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The thymus is a primary lymphoid organ that plays an essential role in T lymphocyte maturation and selection during development of one arm of the mammalian adaptive immune response. Although transcriptional mechanisms have been well documented in thymocyte development, co-/post-transcriptional modifications are also important but have received less attention. Here we demonstrate that the RNA alternative splicing factor MBNL1, which is sequestered in nuclear RNA foci by C(C)UG microsatellite expansions in myotonic dystrophy (DM), is essential for normal thymus development and function. Mbnl1 129S1 knockout mice develop postnatal thymic hyperplasia with thymocyte accumulation. Transcriptome analysis indicates numerous gene expression and RNA mis-splicing events, including transcription factors from the TCF/LEF family. CNBP , the gene containing an intronic CCTG microsatellite expansion in DM type 2 (DM2), is coordinately expressed with MBNL1 in the developing thymus and DM2 CCTG expansions induce similar transcriptome alterations in DM2 blood, which thus serve as disease-specific biomarkers. The activity of the RNA splicing factor MBNL1 is altered in myotonic dystrophy (DM) patients. Here the authors characterize the thymic phenotype of Mbnl1 knockout mice, including developmental defects, transcriptome changes, and RNA mis-splicing of transcripts encoding thymic transcription factors.

Tourette syndrome (TS) and obsessive-compulsive disorder (OCD) are neuropsychiatric disorders with onset in childhood affecting 0.6% and 2.3% of people, respectively. TS and OCD are also highly comorbid with 50-60% of TS patients endorsing OCD, and 10% of OCD patients endorsing TS. Both TS and OCD are highly heritable, with heritability estimates ranging 30% to 60% in family and twin studies. Despite substantial heritability estimates, little is known about underlying genetic mechanisms of OCD and related disorders (OCRD).In this dissertation, I explore OCRDs from both phenomic and genomic aspects. I use rich phenotypes from ABCD Study to investigate OCRD comorbidity and relationships with symptom-level data from the child behavioral checklist (CBCL). I also leverage genome-wide association to explore genetic architecture of OCD and related phenotypes, including polygenic risk score (PRS) analysis with tic disorders within ABCD Study and 12 disorders from the Psychiatric Genomics Consortium. I additionally explore copy-number variation (CNV) among neurodevelopmental disorders, specifically focusing on neurodevelopmental disorders including TS and autism spectrum disorder (ASD).Phenomic analysis of psychopathology in ABCD Study has shown hyperinflated rates of psychiatric disorders in the ABCD Study, likely due to self-endorsement bias. To circumvent that, I define a narrow diagnosis construct that utilizes longitudinal data to refine psychiatric diagnoses. Narrow OCD (nOCD) better reflected childhood OCD prevalence rates and comorbidity patters, and a stronger relationship with symptom-level data from CBCL. Genomic assessment of nOCD has also shown stronger PRS relationship with OCD symptoms compared to broad OCD. Similar effects were also observed in PRS analysis with 12 PGC disorders. CNV analysis of TS has resulted in successful replication of TS-risk contribution by NRXN1 deletions and CNTN6 duplications, as well as identification of 39 additional genes that could potentially contribute to TS pathology. However, genome-wide burdens of CNV numbers or sizes were not replicated.Deconvoluting genetic and phenomic relationships and underpinnings of OCRDs is a complicated task confounded primarily by low sample sizes and suboptimal methodologies. Thus, increased recruitment efforts and improvements to statistical and computational methodologies to analyze these data will likely be the main drivers of discoveries in the OCRD genomics space.

Copy-number variants (CNVs) are a form of genetic structural variation with increasing importance in complex human disorders. Both DNA sequencing and microarray data can be used to call CNVs, which can be used in association tests, such as association between CNV number and disease status. Unlike genotypes, CNV detection in microarrays requires the use of observed intensity signals at each probe, which limits the imputability for analyses that span multiple array types. Thus far, a consensus set of probes (the intersection encompassing the probes that occur in common on all arrays) has been used to circumvent the problem of differing array-specific sensitivities. This has, however, led to excessive reduction in overall sensitivity of CNV calls as arrays can have an undesirably low overlap of probe sets. To overcome this limitation, we developed MarkerMatch, a proximity-based algorithm that matches probes across different genotyping microarrays to maximize the number of probes considered in the CNV calling algorithm, thereby increasing the resolution and sensitivity while preserving precision. By analyzing CNV calls from 4,906 individuals genotyped across three different arrays (Global Screening Array, Omni2.5 array, and Omni Express Exome array), we show that the MarkerMatch approach improves sensitivity by increasing the density of probes available for CNV calling while maintaining precision or improving it relative to the current practice (e.g., use of consensus probes only). We further demonstrate that MarkerMatch exceeds the output from current practice in terms of F1 score, Fowlkes-Mallows index, and Jaccard index. We also optimize MarkerMatch parameters, and , and find an optimal setting at 10kb, with no clear optimal candidate based on , indicating that parameters for this metric should be determined on a use case basis.

Tourette syndrome (TS) is a highly heritable childhood-onset neuropsychiatric disorder characterized by persistent motor and vocal tics. While both common and rare variants contribute to TS susceptibility, the role of rare mutations (DNMs) remains incompletely characterized. Here, we report findings from the largest TS whole-exome sequencing study to date, analyzing 1,466 TS trios alongside 6,714 autism spectrum disorder (ASD) trios and 5,880 unaffected sibling controls from the Simons Simplex Collection (SSC) and SPARK cohorts. Leveraging a trio-based design across these cohorts enabled calibrated assessment of DNM burden while controlling for background mutation rates. We observed a significant exome-wide enrichment of protein-truncating DNMs in TS probands, particularly within genes intolerant to loss-of-function variation (pLI ≥ 0.9), with little contribution from damaging missense variants. Notably, TS probands did not exhibit enrichment in previously implicated ASD or developmental delay (DD) genes, but elsewhere in the genome, suggesting a distinct rare variant architecture. Using a Bayesian statistical framework that integrates both and rare inherited coding variants, we identified three candidate TS risk genes with FDR ≤ 0.05: , , and . Literature shows that they have prior links to neurodevelopmental and psychiatric disorders. These findings reveal a rare variant burden in TS that is genetically distinguishable from ASD, underscore the importance of loss-of-function mutations in TS risk, and nominate novel candidate genes for future functional investigation.

The degree of gene and sequence preservation across species provides valuable insights into the relative necessity of genes from the perspective of natural selection. Here, we developed novel interspecies metrics across 462 mammalian species, GISMO (Gene identity score of mammalian orthologs) and GISMO-mis (GISMO-missense), to quantify gene loss traversing millions of years of evolution. GISMO is a measure of gene loss across mammals weighed by evolutionary distance relative to humans, whereas GISMO-mis quantifies the ratio of missense to synonymous variants across mammalian species for a given gene. Despite large sample sizes, current human constraint metrics are still not well calibrated for short genes. Traversing over 100 million years of evolution across hundreds of mammals can identify the most essential genes and improve gene-disease association. Beyond human genetics, these metrics provide measures of gene constraint to further enable mammalian genetics research. Our analyses showed that both metrics are strongly correlated with measures of human gene constraint for loss-of-function, missense, and copy number dosage derived from upwards of a million human samples, which highlight the power of interspecies constraint. Importantly, neither GISMO nor GISMO-mis are strongly correlated with coding sequence length. Therefore both metrics can identify novel constrained genes that were too small for existing human constraint metrics to capture. We also found that GISMO scores capture rare variant association signals across a range of phenotypes associated with decreased fecundity, such as schizophrenia, autism, and neurodevelopmental disorders. Moreover, common variant heritability of disease traits are highly enriched in the most constrained deciles of both metrics, further underscoring the biological relevance of these metrics in identifying functionally important genes. We further showed that both scores have the lowest duplication and deletion rate in the most constrained deciles for copy number variants in the UK Biobank, suggesting that it may be an important metric for dosage sensitivity. We additionally demonstrate that GISMO can improve prioritization of recessive disorder genes and captures homozygous selection. Overall, we demonstrate that the most constrained genes for gene loss and missense variation capture the largest fraction of heritability, GISMO can help prioritize recessive disorder genes, and identify the most conserved genes across the mammalian tree.

RNA-binding proteins may play a critical role in gene regulation in various diseases or biological processes by controlling post-transcriptional events such as polyadenylation, splicing, and mRNA stabilization via binding activities to RNA molecules. Due to the importance of RNA-binding proteins in gene regulation, a great number of studies have been conducted, resulting in a large amount of RNA-Seq datasets. However, these datasets usually do not have structured organization of metadata, which limits their potentially wide use. To bridge this gap, the metadata of a comprehensive set of publicly available mouse RNA-Seq datasets with perturbed RNA-binding proteins were collected and integrated into a database called RBPMetaDB. This database contains 278 mouse RNA-Seq datasets for a comprehensive list of 163 RNA-binding proteins. These RNA-binding proteins account for only ~10% of all known RNA-binding proteins annotated in Gene Ontology, indicating that most are still unexplored using high-throughput sequencing. This negative information provides a great pool of candidate RNA-binding proteins for biologists to conduct future experimental studies. In addition, we found that DNA-binding activities are significantly enriched among RNA-binding proteins in RBPMetaDB, suggesting that prior studies of these DNA- and RNA-binding factors focus more on DNA-binding activities instead of RNA-binding activities. This result reveals the opportunity to efficiently reuse these data for investigation of the roles of their RNA-binding activities. A web application has also been implemented to enable easy access and wide use of RBPMetaDB. It is expected that RBPMetaDB will be a great resource for improving understanding of the biological roles of RNA-binding proteins. Database URL: http://rbpmetadb.yubiolab.org . Footnotes * Several citations are added.

Although the number of RNA-Seq datasets deposited publicly has increased over the past few years, incomplete annotation of the associated metadata limits their potential use. Because of the importance of RNA splicing in diseases and biological processes, we constructed a database called SFMetaDB by curating datasets related with RNA splicing factors. Our effort focused on the RNA-Seq datasets in which splicing factors were knocked-down, knocked-out or over-expressed, leading to 75 datasets corresponding to 56 splicing factors. These datasets can be used in differential alternative splicing analysis for the identification of the potential targets of these splicing factors and other functional studies. Surprisingly, only ~15% of all the splicing factors have been studied by loss- or gain-of-function experiments using RNA-Seq. In particular, splicing factors with domains from a few dominant Pfam domain families have not been studied. This suggests a significant gap that needs to be addressed to fully elucidate the splicing regulatory landscape. Indeed, there are already mouse models available for ~20 of the unstudied splicing factors, and it can be a fruitful research direction to study these splicing factors in vitro and in vivo using RNA-Seq.