Alu insertion-mediated dsRNA structure formation with pre-existing Alu elements as a disease-causing mechanism.

We previously identified a homozygous Alu insertion variant (Alu_Ins) in the 3'-untranslated region (3'-UTR) of SPINK1 as the cause of severe infantile isolated exocrine pancreatic insufficiency. Although we established that Alu_Ins leads to the complete loss of SPINK1 mRNA expression, the precise mechanisms remained elusive. Here, we aimed to elucidate these mechanisms through a hypothesis-driven approach. Initially, we speculated that, owing to its particular location, Alu_Ins could independently disrupt mRNA 3' end formation and/or affect other post-transcriptional processes such as nuclear export and translation. However, employing a 3'-UTR luciferase reporter assay, Alu_Ins was found to result in only an ∼50% reduction in luciferase activity compared to wild type, which is insufficient to account for the severe pancreatic deficiency in the Alu_Ins homozygote. We then postulated that double-stranded RNA (dsRNA) structures formed between Alu elements, an upstream mechanism regulating gene expression, might be responsible. Using RepeatMasker, we identified two Alu elements within SPINK1's third intron, both oriented oppositely to Alu_Ins. Through RNAfold predictions and full-length gene expression assays, we investigated orientation-dependent interactions between these Alu repeats. We provide compelling evidence to link the detrimental effect of Alu_Ins to extensive dsRNA structures formed between Alu_Ins and pre-existing intronic Alu sequences, including the restoration of SPINK1 mRNA expression by aligning all three Alu elements in the same orientation. Given the widespread presence of Alu elements in the human genome and the potential for new Alu insertions at almost any locus, our findings have important implications for detecting and interpreting Alu insertions in disease genes.

Accurate identification of genes associated with brain disorders by integrating heterogeneous genomic data into a Bayesian framework.

BACKGROUND: Genome-wide association studies (GWAS) have revealed many brain disorder-associated SNPs residing in the noncoding genome, rendering it a challenge to decipher the underlying pathogenic mechanisms. METHODS: Here, we present an unsupervised Bayesian framework to identify disease-associated genes by integrating risk SNPs with long-range chromatin interactions (iGOAT), including SNP-SNP interactions extracted from ∼500,000 patients and controls from the UK Biobank, and enhancer-promoter interactions derived from multiple brain cell types at different developmental stages. FINDINGS: The application of iGOAT to three psychiatric disorders and three neurodegenerative/neurological diseases predicted sets of high-risk (HRGs) and low-risk (LRGs) genes for each disorder. The HRGs were enriched in drug targets, and exhibited higher expression during prenatal brain developmental stages than postnatal stages, indicating their potential to affect brain development at an early stage. The HRGs associated with Alzheimer's disease were found to share genetic architecture with schizophrenia, bipolar disorder and major depressive disorder according to gene co-expression module analysis and rare variants analysis. Comparisons of this method to the eQTL-based method, the TWAS-based method, and the gene-level GWAS method indicated that the genes identified by our method are more enriched in known brain disorder-related genes, and exhibited higher precision. Finally, the method predicted 205 risk genes not previously reported to be associated with any brain disorder, of which one top-risk gene, MLH1, was experimentally validated as being schizophrenia-associated. INTERPRETATION: iGOAT can successfully leverage epigenomic data, phenotype-genotype associations, and protein-protein interactions to advance our understanding of brain disorders, thereby facilitating the development of new therapeutic approaches. FUNDING: The work was funded by the National Key Research and Development Program of China (2024YFF1204902), the Natural Science Foundation of China (82371482), Guangzhou Science and Technology Research Plan (2023A03J0659) and Natural Science Foundation of Guangdong (2024A1515011363).

SEC16A Variants Predispose to Chronic Pancreatitis by Impairing ER-to-Golgi Transport and Inducing ER Stress.

Chronic pancreatitis (CP) is a complex disease with genetic and environmental factors at play. Through trio exome sequencing, a de novo SEC16A frameshift variant in a Chinese teenage CP patient is identified. Subsequent targeted next-generation sequencing of the SEC16A gene in 1,061 Chinese CP patients and 1,196 controls reveals a higher allele frequency of rare nonsynonymous SEC16A variants in patients (4.90% vs 2.93%; odds ratio [OR], 1.71; 95% confidence interval [CI], 1.26-2.33). Similar enrichments are noted in a French cohort (OR, 2.74; 95% CI, 1.67-4.50) and in a biobank meta-analysis (OR, 1.16; 95% CI, 1.04-1.31). Notably, Chinese CP patients with SEC16A variants exhibit a median onset age 5 years earlier than those without (40.0 vs 45.0; p = 0.012). Functional studies using three CRISPR/Cas9-edited HEK293T cell lines show that loss-of-function SEC16A variants disrupt coat protein complex II (COPII) formation, impede secretory protein vesicles trafficking, and induce endoplasmic reticulum (ER) stress due to protein overload. Sec16a+/- mice, which demonstrate impaired zymogen secretion and exacerbated ER stress compared to Sec16a+/+, are further generated. In cerulein-stimulated pancreatitis models, Sec16a+/- mice display heightened pancreatic inflammation and fibrosis compared to wild-type mice. These findings implicate a novel pathogenic mechanism predisposing to CP.

VCAT: an integrated variant function annotation tools.

The development of sequencing technology has promoted discovery of variants in the human genome. Identifying functions of these variants is important for us to link genotype to phenotype, and to diagnose diseases. However, it usually requires researchers to visit multiple databases. Here, we presented a one-stop webserver for variant function annotation tools (VCAT, https://biomed.nscc-gz.cn/zhaolab/VCAT/ ) that is the first one connecting variant to functions via the epigenome, protein, drug and RNA. VCAT is also the first one to make all annotations visualized in interactive charts or molecular structures. VCAT allows users to upload data in VCF format, and download results via a URL. Moreover, VCAT has annotated a huge number (1,262,041,068) of variants collected from dbSNP, 1000 Genomes projects, gnomAD, ICGC, TCGA, and HPRC Pangenome project. For these variants, users are able to searcher their functions, related diseases and drugs from VCAT. In summary, VCAT provides a one-stop webserver to explore the potential functions of human genomic variants including their relationship with diseases and drugs.

Critical assessment of missense variant effect predictors on disease-relevant variant data.

Regular, systematic, and independent assessment of computational tools used to predict the pathogenicity of missense variants is necessary to evaluate their clinical and research utility and suggest directions for future improvement. Here, as part of the sixth edition of the Critical Assessment of Genome Interpretation (CAGI) challenge, we assess missense variant effect predictors (or variant impact predictors) on an evaluation dataset of rare missense variants from disease-relevant databases. Our assessment evaluates predictors submitted to the CAGI6 Annotate-All-Missense challenge, predictors commonly used by the clinical genetics community, and recently developed deep learning methods for variant effect prediction. To explore a variety of settings that are relevant for different clinical and research applications, we assess performance within different subsets of the evaluation data and within high-specificity and high-sensitivity regimes. We find strong performance of many predictors across multiple settings. Meta-predictors tend to outperform their constituent individual predictors; however, several individual predictors have performance similar to that of commonly used meta-predictors. The relative performance of predictors differs in high-specificity and high-sensitivity regimes, suggesting that different methods may be best suited to different use cases. We also characterize two potential sources of bias. Predictors that incorporate allele frequency as a predictive feature tend to have reduced performance when distinguishing pathogenic variants from very rare benign variants, and predictors supervised on pathogenicity labels from curated variant databases often learn label imbalances within genes. Overall, we find notable advances over the oldest and most cited missense variant effect predictors and continued improvements among the most recently developed tools, and the CAGI Annotate-All-Missense challenge (also termed the Missense Marathon) will continue to assess state-of-the-art methods as the field progresses. Together, our results help illuminate the current clinical and research utility of missense variant effect predictors and identify potential areas for future development.

The landscape of rare genetic variation associated with inflammatory bowel disease and Parkinson's disease comorbidity.

BACKGROUND: Inflammatory bowel disease (IBD) and Parkinson's disease (PD) are chronic disorders that have been suggested to share common pathophysiological processes. LRRK2 has been implicated as playing a role in both diseases. Exploring the genetic basis of the IBD-PD comorbidity through studying high-impact rare genetic variants can facilitate the identification of the novel shared genetic factors underlying this comorbidity. METHODS: We analyzed whole exomes from the BioMe BioBank and UK Biobank, and whole genomes from a cohort of 67 European patients diagnosed with both IBD and PD to examine the effects of LRRK2 missense variants on IBD, PD and their co-occurrence (IBD-PD). We performed optimized sequence kernel association test (SKAT-O) and network-based heterogeneity clustering (NHC) analyses using high-impact rare variants in the IBD-PD cohort to identify novel candidate genes, which we further prioritized by biological relatedness approaches. We conducted phenome-wide association studies (PheWAS) employing BioMe BioBank and UK Biobank whole exomes to estimate the genetic relevance of the 14 prioritized genes to IBD-PD. RESULTS: The analysis of LRRK2 missense variants revealed significant associations of the G2019S and N2081D variants with IBD-PD in addition to several other variants as potential contributors to increased or decreased IBD-PD risk. SKAT-O identified two significant genes, LRRK2 and IL10RA, and NHC identified 6 significant gene clusters that are biologically relevant to IBD-PD. We observed prominent overlaps between the enriched pathways in the known IBD, PD, and candidate IBD-PD gene sets. Additionally, we detected significantly enriched pathways unique to the IBD-PD, including MAPK signaling, LPS/IL-1 mediated inhibition of RXR function, and NAD signaling. Fourteen final candidate IBD-PD genes were prioritized by biological relatedness methods. The biological importance scores estimated by protein-protein interaction networks and pathway and ontology enrichment analyses indicated the involvement of genes related to immunity, inflammation, and autophagy in IBD-PD. Additionally, PheWAS provided support for the associations of candidate genes with IBD and PD. CONCLUSIONS: Our study confirms and uncovers new LRRK2 associations in IBD-PD. The identification of novel inflammation and autophagy-related genes supports and expands previous findings related to IBD-PD pathogenesis, and underscores the significance of therapeutic interventions for reducing systemic inflammation.

NextDenovo: an efficient error correction and accurate assembly tool for noisy long reads.

Long-read sequencing data, particularly those derived from the Oxford Nanopore sequencing platform, tend to exhibit high error rates. Here, we present NextDenovo, an efficient error correction and assembly tool for noisy long reads, which achieves a high level of accuracy in genome assembly. We apply NextDenovo to assemble 35 diverse human genomes from around the world using Nanopore long-read data. These genomes allow us to identify the landscape of segmental duplication and gene copy number variation in modern human populations. The use of NextDenovo should pave the way for population-scale long-read assembly using Nanopore long-read data.

Genetic evidence for T-wave area from 12-lead electrocardiograms to monitor cardiovascular diseases in patients taking diabetes medications.

Aims Many studies indicated use of diabetes medications can influence the electrocardiogram (ECG), which remains the simplest and fastest tool for assessing cardiac functions. However, few studies have explored the role of genetic factors in determining the relationship between the use of diabetes medications and ECG trace characteristics (ETC). Methods Genome-wide association studies (GWAS) were performed for 168 ETCs extracted from the 12-lead ECGs of 42,340 Europeans in the UK Biobank. The genetic correlations, causal relationships, and phenotypic relationships of these ETCs with medication usage, as well as the risk of cardiovascular diseases (CVDs), were estimated by linkage disequilibrium score regression (LDSC), Mendelian randomization (MR), and regression model, respectively. Results The GWAS identified 124 independent single nucleotide polymorphisms (SNPs) that were study-wise and genome-wide significantly associated with at least one ETC. Regression model and LDSC identified significant phenotypic and genetic correlations of T-wave area in lead aVR (aVR_T-area) with usage of diabetes medications (ATC code: A10 drugs, and metformin), and the risks of ischemic heart disease (IHD) and coronary atherosclerosis (CA). MR analyses support a putative causal effect of the use of diabetes medications on decreasing aVR_T-area, and on increasing risk of IHD and CA. ConclusionPatients taking diabetes medications are prone to have decreased aVR_T-area and an increased risk of IHD and CA. The aVR_T-area is therefore a potential ECG marker for pre-clinical prediction of IHD and CA in patients taking diabetes medications.

Divergent Evolutionary Rates of Primate Brain Regions as Revealed by Genomics and Transcriptomics.

Although the primate brain contains numerous functionally distinct structures that have experienced diverse genetic changes during the course of evolution and development, these changes remain to be explored in detail. Here we utilize two classic metrics from evolutionary biology, the evolutionary rate index (ERI) and the transcriptome age index (TAI), to investigate the evolutionary alterations that have occurred in each area and developmental stage of the primate brain. We observed a higher evolutionary rate for those genes expressed in the non-cortical areas during primate evolution, particularly in human, with the highest rate of evolution being exhibited at brain developmental stages between late infancy and early childhood. Further, the transcriptome age of the non-cortical areas was lower than that of the cerebral cortex, with the youngest age apparent at brain developmental stages between late infancy and early childhood. Our exploration of the evolutionary patterns manifest in each brain area and developmental stage provides important reference points for further research into primate brain evolution.

Meta-analysis of 46,000 germline de novo mutations linked to human inherited disease.

BACKGROUND: De novo mutations (DNMs) are variants that occur anew in the offspring of noncarrier parents. They are not inherited from either parent but rather result from endogenous mutational processes involving errors of DNA repair/replication. These spontaneous errors play a significant role in the causation of genetic disorders, and their importance in the context of molecular diagnostic medicine has become steadily more apparent as more DNMs have been reported in the literature. In this study, we examined 46,489 disease-associated DNMs annotated by the Human Gene Mutation Database (HGMD) to ascertain their distribution across gene and disease categories. RESULTS: Most disease-associated DNMs reported to date are found to be associated with developmental and psychiatric disorders, a reflection of the focus of sequencing efforts over the last decade. Of the 13,277 human genes in which DNMs have so far been found, the top-10 genes with the highest proportions of DNM relative to gene size were H3-3 A, DDX3X, CSNK2B, PURA, ZC4H2, STXBP1, SCN1A, SATB2, H3-3B and TUBA1A. The distribution of CADD and REVEL scores for both disease-associated DNMs and those mutations not reported to be de novo revealed a trend towards higher deleteriousness for DNMs, consistent with the likely lower selection pressure impacting them. This contrasts with the non-DNMs, which are presumed to have been subject to continuous negative selection over multiple generations. CONCLUSION: This meta-analysis provides important information on the occurrence and distribution of disease-associated DNMs in association with heritable disease and should make a significant contribution to our understanding of this major type of mutation.

Deciphering the Role of Rapidly Evolving Conserved Elements in Primate Brain Development and Exploring Their Potential Involvement in Alzheimer's Disease.

Although previous studies have identified human-specific accelerated regions as playing a key role in the recent evolution of the human brain, the characteristics and cellular functions of rapidly evolving conserved elements (RECEs) in ancestral primate lineages remain largely unexplored. Here, based on large-scale primate genome assemblies, we identify 888 RECEs that have been highly conserved in primates that exhibit significantly accelerated substitution rates in the ancestor of the Simiiformes. This primate lineage exhibits remarkable morphological innovations, including an expanded brain mass. Integrative multiomic analyses reveal that RECEs harbor sequences with potential cis-regulatory functions that are activated in the adult human brain. Importantly, genes linked to RECEs exhibit pronounced expression trajectories in the adult brain relative to the fetal stage. Furthermore, we observed an increase in the chromatin accessibility of RECEs in oligodendrocytes from individuals with Alzheimer's disease (AD) compared to that of a control group, indicating that these RECEs may contribute to brain aging and AD. Our findings serve to expand our knowledge of the genetic underpinnings of brain function during primate evolution.

Development of a human genetics-guided priority score for 19,365 genes and 399 drug indications.

Studies have shown that drug targets with human genetic support are more likely to succeed in clinical trials. Hence, a tool integrating genetic evidence to prioritize drug target genes is beneficial for drug discovery. We built a genetic priority score (GPS) by integrating eight genetic features with drug indications from the Open Targets and SIDER databases. The top 0.83%, 0.28% and 0.19% of the GPS conferred a 5.3-, 9.9- and 11.0-fold increased effect of having an indication, respectively. In addition, we observed that targets in the top 0.28% of the score were 1.7-, 3.7- and 8.8-fold more likely to advance from phase I to phases II, III and IV, respectively. Complementary to the GPS, we incorporated the direction of genetic effect and drug mechanism into a directional version of the score called the GPS with direction of effect. We applied our method to 19,365 protein-coding genes and 399 drug indications and made all results available through a web portal.

Genome-wide prediction of pathogenic gain- and loss-of-function variants from ensemble learning of a diverse feature set.

Gain-of-function (GOF) variants give rise to increased/novel protein functions whereas loss-of-function (LOF) variants lead to diminished protein function. Experimental approaches for identifying GOF and LOF are generally slow and costly, whilst available computational methods have not been optimized to discriminate between GOF and LOF variants. We have developed LoGoFunc, a machine learning method for predicting pathogenic GOF, pathogenic LOF, and neutral genetic variants, trained on a broad range of gene-, protein-, and variant-level features describing diverse biological characteristics. LoGoFunc outperforms other tools trained solely to predict pathogenicity for identifying pathogenic GOF and LOF variants and is available at https://itanlab.shinyapps.io/goflof/ .

Genome-wide detection of human intronic AG-gain variants located between splicing branchpoints and canonical splice acceptor sites.

Human genetic variants that introduce an AG into the intronic region between the branchpoint (BP) and the canonical splice acceptor site (ACC) of protein-coding genes can disrupt pre-mRNA splicing. Using our genome-wide BP database, we delineated the BP-ACC segments of all human introns and found extreme depletion of AG/YAG in the [BP+8, ACC-4] high-risk region. We developed AGAIN as a genome-wide computational approach to systematically and precisely pinpoint intronic AG-gain variants within the BP-ACC regions. AGAIN identified 350 AG-gain variants from the Human Gene Mutation Database, all of which alter splicing and cause disease. Among them, 74% created new acceptor sites, whereas 31% resulted in complete exon skipping. AGAIN also predicts the protein-level products resulting from these two consequences. We performed AGAIN on our exome/genomes database of patients with severe infectious diseases but without known genetic etiology and identified a private homozygous intronic AG-gain variant in the antimycobacterial gene SPPL2A in a patient with mycobacterial disease. AGAIN also predicts a retention of six intronic nucleotides that encode an in-frame stop codon, turning AG-gain into stop-gain. This allele was then confirmed experimentally to lead to loss of function by disrupting splicing. We further showed that AG-gain variants inside the high-risk region led to misspliced products, while those outside the region did not, by two case studies in genes STAT1 and IRF7. We finally evaluated AGAIN on our 14 paired exome-RNAseq samples and found that 82% of AG-gain variants in high-risk regions showed evidence of missplicing. AGAIN is publicly available from https://hgidsoft.rockefeller.edu/AGAIN and https://github.com/casanova-lab/AGAIN.

Frameshift coding sequence variants in the LPL gene: identification of two novel events and exploration of the genotype-phenotype relationship for variants reported to date.

BACKGROUND: Lipoprotein lipase (LPL) is the rate-limiting enzyme for triglyceride hydrolysis. Homozygous or compound heterozygous LPL variants cause autosomal recessive familial chylomicronemia syndrome (FCS), whereas simple heterozygous LPL variants are associated with hypertriglyceridemia (HTG) and HTG-related disorders. LPL frameshift coding sequence variants usually cause complete functional loss of the affected allele, thereby allowing exploration of the impact of different levels of LPL function in human disease. METHODS: All exons and flanking intronic regions of LPL were Sanger sequenced in patients with HTG-related acute pancreatitis (HTG-AP) or HTG-AP in pregnancy. Previously reported LPL frameshift coding sequence variants were collated from the Human Gene Mutation Database and through PubMed keyword searching. Original reports were manually evaluated for the following information: zygosity status of the variant, plasma LPL activity of the variant carrier, disease referred for genetic analysis, patient's age at genetic analysis, and patient's disease history. SpliceAI was employed to predict the potential impact of collated variants on splicing. RESULTS: Two novel rare variants were identified, and 53 known LPL frameshift coding sequence variants were collated. Of the 51 variants informative for zygosity, 30 were simple heterozygotes, 12 were homozygotes, and 9 were compound heterozygotes. Careful evaluation of the 55 variants with respect to their clinical and genetic data generated several interesting findings. First, we conclude that 6-7% residual LPL function could significantly delay the age of onset of FCS and reduce the prevalence of FCS-associated syndromes. Second, whereas a large majority of LPL frameshift coding sequence variants completely disrupt gene function through their "frameshift" nature, a small fraction of these variants may act wholly or partly as "in-frame" variants, leading to the generation of protein products with some residual LPL function. Third, we identified two candidate LPL frameshift coding sequence variants that may retain residual function based on genotype-phenotype correlation or SpliceAI-predicted data. CONCLUSIONS: This study reported two novel LPL variants and yielded new insights into the genotype-phenotype relationship as it pertains to LPL frameshift coding sequence variants.

Classification of PRSS1 variants responsible for chronic pancreatitis: An expert perspective from the Franco-Chinese GREPAN study group.

BACKGROUND: PRSS1 was the first reported chronic pancreatitis (CP) gene. The existence of both gain-of-function (GoF) and gain-of-proteotoxicity (GoP) pathological PRSS1 variants, together with the fact that PRSS1 variants have been identified in CP subtypes spanning the range from monogenic to multifactorial, has made the classification of PRSS1 variants very challenging. METHODS: All currently reported PRSS1 variants (derived primarily from two databases) were manually reviewed with respect to their clinical genetics, functional analysis and population allele frequency. They were classified by variant type and pathological mechanism within the framework of our recently proposed ACMG/AMP guidelines-based seven-category system. RESULTS: The total number of distinct germline PRSS1 variants included for analysis was 100, comprising 3 copy number variants (CNVs), 12 5' and 3' variants, 19 intronic variants, 5 nonsense variants, 1 frameshift deletion variant, 6 synonymous variants, 1 in-frame duplication, 3 gene conversions and 50 missense variants. Based upon a combination of clinical genetic and functional analysis, population data and in silico analysis, we classified 26 variants (all 3 CNVs, the in-frame duplication, all 3 gene conversions and 19 missense) as "pathogenic", 3 variants (missense) as "likely pathogenic", 5 variants (four missense and one promoter) as "predisposing", 13 variants (all missense) as "unknown significance", 2 variants (missense) as "likely benign", and all remaining 51 variants as "benign". CONCLUSIONS: We describe an expert classification of the 100 PRSS1 variants reported to date. The results have immediate implications for reclassifying many ClinVar-registered PRSS1 variants as well as providing optimal guidelines/standards for reporting PRSS1 variants.

Inferring the genetic relationship between brain imaging-derived phenotypes and risk of complex diseases by Mendelian randomization and genome-wide colocalization.

Observational studies consistently disclose brain imaging-derived phenotypes (IDPs) as critical markers for early diagnosis of both brain disorders and cardiovascular diseases. However, it remains unclear about the shared genetic landscape between brain IDPs and the risk of brain disorders and cardiovascular diseases, restricting the applications of potential diagnostic techniques through brain IDPs. Here, we reported genetic correlations and putative causal relationships between 921 brain IDPs, 20 brain disorders and six cardiovascular diseases by leveraging their large-scale genome-wide association study (GWAS) summary statistics. Applications of Mendelian randomization (MR) identified significant putative causal effects of multiple region-specific brain IDPs in relation to the increased risks for amyotrophic lateral sclerosis (ALS), major depressive disorder (MDD), autism spectrum disorder (ASD) and schizophrenia (SCZ). We also found brain IDPs specifically from temporal lobe as a putatively causal consequence of hypertension. The genome-wide colocalization analysis identified three genomic regions in which MDD, ASD and SCZ colocalized with the brain IDPs, and two novel SNPs to be associated with ASD, SCZ, and multiple brain IDPs. Furthermore, we identified a list of candidate genes involved in the shared genetics underlying pairs of brain IDPs and MDD, ASD, SCZ, ALS and hypertension. Our results provide novel insights into the genetic relationships between brain disorders and cardiovascular diseases and brain IDP, which may server as clues for using brain IDPs to predict risks of diseases.

The East Asian-specific LPL p.Ala288Thr (c.862G > A) missense variant exerts a mild effect on protein function.

BACKGROUND: Lipoprotein lipase (LPL) is the key enzyme responsible for the hydrolysis of triglycerides. Loss-of-function variants in the LPL gene are associated with hypertriglyceridemia (HTG) and HTG-related diseases. Unlike nonsense, frameshift and canonical GT-AG splice site variants, a pathogenic role for clinically identified LPL missense variants should generally be confirmed by functional analysis. Herein, we describe the clinical and functional analysis of a rare LPL missense variant. METHODS: Chinese patients with HTG-associated acute pancreatitis (HTG-AP) were screened for rare nonsense, frameshift, missense or canonical GT-AG splice site variants in LPL and four other lipid metabolism-related genes (APOC2, APOA5, GPIHBP1 and LMF1) by Sanger sequencing. The functional consequences of the LPL missense variant of interest were characterized by in vitro expression in HEK-293T and COS-7 cells followed by Western blot and LPL activity assays. RESULTS: Five unrelated HTG-AP patients were found to be heterozygous for a rare East Asian-specific LPL missense variant, c.862G > A (p.Ala288Thr). All five patients were adult males, and all were overweight and had a long history of alcohol consumption. Transfection of LPL wild-type and c.862G > A expression vectors into two cell lines followed by Western blot analysis served to exclude the possibility that the p.Ala288Thr missense variant either impaired protein synthesis or increased protein degradation. Contrary to a previous functional study that claimed that p.Ala288Thr had a severe impact on LPL function (reportedly having 36% normal activity), our experiments consistently demonstrated that the variant had a comparatively mild effect on LPL functional activity, which was mediated through its impact upon LPL protein secretion (~ 20% reduced secretion compared to wild-type). CONCLUSIONS: In this study, we identified the East Asian-specific LPL c.862G > A (p.Ala288Thr) missense variant in five unrelated HTG-AP patients. We demonstrated that this variant exerted only a relatively mild effect on LPL function in two cell lines. Heterozygosity for this LPL variant may have combined with alcohol consumption to trigger HTG-AP in these patients.

Genome-wide identification of dominant polyadenylation hexamers for use in variant classification.

Polyadenylation is an essential process for the stabilization and export of mRNAs to the cytoplasm and the polyadenylation signal hexamer (herein referred to as hexamer) plays a key role in this process. Yet, only 14 Mendelian disorders have been associated with hexamer variants. This is likely an under-ascertainment as hexamers are not well defined and not routinely examined in molecular analysis. To facilitate the interrogation of putatively pathogenic hexamer variants, we set out to define functionally important hexamers genome-wide as a resource for research and clinical testing interrogation. We identified predominant polyA sites (herein referred to as pPAS) and putative predominant hexamers across protein coding genes (PAS usage >50% per gene). As a measure of the validity of these sites, the population constraint of 4532 predominant hexamers were measured. The predominant hexamers had fewer observed variants compared to non-predominant hexamers and trimer controls, and CADD scores for variants in these hexamers were significantly higher than controls. Exome data for 1477 individuals were interrogated for hexamer variants and transcriptome data were generated for 76 individuals with 65 variants in predominant hexamers. 3' RNA-seq data showed these variants resulted in alternate polyadenylation events (38%) and in elongated mRNA transcripts (12%). Our list of pPAS and predominant hexamers are available in the UCSC genome browser and on GitHub. We suggest this list of predominant hexamers can be used to interrogate exome and genome data. Variants in these predominant hexamers should be considered candidates for pathogenic variation in human disease, and to that end we suggest pathogenicity criteria for classifying hexamer variants.

Integrative Omics Reveals Rapidly Evolving Regulatory Sequences Driving Primate Brain Evolution.

Although the continual expansion of the brain during primate evolution accounts for our enhanced cognitive capabilities, the drivers of brain evolution have scarcely been explored in these ancestral nodes. Here, we performed large-scale comparative genomic, transcriptomic, and epigenomic analyses to investigate the evolutionary alterations acquired by brain genes and provide comprehensive listings of innovatory genetic elements along the evolutionary path from ancestral primates to human. The regulatory sequences associated with brain-expressed genes experienced rapid change, particularly in the ancestor of the Simiiformes. Extensive comparisons of single-cell and bulk transcriptomic data between primate and nonprimate brains revealed that these regulatory sequences may drive the high expression of certain genes in primate brains. Employing in utero electroporation into mouse embryonic cortex, we show that the primate-specific brain-biased gene BMP7 was recruited, probably in the ancestor of the Simiiformes, to regulate neuronal proliferation in the primate ventricular zone. Our study provides a comprehensive listing of genes and regulatory changes along the brain evolution lineage of ancestral primates leading to human. These data should be invaluable for future functional studies that will deepen our understanding not only of the genetic basis of human brain evolution but also of inherited disease.