Integrating whole-genome sequencing with multi-omic data reveals the impact of structural variants on gene regulation in the human brain

Genetic analysis of psychosis Biotypes: shared Ancestry-adjusted polygenic risk and unique genomic associations

The Bipolar-Schizophrenia Network for Intermediate Phenotypes (B-SNIP) created psychosis Biotypes based on neurobiological measurements in a multi-ancestry sample. These Biotypes cut across DSM diagnoses of schizophrenia, schizoaffective disorder, and bipolar disorder with psychosis. Two recently developed post hoc ancestry adjustment methods of Polygenic Risk Scores (PRSs) generate Ancestry-Adjusted PRSs (AAPRSs), which allow for PRS analysis of multi-ancestry samples. Applied to schizophrenia PRS, we found the Khera AAPRS method to show superior portability and comparable prediction accuracy as compared with the Ge method. The three Biotypes of psychosis disorders had similar AAPRSs across ancestries. In genomic analysis of Biotypes, 12 genes, and isoforms showed significant genomic associations with specific Biotypes in a Transcriptome-Wide Association Study (TWAS) of genetically regulated expression (GReX) in the adult brain and fetal brain. TWAS inflation was addressed by the inclusion of genotype principal components in the association analyses. Seven of these 12 genes/isoforms satisfied Mendelian Randomization (MR) criteria for putative causality, including four genes TMEM140, ARTN, C1orf115, CYREN, and three transcripts ENSG00000272941, ENSG00000257176, ENSG00000287733. These genes are enriched in the biological pathways of Rearranged during Transfection (RET) signaling, Neural Cell Adhesion Molecule 1 (NCAM1) interactions, and NCAM signaling for neurite out-growth. The specific associations with Biotypes suggest that pharmacological clinical trials and biological investigations might benefit from analyzing Biotypes separately.

Alternative spliceosomal protein Eftud2 mediated Kif3a exon skipping promotes SHH-subgroup medulloblastoma progression

Alternative splicing plays a pivotal role in various facets of organogenesis, immune response, and tumorigenesis. Medulloblastoma represents a prevalent childhood brain tumor, with approximately one-third classified as the Sonic Hedgehog (SHH) subgroup. Nevertheless, the contribution of alternative splicing to medulloblastoma oncogenesis remains elusive. This investigation delineated an upregulation of the spliceosomal protein Eftud2 in the SHH-subgroup medulloblastoma mouse model and human medulloblastoma patients. Targeted ablation of Eftud2 in granule precursor cells (GNPs) within the cerebellum prolonged the survival of SHH-subgroup medulloblastoma mice, indicating a putative association between Eftud2 expression and medulloblastoma prognosis. Functional assays unveiled that EFTUD2 depletion in human medulloblastoma cells significantly curtailed cellular proliferation by impeding the activation of the SHH signaling pathway. Through multi-omics sequencing analysis, it was discerned that Eftud2 influences exons 10-11 skipping of Kif3a, a kinesin motor critical for primary cilia formation. Notably, exons 10-11 skipping in Kif3a augmented human medulloblastoma cell proliferation by potentiating the transcriptional activity of Gli2. These findings underscore a robust correlation between Eftud2 and SHH-subgroup medulloblastoma, emphasizing its regulatory role in modulating downstream transcription factors through the alternative splicing of pivotal genes within the SHH signaling pathway, thereby propelling the aggressive proliferation of SHH-subgroup medulloblastoma.

Cross-ancestry genome-wide association study and systems-level integrative analyses implicate new risk genes and therapeutic targets for depression

Deciphering the genetic architecture of depression is pivotal for characterizing the associated pathophysiological processes and development of new therapeutics. Here we conducted a cross-ancestry genome-wide meta-analysis on depression (416,437 cases and 1,308,758 controls) and identified 287 risk loci, of which 49 are new. Variant-level fine mapping prioritized potential causal variants and functional genomic analysis identified variants that regulate the binding of transcription factors. We validated that 80% of the identified functional variants are regulatory variants, and expression quantitative trait loci analysis uncovered the potential target genes regulated by the prioritized risk variants. Gene-level analysis, including transcriptome and proteome-wide association studies, colocalization and Mendelian randomization-based analyses, prioritized potential causal genes and drug targets. Gene prioritization analyses highlighted likely causal genes, including TMEM106B, CTNND1, AREL1 and so on. Pathway analysis indicated significant enrichment of depression risk genes in synapse-related pathways. Finally, knockdown of Tmem106b in mice resulted in depression-like behaviours, supporting the involvement of Tmem106b in depression. Our study identified new risk loci, likely causal variants and genes for depression, providing important insights into the genetic architecture of depression and potential therapeutic targets.

Structural variants linked to Alzheimer's disease and other common age-related clinical and neuropathologic traits

BACKGROUND

Alzheimer's disease (AD) is a complex neurodegenerative disorder with substantial genetic influence. While genome-wide association studies (GWAS) have identified numerous risk loci for late-onset AD (LOAD), the functional mechanisms underlying most of these associations remain unresolved. Large genomic rearrangements, known as structural variants (SVs), represent a promising avenue for elucidating such mechanisms within some of these loci.

METHODS

By leveraging data from two ongoing cohort studies of aging and dementia, the Religious Orders Study and Rush Memory and Aging Project (ROS/MAP), we performed genome-wide association analysis testing 20,205 common SVs from 1088 participants with whole genome sequencing (WGS) data. A range of Alzheimer's disease and other common age-related clinical and neuropathologic traits were examined.

RESULTS

First, we mapped SVs across 81 AD risk loci and discovered 22 SVs in linkage disequilibrium (LD) with GWAS lead variants and directly associated with the phenotypes tested. The strongest association was a deletion of an Alu element in the 3'UTR of the TMEM106B gene, in high LD with the respective AD GWAS locus and associated with multiple AD and AD-related disorders (ADRD) phenotypes, including tangles density, TDP-43, and cognitive resilience. The deletion of this element was also linked to lower TMEM106B protein abundance. We also found a 22-kb deletion associated with depression in ROS/MAP and bearing similar association patterns as GWAS SNPs at the IQCK locus. In addition, we leveraged our catalog of SV-GWAS to replicate and characterize independent findings in SV-based GWAS for AD and five other neurodegenerative diseases. Among these findings, we highlight the replication of genome-wide significant SVs for progressive supranuclear palsy (PSP), including markers for the 17q21.31 MAPT locus inversion and a 1483-bp deletion at the CYP2A13 locus, along with other suggestive associations, such as a 994-bp duplication in the LMNTD1 locus, suggestively linked to AD and a 3958-bp deletion at the DOCK5 locus linked to Lewy body disease (LBD) (P = 3.36 × 10-4).

CONCLUSIONS

While still limited in sample size, this study highlights the utility of including analysis of SVs for elucidating mechanisms underlying GWAS loci and provides a valuable resource for the characterization of the effects of SVs in neurodegenerative disease pathogenesis.

SV4GD: a comprehensive structural variation database specially for genetic diseases

Structural variations (SVs) contribute to a large extent to genomic diversity and are highly relevant for various human genetic diseases. The sensitivity and specificity of SV identification have significantly improved with the development and widespread application of high-throughput sequencing, making clinical diagnosis and treatment more accurate. Therefore, the SV4GD (Structural Variation for Genetic Diseases, https://bio-computing.hrbmu.edu.cn/SV4GD/), a manually curated database, was constructed to provide a comprehensive, standardized and user-friendly data resource for selective batch browsing, searching, downloading and comparing those genetic disease-relevant SVs. This database compiles 10 305 records of germline structural variants from 58 human neoplastic diseases and 232 non-neoplastic genetic diseases, including 2695 disease-related SVs, and other 7610 pending research SVs detected from patients. SV4GD provides a browser and search engine to query for the detailed information of SVs, human genetic diseases and the clinical information of patients, providing an easy-to-use online tool for clinical and molecular genetics research.

Long-read sequencing of hundreds of diverse brains provides insight into the impact of structural variation on gene expression and DNA methylation

Structural variants (SVs) drive gene expression in the human brain and are causative of many neurological conditions. However, most existing genetic studies have been based on short-read sequencing methods, which capture fewer than half of the SVs present in any one individual. Long-read sequencing (LRS) enhances our ability to detect disease-associated and functionally relevant structural variants (SVs); however, its application in large-scale genomic studies has been limited by challenges in sample preparation and high costs. Here, we leverage a new scalable wet-lab protocol and computational pipeline for whole-genome Oxford Nanopore Technologies sequencing and apply it to neurologically normal control samples from the North American Brain Expression Consortium (NABEC) (European ancestry) and Human Brain Collection Core (HBCC) (African or African admixed ancestry) cohorts. Through this work, we present a publicly available long-read resource from 351 human brain samples (median N50: 27 Kbp and at an average depth of ~40x genome coverage). We discover approximately 234,905 SVs and produce locally phased assemblies that cover 95% of all protein-coding genes in GRCh38. Utilizing matched expression datasets for these samples, we apply quantitative trait locus (QTL) analyses and identify SVs that impact gene expression in post-mortem frontal cortex brain tissue. Further, we determine haplotype-specific methylation signatures at millions of CpGs and, with this data, identify cis-acting SVs. In summary, these results highlight that large-scale LRS can identify complex regulatory mechanisms in the brain that were inaccessible using previous approaches. We believe this new resource provides a critical step toward understanding the biological effects of genetic variation in the human brain.

Genetic Analysis of Psychosis Biotypes: Shared Ancestry-Adjusted Polygenic Risk and Unique Genomic Associations

The Bipolar-Schizophrenia Network for Intermediate Phenotypes (B-SNIP) created psychosis Biotypes based on neurobiological measurements in a multi-ancestry sample. These Biotypes cut across DSM diagnoses of schizophrenia, schizoaffective disorder and bipolar disorder with psychosis. Two recently developed post hoc ancestry adjustment methods of Polygenic Risk Scores (PRSs) generate Ancestry-Adjusted PRSs (AAPRSs), which allow for PRS analysis of multi-ancestry samples. Applied to schizophrenia PRS, we found the Khera AAPRS method to show superior portability and comparable prediction accuracy as compared with the Ge method. The three Biotypes of psychosis disorders had similar AAPRSs across ancestries. In genomic analysis of Biotypes, 12 genes and isoforms showed significant genomic associations with specific Biotypes in Transcriptome-Wide Association Study (TWAS) of genetically regulated expression (GReX) in adult brain and fetal brain. TWAS inflation was addressed by inclusion of genotype principal components in the association analyses. Seven of these 12 genes/isoforms satisfied Mendelian Randomization (MR) criteria for putative causality, including four genes TMEM140, ARTN, C1orf115, CYREN, and three transcripts ENSG00000272941, ENSG00000257176, ENSG00000287733. These genes are enriched in the biological pathways of Rearranged during Transfection (RET) signaling, Neural Cell Adhesion Molecule 1 (NCAM1) interactions, and NCAM signaling for neurite out-growth. The specific associations with Biotypes suggest that pharmacological clinical trials and biological investigations might benefit from analyzing Biotypes separately.

Engineering structural variants to interrogate genome function

Structural variation, such as deletions, duplications, inversions and complex rearrangements, can have profound effects on gene expression, genome stability, phenotypic diversity and disease susceptibility. Structural variants can encompass up to millions of bases and have the potential to rearrange substantial segments of the genome. They contribute considerably more to genetic diversity in human populations and have larger effects on phenotypic traits than point mutations. Until recently, our understanding of the effects of structural variants was driven mainly by studying naturally occurring variation. New genome-engineering tools capable of generating deletions, insertions, inversions and translocations, together with the discovery of new recombinases and advances in creating synthetic DNA constructs, now enable the design and generation of an extended range of structural variation. Here, we discuss these tools and examples of their application and highlight existing challenges that will need to be overcome to fully harness their potential.

Population-level exploration of alternative splicing and its unique role in controlling agronomic traits of rice

Alternative splicing (AS) plays crucial roles in regulating various biological processes in plants. However, the genetic mechanisms underlying AS and its role in controlling important agronomic traits in rice (Oryza sativa) remain poorly understood. In this study, we explored AS in rice leaves and panicles using the rice minicore collection. Our analysis revealed a high level of transcript isoform diversity, with approximately one-fifth of the potential isoforms acting as major transcripts in both tissues. Regarding the genetic mechanism of AS, we found that the splicing of 833 genes in the leaf and 1,230 genes in the panicle was affected by cis-genetic variation. Twenty-one percent of these AS events could only be explained by large structural variations. Approximately 77.5% of genes with significant splicing quantitative trait loci (sGenes) exhibited tissue-specific regulation, and AS can cause 26.9% (leaf) and 23.6% (panicle) of sGenes to have altered, lost, or gained functional domains. Additionally, through splicing-phenotype association analysis, we identified phosphate-starvation-induced RING-type E3 ligase (OsPIE1; LOC_Os01g72480), whose splicing ratio was significantly associated with plant height. In summary, this study provides an understanding of AS in rice and its contribution to the regulation of important agronomic traits.

The ROSMAP project: aging and neurodegenerative diseases through omic sciences

The Religious Order Study and Memory and Aging Project (ROSMAP) is an initiative that integrates two longitudinal cohort studies, which have been collecting clinicopathological and molecular data since the early 1990s. This extensive dataset includes a wide array of omic data, revealing the complex interactions between molecular levels in neurodegenerative diseases (ND) and aging. Neurodegenerative diseases (ND) are frequently associated with morbidity and cognitive decline in older adults. Omics research, in conjunction with clinical variables, is crucial for advancing our understanding of the diagnosis and treatment of neurodegenerative diseases. This summary reviews the extensive omics research-encompassing genomics, transcriptomics, proteomics, metabolomics, epigenomics, and multiomics-conducted through the ROSMAP study. It highlights the significant advancements in understanding the mechanisms underlying neurodegenerative diseases, with a particular focus on Alzheimer's disease.

Prioritizing disease-related rare variants by integrating gene expression data

Rare variants, comprising the vast majority of human genetic variations, are likely to have more deleterious impact in the context of human diseases compared to common variants. Here we present carrier statistic, a statistical framework to prioritize disease-related rare variants by integrating gene expression data. By quantifying the impact of rare variants on gene expression, carrier statistic can prioritize those rare variants that have large functional consequence in the patients. Through simulation studies and analyzing real multi-omics dataset, we demonstrated that carrier statistic is applicable in studies with limited sample size (a few hundreds) and achieves substantially higher sensitivity than existing rare variants association methods. Application to Alzheimer's disease reveals 16 rare variants within 15 genes with extreme carrier statistics. We also found strong excess of rare variants among the top prioritized genes in patients compared to that in healthy individuals. The carrier statistic method can be applied to various rare variant types and is adaptable to other omics data modalities, offering a powerful tool for investigating the molecular mechanisms underlying complex diseases.

Structural variants linked to Alzheimer's Disease and other common age-related clinical and neuropathologic traits

Advances have led to a greater understanding of the genetics of Alzheimer's Disease (AD). However, the gap between the predicted and observed genetic heritability estimates when using single nucleotide polymorphisms (SNPs) and small indel data remains. Large genomic rearrangements, known as structural variants (SVs), have the potential to account for this missing genetic heritability. By leveraging data from two ongoing cohort studies of aging and dementia, the Religious Orders Study and Rush Memory and Aging Project (ROS/MAP), we performed genome-wide association analysis testing around 20,000 common SVs from 1,088 participants with whole genome sequencing (WGS) data. A range of Alzheimer's Disease and Related Disorders (AD/ADRD) clinical and pathologic traits were examined. Given the limited sample size, no genome-wide significant association was found, but we mapped SVs across 81 AD risk loci and discovered 22 SVs in linkage disequilibrium (LD) with GWAS lead variants and directly associated with AD/ADRD phenotypes (nominal P < 0.05). The strongest association was a deletion of an Alu element in the 3'UTR of the TMEM106B gene. This SV was in high LD with the respective AD GWAS locus and was associated with multiple AD/ADRD phenotypes, including tangle density, TDP-43, and cognitive resilience. The deletion of this element was also linked to lower TMEM106B protein abundance. We also found a 22 kb deletion associated with depression in ROSMAP and bearing similar association patterns as AD GWAS SNPs at the IQCK locus. In addition, genome-wide scans allowed the identification of 7 SVs, with no LD with SNPs and nominally associated with AD/ADRD traits. This result suggests potentially new ADRD risk loci not discoverable using SNP data. Among these findings, we highlight a 5.6 kb duplication of coding regions of the gene C1orf186 at chromosome 1 associated with indices of cognitive impairment, decline, and resilience. While further replication in independent datasets is needed to validate these findings, our results support the potential roles of common structural variations in the pathogenesis of AD/ADRD.

Critical reasoning on the co-expression module QTL in the dorsolateral prefrontal cortex

Expression quantitative trait locus (eQTL) analysis is a popular method of gaining insight into the function of regulatory variation. While cis-eQTL resources have been instrumental in linking genome-wide association study variants to gene function, complex trait heritability may be additionally mediated by other forms of gene regulation. Toward this end, novel eQTL methods leverage gene co-expression (module-QTL) to investigate joint regulation of gene modules by single genetic variants. Here we broadly define a "module-QTL" as the association of a genetic variant with a summary measure of gene co-expression. This approach aims to reduce the multiple testing burden of a trans-eQTL search through the consolidation of gene-based testing and provide biological context to eQTLs shared between genes. In this article we provide an in-depth examination of the co-expression module eQTL (module-QTL) through literature review, theoretical investigation, and real-data application of the module-QTL to three large prefrontal cortex genotype-RNA sequencing datasets. We find module-QTLs in our study that are disease associated and reproducible are not additionally informative beyond cis- or trans-eQTLs for module genes. Through comparison to prior studies, we highlight promises and limitations of the module-QTL across study designs and provide recommendations for further investigation of the module-QTL framework.

Analysis of 3760 hematologic malignancies reveals rare transcriptomic aberrations of driver genes

BACKGROUND

Rare oncogenic driver events, particularly affecting the expression or splicing of driver genes, are suspected to substantially contribute to the large heterogeneity of hematologic malignancies. However, their identification remains challenging.

METHODS

To address this issue, we generated the largest dataset to date of matched whole genome sequencing and total RNA sequencing of hematologic malignancies from 3760 patients spanning 24 disease entities. Taking advantage of our dataset size, we focused on discovering rare regulatory aberrations. Therefore, we called expression and splicing outliers using an extension of the workflow DROP (Detection of RNA Outliers Pipeline) and AbSplice, a variant effect predictor that identifies genetic variants causing aberrant splicing. We next trained a machine learning model integrating these results to prioritize new candidate disease-specific driver genes.

RESULTS

We found a median of seven expression outlier genes, two splicing outlier genes, and two rare splice-affecting variants per sample. Each category showed significant enrichment for already well-characterized driver genes, with odds ratios exceeding three among genes called in more than five samples. On held-out data, our integrative modeling significantly outperformed modeling based solely on genomic data and revealed promising novel candidate driver genes. Remarkably, we found a truncated form of the low density lipoprotein receptor LRP1B transcript to be aberrantly overexpressed in about half of hairy cell leukemia variant (HCL-V) samples and, to a lesser extent, in closely related B-cell neoplasms. This observation, which was confirmed in an independent cohort, suggests LRP1B as a novel marker for a HCL-V subclass and a yet unreported functional role of LRP1B within these rare entities.

CONCLUSIONS

Altogether, our census of expression and splicing outliers for 24 hematologic malignancy entities and the companion computational workflow constitute unique resources to deepen our understanding of rare oncogenic events in hematologic cancers.

Prioritizing disease-related rare variants by integrating gene expression data

Rare variants, comprising a vast majority of human genetic variations, are likely to have more deleterious impact on human diseases compared to common variants. Here we present carrier statistic, a statistical framework to prioritize disease-related rare variants by integrating gene expression data. By quantifying the impact of rare variants on gene expression, carrier statistic can prioritize those rare variants that have large functional consequence in the diseased patients. Through simulation studies and analyzing real multi-omics dataset, we demonstrated that carrier statistic is applicable in studies with limited sample size (a few hundreds) and achieves substantially higher sensitivity than existing rare variants association methods. Application to Alzheimer's disease reveals 16 rare variants within 15 genes with extreme carrier statistics. We also found strong excess of rare variants among the top prioritized genes in diseased patients compared to that in healthy individuals. The carrier statistic method can be applied to various rare variant types and is adaptable to other omics data modalities, offering a powerful tool for investigating the molecular mechanisms underlying complex diseases.

The Role of Structural Variants in the Genetic Architecture of Parkinson's Disease

Parkinson's disease (PD) significantly impacts millions of individuals worldwide. Although our understanding of the genetic foundations of PD has advanced, a substantial portion of the genetic variation contributing to disease risk remains unknown. Current PD genetic studies have primarily focused on one form of genetic variation, single nucleotide variants (SNVs), while other important forms of genetic variation, such as structural variants (SVs), are mostly ignored due to the complexity of detecting these variants with traditional sequencing methods. Yet, these forms of genetic variation play crucial roles in gene expression and regulation in the human brain and are causative of numerous neurological disorders, including forms of PD. This review aims to provide a comprehensive overview of our current understanding of the involvement of coding and noncoding SVs in the genetic architecture of PD.

Pangenome analysis reveals transposon-driven genome evolution in cotton

BACKGROUND

Transposable elements (TEs) have a profound influence on the trajectory of plant evolution, driving genome expansion and catalyzing phenotypic diversification. The pangenome, a comprehensive genetic pool encompassing all variations within a species, serves as an invaluable tool, unaffected by the confounding factors of intraspecific diversity. This allows for a more nuanced exploration of plant TE evolution.

RESULTS

Here, we constructed a pangenome for diploid A-genome cotton using 344 accessions from representative geographical regions, including 223 from China as the main component. We found 511 Mb of non-reference sequences (NRSs) and revealed the presence of 5479 previously undiscovered protein-coding genes. Our comprehensive approach enabled us to decipher the genetic underpinnings of the distinct geographic distributions of cotton. Notably, we identified 3301 presence-absence variations (PAVs) that are closely tied to gene expression patterns within the pangenome, among which 2342 novel expression quantitative trait loci (eQTLs) were found residing in NRSs. Our investigation also unveiled contrasting patterns of transposon proliferation between diploid and tetraploid cotton, with long terminal repeat (LTR) retrotransposons exhibiting a synchronized surge in polyploids. Furthermore, the invasion of LTR retrotransposons from the A subgenome to the D subgenome triggered a substantial expansion of the latter following polyploidization. In addition, we found that TE insertions were responsible for the loss of 36.2% of species-specific genes, as well as the generation of entirely new species-specific genes.

CONCLUSIONS

Our pangenome analyses provide new insights into cotton genomics and subgenome dynamics after polyploidization and demonstrate the power of pangenome approaches for elucidating transposon impacts and genome evolution.

Prioritizing disease-related rare variants by integrating gene expression data

Rare variants, comprising a vast majority of human genetic variations, are likely to have more deleterious impact on human diseases compared to common variants. Here we present carrier statistic, a statistical framework to prioritize disease-related rare variants by integrating gene expression data. By quantifying the impact of rare variants on gene expression, carrier statistic can prioritize those rare variants that have large functional consequence in the diseased patients. Through simulation studies and analyzing real multi-omics dataset, we demonstrated that carrier statistic is applicable in studies with limited sample size (a few hundreds) and achieves substantially higher sensitivity than existing rare variants association methods. Application to Alzheimer's disease reveals 16 rare variants within 15 genes with extreme carrier statistics. The carrier statistic method can be applied to various rare variant types and is adaptable to other omics data modalities, offering a powerful tool for investigating the molecular mechanisms underlying complex diseases.

Multi-modal molecular determinants of clinically relevant osteoporosis subtypes

Due to a rapidly aging global population, osteoporosis and the associated risk of bone fractures have become a wide-spread public health problem. However, osteoporosis is very heterogeneous, and the existing standard diagnostic measure is not sufficient to accurately identify all patients at risk of osteoporotic fractures and to guide therapy. Here, we constructed the first prospective multi-omics atlas of the largest osteoporosis cohort to date (longitudinal data from 366 participants at three time points), and also implemented an explainable data-intensive analysis framework (DLSF: Deep Latent Space Fusion) for an omnigenic model based on a multi-modal approach that can capture the multi-modal molecular signatures (M3S) as explicit functional representations of hidden genotypes. Accordingly, through DLSF, we identified two subtypes of the osteoporosis population in Chinese individuals with corresponding molecular phenotypes, i.e., clinical intervention relevant subtypes (CISs), in which bone mineral density benefits response to calcium supplements in 2-year follow-up samples. Many snpGenes associated with these molecular phenotypes reveal diverse candidate biological mechanisms underlying osteoporosis, with xQTL preferences of osteoporosis and its subtypes indicating an omnigenic effect on different biological domains. Finally, these two subtypes were found to have different relevance to prior fracture and different fracture risk according to 4-year follow-up data. Thus, in clinical application, M3S could help us further develop improved diagnostic and treatment strategies for osteoporosis and identify a new composite index for fracture prediction, which were remarkably validated in an independent cohort (166 participants).

Innovative approach for high-throughput exploiting sex-specific markers in Japanese parrotfish Oplegnathus fasciatus

BACKGROUND

The use of sex-specific molecular markers has become a prominent method in enhancing fish production and economic value, as well as providing a foundation for understanding the complex molecular mechanisms involved in fish sex determination. Over the past decades, research on male and female sex identification has predominantly employed molecular biology methodologies such as restriction fragment length polymorphism, random amplification of polymorphic DNA, simple sequence repeat, and amplified fragment length polymorphism. The emergence of high-throughput sequencing technologies, particularly Illumina, has led to the utilization of single nucleotide polymorphism and insertion/deletion variants as significant molecular markers for investigating sex identification in fish. The advancement of sex-controlled breeding encounters numerous challenges, including the inefficiency of current methods, intricate experimental protocols, high costs of development, elevated rates of false positives, marker instability, and cumbersome field-testing procedures. Nevertheless, the emergence and swift progress of PacBio high-throughput sequencing technology, characterized by its long-read output capabilities, offers novel opportunities to overcome these obstacles.

FINDINGS

Utilizing male/female assembled genome information in conjunction with short-read sequencing data survey and long-read PacBio sequencing data, a catalog of large-segment (>100 bp) insertion/deletion genetic variants was generated through a genome-wide variant site-scanning approach with bidirectional comparisons. The sequence tagging sites were ranked based on the long-read depth of the insertion/deletion site, with markers exhibiting lower long-read depth being considered more effective for large-segment deletion variants. Subsequently, a catalog of bulk primers and simulated PCR for the male/female variant loci was developed, incorporating primer design for the target region and electronic PCR (e-PCR) technology. The Japanese parrotfish (Oplegnathus fasciatus), belonging to the Oplegnathidae family within the Centrarchiformes order, holds significant economic value as a rocky reef fish indigenous to East Asia. The criteria for rapid identification of male and female differences in Japanese parrotfish were established through agarose gel electrophoresis, which revealed 2 amplified bands for males and 1 amplified band for females. A high-throughput identification catalog of sex-specific markers was then constructed using this method, resulting in the identification of 3,639 (2,786 INS/853 DEL, ♀ as reference) and 3,672 (2,876 INS/833 DEL, ♂ as reference) markers in conjunction with 1,021 and 894 high-quality genetic sex identification markers, respectively. Sixteen differential loci were randomly chosen from the catalog for validation, with 11 of them meeting the criteria for male/female distinctions. The implementation of cost-effective and efficient technological processes would facilitate the rapid advancement of genetic breeding through expediting the high-throughput development of sex genetic markers for various species.

CONCLUSIONS

Our study utilized assembled genome information from male and female individuals obtained from PacBio, in addition to data from short-read sequencing data survey and long-read PacBio sequencing data. We extensively employed genome-wide variant site scanning and identification, high-throughput primer design of target regions, and e-PCR batch amplification, along with statistical analysis and ranking of the long-read depth of the variant sites. Through this integrated approach, we successfully compiled a catalog of large insertion/deletion sites (>100 bp) in both male and female Japanese parrotfish.

Unraveling the intercellular communication disruption and key pathways in Alzheimer's disease: an integrative study of single-nucleus transcriptomes and genetic association

BACKGROUND

Recently, single-nucleus RNA-seq (snRNA-seq) analyses have revealed important cellular and functional features of Alzheimer's disease (AD), a prevalent neurodegenerative disease. However, our knowledge regarding intercellular communication mediated by dysregulated ligand-receptor (LR) interactions remains very limited in AD brains.

METHODS

We systematically assessed the intercellular communication networks by using a discovery snRNA-seq dataset comprising 69,499 nuclei from 48 human postmortem prefrontal cortex (PFC) samples. We replicated the findings using an independent snRNA-seq dataset of 56,440 nuclei from 18 PFC samples. By integrating genetic signals from AD genome-wide association studies (GWAS) summary statistics and whole genome sequencing (WGS) data, we prioritized AD-associated Gene Ontology (GO) terms containing dysregulated LR interactions. We further explored drug repurposing for the prioritized LR pairs using the Therapeutic Targets Database.

RESULTS

We identified 190 dysregulated LR interactions across six major cell types in AD PFC, of which 107 pairs were replicated. Among the replicated LR signals, we found globally downregulated communications in the astrocytes-to-neurons signaling axis, characterized, for instance, by the downregulation of APOE-related and Calmodulin (CALM)-related LR interactions and their potential regulatory connections to target genes. Pathway analyses revealed 44 GO terms significantly linked to AD, highlighting Biological Processes such as 'amyloid precursor protein processing' and 'ion transmembrane transport,' among others. We prioritized several drug repurposing candidates, such as cromoglicate, targeting the identified dysregulated LR pairs.

CONCLUSIONS

Our integrative analysis identified key dysregulated LR interactions in a cell type-specific manner and the associated GO terms in AD, offering novel insights into potential therapeutic targets involved in disrupted cell-cell communication in AD.

Artificial intelligence for dementia genetics and omics

Genetics and omics studies of Alzheimer's disease and other dementia subtypes enhance our understanding of underlying mechanisms and pathways that can be targeted. We identified key remaining challenges: First, can we enhance genetic studies to address missing heritability? Can we identify reproducible omics signatures that differentiate between dementia subtypes? Can high-dimensional omics data identify improved biomarkers? How can genetics inform our understanding of causal status of dementia risk factors? And which biological processes are altered by dementia-related genetic variation? Artificial intelligence (AI) and machine learning approaches give us powerful new tools in helping us to tackle these challenges, and we review possible solutions and examples of best practice. However, their limitations also need to be considered, as well as the need for coordinated multidisciplinary research and diverse deeply phenotyped cohorts. Ultimately AI approaches improve our ability to interrogate genetics and omics data for precision dementia medicine. HIGHLIGHTS: We have identified five key challenges in dementia genetics and omics studies. AI can enable detection of undiscovered patterns in dementia genetics and omics data. Enhanced and more diverse genetics and omics datasets are still needed. Multidisciplinary collaborative efforts using AI can boost dementia research.

Isoform-level transcriptome-wide association uncovers genetic risk mechanisms for neuropsychiatric disorders in the human brain

Methods integrating genetics with transcriptomic reference panels prioritize risk genes and mechanisms at only a fraction of trait-associated genetic loci, due in part to an overreliance on total gene expression as a molecular outcome measure. This challenge is particularly relevant for the brain, in which extensive splicing generates multiple distinct transcript-isoforms per gene. Due to complex correlation structures, isoform-level modeling from cis-window variants requires methodological innovation. Here we introduce isoTWAS, a multivariate, stepwise framework integrating genetics, isoform-level expression and phenotypic associations. Compared to gene-level methods, isoTWAS improves both isoform and gene expression prediction, yielding more testable genes, and increased power for discovery of trait associations within genome-wide association study loci across 15 neuropsychiatric traits. We illustrate multiple isoTWAS associations undetectable at the gene-level, prioritizing isoforms of AKT3, CUL3 and HSPD1 in schizophrenia and PCLO with multiple disorders. Results highlight the importance of incorporating isoform-level resolution within integrative approaches to increase discovery of trait associations, especially for brain-relevant traits.

Pharmacogenomics of Dementia: Personalizing the Treatment of Cognitive and Neuropsychiatric Symptoms

Dementia is a syndrome of global and progressive deterioration of cognitive skills, especially memory, learning, abstract thinking, and orientation, usually affecting the elderly. The most common forms are Alzheimer's disease, vascular dementia, and other (frontotemporal, Lewy body disease) dementias. The etiology of these multifactorial disorders involves complex interactions of various environmental and (epi)genetic factors and requires multiple forms of pharmacological intervention, including anti-dementia drugs for cognitive impairment, antidepressants, antipsychotics, anxiolytics and sedatives for behavioral and psychological symptoms of dementia, and other drugs for comorbid disorders. The pharmacotherapy of dementia patients has been characterized by a significant interindividual variability in drug response and the development of adverse drug effects. The therapeutic response to currently available drugs is partially effective in only some individuals, with side effects, drug interactions, intolerance, and non-compliance occurring in the majority of dementia patients. Therefore, understanding the genetic basis of a patient's response to pharmacotherapy might help clinicians select the most effective treatment for dementia while minimizing the likelihood of adverse reactions and drug interactions. Recent advances in pharmacogenomics may contribute to the individualization and optimization of dementia pharmacotherapy by increasing its efficacy and safety via a prediction of clinical outcomes. Thus, it can significantly improve the quality of life in dementia patients.

Leveraging molecular quantitative trait loci to comprehend complex diseases/traits from the omics perspective

Comprehending the molecular basis of quantitative genetic variation is a principal goal for complex diseases or traits. Molecular quantitative trait loci (molQTLs) have made it possible to investigate the effects of genetic variants hiding behind large-scale omics data. A deeper understanding of molQTL is urgently required in light of the multi-dimensionalization of omics data to more fully elucidate the pertinent biological mechanisms. Herein, we reviewed molQTLs with the corresponding resource from the omics perspective and further discussed the integrative strategy of GWAS-molQTL to infer their causal effects. Subsequently, we described the opportunities and challenges encountered by molQTL. The case studies showed that molQTL is essential for complex diseases and traits, whether single- or multi-omics QTLs. Overall, we highlighted the functional significance of genetic variants to employ the discovery of molQTL in complex diseases and traits.

Structural variants and short tandem repeats impact gene expression and splicing in bovine testis tissue

Structural variants (SVs) and short tandem repeats (STRs) are significant sources of genetic variation. However, the impacts of these variants on gene regulation have not been investigated in cattle. Here, we genotyped and characterized 19,408 SVs and 374,821 STRs in 183 bovine genomes and investigated their impact on molecular phenotypes derived from testis transcriptomes. We found that 71% STRs were multiallelic. The vast majority (95%) of STRs and SVs were in intergenic and intronic regions. Only 37% SVs and 40% STRs were in high linkage disequilibrium (LD) (R2 > 0.8) with surrounding SNPs/insertions and deletions (Indels), indicating that SNP-based association testing and genomic prediction are blind to a nonnegligible portion of genetic variation. We showed that both SVs and STRs were more than 2-fold enriched among expression and splicing QTL (e/sQTL) relative to SNPs/Indels and were often associated with differential expression and splicing of multiple genes. Deletions and duplications had larger impacts on splicing and expression than any other type of SV. Exonic duplications predominantly increased gene expression either through alternative splicing or other mechanisms, whereas expression- and splicing-associated STRs primarily resided in intronic regions and exhibited bimodal effects on the molecular phenotypes investigated. Most e/sQTL resided within 100 kb of the affected genes or splicing junctions. We pinpoint candidate causal STRs and SVs associated with the expression of SLC13A4 and TTC7B and alternative splicing of a lncRNA and CAPP1. We provide a catalog of STRs and SVs for taurine cattle and show that these variants contribute substantially to gene expression and splicing variation.

Unraveling the intercellular communication disruption and key pathways in Alzheimer's disease: An integrative study of single-nucleus transcriptomes and genetic association

Background

Recently, single-nucleus RNA-seq (snRNA-seq) analyses have revealed important cellular and functional features of Alzheimer's disease (AD), a prevalent neurodegenerative disease. However, our knowledge regarding intercellular communication mediated by dysregulated ligand-receptor (LR) interactions remains very limited in AD brains.

Methods

We systematically assessed the intercellular communication networks by using a discovery snRNA-seq dataset comprising 69,499 nuclei from 48 human postmortem prefrontal cortex (PFC) samples. We replicated the findings using an independent snRNA-seq dataset of 56,440 nuclei from 18 PFC samples. By integrating genetic signals from AD genome-wide association studies (GWAS) summary statistics and whole genome sequencing (WGS) data, we prioritized AD-associated Gene Ontology (GO) terms containing dysregulated LR interactions. We further explored drug repurposing for the prioritized LR pairs using the Therapeutic Targets Database.

Results

We identified 316 dysregulated LR interactions across six major cell types in AD PFC, of which 210 pairs were replicated. Among the replicated LR signals, we found globally downregulated communications in astrocytes-to-neurons signaling axis, characterized, for instance, by the downregulation of APOE-related and Calmodulin (CALM)-related LR interactions and their potential regulatory connections to target genes. Pathway analyses revealed 60 GO terms significantly linked to AD, highlighting Biological Processes such as 'amyloid precursor protein processing' and 'ion transmembrane transport', among others. We prioritized several drug repurposing candidates, such as cromoglicate, targeting the identified dysregulated LR pairs.

Conclusions

Our integrative analysis identified key dysregulated LR interactions in a cell type-specific manner and the associated GO terms in AD, offering novel insights into potential therapeutic targets involved in disrupted cell-cell communication in AD.

Applications of multi-omics analysis in human diseases

Multi-omics usually refers to the crossover application of multiple high-throughput screening technologies represented by genomics, transcriptomics, single-cell transcriptomics, proteomics and metabolomics, spatial transcriptomics, and so on, which play a great role in promoting the study of human diseases. Most of the current reviews focus on describing the development of multi-omics technologies, data integration, and application to a particular disease; however, few of them provide a comprehensive and systematic introduction of multi-omics. This review outlines the existing technical categories of multi-omics, cautions for experimental design, focuses on the integrated analysis methods of multi-omics, especially the approach of machine learning and deep learning in multi-omics data integration and the corresponding tools, and the application of multi-omics in medical researches (e.g., cancer, neurodegenerative diseases, aging, and drug target discovery) as well as the corresponding open-source analysis tools and databases, and finally, discusses the challenges and future directions of multi-omics integration and application in precision medicine. With the development of high-throughput technologies and data integration algorithms, as important directions of multi-omics for future disease research, single-cell multi-omics and spatial multi-omics also provided a detailed introduction. This review will provide important guidance for researchers, especially who are just entering into multi-omics medical research.

Taming transposable elements in livestock and poultry: a review of their roles and applications

Livestock and poultry play a significant role in human nutrition by converting agricultural by-products into high-quality proteins. To meet the growing demand for safe animal protein, genetic improvement of livestock must be done sustainably while minimizing negative environmental impacts. Transposable elements (TE) are important components of livestock and poultry genomes, contributing to their genetic diversity, chromatin states, gene regulatory networks, and complex traits of economic value. However, compared to other species, research on TE in livestock and poultry is still in its early stages. In this review, we analyze 72 studies published in the past 20 years, summarize the TE composition in livestock and poultry genomes, and focus on their potential roles in functional genomics. We also discuss bioinformatic tools and strategies for integrating multi-omics data with TE, and explore future directions, feasibility, and challenges of TE research in livestock and poultry. In addition, we suggest strategies to apply TE in basic biological research and animal breeding. Our goal is to provide a new perspective on the importance of TE in livestock and poultry genomes.

A Genome-Wide Screen for the Exonisation of Reference SINE-VNTR-Alus and Their Expression in CNS Tissues of Individuals with Amyotrophic Lateral Sclerosis

The hominid-specific retrotransposon SINE-VNTR-Alu (SVA) is a composite element that has contributed to the genetic variation between individuals and influenced genomic structure and function. SVAs are involved in modulating gene expression and splicing patterns, altering mRNA levels and sequences, and have been associated with the development of disease. We evaluated the genome-wide effects of SVAs present in the reference genome on transcript sequence and expression in the CNS of individuals with and without the neurodegenerative disorder Amyotrophic Lateral Sclerosis (ALS). This study identified SVAs in the exons of 179 known transcripts, several of which were expressed in a tissue-specific manner, as well as 92 novel exonisation events occurring in the motor cortex. An analysis of 65 reference genome SVAs polymorphic for their presence/absence in the ALS consortium cohort did not identify any elements that were significantly associated with disease status, age at onset, and survival. However, there were transcripts, such as transferrin and HLA-A, that were differentially expressed between those with or without disease, and expression levels were associated with the genotype of proximal SVAs. This study demonstrates the functional consequences of several SVA elements altering mRNA splicing patterns and expression levels in tissues of the CNS.

Genome-wide structural variant analysis identifies risk loci for non-Alzheimer's dementias

We characterized the role of structural variants, a largely unexplored type of genetic variation, in two non-Alzheimer's dementias, namely Lewy body dementia (LBD) and frontotemporal dementia (FTD)/amyotrophic lateral sclerosis (ALS). To do this, we applied an advanced structural variant calling pipeline (GATK-SV) to short-read whole-genome sequence data from 5,213 European-ancestry cases and 4,132 controls. We discovered, replicated, and validated a deletion in TPCN1 as a novel risk locus for LBD and detected the known structural variants at the C9orf72 and MAPT loci as associated with FTD/ALS. We also identified rare pathogenic structural variants in both LBD and FTD/ALS. Finally, we assembled a catalog of structural variants that can be mined for new insights into the pathogenesis of these understudied forms of dementia.

Mobile element variation contributes to population-specific genome diversification, gene regulation and disease risk

Mobile genetic elements (MEs) are heritable mutagens that recursively generate structural variants (SVs). ME variants (MEVs) are difficult to genotype and integrate in statistical genetics, obscuring their impact on genome diversification and traits. We developed a tool that accurately genotypes MEVs using short-read whole-genome sequencing (WGS) and applied it to global human populations. We find unexpected population-specific MEV differences, including an Alu insertion distribution distinguishing Japanese from other populations. Integrating MEVs with expression quantitative trait loci (eQTL) maps shows that MEV classes regulate tissue-specific gene expression by shared mechanisms, including creating or attenuating enhancers and recruiting post-transcriptional regulators, supporting class-wide interpretability. MEVs more often associate with gene expression changes than SNVs, thus plausibly impacting traits. Performing genome-wide association study (GWAS) with MEVs pinpoints potential causes of disease risk, including a LINE-1 insertion associated with keloid and fasciitis. This work implicates MEVs as drivers of human divergence and disease risk.

The complex genetic architecture of Alzheimer's disease: novel insights and future directions

BACKGROUND

Alzheimer's disease (AD) is a complex multifactorial neurodegenerative disorder and the most common form of dementia. AD is highly heritable, with heritability estimates of ∼70% from twin studies. Progressively larger genome-wide association studies (GWAS) have continued to expand our knowledge of AD/dementia genetic architecture. Until recently these efforts had identified 39 disease susceptibility loci in European ancestry populations.

RECENT DEVELOPMENTS

Two new AD/dementia GWAS have dramatically expanded the sample sizes and the number of disease susceptibility loci. The first increased total sample size to 1,126,563-with an effective sample size of 332,376-by predominantly including new biobank and population-based dementia datasets. The second, expands on an earlier GWAS from the International Genomics of Alzheimer's Project (IGAP) by increasing the number of clinically-defined AD cases/controls in addition to incorporating biobank dementia datasets, resulting in a total sample size to 788,989 and an effective sample size of 382,472. Collectively both GWAS identified 90 independent variants across 75 AD/dementia susceptibility loci, including 42 novel loci. Pathway analyses indicate the susceptibility loci are enriched for genes involved in amyloid plaque and neurofibrillary tangle formation, cholesterol metabolism, endocytosis/phagocytosis, and the innate immune system. Gene prioritization efforts for the novel loci identified 62 candidate causal genes. Many of the candidate genes from known and newly discovered loci play key roles in macrophages and highlight phagocytic clearance of cholesterol-rich brain tissue debris by microglia (efferocytosis) as a core pathogenetic hub and putative therapeutic target for AD. WHERE NEXT?: While GWAS in European ancestry populations have substantially enhanced our understanding of AD genetic architecture, heritability estimates from population based GWAS cohorts are markedly smaller than those from twin studies. While this missing heritability is likely due to a combination of factors, it highlights that our understanding of AD genetic architecture and genetic risk mechanisms remains incomplete. These knowledge gaps result from several underexplored areas in AD research. First, rare variants remain understudied due to methodological issues in identifying them and the cost of generating sufficiently powered whole exome/genome sequencing datasets. Second, sample sizes of non-European ancestry populations in AD GWAS remain small. Third, GWAS of AD neuroimaging and cerebrospinal fluid endophenotypes remains limited due to low compliance and high costs associated with measuring amyloid-β and tau levels and other disease-relevant biomarkers. Studies generating sequencing data, including diverse populations, and incorporating blood-based AD biomarkers are set to substantially improve our knowledge of AD genetic architecture.

Genome-Wide Association Study Reveals the Genetic Basis of Duck Plumage Colors

Plumage color is an artificially and naturally selected trait in domestic ducks. Black, white, and spotty are the main feather colors in domestic ducks. Previous studies have shown that black plumage color is caused by MC1R, and white plumage color is caused by MITF. We performed a genome-wide association study (GWAS) to identify candidate genes associated with white, black, and spotty plumage in ducks. Two non-synonymous SNPs in MC1R (c.52G>A and c.376G>A) were significantly related to duck black plumage, and three SNPs in MITF (chr13:15411658A>G, chr13:15412570T>C and chr13:15412592C>G) were associated with white plumage. Additionally, we also identified the epistatic interactions between causing loci. Some ducks with white plumage carry the c.52G>A and c.376G>A in MC1R, which also compensated for black and spotty plumage color phenotypes, suggesting that MC1R and MITF have an epistatic effect. The MITF locus was supposed to be an upstream gene to MC1R underlying the white, black, and spotty colors. Although the specific mechanism remains to be further clarified, these findings support the importance of epistasis in plumage color variation in ducks.

The role of structural variations in Alzheimer's disease and other neurodegenerative diseases

Dozens of single nucleotide polymorphisms (SNPs) related to Alzheimer's disease (AD) have been discovered by large scale genome-wide association studies (GWASs). However, only a small portion of the genetic component of AD can be explained by SNPs observed from GWAS. Structural variation (SV) can be a major contributor to the missing heritability of AD; while SV in AD remains largely unexplored as the accurate detection of SVs from the widely used array-based and short-read technology are still far from perfect. Here, we briefly summarized the strengths and weaknesses of available SV detection methods. We reviewed the current landscape of SV analysis in AD and SVs that have been found associated with AD. Particularly, the importance of currently less explored SVs, including insertions, inversions, short tandem repeats, and transposable elements in neurodegenerative diseases were highlighted.

Advances in sequencing technologies for amyotrophic lateral sclerosis research

Amyotrophic lateral sclerosis (ALS) is caused by upper and lower motor neuron loss and has a fairly rapid disease progression, leading to fatality in an average of 2-5 years after symptom onset. Numerous genes have been implicated in this disease; however, many cases remain unexplained. Several technologies are being used to identify regions of interest and investigate candidate genes. Initial approaches to detect ALS genes include, among others, linkage analysis, Sanger sequencing, and genome-wide association studies. More recently, next-generation sequencing methods, such as whole-exome and whole-genome sequencing, have been introduced. While those methods have been particularly useful in discovering new ALS-linked genes, methodological advances are becoming increasingly important, especially given the complex genetics of ALS. Novel sequencing technologies, like long-read sequencing, are beginning to be used to uncover the contribution of repeat expansions and other types of structural variation, which may help explain missing heritability in ALS. In this review, we discuss how popular and/or upcoming methods are being used to discover ALS genes, highlighting emerging long-read sequencing platforms and their role in aiding our understanding of this challenging disease.

Omics-based biomarkers discovery for Alzheimer's disease

Alzheimer's disease (AD) is the most common neurodegenerative disorders presenting with the pathological hallmarks of amyloid plaques and tau tangles. Over the past few years, great efforts have been made to explore reliable biomarkers of AD. High-throughput omics are a technology driven by multiple levels of unbiased data to detect the complex etiology of AD, and it provides us with new opportunities to better understand the pathophysiology of AD and thereby identify potential biomarkers. Through revealing the interaction networks between different molecular levels, the ultimate goal of multi-omics is to improve the diagnosis and treatment of AD. In this review, based on the current AD pathology and the current status of AD diagnostic biomarkers, we summarize how genomics, transcriptomics, proteomics and metabolomics are all conducing to the discovery of reliable AD biomarkers that could be developed and used in clinical AD management.

PGG.SV: a whole-genome-sequencing-based structural variant resource and data analysis platform

Structural variations (SVs) play important roles in human evolution and diseases, but there is a lack of data resources concerning representative samples, especially for East Asians. Taking advantage of both next-generation sequencing and third-generation sequencing data at the whole-genome level, we developed the database PGG.SV to provide a practical platform for both regionally and globally representative structural variants. In its current version, PGG.SV archives 584 277 SVs obtained from whole-genome sequencing data of 6048 samples, including 1030 long-read sequencing genomes representing 177 global populations. PGG.SV provides (i) high-quality SVs with fine-scale and precise genomic locations in both GRCh37 and GRCh38, covering underrepresented SVs in existing sequencing and microarray data; (ii) hierarchical estimation of SV prevalence in geographical populations; (iii) informative annotations of SV-related genes, potential functions and clinical effects; (iv) an analysis platform to facilitate SV-based case-control association studies and (v) various visualization tools for understanding the SV structures in the human genome. Taken together, PGG.SV provides a user-friendly online interface, easy-to-use analysis tools and a detailed presentation of results. PGG.SV is freely accessible via https://www.biosino.org/pggsv.

No association between initiation of phosphodiesterase-5 inhibitors and risk of incident Alzheimer's disease and related dementia: results from the Drug Repurposing for Effective Alzheimer's Medicines study

We evaluated the hypothesis that phosphodiesterase-5 inhibitors, including sildenafil and tadalafil, may be associated with reduced incidence of Alzheimer's disease and related dementia using a patient-level cohort study of Medicare claims and cell culture-based phenotypic assays. We compared incidence of Alzheimer's disease and related dementia after phosphodiesterase-5 inhibitor initiation versus endothelin receptor antagonist initiation among patients with pulmonary hypertension after controlling for 76 confounding variables through propensity score matching. Across four separate analytic approaches designed to address specific types of biases including informative censoring, reverse causality, and outcome misclassification, we observed no evidence for a reduced risk of Alzheimer's disease and related dementia with phosphodiesterase-5 inhibitors;hazard ratio (95% confidence interval): 0.99 (0.69-1.43), 1.00 (0.71-1.42), 0.67 (0.43-1.06), and 1.15 (0.57-2.34). We also did not observe evidence that sildenafil ameliorated molecular abnormalities relevant to Alzheimer's disease in most cell culture-based phenotypic assays. These results do not provide support to the hypothesis that phosphodiesterase-5 inhibitors are promising repurposing candidates for Alzheimer's disease and related dementia.

Genetic control of RNA splicing and its distinct role in complex trait variation

Most genetic variants identified from genome-wide association studies (GWAS) in humans are noncoding, indicating their role in gene regulation. Previous studies have shown considerable links of GWAS signals to expression quantitative trait loci (eQTLs) but the links to other genetic regulatory mechanisms, such as splicing QTLs (sQTLs), are underexplored. Here, we introduce an sQTL mapping method, testing for heterogeneity between isoform-eQTLeffects (THISTLE), with improved power over competing methods. Applying THISTLE together with a complementary sQTL mapping strategy to brain transcriptomic (n = 2,865) and genotype data, we identified 12,794 genes with cis-sQTLs at P < 5 × 10⁻⁸, approximately 61% of which were distinct from eQTLs. Integrating the sQTL data into GWAS for 12 brain-related complex traits (including diseases), we identified 244 genes associated with the traits through cis-sQTLs, approximately 61% of which could not be discovered using the corresponding eQTL data. Our study demonstrates the distinct role of most sQTLs in the genetic regulation of transcription and complex trait variation.

A powerful method for splicing quantitative trait loci (sQTL) mapping, THISTLE, is presented and applied to a collection of 2,865 brain samples. Integration with GWAS identifies 244 genes associated via cis-sQTLs, of which 61% were not identified using expression QTLs.