spatial molecular approaches to reveal simultaneous transcriptional events across the hippocampus during memory consolidation.

Activity-induced gene expression in the human brain

Activity-induced gene expression underlies synaptic plasticity and brain function. Here, using molecular sequencing techniques, we define activity-dependent transcriptomic and epigenomic changes at the tissue and single-cell level in the human brain following direct electrical stimulation of the anterior temporal lobe in patients undergoing neurosurgery. Genes related to transcriptional regulation and microglia-specific cytokine activity displayed the greatest induction pattern, revealing a precise molecular signature of neuronal activation in the human brain.

Language and reading impairments are associated with increased prevalence of non-right-handedness

Handedness has been studied for association with language-related disorders because of its link with language hemispheric dominance. No clear pattern has emerged, possibly because of small samples, publication bias, and heterogeneous criteria across studies. Non-right-handedness (NRH) frequency was assessed in N = 2503 cases with reading and/or language impairment and N = 4316 sex-matched controls identified from 10 distinct cohorts (age range 6-19 years old; European ethnicity) using a priori set criteria. A meta-analysis (Ncases  = 1994) showed elevated NRH % in individuals with language/reading impairment compared with controls (OR = 1.21, CI = 1.06-1.39, p = .01). The association between reading/language impairments and NRH could result from shared pathways underlying brain lateralization, handedness, and cognitive functions.

Mapping the spatial transcriptomic signature of the hippocampus during memory consolidation

Memory consolidation involves discrete patterns of transcriptional events in the hippocampus. Despite the emergence of single-cell transcriptomic profiling techniques, defining learning-responsive gene expression across subregions of the hippocampus has remained challenging. Here, we utilized unbiased spatial sequencing to elucidate transcriptome-wide changes in gene expression in the hippocampus following learning, enabling us to define molecular signatures unique to each hippocampal subregion. We find that each subregion of the hippocampus exhibits distinct yet overlapping transcriptomic signatures. Although the CA1 region exhibited increased expression of genes related to transcriptional regulation, the DG showed upregulation of genes associated with protein folding. We demonstrate the functional relevance of subregion-specific gene expression by genetic manipulation of a transcription factor selectively in the CA1 hippocampal subregion, leading to long-term memory deficits. This work demonstrates the power of using spatial molecular approaches to reveal transcriptional events during memory consolidation.

The combination of autism and exceptional cognitive ability is associated with suicidal ideation

Autism with co-occurring exceptional cognitive ability is often accompanied by severe internalizing symptoms and feelings of inadequacy. Whether cognitive ability also translates into greater risk for suicidal ideation is unclear. To investigate this urgent question, we examined two samples of high-ability autistic individuals for factors that were predictive of suicidal ideation. In the first sample (N = 1,074 individuals seen at a clinic specializing in gifted/talented youth), we observed a striking excess of parent-reported suicidal ideation in autistic individuals with IQ ≥ 120 (Odds Ratio = 5.9, p=0.0007). In a separate sample of SPARK participants, we confirmed higher rates of suicidal thoughts compared to non-autistic children from the ABCD cohort (combined N = 16,049, Odds Ratio = 6.8, p<2.2e-16), and further that autistic children with suicidal thoughts had significantly higher cognitive ability (p<2.2e-16) than those without. Elevated polygenic scores (PGS) for cognitive performance were associated with increased suicidal thoughts (N = 1,983, Z=2.16,p=0.03), with PGS for educational attainment trending in the same direction (Z=1.4,p=0.17). Notably, similar results were found in parents of these autistic youth, where higher PGS for educational attainment was associated with increasing thoughts of suicide (N = 736, Z=2.28,p=0.02). Taken together, these results suggest that on a phenotypic and genetic level, increasing cognitive ability is an unexpected risk factor for suicidal ideation in individuals diagnosed with, or at risk for autism.

Integrating de novo and inherited variants in 42,607 autism cases identifies mutations in new moderate-risk genes

To capture the full spectrum of genetic risk for autism, we performed a two-stage analysis of rare de novo and inherited coding variants in 42,607 autism cases, including 35,130 new cases recruited online by SPARK. We identified 60 genes with exome-wide significance (P < 2.5 × 10^-6), including five new risk genes (NAV3, ITSN1, MARK2, SCAF1 and HNRNPUL2). The association of NAV3 with autism risk is primarily driven by rare inherited loss-of-function (LoF) variants, with an estimated relative risk of 4, consistent with moderate effect. Autistic individuals with LoF variants in the four moderate-risk genes (NAV3, ITSN1, SCAF1 and HNRNPUL2; n = 95) have less cognitive impairment than 129 autistic individuals with LoF variants in highly penetrant genes (CHD8, SCN2A, ADNP, FOXP1 and SHANK3) (59% vs 88%, P = 1.9 × 10^-6). Power calculations suggest that much larger numbers of autism cases are needed to identify additional moderate-risk genes.

Genome-wide analyses of individual differences in quantitatively assessed reading- and language-related skills in up to 34,000 people

The use of spoken and written language is a fundamental human capacity. Individual differences in reading- and language-related skills are influenced by genetic variation, with twin-based heritability estimates of 30 to 80% depending on the trait. The genetic architecture is complex, heterogeneous, and multifactorial, but investigations of contributions of single-nucleotide polymorphisms (SNPs) were thus far underpowered. We present a multicohort genome-wide association study (GWAS) of five traits assessed individually using psychometric measures (word reading, nonword reading, spelling, phoneme awareness, and nonword repetition) in samples of 13,633 to 33,959 participants aged 5 to 26 y. We identified genome-wide significant association with word reading (rs11208009, P = 1.098 × 10-8) at a locus that has not been associated with intelligence or educational attainment. All five reading-/language-related traits showed robust SNP heritability, accounting for 13 to 26% of trait variability. Genomic structural equation modeling revealed a shared genetic factor explaining most of the variation in word/nonword reading, spelling, and phoneme awareness, which only partially overlapped with genetic variation contributing to nonword repetition, intelligence, and educational attainment. A multivariate GWAS of word/nonword reading, spelling, and phoneme awareness maximized power for follow-up investigation. Genetic correlation analysis with neuroimaging traits identified an association with the surface area of the banks of the left superior temporal sulcus, a brain region linked to the processing of spoken and written language. Heritability was enriched for genomic elements regulating gene expression in the fetal brain and in chromosomal regions that are depleted of Neanderthal variants. Together, these results provide avenues for deciphering the biological underpinnings of uniquely human traits.

Calculating genetic risk for dysfunction in pleiotropic biological processes using whole exome sequencing data

BACKGROUND

Numerous genes are implicated in autism spectrum disorder (ASD). ASD encompasses a wide-range and severity of symptoms and co-occurring conditions; however, the details of how genetic variation contributes to phenotypic differences are unclear. This creates a challenge for translating genetic evidence into clinically useful knowledge. Sleep disturbances are particularly prevalent co-occurring conditions in ASD, and genetics may inform treatment. Identifying convergent mechanisms with evidence for dysfunction that connect ASD and sleep biology could help identify better treatments for sleep disturbances in these individuals.

METHODS

To identify mechanisms that influence risk for ASD and co-occurring sleep disturbances, we analyzed whole exome sequence data from individuals in the Simons Simplex Collection (n = 2380). We predicted protein damaging variants (PDVs) in genes currently implicated in either ASD or sleep duration in typically developing children. We predicted a network of ASD-related proteins with direct evidence for interaction with sleep duration-related proteins encoded by genes with PDVs. Overrepresentation analyses of Gene Ontology-defined biological processes were conducted on the resulting gene set. We calculated the likelihood of dysfunction in the top overrepresented biological process. We then tested if scores reflecting genetic dysfunction in the process were associated with parent-reported sleep duration.

RESULTS

There were 29 genes with PDVs in the ASD dataset where variation was reported in the literature to be associated with both ASD and sleep duration. A network of 108 proteins encoded by ASD and sleep duration candidate genes with PDVs was identified. The mechanism overrepresented in PDV-containing genes that encode proteins in the interaction network with the most evidence for dysfunction was cerebral cortex development (GO:0,021,987). Scores reflecting dysfunction in this process were associated with sleep durations; the largest effects were observed in adolescents (p = 4.65 × 10^-3).

CONCLUSIONS

Our bioinformatic-driven approach detected a biological process enriched for genes encoding a protein-protein interaction network linking ASD gene products with sleep duration gene products where accumulation of potentially damaging variants in individuals with ASD was associated with sleep duration as reported by the parents. Specifically, genetic dysfunction impacting development of the cerebral cortex may affect sleep by disrupting sleep homeostasis which is evidenced to be regulated by this brain region. Future functional assessments and objective measurements of sleep in adolescents with ASD could provide the basis for more informed treatment of sleep problems in these individuals.

Clinical autism subscales have common genetic liabilities that are heritable, pleiotropic, and generalizable to the general population

The complexity of autism's phenotypic spectra is well-known, yet most genetic research uses case-control status as the target trait. It is undetermined if autistic symptom domain severity underlying this heterogeneity is heritable and pleiotropic with other psychiatric and behavior traits in the same manner as autism case-control status. In N = 6064 autistic children in the SPARK cohort, we investigated the common genetic properties of twelve subscales from three clinical autism instruments measuring autistic traits: the Social Communication Questionnaire (SCQ), the Repetitive Behavior Scale-Revised (RBS-R), and the Developmental Coordination Disorder Questionnaire (DCDQ). Educational attainment polygenic scores (PGS) were significantly negatively correlated with eleven subscales, while ADHD and major depression PGS were positively correlated with ten and eight of the autism subscales, respectively. Loneliness and neuroticism PGS were also positively correlated with many subscales. Significant PGS by sex interactions were found-surprisingly, the autism case-control PGS was negatively correlated in females and had no strong correlation in males. SNP-heritability of the DCDQ subscales ranged from 0.04 to 0.08, RBS-R subscales ranged from 0.09 to 0.24, and SCQ subscales ranged from 0 to 0.12. GWAS in SPARK followed by estimation of polygenic scores (PGS) in the typically-developing ABCD cohort (N = 5285), revealed significant associations of RBS-R subscale PGS with autism-related behavioral traits, with several subscale PGS more strongly correlated than the autism case-control PGS. Overall, our analyses suggest that the clinical autism subscale traits show variability in SNP-heritability, PGS associations, and significant PGS by sex interactions, underscoring the heterogeneity in autistic traits at a genetic level. Furthermore, of the three instruments investigated, the RBS-R shows the greatest evidence of genetic signal in both (1) autistic samples (greater heritability) and (2) general population samples (strongest PGS associations).

p53-mediated neurodegeneration in the absence of the nuclear protein Akirin2

Proper gene regulation is critical for both neuronal development and maintenance as the brain matures. We previously demonstrated that Akirin2, an essential nuclear protein that interacts with transcription factors and chromatin remodeling complexes, is required for the embryonic formation of the cerebral cortex. Here we show that Akirin2 plays a mechanistically distinct role in maintaining healthy neurons during cortical maturation. Restricting Akirin2 loss to excitatory cortical neurons resulted in progressive neurodegeneration via necroptosis and severe cortical atrophy with age. Comparing transcriptomes from Akirin2-null postnatal neurons and cortical progenitors revealed that targets of the tumor suppressor p53, a regulator of both proliferation and cell death encoded by Trp53, were consistently upregulated. Reduction of Trp53 rescued neurodegeneration in Akirin2-null neurons. These data: (1) implicate Akirin2 as a critical neuronal maintenance protein, (2) identify p53 pathways as mediators of Akirin2 functions, and (3) suggest Akirin2 dysfunction may be relevant to neurodegenerative diseases.

Altered hippocampal transcriptome dynamics following sleep deprivation

Widespread sleep deprivation is a continuing public health problem in the United States and worldwide affecting adolescents and adults. Acute sleep deprivation results in decrements in spatial memory and cognitive impairments. The hippocampus is vulnerable to acute sleep deprivation with changes in gene expression, cell signaling, and protein synthesis. Sleep deprivation also has long lasting effects on memory and performance that persist after recovery sleep, as seen in behavioral studies from invertebrates to humans. Although previous research has shown that acute sleep deprivation impacts gene expression, the extent to which sleep deprivation affects gene regulation remains unknown. Using an unbiased deep RNA sequencing approach, we investigated the effects of acute sleep deprivation on gene expression in the hippocampus. We identified 1,146 genes that were significantly dysregulated following sleep deprivation with 507 genes upregulated and 639 genes downregulated, including protein coding genes and long non-coding RNAs not previously identified as impacted by sleep deprivation. Notably, genes significantly upregulated after sleep deprivation were associated with RNA splicing and the nucleus. In contrast, downregulated genes were associated with cell adhesion, dendritic localization, the synapse, and postsynaptic membrane. Furthermore, we found through independent experiments analyzing a subset of genes that three hours of recovery sleep following acute sleep deprivation was sufficient to normalize mRNA abundance for most genes, although exceptions occurred for some genes that may affect RNA splicing or transcription. These results clearly demonstrate that sleep deprivation differentially regulates gene expression on multiple transcriptomic levels to impact hippocampal function.

Targeting the Coronavirus Nucleocapsid Protein through GSK-3 Inhibition

The coronaviruses responsible for severe acute respiratory syndrome (SARS-CoV), COVID-19 (SARS-CoV-2), Middle East respiratory syndrome (MERS-CoV), and other coronavirus infections express a nucleocapsid protein (N) that is essential for viral replication, transcription, and virion assembly. Phosphorylation of N from SARS-CoV by glycogen synthase kinase 3 (GSK-3) is required for its function and inhibition of GSK-3 with lithium impairs N phosphorylation, viral transcription, and replication. Here we report that the SARS-CoV-2 N protein contains GSK-3 consensus sequences and that this motif is conserved in diverse coronaviruses, raising the possibility that SARS-CoV-2 may be sensitive to GSK-3 inhibitors including lithium. We conducted a retrospective analysis of lithium use in patients from three major health systems who were PCR tested for SARS-CoV-2. We found that patients taking lithium have a significantly reduced risk of COVID-19 (odds ratio = 0.51 [0.35 - 0.74], p = 0.005). We also show that the SARS-CoV-2 N protein is phosphorylated by GSK-3. Knockout of GSK3A and GSK3B demonstrates that GSK-3 is essential for N phosphorylation. Alternative GSK-3 inhibitors block N phosphorylation and impair replication in SARS-CoV-2 infected lung epithelial cells in a cell-type dependent manner. Targeting GSK-3 may therefore provide a new approach to treat COVID-19 and future coronavirus outbreaks.

Significance

COVID-19 is taking a major toll on personal health, healthcare systems, and the global economy. With three betacoronavirus epidemics in less than 20 years, there is an urgent need for therapies to combat new and existing coronavirus outbreaks. Our analysis of clinical data from over 300,000 patients in three major health systems demonstrates a 50% reduced risk of COVID-19 in patients taking lithium, a direct inhibitor of glycogen synthase kinase-3 (GSK-3). We further show that GSK-3 is essential for phosphorylation of the SARS-CoV-2 nucleocapsid protein and that GSK-3 inhibition blocks SARS-CoV-2 infection in human lung epithelial cells. These findings suggest an antiviral strategy for COVID-19 and new coronaviruses that may arise in the future.

Genetic and morphological estimates of androgen exposure predict social deficits in multiple neurodevelopmental disorder cohorts

BACKGROUND

Neurodevelopmental disorders (NDDs) such as autism spectrum disorder (ASD) display a strong male bias. Androgen exposure is profoundly increased in typical male development, but it also varies within the sexes, and previous work has sought to connect morphological proxies of androgen exposure, including digit ratio and facial morphology, to neurodevelopmental outcomes. The results of these studies have been mixed, and the relationships between androgen exposure and behavior remain unclear.

METHODS

Here, we measured both digit ratio masculinity (DRM) and facial landmark masculinity (FLM) in the same neurodevelopmental cohort (N = 763) and compared these proxies of androgen exposure to clinical and parent-reported features as well as polygenic risk scores.

RESULTS

We found that FLM was significantly associated with NDD diagnosis (ASD, ADHD, ID; all [Formula: see text]), while DRM was not. When testing for association with parent-reported problems, we found that both FLM and DRM were positively associated with concerns about social behavior ([Formula: see text], [Formula: see text]; [Formula: see text], [Formula: see text], respectively). Furthermore, we found evidence via polygenic risk scores (PRS) that DRM indexes masculinity via testosterone levels ([Formula: see text], [Formula: see text]), while FLM indexes masculinity through a negative relationship with sex hormone binding globulin (SHBG) levels ([Formula: see text], [Formula: see text]). Finally, using the SPARK cohort (N = 9419) we replicated the observed relationship between polygenic estimates of testosterone, SHBG, and social functioning ([Formula: see text], [Formula: see text], and [Formula: see text], [Formula: see text] for testosterone and SHBG, respectively). Remarkably, when considered over the extremes of each variable, these quantitative sex effects on social functioning were comparable to the effect of binary sex itself (binary male: [Formula: see text]; testosterone: [Formula: see text] from 0.1%-ile to 99.9%-ile; SHBG: [Formula: see text] from 0.1%-ile to 99.9%-ile).

LIMITATIONS

In the devGenes and SPARK cohorts, our analyses rely on indirect, rather than direct measurement of androgens and related molecules.

CONCLUSIONS

These findings and their replication in the large SPARK cohort lend support to the hypothesis that increasing net androgen exposure diminishes capacity for social functioning in both males and females.

Estimating the Prevalence and Genetic Risk Mechanisms of ARFID in a Large Autism Cohort

This study is the first genetically-informed investigation of avoidant/restrictive food intake disorder (ARFID), an eating disorder that profoundly impacts quality of life for those affected. ARFID is highly comorbid with autism, and we provide the first estimate of its prevalence in a large and phenotypically diverse autism cohort (a subsample of the SPARK study, N = 5,157 probands). This estimate, 21% (at a balanced accuracy 80%), is at the upper end of previous estimates from studies based on clinical samples, suggesting under-diagnosis and potentially lack of awareness among caretakers and clinicians. Although some studies suggest a decrease of disordered eating symptoms by age 6, our estimates indicate that up to 17% (at a balanced accuracy 87%) of parents of autistic children are also at heightened risk for ARFID, suggesting a lifelong risk for disordered eating. We were also able to provide the first estimates of narrow-sense heritability (h²) for ARFID risk, at 0.45. Genome-wide association revealed a single hit near ZSWIM6, a gene previously implicated in neurodevelopmental conditions. While, the current sample was not well-powered for GWAS, effect size and heritability estimates allowed us to project the sample sizes necessary to more robustly discover ARFID-linked loci via common variants. Further genetic analysis using polygenic risk scores (PRS) affirmed genetic links to autism as well as neuroticism and metabolic syndrome.

The CBP KIX domain regulates long-term memory and circadian activity

Background

CREB-dependent transcription necessary for long-term memory is driven by interactions with CREB-binding protein (CBP), a multi-domain protein that binds numerous transcription factors potentially affecting expression of thousands of genes. Identifying specific domain functions for multi-domain proteins is essential to understand processes such as cognitive function and circadian clocks. We investigated the function of the CBP KIX domain in hippocampal memory and gene expression using CBP^KIX/KIX mice with mutations that prevent phospho-CREB (Ser133) binding.

Results

We found that CBP^KIX/KIX mice were impaired in long-term memory, but not learning acquisition or short-term memory for the Morris water maze. Using an unbiased analysis of gene expression in the dorsal hippocampus after training in the Morris water maze or contextual fear conditioning, we discovered dysregulation of CREB, CLOCK, and BMAL1 target genes and downregulation of circadian genes in CBP^KIX/KIX mice. Given our finding that the CBP KIX domain was important for transcription of circadian genes, we profiled circadian activity and phase resetting in CBP^KIX/KIX mice. CBP^KIX/KIX mice exhibited delayed activity peaks after light offset and longer free-running periods in constant dark. Interestingly, CBP^KIX/KIX mice displayed phase delays and advances in response to photic stimulation comparable to wildtype littermates. Thus, this work delineates site-specific regulation of the circadian clock by a multi-domain protein.

Conclusions

These studies provide insight into the significance of the CBP KIX domain by defining targets of CBP transcriptional co-activation in memory and the role of the CBP KIX domain in vivo on circadian rhythms.

Graphical abstract

Large-scale targeted sequencing identifies risk genes for neurodevelopmental disorders

Most genes associated with neurodevelopmental disorders (NDDs) were identified with an excess of de novo mutations (DNMs) but the significance in case-control mutation burden analysis is unestablished. Here, we sequence 63 genes in 16,294 NDD cases and an additional 62 genes in 6,211 NDD cases. By combining these with published data, we assess a total of 125 genes in over 16,000 NDD cases and compare the mutation burden to nonpsychiatric controls from ExAC. We identify 48 genes (25 newly reported) showing significant burden of ultra-rare (MAF < 0.01%) gene-disruptive mutations (FDR 5%), six of which reach family-wise error rate (FWER) significance (p < 1.25E-06). Among these 125 targeted genes, we also reevaluate DNM excess in 17,426 NDD trios with 6,499 new autism trios. We identify 90 genes enriched for DNMs (FDR 5%; e.g., GABRG2 and UIMC1); of which, 61 reach FWER significance (p < 3.64E-07; e.g., CASZ1). In addition to doubling the number of patients for many NDD risk genes, we present phenotype-genotype correlations for seven risk genes (CTCF, HNRNPU, KCNQ3, ZBTB18, TCF12, SPEN, and LEO1) based on this large-scale targeted sequencing effort.

Community attitudes on genetic research of gender identity, sexual orientation, and mental health

Sex is an important factor in mental health, and a non-binary view of how variation in sex and gender influence mental health represents a new research frontier that may yield new insights. The recent acceleration of research into sexual orientation, gender identity, and mental health has generally been conducted without sufficient understanding of the opinions of sexual and gender minorities (SGM) toward this research. We surveyed 768 individuals, with an enrichment of LGBTQ+ stakeholders, for their opinions regarding genetic research of SGM and mental health. We found that the key predictors of attitudes toward genetic research specifically on SGM are 1) general attitudes toward genetic and mental health research 2) tolerance of SGM and associated behaviors and 3) age of the participant. Non-heterosexual stakeholder status was significantly associated with increased willingness to participate in genetic research if a biological basis for gender identity were discovered. We also found that heterosexual, cisgender participants with a low tolerance for SGM indicated their SGM views would be positively updated if science showed a biological basis for their behaviors and identities. These findings represent an important first step in understanding and engaging the LGBTQ+ stakeholder community in the context of genetic research.

Forecasting risk gene discovery in autism with machine learning and genome-scale data

Genetics has been one of the most powerful windows into the biology of autism spectrum disorder (ASD). It is estimated that a thousand or more genes may confer risk for ASD when functionally perturbed, however, only around 100 genes currently have sufficient evidence to be considered true "autism risk genes". Massive genetic studies are currently underway producing data to implicate additional genes. This approach - although necessary - is costly and slow-moving, making identification of putative ASD risk genes with existing data vital. Here, we approach autism risk gene discovery as a machine learning problem, rather than a genetic association problem, by using genome-scale data as predictors to identify new genes with similar properties to established autism risk genes. This ensemble method, forecASD, integrates brain gene expression, heterogeneous network data, and previous gene-level predictors of autism association into an ensemble classifier that yields a single score indexing evidence of each gene's involvement in the etiology of autism. We demonstrate that forecASD has substantially better performance than previous predictors of autism association in three independent trio-based sequencing studies. Studying forecASD prioritized genes, we show that forecASD is a robust indicator of a gene's involvement in ASD etiology, with diverse applications to gene discovery, differential expression analysis, eQTL prioritization, and pathway enrichment analysis.

TiSAn: estimating tissue-specific effects of coding and non-coding variants

Motivation

Model-based estimates of general deleteriousness, like CADD, DANN or PolyPhen, have become indispensable tools in the interpretation of genetic variants. However, these approaches say little about the tissues in which the effects of deleterious variants will be most meaningful. Tissue-specific annotations have been recently inferred for dozens of tissues/cell types from large collections of cross-tissue epigenomic data, and have demonstrated sensitivity in predicting affected tissues in complex traits. It remains unclear, however, whether including additional genome-scale data specific to the tissue of interest would appreciably improve functional annotations.

Results

Herein, we introduce TiSAn, a tool that integrates multiple genome-scale data sources, defined by expert knowledge. TiSAn uses machine learning to discriminate variants relevant to a tissue from those with no bearing on the function of that tissue. Predictions are made genome-wide, and can be used to contextualize and filter variants of interest in whole genome sequencing or genome-wide association studies. We demonstrate the accuracy and flexibility of TiSAn by producing predictive models for human heart and brain, and detecting tissue-relevant variations in large cohorts for autism spectrum disorder (TiSAn-brain) and coronary artery disease (TiSAn-heart). We find the multiomics TiSAn model is better able to prioritize genetic variants according to their tissue-specific action than the current state-of-the-art method, GenoSkyLine.

Availability and implementation

Software and vignettes are available at http://github.com/kevinVervier/TiSAn.

Supplementary information

Supplementary data are available at Bioinformatics online.

Exome sequencing of 457 autism families recruited online provides evidence for autism risk genes

Autism spectrum disorder (ASD) is a genetically heterogeneous condition, caused by a combination of rare de novo and inherited variants as well as common variants in at least several hundred genes. However, significantly larger sample sizes are needed to identify the complete set of genetic risk factors. We conducted a pilot study for SPARK (SPARKForAutism.org) of 457 families with ASD, all consented online. Whole exome sequencing (WES) and genotyping data were generated for each family using DNA from saliva. We identified variants in genes and loci that are clinically recognized causes or significant contributors to ASD in 10.4% of families without previous genetic findings. In addition, we identified variants that are possibly associated with ASD in an additional 3.4% of families. A meta-analysis using the TADA framework at a false discovery rate (FDR) of 0.1 provides statistical support for 26 ASD risk genes. While most of these genes are already known ASD risk genes, BRSK2 has the strongest statistical support and reaches genome-wide significance as a risk gene for ASD (p-value = 2.3e-06). Future studies leveraging the thousands of individuals with ASD who have enrolled in SPARK are likely to further clarify the genetic risk factors associated with ASD as well as allow accelerate ASD research that incorporates genetic etiology.

Drug repositioning in epilepsy reveals novel antiseizure candidates

Objective

Epilepsy treatment falls short in ~30% of cases. A better understanding of epilepsy pathophysiology can guide rational drug development in this difficult to treat condition. We tested a low-cost, drug-repositioning strategy to identify candidate epilepsy drugs that are already FDA-approved and might be immediately tested in epilepsy patients who require new therapies.

Methods

Biopsies of spiking and nonspiking hippocampal brain tissue from six patients with unilateral mesial temporal lobe epilepsy were analyzed by RNA-Seq. These profiles were correlated with transcriptomes from cell lines treated with FDA-approved drugs, identifying compounds which were tested for therapeutic efficacy in a zebrafish seizure assay.

Results

In spiking versus nonspiking biopsies, RNA-Seq identified 689 differentially expressed genes, 148 of which were previously cited in articles mentioning seizures or epilepsy. Differentially expressed genes were highly enriched for protein-protein interactions and formed three clusters with associated GO-terms including myelination, protein ubiquitination, and neuronal migration. Among the 184 compounds, a zebrafish seizure model tested the therapeutic efficacy of doxycycline, metformin, nifedipine, and pyrantel tartrate, with metformin, nifedipine, and pyrantel tartrate all showing efficacy.

Interpretation

This proof-of-principle analysis suggests our powerful, rapid, cost-effective approach can likely be applied to other hard-to-treat diseases.

Whole-genome sequencing in a family with twin boys with autism and intellectual disability suggests multimodal polygenic risk

Over the past decade, a focus on de novo mutations has rapidly accelerated gene discovery in autism spectrum disorder (ASD), intellectual disability (ID), and other neurodevelopmental disorders (NDDs). However, recent studies suggest that only a minority of cases are attributable to de novo mutations, and instead these disorders often result from an accumulation of various forms of genetic risk. Consequently, we adopted an inclusive approach to investigate the genetic risk contributing to a case of male monozygotic twins with ASD and ID. At the time of the study, the probands were 7 yr old and largely nonverbal. Medical records indicated a history of motor delays, sleep difficulties, and significant cognitive deficits. Through whole-genome sequencing of the probands and their parents, we uncovered elevated common polygenic risk, a coding de novo point mutation in CENPE, an ultra-rare homozygous regulatory variant in ANK3, inherited rare variants in NRXN3, and a maternally inherited X-linked deletion situated in a noncoding regulatory region between ZNF81 and ZNF182 Although each of these genes has been directly or indirectly associated with NDDs, evidence suggests that no single variant adequately explains the probands' phenotype. Instead, we propose that the probands' condition is due to the confluence of multiple rare variants in the context of a high-risk genetic background. This case emphasizes the multifactorial nature of genetic risk underlying most instances of NDDs and aligns with the "female protective model" of ASD.

cerebroViz: an R package for anatomical visualization of spatiotemporal brain data

Summary

Spatiotemporal transcriptomic profiling has provided valuable insight into the patterning of gene expression throughout the human brain from early fetal development to adulthood. When combined with prior knowledge of a disease's age at onset and region-specificity, these expression profiles have provided the necessary context to both strengthen putative gene-disease associations and infer new associations. While a wealth of spatiotemporal expression data exists, there are currently no tools available to visualize this data within the anatomical context of the brain, thus limiting the intuitive interpretation of many such findings. We present cerebroViz, an R package to map spatiotemporal brain data to vector graphic diagrams of the human brain. Our tool allows rapid generation of publication-quality figures that highlight spatiotemporal trends in the input data, while striking a balance between usability and customization. cerebroViz is generalizable to any data quantifiable at a brain-regional resolution and currently supports visualization of up to thirty regions of the brain found in databases such as BrainSpan, GTEx and Roadmap Epigenomics.

Availability and Implementation

cerebroViz is freely available through GitHub ( https://github.com/ethanbahl/cerebroViz ). The tutorial is available at http://ethanbahl.github.io/cerebroViz/.

Contacts

ethan-bahl@uiowa.edu or jacob-michaelson@uiowa.edu.

Supplementary information

Supplementary data are available at Bioinformatics online.

Integrated genetic and epigenetic prediction of coronary heart disease in the Framingham Heart Study

An improved method for detecting coronary heart disease (CHD) could have substantial clinical impact. Building on the idea that systemic effects of CHD risk factors are a conglomeration of genetic and environmental factors, we use machine learning techniques and integrate genetic, epigenetic and phenotype data from the Framingham Heart Study to build and test a Random Forest classification model for symptomatic CHD. Our classifier was trained on n = 1,545 individuals and consisted of four DNA methylation sites, two SNPs, age and gender. The methylation sites and SNPs were selected during the training phase. The final trained model was then tested on n = 142 individuals. The test data comprised of individuals removed based on relatedness to those in the training dataset. This integrated classifier was capable of classifying symptomatic CHD status of those in the test set with an accuracy, sensitivity and specificity of 78%, 0.75 and 0.80, respectively. In contrast, a model using only conventional CHD risk factors as predictors had an accuracy and sensitivity of only 65% and 0.42, respectively, but with a specificity of 0.89 in the test set. Regression analyses of the methylation signatures illustrate our ability to map these signatures to known risk factors in CHD pathogenesis. These results demonstrate the capability of an integrated approach to effectively model symptomatic CHD status. These results also suggest that future studies of biomaterial collected from longitudinally informative cohorts that are specifically characterized for cardiac disease at follow-up could lead to the introduction of sensitive, readily employable integrated genetic-epigenetic algorithms for predicting onset of future symptomatic CHD.

Novel and ultra-rare damaging variants in neuropeptide signaling are associated with disordered eating behaviors

Objective

Eating disorders develop through a combination of genetic vulnerability and environmental stress, however the genetic basis of this risk is unknown.

Methods

To understand the genetic basis of this risk, we performed whole exome sequencing on 93 unrelated individuals with eating disorders (38 restricted-eating and 55 binge-eating) to identify novel damaging variants. Candidate genes with an excessive burden of predicted damaging variants were then prioritized based upon an unbiased, data-driven bioinformatic analysis. One top candidate pathway was empirically tested for therapeutic potential in a mouse model of binge-like eating.

Results

An excessive burden of novel damaging variants was identified in 186 genes in the restricted-eating group and 245 genes in the binge-eating group. This list is significantly enriched (OR = 4.6, p<0.0001) for genes involved in neuropeptide/neurotrophic pathways implicated in appetite regulation, including neurotensin-, glucagon-like peptide 1- and BDNF-signaling. Administration of the glucagon-like peptide 1 receptor agonist exendin-4 significantly reduced food intake in a mouse model of ‘binge-like’ eating.

Conclusions

These findings implicate ultra-rare and novel damaging variants in neuropeptide/neurotropic factor signaling pathways in the development of eating disorder behaviors and identify glucagon-like peptide 1-receptor agonists as a potential treatment for binge eating.

Neuronal PAS Domain Proteins 1 and 3 Are Master Regulators of Neuropsychiatric Risk Genes

BACKGROUND

NPAS3 has been established as a robust genetic risk factor in major mental illness. In mice, loss of neuronal PAS domain protein 3 (NPAS3) impairs postnatal hippocampal neurogenesis, while loss of the related protein NPAS1 promotes it. These and other findings suggest a critical role for NPAS proteins in neuropsychiatric functioning, prompting interest in the molecular pathways under their control.

METHODS

We used RNA sequencing coupled with chromatin immunoprecipitation sequencing to identify genes directly regulated by NPAS1 and NPAS3 in the hippocampus of wild-type, Npas1^-/-, and Npas3^-/- mice. Computational integration with human genetic and expression data revealed the disease relevance of NPAS-regulated genes and pathways. Specific findings were confirmed at the protein level by Western blot.

RESULTS

This is the first in vivo, transcriptome-scale investigation of genes regulated by NPAS1 and NPAS3. These transcription factors control an ensemble of genes that are themselves also major regulators of neuropsychiatric function. Specifically, Fmr1 (fragile X syndrome) and Ube3a (Angelman syndrome) are transcriptionally regulated by NPAS3, as is the neurogenesis regulator Notch. Dysregulation of these pathways was confirmed at the protein level. Furthermore, NPAS1/3 targets show increased human genetic burden for schizophrenia and intellectual disability.

CONCLUSIONS

Together, these data provide a clear, unbiased view of the full spectrum of genes regulated by NPAS1 and NPAS3 and show that these transcription factors are master regulators of neuropsychiatric function. These findings expose the molecular pathophysiology of NPAS1/3 mutations and provide a striking example of the shared, combinatorial nature of molecular pathways that underlie diagnostically distinct neuropsychiatric conditions.

Hotspots of missense mutation identify neurodevelopmental disorder genes and functional domains

Although de novo missense mutations have been predicted to account for more cases of autism than gene-truncating mutations, most research has focused on the latter. We identified the properties of de novo missense mutations in patients with neurodevelopmental disorders (NDDs) and highlight 35 genes with excess missense mutations. Additionally, 40 amino acid sites were recurrently mutated in 36 genes, and targeted sequencing of 20 sites in 17,688 patients with NDD identified 21 new patients with identical missense mutations. One recurrent site substitution (p.A636T) occurs in a glutamate receptor subunit, GRIA1. This same amino acid substitution in the homologous but distinct mouse glutamate receptor subunit Grid2 is associated with Lurcher ataxia. Phenotypic follow-up in five individuals with GRIA1 mutations shows evidence of specific learning disabilities and autism. Overall, we find significant clustering of de novo mutations in 200 genes, highlighting specific functional domains and synaptic candidate genes important in NDD pathology.

Genetic Approaches to Understanding Psychiatric Disease

Human genetic studies have been the driving force in bringing to light the underlying biology of psychiatric conditions. As these studies fill in the gaps in our knowledge of the mechanisms at play, we will be better equipped to design therapies in rational and targeted ways, or repurpose existing therapies in previously unanticipated ways. This review is intended for those unfamiliar with psychiatric genetics as a field and provides a primer on different modes of genetic variation, the technologies currently used to probe them, and concepts that provide context for interpreting the gene-phenotype relationship. Like other subfields in human genetics, psychiatric genetics is moving from microarray technology to sequencing-based approaches as barriers of cost and expertise are removed, and the ramifications of this transition are discussed here. A summary is then given of recent genetic discoveries in a number of neuropsychiatric conditions, with particular emphasis on neurodevelopmental conditions. The general impact of genetics on drug development has been to underscore the extensive etiological heterogeneity in seemingly cohesive diagnostic categories. Consequently, the path forward is not in therapies hoping to reach large swaths of patients sharing a clinically defined diagnosis, but rather in targeting patients belonging to specific "biotypes" defined through a combination of objective, quantifiable data, including genotype.

SLINGER: large-scale learning for predicting gene expression

Recent studies have established that single nucleotide polymorphisms are sufficient to build accurate predictive models of gene expression. Gamazon, et al., found that gene expression values predicted from cis neighborhood SNPs show statistical association with disease status. In this work, we remove the cis neighborhood constraint during the learning process, and propose a novel predictive approach called SLINGER. We demonstrate that models drawing from a genome-wide set of SNPs are able to predict expression for more genes than the ones built on cis neighborhood only. Results indicate that these new models significantly improve accuracy for a large number of genes. Thanks to a penalized linear model, we also show that the number of features used in our models remains comparable to the cis-only models. Finally, SLINGER application on seven Wellcome Trust Case-Control Consortium genome-wide association studies demonstrate that compared to a cis-only approach, our models lead to associations with greater fidelity to actual gene expression values.

Common Genetic Variants in FOXP2 Are Not Associated with Individual Differences in Language Development

Much of our current knowledge regarding the association of FOXP2 with speech and language development comes from singleton and small family studies where a small number of rare variants have been identified. However, neither genome-wide nor gene-specific studies have provided evidence that common polymorphisms in the gene contribute to individual differences in language development in the general population. One explanation for this inconsistency is that previous studies have been limited to relatively small samples of individuals with low language abilities, using low density gene coverage. The current study examined the association between common variants in FOXP2 and a quantitative measure of language ability in a population-based cohort of European decent (n = 812). No significant associations were found for a panel of 13 SNPs that covered the coding region of FOXP2 and extended into the promoter region. Power analyses indicated we should have been able to detect a QTL variance of 0.02 for an associated allele with MAF of 0.2 or greater with 80% power. This suggests that, if a common variant associated with language ability in this gene does exist, it is likely of small effect. Our findings lead us to conclude that while genetic variants in FOXP2 may be significant for rare forms of language impairment, they do not contribute appreciably to individual variation in the normal range as found in the general population.

Adaptation of the targeted capture Methyl-Seq platform for the mouse genome identifies novel tissue-specific DNA methylation patterns of genes involved in neurodevelopment

Methyl-Seq was recently developed as a targeted approach to assess DNA methylation (DNAm) at a genome-wide level in human. We adapted it for mouse and sought to examine DNAm differences across liver and 2 brain regions: cortex and hippocampus. A custom hybridization array was designed to isolate 99 Mb of CpG islands, shores, shelves, and regulatory elements in the mouse genome. This was followed by bisulfite conversion and sequencing on the Illumina HiSeq2000. The majority of differentially methylated cytosines (DMCs) were present at greater than expected frequency in introns, intergenic regions, near CpG islands, and transcriptional enhancers. Liver-specific enhancers were observed to be methylated in cortex, while cortex specific enhancers were methylated in the liver. Interestingly, commonly shared enhancers were differentially methylated between the liver and cortex. Gene ontology and pathway analysis showed that genes that were hypomethylated in the cortex and hippocampus were enriched for neuronal components and neuronal function. In contrast, genes that were hypomethylated in the liver were enriched for cellular components important for liver function. Bisulfite-pyrosequencing validation of 75 DMCs from 19 different loci showed a correlation of r = 0.87 with Methyl-Seq data. We also identified genes involved in neurodevelopment that were not previously reported to be differentially methylated across brain regions. This platform constitutes a valuable tool for future genome-wide studies involving mouse models of disease.

Protein interaction network of alternatively spliced isoforms from brain links genetic risk factors for autism

Increased risk for autism spectrum disorders (ASD) is attributed to hundreds of genetic loci. The convergence of ASD variants have been investigated using various approaches, including protein interactions extracted from the published literature. However, these datasets are frequently incomplete, carry biases and are limited to interactions of a single splicing isoform, which may not be expressed in the disease-relevant tissue. Here we introduce a new interactome mapping approach by experimentally identifying interactions between brain-expressed alternatively spliced variants of ASD risk factors. The Autism Spliceform Interaction Network reveals that almost half of the detected interactions and about 30% of the newly identified interacting partners represent contribution from splicing variants, emphasizing the importance of isoform networks. Isoform interactions greatly contribute to establishing direct physical connections between proteins from the de novo autism CNVs. Our findings demonstrate the critical role of spliceform networks for translating genetic knowledge into a better understanding of human diseases.

Whole-genome sequencing in autism identifies hot spots for de novo germline mutation

De novo mutation plays an important role in autism spectrum disorders (ASDs). Notably, pathogenic copy number variants (CNVs) are characterized by high mutation rates. We hypothesize that hypermutability is a property of ASD genes and may also include nucleotide-substitution hot spots. We investigated global patterns of germline mutation by whole-genome sequencing of monozygotic twins concordant for ASD and their parents. Mutation rates varied widely throughout the genome (by 100-fold) and could be explained by intrinsic characteristics of DNA sequence and chromatin structure. Dense clusters of mutations within individual genomes were attributable to compound mutation or gene conversion. Hypermutability was a characteristic of genes involved in ASD and other diseases. In addition, genes impacted by mutations in this study were associated with ASD in independent exome-sequencing data sets. Our findings suggest that regional hypermutation is a significant factor shaping patterns of genetic variation and disease risk in humans.

A complete mass-spectrometric map of the yeast proteome applied to quantitative trait analysis

Experience from different fields of life sciences suggests that accessible, complete reference maps of the components of the system under study are highly beneficial research tools. Examples of such maps include libraries of the spectroscopic properties of molecules, or databases of drug structures in analytical or forensic chemistry. Such maps, and methods to navigate them, constitute reliable assays to probe any sample for the presence and amount of molecules contained in the map. So far, attempts to generate such maps for any proteome have failed to reach complete proteome coverage. Here we use a strategy based on high-throughput peptide synthesis and mass spectrometry to generate an almost complete reference map (97% of the genome-predicted proteins) of the Saccharomyces cerevisiae proteome. We generated two versions of this mass-spectrometric map, one supporting discovery-driven (shotgun) and the other supporting hypothesis-driven (targeted) proteomic measurements. Together, the two versions of the map constitute a complete set of proteomic assays to support most studies performed with contemporary proteomic technologies. To show the utility of the maps, we applied them to a protein quantitative trait locus (QTL) analysis, which requires precise measurement of the same set of peptides over a large number of samples. Protein measurements over 78 S. cerevisiae strains revealed a complex relationship between independent genetic loci, influencing the levels of related proteins. Our results suggest that selective pressure favours the acquisition of sets of polymorphisms that adapt protein levels but also maintain the stoichiometry of functionally related pathway members.

Differential relationship of DNA replication timing to different forms of human mutation and variation

Human genetic variation is distributed nonrandomly across the genome, though the principles governing its distribution are only partially known. DNA replication creates opportunities for mutation, and the timing of DNA replication correlates with the density of SNPs across the human genome. To enable deeper investigation of how DNA replication timing relates to human mutation and variation, we generated a high-resolution map of the human genome's replication timing program and analyzed its relationship to point mutations, copy number variations, and the meiotic recombination hotspots utilized by males and females. DNA replication timing associated with point mutations far more strongly than predicted from earlier analyses and showed a stronger relationship to transversion than transition mutations. Structural mutations arising from recombination-based mechanisms and recombination hotspots used more extensively by females were enriched in early-replicating parts of the genome, though these relationships appeared to relate more strongly to the genomic distribution of causative sequence features. These results indicate differential and sex-specific relationship of DNA replication timing to different forms of mutation and recombination.

Teamwork: improved eQTL mapping using combinations of machine learning methods

Expression quantitative trait loci (eQTL) mapping is a widely used technique to uncover regulatory relationships between genes. A range of methodologies have been developed to map links between expression traits and genotypes. The DREAM (Dialogue on Reverse Engineering Assessments and Methods) initiative is a community project to objectively assess the relative performance of different computational approaches for solving specific systems biology problems. The goal of one of the DREAM5 challenges was to reverse-engineer genetic interaction networks from synthetic genetic variation and gene expression data, which simulates the problem of eQTL mapping. In this framework, we proposed an approach whose originality resides in the use of a combination of existing machine learning algorithms (committee). Although it was not the best performer, this method was by far the most precise on average. After the competition, we continued in this direction by evaluating other committees using the DREAM5 data and developed a method that relies on Random Forests and LASSO. It achieved a much higher average precision than the DREAM best performer at the cost of slightly lower average sensitivity.

High frequencies of de novo CNVs in bipolar disorder and schizophrenia

While it is known that rare copy-number variants (CNVs) contribute to risk for some neuropsychiatric disorders, the role of CNVs in bipolar disorder is unclear. Here, we reasoned that a contribution of CNVs to mood disorders might be most evident for de novo mutations. We performed a genome-wide analysis of de novo CNVs in a cohort of 788 trios. Diagnoses of offspring included bipolar disorder (n = 185), schizophrenia (n = 177), and healthy controls (n = 426). Frequencies of de novo CNVs were significantly higher in bipolar disorder as compared with controls (OR = 4.8 [1.4,16.0], p = 0.009). De novo CNVs were particularly enriched among cases with an age at onset younger than 18 (OR = 6.3 [1.7,22.6], p = 0.006). We also confirmed a significant enrichment of de novo CNVs in schizophrenia (OR = 5.0 [1.5,16.8], p = 0.007). Our results suggest that rare spontaneous mutations are an important contributor to risk for bipolar disorder and other major neuropsychiatric diseases.

Transcriptional signatures of regulatory and toxic responses to benzo-[a]-pyrene exposure

Background

Small molecule ligands often have multiple effects on the transcriptional program of a cell: they trigger a receptor specific response and additional, indirect responses ("side effects"). Distinguishing those responses is important for understanding side effects of drugs and for elucidating molecular mechanisms of toxic chemicals.

Results

We explored this problem by exposing cells to the environmental contaminant benzo-[a]-pyrene (B[a]P). B[a]P exposure activates the aryl hydrocarbon receptor (Ahr) and causes toxic stress resulting in transcriptional changes that are not regulated through Ahr. We sought to distinguish these two types of responses based on a time course of expression changes measured after B[a]P exposure. Using Random Forest machine learning we classified 81 primary Ahr responders and 1,308 genes regulated as side effects. Subsequent weighted clustering gave further insight into the connection between expression pattern, mode of regulation, and biological function. Finally, the accuracy of the predictions was supported through extensive experimental validation.

Conclusion

Using a combination of machine learning followed by extensive experimental validation, we have further expanded the known catalog of genes regulated by the environmentally sensitive transcription factor Ahr. More broadly, this study presents a strategy for distinguishing receptor-dependent responses and side effects based on expression time courses.

Integrative analysis of low- and high-resolution eQTL

The study of expression quantitative trait loci (eQTL) is a powerful way of detecting transcriptional regulators at a genomic scale and for elucidating how natural genetic variation impacts gene expression. Power and genetic resolution are heavily affected by the study population: whereas recombinant inbred (RI) strains yield greater statistical power with low genetic resolution, using diverse inbred or outbred strains improves genetic resolution at the cost of lower power. In order to overcome the limitations of both individual approaches, we combine data from RI strains with genetically more diverse strains and analyze hippocampus eQTL data obtained from mouse RI strains (BXD) and from a panel of diverse inbred strains (Mouse Diversity Panel, MDP). We perform a systematic analysis of the consistency of eQTL independently obtained from these two populations and demonstrate that a significant fraction of eQTL can be replicated. Based on existing knowledge from pathway databases we assess different approaches for using the high-resolution MDP data for fine mapping BXD eQTL. Finally, we apply this framework to an eQTL hotspot on chromosome 1 (Qrr1), which has been implicated in a range of neurological traits. Here we present the first systematic examination of the consistency between eQTL obtained independently from the BXD and MDP populations. Our analysis of fine-mapping approaches is based on 'real life' data as opposed to simulated data and it allows us to propose a strategy for using MDP data to fine map BXD eQTL. Application of this framework to Qrr1 reveals that this eQTL hotspot is not caused by just one (or few) 'master regulators', but actually by a set of polymorphic genes specific to the central nervous system.

Data-driven assessment of eQTL mapping methods

Background

The analysis of expression quantitative trait loci (eQTL) is a potentially powerful way to detect transcriptional regulatory relationships at the genomic scale. However, eQTL data sets often go underexploited because legacy QTL methods are used to map the relationship between the expression trait and genotype. Often these methods are inappropriate for complex traits such as gene expression, particularly in the case of epistasis.

Results

Here we compare legacy QTL mapping methods with several modern multi-locus methods and evaluate their ability to produce eQTL that agree with independent external data in a systematic way. We found that the modern multi-locus methods (Random Forests, sparse partial least squares, lasso, and elastic net) clearly outperformed the legacy QTL methods (Haley-Knott regression and composite interval mapping) in terms of biological relevance of the mapped eQTL. In particular, we found that our new approach, based on Random Forests, showed superior performance among the multi-locus methods.

Conclusions

Benchmarks based on the recapitulation of experimental findings provide valuable insight when selecting the appropriate eQTL mapping method. Our battery of tests suggests that Random Forests map eQTL that are more likely to be validated by independent data, when compared to competing multi-locus and legacy eQTL mapping methods.