Identification of the hybrid gene LILRB5-3 by long-read sequencing and implication of its novel signaling function.

Leukocyte immunoglobulin (Ig)-like receptors (LILRs) on human chromosome 19q13.4 encode 11 immunoglobulin superfamily receptors, exhibiting genetic diversity within and between human populations. Among the LILR genes, the genomic region surrounding LILRB3 and LILRA6 has yet to be fully characterized due to their significant sequence homology, which makes it difficult to differentiate between them. To examine the LILRB3 and LILRA6 genomic region, a tool named JoGo-LILR CN Caller, which can call copy number from short-read whole genome sequencing (srWGS) data, was applied to an extensive international srWGS dataset comprising 2,504 samples. During this process, a previously unreported loss of both LILRB3 and LILRA6 was detected in three samples. Using long-read sequencing of these samples, we have discovered a novel large deletion (33,692 bp) in the LILRB3 and LILRA6 genomic regions in the Japanese population. This deletion spanned three genes, LILRB3, LILRA6, and LILRB5, resulting in LILRB3 exons 12-13 being located immediately downstream of LILRB5 exons 1-12 with the loss of LILRA6, suggesting the potential expression of a hybrid gene between LILRB5 and LILRB3 (LILRB5-3). Transcription and subsequent translation of the LILRB5-3 hybrid gene were also verified. The hybrid junction was located within the intracellular domain, resulting in an LILRB5 extracellular domain fused to a partial LILRB3 intracellular domain with three immunoreceptor tyrosine-based inhibitory motifs (ITIMs), suggesting that LILRB5-3 acquired a novel signaling function. Further application of the JoGo-LILR tool to srWGS samples suggested the presence of the LILRB5-3 hybrid gene in the CEU population. Our findings provide insight into the genetic and functional diversity of the LILR family.

Discrimination between healthy participants and people with panic disorder based on polygenic scores for psychiatric disorders and for intermediate phenotypes using machine learning.

OBJECTIVE: Panic disorder is a modestly heritable condition. Currently, diagnosis is based only on clinical symptoms; identifying objective biomarkers and a more reliable diagnostic procedure is desirable. We investigated whether people with panic disorder can be reliably diagnosed utilizing combinations of multiple polygenic scores for psychiatric disorders and their intermediate phenotypes, compared with single polygenic score approaches, by applying specific machine learning techniques. METHODS: Polygenic scores for 48 psychiatric disorders and intermediate phenotypes based on large-scale genome-wide association studies (n = 7556-1,131,881) were calculated for people with panic disorder (n = 718) and healthy controls (n = 1717). Discrimination between people with panic disorder and healthy controls was based on the 48 polygenic scores using five methods for classification: logistic regression, neural networks, quadratic discriminant analysis, random forests and a support vector machine. Differences in discrimination accuracy (area under the curve) due to an increased number of polygenic score combinations and differences in the accuracy across five classifiers were investigated. RESULTS: All five classifiers performed relatively well for distinguishing people with panic disorder from healthy controls by increasing the number of polygenic scores. Of the 48 polygenic scores, the polygenic score for anxiety UK Biobank was the most useful for discrimination by the classifiers. In combinations of two or three polygenic scores, the polygenic score for anxiety UK Biobank was included as one of polygenic scores in all classifiers. When all 48 polygenic scores were used in combination, the greatest areas under the curve significantly differed among the five classifiers. Support vector machine and logistic regression had higher accuracy than quadratic discriminant analysis and random forests. For each classifier, the greatest area under the curve was 0.600 ± 0.030 for logistic regression (polygenic score combinations N = 14), 0.591 ± 0.039 for neural networks (N = 9), 0.603 ± 0.033 for quadratic discriminant analysis (N = 10), 0.572 ± 0.039 for random forests (N = 25) and 0.617 ± 0.041 for support vector machine (N = 11). The greatest areas under the curve at the best polygenic score combination significantly differed among the five classifiers. Random forests had the lowest accuracy among classifiers. Support vector machine had higher accuracy than neural networks. CONCLUSIONS: These findings suggest that increasing the number of polygenic score combinations up to approximately 10 effectively improved the discrimination accuracy and that support vector machine exhibited greater accuracy among classifiers. However, the discrimination accuracy for panic disorder, when based solely on polygenic score combinations, was found to be modest.

Identification of region-specific gene isoforms in the human brain using long-read transcriptome sequencing.

In neurological and neuropsychiatric diseases, different brain regions are affected, and differences in gene expression patterns could potentially explain this mechanism. However, limited studies have precisely explored gene expression in different regions of the human brain. In this study, we performed long-read RNA sequencing on three different brain regions of the same individuals: the cerebellum, hypothalamus, and temporal cortex. Despite stringent filtering criteria excluding isoforms predicted to be artifacts, over half of the isoforms expressed in multiple samples across multiple regions were found to be unregistered in the GENCODE reference. We then especially focused on genes with different major isoforms in each brain region, even with similar overall expression levels, and identified that many of such genes including GAS7 might have distinct roles in dendritic spine and neuronal formation in each region. We also found that DNA methylation might, in part, drive different isoform expressions in different regions. These findings highlight the significance of analyzing isoforms expressed in disease-relevant sites.

Genome-wide DNA methylation risk scores for schizophrenia derived from blood and brain tissues further explain the genetic risk in patients stratified by polygenic risk scores for schizophrenia and bipolar disorder.

BACKGROUND: Genetic and environmental factors contribute to the pathogenesis of schizophrenia (SZ) and bipolar disorder (BD). Among genetic risk groups stratified by combinations of Polygenic Risk Score (PRS) deciles for SZ, BD and SZ versus BD, genetic SZ risk groups had high SZ risk and prominent cognitive impairments. Furthermore, epigenetic alterations are implicated in these disorders. However, it was unclear whether DNA Methylation Risk Scores (MRSs) for SZ risk derived from blood and brain tissues were associated with SZ risk, particularly the PRS-stratified genetic SZ risk group. METHODS: Epigenome-wide association studies (EWASs) of SZ risk in whole blood were preliminarily conducted between 66 SZ patients and 30 healthy controls (HCs) and among genetic risk groups (individuals with low genetic risk for SZ and BD in HCs (n=30) and in SZ patients (n=11), genetic BD risk in SZ patients (n=25) and genetic SZ risk in SZ patients (n=30)) stratified by combinations of PRSs for SZ, BD and SZ versus BD. Next, differences in MRSs based on independent EWASs of SZ risk in whole blood, postmortem frontal cortex (FC) and superior temporal gyrus (STG) were investigated among our case‒control and PRS-stratified genetic risk status groups. RESULTS: Among case‒control and genetic risk status groups, 33 and 351 genome-wide significant differentially methylated positions (DMPs) associated with SZ were identified, respectively, many of which were hypermethylated. Compared with the low genetic risk in HCs group, the genetic SZ risk in SZ group had 39 genome-wide significant DMPs, while the genetic BD risk in SZ group had only six genome-wide significant DMPs. The MRSs for SZ risk derived from whole blood, FC and STG were higher in our SZ patients than in HCs in whole blood and were particularly higher in the genetic SZ risk in SZ group than in the low genetic risk in HCs and genetic BD risk in SZ groups. Conversely, the MRSs for SZ risk based on our whole-blood EWASs among genetic risk groups were also associated with SZ in the FC and STG. There were no correlations between the MRSs and PRSs. CONCLUSIONS: These findings suggest that the MRS is a potential genetic marker in understanding SZ, particularly in patients with a genetic SZ risk.

Genome-wide association study of idiopathic hypersomnia in a Japanese population.

Idiopathic hypersomnia (IH) is a rare sleep disorder characterized by excessive daytime sleepiness, great difficulty upon awakening, and prolonged sleep time. In contrast to narcolepsy type 1, which is a well-recognized hypersomnia, the etiology of IH remains poorly understood. No susceptibility loci for IH have been identified, although familial aggregations have been observed among patients with IH. Narcolepsy type 1 is strongly associated with human leukocyte antigen (HLA)-DQB1*06:02; however, no significant associations between IH and HLA alleles have been reported. To identify genetic variants that affect susceptibility to IH, we performed a genome-wide association study (GWAS) and two replication studies involving a total of 414 Japanese patients with IH and 6587 healthy Japanese individuals. A meta-analysis of the three studies found no single-nucleotide polymorphisms (SNPs) that reached the genome-wide significance level. However, we identified several candidate SNPs for IH. For instance, a common genetic variant (rs2250870) within an intron of PDE9A was suggestively associated with IH. rs2250870 was significantly associated with expression levels of PDE9A in not only whole blood but also brain tissues. The leading SNP in the PDE9A region was the same in associations with both IH and PDE9A expression. PDE9A is a potential target in the treatment of several brain diseases, such as depression, schizophrenia, and Alzheimer's disease. It will be necessary to examine whether PDE9A inhibitors that have demonstrated effects on neurophysiologic and cognitive function can contribute to the development of new treatments for IH, as higher expression levels of PDE9A were observed with regard to the risk allele of rs2250870. The present study constitutes the first GWAS of genetic variants associated with IH. A larger replication study will be required to confirm these associations. Supplementary Information: The online version contains supplementary material available at 10.1007/s41105-021-00349-2.