Splicing QTL analysis focusing on coding sequences reveals mechanisms for disease susceptibility loci

Optimal variable identification for accurate detection of causal expression Quantitative Trait Loci with applications in heart-related diseases

Gene expression plays a pivotal role in various diseases, contributing significantly to their mechanisms. Most GWAS risk loci are in non-coding regions, potentially affecting disease risk by altering gene expression in specific tissues. This expression is notably tissue-specific, with genetic variants substantially influencing it. However, accurately detecting the expression Quantitative Trait Loci (eQTL) is challenging due to limited heritability in gene expression, extensive linkage disequilibrium (LD), and multiple causal variants. The single variant association approach in eQTL analysis is limited by its susceptibility to capture the combined effects of multiple variants, and a bias towards common variants, underscoring the need for a more robust method to accurately identify causal eQTL variants. To address this, we developed an algorithm, CausalEQTL, which integrates L 0 +L 1 penalized regression with an ensemble approach to localize eQTL, thereby enhancing prediction performance precisely. Our results demonstrate that CausalEQTL outperforms traditional models, including LASSO, Elastic Net, Ridge, in terms of power and overall performance. Furthermore, analysis of heart tissue data from the GTEx project revealed that eQTL sites identified by our algorithm provide deeper insights into heart-related tissue eQTL detection. This advancement in eQTL mapping promises to improve our understanding of the genetic basis of tissue-specific gene expression and its implications in disease. The source code and identified causal eQTLs for CausalEQTL are available on GitHub: https://github.com/zhc-moushang/CausalEQTL.

Perturbation of the insomnia WDR90 genome-wide association studies locus pinpoints rs3752495 as a causal variant influencing distal expression of neighboring gene, PIG-Q

Although genome-wide association studies (GWAS) have identified loci for sleep-related traits, they do not directly uncover the underlying causal variants and corresponding effector genes. The majority of such variants reside in non-coding regions and are therefore presumed to impact cis-regulatory elements. Our previously reported 'variant-to-gene mapping' effort in human induced pluripotent stem cell (iPSC)-derived neural progenitor cells (NPCs), combined with validation in both Drosophila and zebrafish, implicated phosphatidyl inositol glycan (PIG)-Q as a functionally relevant gene at the insomnia "WDR90" GWAS locus. However, importantly that effort did not characterize the corresponding underlying causal variant. Specifically, our previous 3D genomic datasets nominated a shortlist of three neighboring single nucleotide polymorphisms (SNPs) in strong linkage disequilibrium within an intronic enhancer region of WDR90 that contacted the open PIG-Q promoter. We sought to investigate the influence of these SNPs collectively and then individually on PIG-Q modulation to pinpoint the causal "regulatory" variant. Starting with gross level perturbation, deletion of the entire region in NPCs via CRISPR-Cas9 editing and subsequent RNA sequencing revealed expression changes in specific PIG-Q transcripts. Results from individual luciferase reporter assays for each SNP in iPSCs revealed that the region with the rs3752495 risk allele (RA) induced a ~2.5-fold increase in luciferase expression. Importantly, rs3752495 also exhibited an allele-specific effect, with the RA increasing the luciferase expression by ~2-fold versus the non-RA. In conclusion, our variant-to-function approach and in vitro validation implicate rs3752495 as a causal insomnia variant embedded within WDR90 while modulating the expression of the distally located PIG-Q.

Dynamic stress- and inflammatory-based regulation of psychiatric risk loci in human neurons

The prenatal environment can alter neurodevelopmental and clinical trajectories, markedly increasing risk for psychiatric disorders in childhood and adolescence. To understand if and how fetal exposures to stress and inflammation exacerbate manifestation of genetic risk for complex brain disorders, we report a large-scale context-dependent massively parallel reporter assay (MPRA) in human neurons designed to catalogue genotype x environment (GxE) interactions. Across 240 genome-wide association study (GWAS) loci linked to ten brain traits/disorders, the impact of hydrocortisone, interleukin 6, and interferon alpha on transcriptional activity is empirically evaluated in human induced pluripotent stem cell (hiPSC)-derived glutamatergic neurons. Of ~3,500 candidate regulatory risk elements (CREs), 11% of variants are active at baseline, whereas cue-specific CRE regulatory activity range from a high of 23% (hydrocortisone) to a low of 6% (IL-6). Cue-specific regulatory activity is driven, at least in part, by differences in transcription factor binding activity, the gene targets of which show unique enrichments for brain disorders as well as co-morbid metabolic and immune syndromes. The dynamic nature of genetic regulation informs the influence of environmental factors, reveals a mechanism underlying pleiotropy and variable penetrance, and identifies specific risk variants that confer greater disorder susceptibility after exposure to stress or inflammation. Understanding neurodevelopmental GxE interactions will inform mental health trajectories and uncover novel targets for therapeutic intervention.

Long-read sequencing for 29 immune cell subsets reveals disease-linked isoforms

Alternative splicing events are a major causal mechanism for complex traits, but they have been understudied due to the limitation of short-read sequencing. Here, we generate a full-length isoform annotation of human immune cells from an individual by long-read sequencing for 29 cell subsets. This contains a number of unannotated transcripts and isoforms such as a read-through transcript of TOMM40-APOE in the Alzheimer's disease locus. We profile characteristics of isoforms and show that repetitive elements significantly explain the diversity of unannotated isoforms, providing insight into the human genome evolution. In addition, some of the isoforms are expressed in a cell-type specific manner, whose alternative 3'-UTRs usage contributes to their specificity. Further, we identify disease-associated isoforms by isoform switch analysis and by integration of several quantitative trait loci analyses with genome-wide association study data. Our findings will promote the elucidation of the mechanism of complex diseases via alternative splicing.

Genome-wide association analysis of the resistance to infectious hematopoietic necrosis virus in two rainbow trout aquaculture lines confirms oligogenic architecture with several moderate effect quantitative trait loci

Infectious hematopoietic necrosis (IHN) is a disease of salmonid fish that is caused by the IHN virus (IHNV), which can cause substantial mortality and economic losses in rainbow trout aquaculture and fisheries enhancement hatchery programs. In a previous study on a commercial rainbow trout breeding line that has undergone selection, we found that genetic resistance to IHNV is controlled by the oligogenic inheritance of several moderate and many small effect quantitative trait loci (QTL). Here we used genome wide association analyses in two different commercial aquaculture lines that were naïve to previous exposure to IHNV to determine whether QTL were shared across lines, and to investigate whether there were major effect loci that were still segregating in the naïve lines. A total of 1,859 and 1,768 offspring from two commercial aquaculture strains were phenotyped for resistance to IHNV and genotyped with the rainbow trout Axiom 57K SNP array. Moderate heritability values (0.15-0.25) were estimated. Two statistical methods were used for genome wide association analyses in the two populations. No major QTL were detected despite the naïve status of the two lines. Further, our analyses confirmed an oligogenic architecture for genetic resistance to IHNV in rainbow trout. Overall, 17 QTL with notable effect (≥1.9% of the additive genetic variance) were detected in at least one of the two rainbow trout lines with at least one of the two statistical methods. Five of those QTL were mapped to overlapping or adjacent chromosomal regions in both lines, suggesting that some loci may be shared across commercial lines. Although some of the loci detected in this GWAS merit further investigation to better understand the biological basis of IHNV disease resistance across populations, the overall genetic architecture of IHNV resistance in the two rainbow trout lines suggests that genomic selection may be a more effective strategy for genetic improvement in this trait.

Elucidating the Role of OXPHOS Variants in Asthenozoospermia: Insights from Whole Genome Sequencing and an In Silico Analysis

Infertility is a global health challenge that affects an estimated 72.4 million people worldwide. Between 30 and 50% of these cases involve male factors, showcasing the complex nature of male infertility, which can be attributed to both environmental and genetic determinants. Asthenozoospermia, a condition characterized by reduced sperm motility, stands out as a significant contributor to male infertility. This study explores the involvement of the mitochondrial oxidative phosphorylation (OXPHOS) system, crucial for ATP production and sperm motility, in asthenozoospermia. Through whole-genome sequencing and in silico analysis, our aim was to identify and characterize OXPHOS gene variants specific to individuals with asthenozoospermia. Our analysis identified 680,099 unique variants, with 309 located within OXPHOS genes. Nine of these variants were prioritized due to their significant implications, such as potential associations with diseases, effects on gene expression, protein function, etc. Interestingly, none of these variants had been previously associated with male infertility, opening up new avenues for research. Thus, through our comprehensive approach, we provide valuable insights into the genetic factors that influence sperm motility, laying the foundation for future research in the field of male infertility.

Pangenome-genotyped structural variation improves molecular phenotype mapping in cattle

Expression and splicing quantitative trait loci (e/sQTL) are large contributors to phenotypic variability. Achieving sufficient statistical power for e/sQTL mapping requires large cohorts with both genotypes and molecular phenotypes, and so, the genomic variation is often called from short-read alignments, which are unable to comprehensively resolve structural variation. Here we build a pangenome from 16 HiFi haplotype-resolved cattle assemblies to identify small and structural variation and genotype them with PanGenie in 307 short-read samples. We find high (>90%) concordance of PanGenie-genotyped and DeepVariant-called small variation and confidently genotype close to 21 million small and 43,000 structural variants in the larger population. We validate 85% of these structural variants (with MAF > 0.1) directly with a subset of 25 short-read samples that also have medium coverage HiFi reads. We then conduct e/sQTL mapping with this comprehensive variant set in a subset of 117 cattle that have testis transcriptome data, and find 92 structural variants as causal candidates for eQTL and 73 for sQTL. We find that roughly half of the top associated structural variants affecting expression or splicing are transposable elements, such as SV-eQTL for STN1 and MYH7 and SV-sQTL for CEP89 and ASAH2 Extensive linkage disequilibrium between small and structural variation results in only 28 additional eQTL and 17 sQTL discovered when including SVs, although many top associated SVs are compelling candidates.

Biosurfer for systematic tracking of regulatory mechanisms leading to protein isoform diversity

Long-read RNA sequencing has shed light on transcriptomic complexity, but questions remain about the functionality of downstream protein products. We introduce Biosurfer, a computational approach for comparing protein isoforms, while systematically tracking the transcriptional, splicing, and translational variations that underlie differences in the sequences of the protein products. Using Biosurfer, we analyzed the differences in 32,799 pairs of GENCODE annotated protein isoforms, finding a majority (70%) of variable N-termini are due to the alternative transcription start sites, while only 9% arise from 5' UTR alternative splicing. Biosurfer's detailed tracking of nucleotide-to-residue relationships helped reveal an uncommonly tracked source of single amino acid residue changes arising from the codon splits at junctions. For 17% of internal sequence changes, such split codon patterns lead to single residue differences, termed "ragged codons". Of variable C-termini, 72% involve splice- or intron retention-induced reading frameshifts. We found an unusual pattern of reading frame changes, in which the first frameshift is closely followed by a distinct second frameshift that restores the original frame, which we term a "snapback" frameshift. We analyzed long read RNA-seq-predicted proteome of a human cell line and found similar trends as compared to our GENCODE analysis, with the exception of a higher proportion of isoforms predicted to undergo nonsense-mediated decay. Biosurfer's comprehensive characterization of long-read RNA-seq datasets should accelerate insights of the functional role of protein isoforms, providing mechanistic explanation of the origins of the proteomic diversity driven by the alternative splicing. Biosurfer is available as a Python package at https://github.com/sheynkman-lab/biosurfer.

Alternative splicing and environmental adaptation in wild house mice

A major goal of evolutionary genetics is to understand the genetic and molecular mechanisms underlying adaptation. Previous work has established that changes in gene regulation may contribute to adaptive evolution, but most studies have focused on mRNA abundance and only a few studies have investigated the role of post-transcriptional processing. Here, we use a combination of exome sequences and short-read RNA-Seq data from wild house mice (Mus musculus domesticus) collected along a latitudinal transect in eastern North America to identify candidate genes for local adaptation through alternative splicing. First, we identified alternatively spliced transcripts that differ in frequency between mice from the northern-most and southern-most populations in this transect. We then identified the subset of these transcripts that exhibit clinal patterns of variation among all populations in the transect. Finally, we conducted association studies to identify cis-acting splicing quantitative trait loci (cis-sQTL), and we identified cis-sQTL that overlapped with previously ascertained targets of selection from genome scans. Together, these analyses identified a small set of alternatively spliced transcripts that may underlie environmental adaptation in house mice. Many of these genes have known phenotypes associated with body size, a trait that varies clinally in these populations. We observed no overlap between these genes and genes previously identified by changes in mRNA abundance, indicating that alternative splicing and changes in mRNA abundance may provide separate molecular mechanisms of adaptation.

Whole genome sequencing of 4,787 individuals identifies gene-based rare variants in age-related macular degeneration

Genome-wide association studies have contributed extensively to the discovery of disease-associated common variants. However, the genetic contribution to complex traits is still largely difficult to interpret. We report a genome-wide association study of 2394 cases and 2393 controls for age-related macular degeneration (AMD) via whole-genome sequencing, with 46.9 million genetic variants. Our study reveals significant single-variant association signals at four loci and independent gene-based signals in CFH, C2, C3, and NRTN. Using data from the Exome Aggregation Consortium (ExAC) for a gene-based test, we demonstrate an enrichment of predicted rare loss-of-function variants in CFH, CFI, and an as-yet unreported gene in AMD, ORMDL2. Our method of using a large variant list without individual-level genotypes as an external reference provides a flexible and convenient approach to leverage the publicly available variant datasets to augment the search for rare variant associations, which can explain additional disease risk in AMD.

Molecular quantitative trait loci in reproductive tissues impact male fertility in cattle

Breeding bulls are well suited to investigate inherited variation in male fertility because they are genotyped and their reproductive success is monitored through semen analyses and thousands of artificial inseminations. However, functional data from relevant tissues are lacking in cattle, which prevents fine-mapping fertility-associated genomic regions. Here, we characterize gene expression and splicing variation in testis, epididymis, and vas deferens transcriptomes of 118 mature bulls and conduct association tests between 414,667 molecular phenotypes and 21,501,032 genome-wide variants to identify 41,156 regulatory loci. We show broad consensus in tissue-specific and tissue-enriched gene expression between the three bovine tissues and their human and murine counterparts. Expression- and splicing-mediating variants are more than three times as frequent in testis than epididymis and vas deferens, highlighting the transcriptional complexity of testis. Finally, we identify genes (WDR19, SPATA16, KCTD19, ZDHHC1) and molecular phenotypes that are associated with quantitative variation in male fertility through transcriptome-wide association and colocalization analyses.

Identifying genetic variants that influence the abundance of cell states in single-cell data

To understand genetic mechanisms driving disease, it is essential but difficult to map how risk alleles affect the composition of cells present in the body. Single-cell profiling quantifies granular information about tissues, but variant-associated cell states may reflect diverse combinations of the profiled cell features that are challenging to predefine. We introduce GeNA (Genotype-Neighborhood Associations), a statistical tool to identify cell state abundance quantitative trait loci (csaQTLs) in high-dimensional single-cell datasets. Instead of testing associations to predefined cell states, GeNA flexibly identifies the cell states whose abundance is most associated with genetic variants. In a genome-wide survey of scRNA-seq peripheral blood profiling from 969 individuals,1GeNA identifies five independent loci associated with shifts in the relative abundance of immune cell states. For example, rs3003-T (p=1.96×10-11) associates with increased abundance of NK cells expressing TNF-α response programs. This csaQTL colocalizes with increased risk for psoriasis, an autoimmune disease that responds to anti-TNF treatments. Flexibly characterizing csaQTLs for granular cell states may help illuminate how genetic background alters cellular composition to confer disease risk.

CamoTSS: analysis of alternative transcription start sites for cellular phenotypes and regulatory patterns from 5' scRNA-seq data

Five-prime single-cell RNA-seq (scRNA-seq) has been widely employed to profile cellular transcriptomes, however, its power of analysing transcription start sites (TSS) has not been fully utilised. Here, we present a computational method suite, CamoTSS, to precisely identify TSS and quantify its expression by leveraging the cDNA on read 1, which enables effective detection of alternative TSS usage. With various experimental data sets, we have demonstrated that CamoTSS can accurately identify TSS and the detected alternative TSS usages showed strong specificity in different biological processes, including cell types across human organs, the development of human thymus, and cancer conditions. As evidenced in nasopharyngeal cancer, alternative TSS usage can also reveal regulatory patterns including systematic TSS dysregulations.

Perturbation of the insomnia WDR90 GWAS locus pinpoints rs3752495 as a causal variant influencing distal expression of neighboring gene, PIG-Q

Although genome wide association studies (GWAS) have been crucial for the identification of loci associated with sleep traits and disorders, the method itself does not directly uncover the underlying causal variants and corresponding effector genes. The overwhelming majority of such variants reside in non-coding regions and are therefore presumed to impact the activity of cis-regulatory elements, such as enhancers. Our previously reported 'variant-to-gene mapping' effort in human induced pluripotent stem cell (iPSC)-derived neural progenitor cells (NPCs), combined with validation in both Drosophila and zebrafish, implicated PIG-Q as a functionally relevant gene at the insomnia 'WDR90' locus. However, importantly that effort did not characterize the corresponding underlying causal variant at this GWAS signal. Specifically, our genome-wide ATAC-seq and high-resolution promoter-focused Capture C datasets generated in this cell setting brought our attention to a shortlist of three tightly neighboring single nucleotide polymorphisms (SNPs) in strong linkage disequilibrium in a candidate intronic enhancer region of WDR90 that contacted the open PIG-Q promoter. The objective of this study was to investigate the influence of the proxy SNPs collectively and then individually on PIG-Q modulation and to pinpoint the causal "regulatory" variant among the three SNPs. Starting at a gross level perturbation, deletion of the entire region harboring all three SNPs in human iPSC-derived neural progenitor cells via CRISPR-Cas9 editing and subsequent RNA sequencing revealed expression changes in specific PIG-Q transcripts. Results from more refined individual luciferase reporter assays for each of the three SNPs in iPSCs revealed that the intronic region with the rs3752495 risk allele induced a ~2.5-fold increase in luciferase expression (n=10). Importantly, rs3752495 also exhibited an allele specific effect, with the risk allele increasing the luciferase expression by ~2-fold compared to the non-risk allele. In conclusion, our variant-to-function approach and subsequent in vitro validation implicates rs3752495 as a causal insomnia risk variant embedded at the WDR90-PIG-Q locus.

Expression of the readthrough transcript CiDRE in alveolar macrophages boosts SARS-CoV-2 susceptibility and promotes COVID-19 severity

Lung infection during severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) via the angiotensin-I-converting enzyme 2 (ACE2) receptor induces a cytokine storm. However, the precise mechanisms involved in severe COVID-19 pneumonia are unknown. Here, we showed that interleukin-10 (IL-10) induced the expression of ACE2 in normal alveolar macrophages, causing them to become vectors for SARS-CoV-2. The inhibition of this system in hamster models attenuated SARS-CoV-2 pathogenicity. Genome-wide association and quantitative trait locus analyses identified a IFNAR2-IL10RB readthrough transcript, COVID-19 infectivity-enhancing dual receptor (CiDRE), which was highly expressed in patients harboring COVID-19 risk variants at the IFNAR2 locus. We showed that CiDRE exerted synergistic effects via the IL-10-ACE2 axis in alveolar macrophages and functioned as a decoy receptor for type I interferons. Collectively, our data show that high IL-10 and CiDRE expression are potential risk factors for severe COVID-19. Thus, IL-10R and CiDRE inhibitors might be useful COVID-19 therapies.

Brain Region-Dependent Alternative Splicing of Alzheimer Disease (AD)-Risk Genes Is Associated With Neuropathological Features in AD

PURPOSE

Alzheimer disease (AD) is one of the most complex diseases and is characterized by AD-related neuropathological features, including accumulation of amyloid-β plaques and tau neurofibrillary tangles. Dysregulation of alternative splicing (AS) contributes to these features, and there is heterogeneity in features across brain regions between AD patients, leading to different severity and progression rates; however, brain region-specific AS mechanisms still remain unclear. Therefore, we aimed to systemically investigate AS in multiple brain regions of AD patients and how they affect clinical features.

METHODS

We analyzed RNA sequencing (RNA-Seq) data obtained from brain regions (frontal and temporal) of AD patients. Reads were mapped to the hg19 reference genome using the STAR aligner, and exon skipping (ES) rates were estimated as percent spliced in (PSI) by rMATs. We focused on AD-risk genes discovered by genome-wide association studies, and accordingly evaluated associations between PSI of skipped exons in AD-risk genes and Braak stage and plaque density mean (PM) for each brain region. We also integrated whole-genome sequencing data of the ascertained samples with RNA-Seq data to identify genetic regulators of feature-associated ES.

RESULTS

We identified 26 and 41 ES associated with Braak stage in frontal and temporal regions, respectively, and 10 and 50 ES associated with PM. Among those, 10 were frontal-specific (CLU and NTRK2), 65 temporal-specific (HIF1A and TRPC4AP), and 26 shared ES (APP) that accompanied functional Gene Ontology terms, including axonogenesis in shared-ES genes. We further identified genetic regulators that account for 44 ES (44% of the total). Finally, we present as a case study the systematic regulation of an ES in APP, which is important in AD pathogenesis.

CONCLUSION

This study provides new insights into brain region-dependent AS regulation of the architecture of AD-risk genes that contributes to AD pathologies, ultimately allowing identification of a treatment target and region-specific biomarkers for AD.