Gene-level Analysis Research Articles

Investigating the shared genetic architecture of post-traumatic stress disorder and gastrointestinal tract disorders: a genome-wide cross-trait analysis.

Observational studies suggest a correlation between post-traumatic stress disorder (PTSD) and gastrointestinal tract (GIT) disorders. However, the genetic overlap, causal relationships, and underlining mechanisms between PTSD and GIT disorders were absent. We obtained genome-wide association study statistics for PTSD (23 212 cases, 151 447 controls), peptic ulcer disease (PUD; 16 666 cases, 439 661 controls), gastroesophageal reflux disease (GORD; 54 854 cases, 401 473 controls), PUD and/or GORD and/or medications (PGM; 90 175 cases, 366 152 controls), irritable bowel syndrome (IBS; 28 518 cases, 426 803 controls), and inflammatory bowel disease (IBD; 7045 cases, 449 282 controls). We quantified genetic correlations, identified pleiotropic loci, and performed multi-marker analysis of genomic annotation, fast gene-based association analysis, transcriptome-wide association study analysis, and bidirectional Mendelian randomization analysis. PTSD globally correlates with PUD (rg = 0.526, p = 9.355 × 10-7), GORD (rg = 0.398, p = 5.223 × 10-9), PGM (rg = 0.524, p = 1.251 × 10-15), and IBS (rg = 0.419, p = 8.825 × 10-6). Cross-trait meta-analyses identify seven genome-wide significant loci between PTSD and PGM (rs13107325, rs1632855, rs1800628, rs2188100, rs3129953, rs6973700, and rs73154693); three between PTSD and GORD (rs13107325, rs1632855, and rs3132450); one between PTSD and IBS/IBD (rs4937872 and rs114969413, respectively). Proximal pleiotropic genes are mainly enriched in immune response regulatory pathways, and in brain, digestive, and immune systems. Gene-level analyses identify five candidates: ABT1, BTN3A2, HIST1H3J, ZKSCAN4, and ZKSCAN8. We found significant causal effects of GORD, PGM, IBS, and IBD on PTSD. We observed no reverse causality of PTSD with GIT disorders, except for GORD. PTSD and GIT disorders share common genetic architectures. Our work offers insights into the biological mechanisms, and provides genetic basis for translational research studies.

Gene-level association analysis of bivariate ordinal traits with functional regressions.

In genetic studies, many phenotypes have multiple naturally ordered discrete values. The phenotypes can be correlated with each other. If multiple correlated ordinal traits are analyzed simultaneously, the power of analysis may increase significantly while the false positives can be controlled well. In this study, we propose bivariate functional ordinal linear regression (BFOLR) models using latent regressions with cumulative logit link or probit link to perform a gene-based analysis for bivariate ordinal traits and sequencing data. In the proposed BFOLR models, genetic variant data are viewed as stochastic functions of physical positions, and the genetic effects are treated as a function of physical positions. The BFOLR models take the correlation of the two ordinal traits into account via latent variables. The BFOLR models are built upon functional data analysis which can be revised to analyze the bivariate ordinal traits and high-dimension genetic data. The methods are flexible and can analyze three types of genetic data: (1) rare variants only, (2) common variants only, and (3) a combination of rare and common variants. Extensive simulation studies show that the likelihood ratio tests of the BFOLR models control type I errors well and have good power performance. The BFOLR models are applied to analyze Age-Related Eye Disease Studydata, in which two genes, CFH and ARMS2, are found to strongly associate with eye drusen size, drusen area, age-related macular degeneration (AMD) categories, and AMD severity scale.

Differentially expressed transcript isoforms associate with resistance to tuberculin skin test and interferon gamma release assay conversion.

A mechanistic understanding of uncommon immune outcomes such as resistance to infection has led to the development of novel therapies. Using gene level analytic methods, we previously found distinct monocyte transcriptional responses associated with resistance to Mycobacterium tuberculosis (Mtb) infection defined as persistently negative tuberculin skin test (TST) and interferon gamma release assay (IGRA) reactivity among highly exposed contacts (RSTR phenotype). Using transcript isoform analyses, we aimed to identify novel RSTR-associated genes hypothesizing that previous gene-level differential expression analysis obscures isoform-specific differences that contribute to phenotype. Monocytes from 49 RSTR versus 52 subjects with latent Mtb infection (LTBI) were infected with M. tuberculosis (H37Rv) or left unstimulated (media) prior to RNA isolation and sequencing. RSTR-associated gene expression was then identified using differential transcript isoform analysis. We identified 81 differentially expressed transcripts (DETs) in 70 genes (FDR <0.05) comparing RSTR and LTBI phenotypes with the majority (n = 79 DETs) identified under Mtb-stimulated conditions. Seventeen of these genes were previously identified with gene-level bulk RNAseq analyses including genes in the IFNγ response that had increased expression among LTBI subjects, findings consistent with a clinical phenotype based on IGRA reactivity. Among the subset of 23 genes with positive differential expression among Mtb-infected RSTR monocytes, 13 were not previously identified. These novel DET genes included PDE4A and ZEB2, which each had multiple DETs with higher expression among RSTR subjects, and ACSL4 and GAPDH that each had a single transcript isoform associated with RSTR. Transcript isoform-specific analyses identify transcriptional associations, such as those associated with resistance to TST/IGRA conversion, that are obscured when using gene-level approaches. These findings should be validated with additional RSTR cohorts and whether the newly identified candidate resistance genes directly influence the monocyte Mtb response requires functional study.

Polygenic influences on the behavioral effects of alcohol withdrawal in a mixed-ancestry population from the collaborative study on the genetics of alcoholism (COGA)

Alcohol withdrawal (AW) is a feature of alcohol use disorder that may occur in up to half of individuals with chronic, heavy alcohol consumption whenever alcohol use is abruptly stopped or significantly reduced. To date, few genes have been robustly associated with AW; this may be partly due to most studies defining AW as a binary construct despite the multiple symptoms and their range in severity from mild to severe. The current study examined the effects of genome-wide loci on a factor score for AW in high risk and community family samples in the Collaborative Study for the Genetics of Alcoholism (COGA). In addition, we tested whether differentially expressed genes associated with alcohol withdrawal in model organisms are enriched in human genome-wide association study (GWAS) effects. Analyses employed roughly equal numbers of males and females (mean age 35, standard deviation = 15; total N = 8009) and included individuals from multiple ancestral backgrounds. Genomic data were imputed to the HRC reference panel and underwent strict quality control procedures using Plink2. Analyses controlled for age, sex, and population stratification effects using ancestral principal components. We found support that AW is a polygenic disease (SNP-heritability = 0.08 [95 % CI = 0.01, 0.15; pedigree-based heritability = 0.12 [0.08,0.16]. We identified five single nucleotide variants that met genomewide significance, some of which have previously been associated with alcohol phenotypes. Gene-level analyses suggest a role for COL19A1 in AW; H-MAGMA analyses implicated 12 genes associated with AW. Cross-species enrichment analyses indicated that variation within genes identified in model organism studies explained <1 % of the phenotypic variability in human AW. Notably, the surrounding regulatory regions of model organism genes explained more variance than expected by chance, indicating that these regulatory regions and gene sets may be important for human AW. Lastly, when comparing the overlap in genes identified from the human GWAS and H-MAGMA analyses with the genes identified from the animal studies, there was modest overlap, indicating some convergence between the methods and organisms.

Abstract 5219: Telomere length distribution, metabolic trait association, and genome-wide association study among Singaporean cohorts

Abstract Leukocyte telomere length (LTL) is known as a heritable marker of cellular aging and is a risk factor for several chronic diseases. LTL distribution and association studies are essential in providing insights into telomere homeostasis, its relationship with different metabolic traits, and age-related conditions. Such studies have been underperformed in Asian population groups. Here we estimated LTL using whole-genome sequencing data in 8,045 Singaporean samples across three major ethnic groups - Chinese, Indian, and Malay divided into six cohorts: GUSTO, HELIOS, MEC, PRISM, SEED, and TTSH. LTLs were measured using TelSeq, with normalized LTL as phenotype in subsequent analyses. We demonstrated that average LTL was robustly longer in the childhood cohort (GUSTO) compared to the adult cohorts (P&amp;lt;1.19 × 10−195). We observed ethnicity differences in LTL where adult Singaporean Chinese samples had the longest LTL (5.08kb) as compared to Malay (4.87kb) and Indian (4.65kb) samples in our datasets (P&amp;lt;4.98 × 10−12). Our genetic association analyses replicated multiple variants previously reported to be associated with LTL (binomial P&amp;lt;0.00001) in Asian population groups, providing added confidence in the methodologies used. At the same time, we identified a novel East-Asian-specific novel variant beyond genome-wide association levels (meta P=2.05 × 10−08) at the COL28A1/MIOS gene loci to be associated with LTL in our study. Individual analysis of the GUSTO childhood dataset highlighted a second novel genome-wide hit at RAD17 gene locus to be associated with LTL in our study and indicated that the previously reported variant at the TINF2 locus may have significantly opposing effects (compared to adults) during early developmental stages. Gene-level burden analysis for rare variants (MAF &amp;lt;5%) in adult Chinese cohorts revealed potential cancer association with RET gene (SKAT, P=5.22 × 10−03) and MLH1 gene (SKAT, P=0.037), as well as telomere biology condition association with RECQL4 gene (CMC, P=0.0399, and VT, P=0.0379). Our findings have broadened the understanding of LTL distribution and the underlying associated genetic characteristics in both childhood and adult Singaporean populations. Citation Format: Trang Nguyen, Penny Chan, Ives Lim, Brian Kennedy, Neerja Karnani, Rajkumar Dorajoo, Joanne Ngeow. Telomere length distribution, metabolic trait association, and genome-wide association study among Singaporean cohorts. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5219.

Abstract 674: CRISPR and drug screens identify ERK as the mediator of IFNg-induced melanoma growth inhibition

Abstract Interferon-gamma (IFNg) is critical for the success of immune checkpoint blockade therapy. Disruption of IFNg signaling makes tumors resistant to anti-PD-1/L1 treatment primarily by evasion of the growth inhibition (GI) induced by this cytokine. However, the pathways mediating IFNg-induced GI are largely unknown. We used two independent approaches to delineate the IFNg GI pathway, a chemical genomics screen and a whole genome targeting CRISPR/Cas9 screen in an IFNg GI-sensitive patient-derived melanoma (PDM) cell line. Drug screening revealed that treatment with RAF and ERK inhibitors disrupted IFNg GI in PDM cells. For the CRISPR screen, gene-level analysis showed that the magnitude of enrichment for guide RNAs targeting ERK2 in IFNg-treated cells was comparable to proximal IFNg signaling proteins like JAK1, STAT1, IFNGR2, and JAK2 (&amp;gt;26-fold enrichment for ERK2, p = 2.28e-07). Thus, results from both these experiments converged upon ERK as the primary downstream target of IFNg essential for GI induction. We validated the involvement of ERK by detecting a sustained increase in phospho-ERK1/2 levels following IFNg treatment. In live imaging experiments, we found that blocking ERK activity with the ERK inhibitor Ulixertinib blocks the induction of cell death after IFNg treatment in 17 of 23 (~74%) IFNg-sensitive PDM lines covering all the MAPK mutant and triple wildtype molecular subtypes of melanoma. Decreasing and increasing ERK activity levels using shRNA and overexpression approaches also led to a decrease and increase in IFNg-mediated GI, respectively. We have identified NOXA and DR5 as likely mediators of cell death through the IFNg-ERK GI pathway by comparing gene expression profiles of control and IFNg-treated PDM cells. Finally, phorbol 12-myristate 13-acetate (PMA), a known ERK activator, synergizes with IFNg treatment to induce increased GI in PDM cells (Bliss synergy score 16.87, p = 1.83e-123). In summary, the IFNg-ERK signaling axis mediates cell death downstream of IFNg treatment in melanoma cells. This pathway is active in all melanoma subtypes, making it an attractive target to enhance IFNg GI in tumor cells. Our results provide a new understanding of the IFNg GI pathway that will also be crucial to define mechanisms of GI resistance in tumor cells. Citation Format: Ameya S. Champhekar, Rachel Heymans, Cynthia Gonzalez, Guillem Turon Font, Daniel Karin, June Lee, Robert Damoiseaux, Antoni Ribas. CRISPR and drug screens identify ERK as the mediator of IFNg-induced melanoma growth inhibition [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 674.

Whole exome sequencing study identifies candidate loss of function variants and locus heterogeneity in familial cholesteatoma.

Cholesteatoma is a rare progressive disease of the middle ear. Most cases are sporadic, but some patients report a positive family history. Identifying functionally important gene variants associated with this disease has the potential to uncover the molecular basis of cholesteatoma pathology with implications for disease prevention, surveillance, or management. We performed an observational WES study of 21 individuals treated for cholesteatoma who were recruited from ten multiply affected families. These family studies were complemented with gene-level mutational burden analysis. We also applied functional enrichment analyses to identify shared properties and pathways for candidate genes and their products. Filtered data collected from pairs and trios of participants within the ten families revealed 398 rare, loss of function (LOF) variants co-segregating with cholesteatoma in 389 genes. We identified six genes DENND2C, DNAH7, NBEAL1, NEB, PRRC2C, and SHC2, for which we found LOF variants in two or more families. The parallel gene-level analysis of mutation burden identified a significant mutation burden for the genes in the DNAH gene family, which encode products involved in ciliary structure. Functional enrichment analyses identified common pathways for the candidate genes which included GTPase regulator activity, calcium ion binding, and degradation of the extracellular matrix. The number of candidate genes identified and the locus heterogeneity that we describe within and between multiply affected families suggest that the genetic architecture for familial cholesteatoma is complex.

Genetic Modifiers of Cystic Fibrosis Lung Disease Severity: Whole-Genome Analysis of 7,840 Patients.

Rationale: Lung disease is the major cause of morbidity and mortality in persons with cystic fibrosis (pwCF). Variability in CF lung disease has substantial non-CFTR (CF transmembrane conductance regulator) genetic influence. Identification of genetic modifiers has prognostic and therapeutic importance. Objectives: Identify genetic modifier loci and genes/pathways associated with pulmonary disease severity. Methods: Whole-genome sequencing data on 4,248 unique pwCF with pancreatic insufficiency and lung function measures were combined with imputed genotypes from an additional 3,592 patients with pancreatic insufficiency from the United States, Canada, and France. This report describes association of approximately 15.9 million SNPs using the quantitative Kulich normal residual mortality-adjusted (KNoRMA) lung disease phenotype in 7,840 pwCF using premodulator lung function data. Measurements and Main Results: Testing included common and rare SNPs, transcriptome-wide association, gene-level, and pathway analyses. Pathway analyses identified novel associations with genes that have key roles in organ development, and we hypothesize that these genes may relate to dysanapsis and/or variability in lung repair. Results confirmed and extended previous genome-wide association study findings. These whole-genome sequencing data provide finely mapped genetic information to support mechanistic studies. No novel primary associations with common single variants or rare variants were found. Multilocus effects at chr5p13 (SLC9A3/CEP72) and chr11p13 (EHF/APIP) were identified. Variant effect size estimates at associated loci were consistently ordered across the cohorts, indicating possible age or birth cohort effects. Conclusions: This premodulator genomic, transcriptomic, and pathway association study of 7,840 pwCF will facilitate mechanistic and postmodulator genetic studies and the development of novel therapeutics for CF lung disease.

Replacement Flame-Retardant 2-Ethylhexyldiphenyl Phosphate (EHDPP) Disrupts Hepatic Lipidome: Evidence from Human 3D Hepatospheroid Cell Culture.

The present study aims to evaluate the effects of repeated exposure to 2-ethylhexyldiphenyl phosphate (EHDPP) on human liver cells. In vitro three-dimensional (3D) hepatospheroid cell culture was utilized to explore the potential mechanisms of EHDPP-mediated metabolic disruption through morphological, transcriptional, and biochemical assays. Lipidomics analysis was performed on the individual hepatospheroids to investigate the effects on intracellular lipid profiles, followed by hepatospheroid morphology, growth, functional parameters, and cytotoxicity evaluation. The possible mechanisms were delineated using the gene-level analysis by assessing the expression of key genes encoding for hepatic lipid metabolism. We revealed that exposure to EHDPP at 1 and 10 μM for 7 days alters the lipid profile of human 3D hepatospheroids. Dysregulation in several lipid classes, including sterol lipids (cholesterol esters), sphingolipids (dihydroceramide, hexosylceramide, ceramide, sphingomyelin), glycerolipids (triglycerides), glycerophospholipids, and fatty acyls, was noted along with alteration in genes including ACAT1, ACAT2, CYP27A1, ABCA1, GPAT2, PNPLA2, PGC1α, and Nrf2. Our study brings a novel insight into the metabolic disrupting effects of EHDPP and demonstrates the utility of hepatospheroids as an in vitro cell culture model complemented with omics technology (e.g., lipidomics) for mechanistic toxicity studies.

Rare variants confer shared susceptibility to gastrointestinal tract cancer risk.

Cancers arising within the gastrointestinal tract are complex disorders involving genetic events that cause the conversion of normal tissue to premalignant lesions and malignancy. Shared genetic features are reported in epithelial-based gastrointestinal cancers which indicate common susceptibility among this group of malignancies. In addition, the contribution of rare variants may constitute parts of genetic susceptibility. A cross-cancer analysis of 38,171 shared rare genetic variants from genome-wide association assays was conducted, which included data from 3,194 cases and 1,455 controls across three cancer sites (esophageal, gastric and colorectal). The SNP-level association was performed by multivariate logistic regression analyses for single cancer, followed by association analysis for SubSETs (ASSET) to adjust the bias of overlapping controls. Gene-level analyses were conducted by SKAT-O, with multiple comparison adjustments by false discovery rate (FDR). Based on the significant genes indicated by SKATO analysis, pathways analysis was conducted using Gene Ontology (GO), the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Reactome databases. Meta-analysis in three gastrointestinal (GI) cancers identified 13 novel susceptibility loci that reached genome-wide significance (P ASSET< 5×10-8). SKAT-O analysis revealed EXOC6, LRP5L and MIR1263/LINC01324 to be significant genes shared by GI cancers (P adj<0.05, P FDR<0.05). Furthermore, GO pathway analysis identified significant enrichment of synaptic transmission and neuron development pathways shared by all three cancer types. Rare variants and the corresponding genes potentially contribute to shared susceptibility in different GI cancer types. The discovery of these novel variants and genes offers new insights for the carcinogenic mechanisms and missing heritability of GI cancers.

Comprehensive characterization of putative genetic influences on plasma metabolome in a pediatric cohort.

The human exposome is composed of diverse metabolites and small chemical compounds originated from endogenous and exogenous sources, respectively. Genetic and environmental factors influence metabolite levels, while the extent of genetic contributions across metabolic pathways is not yet known. Untargeted profiling of human metabolome using high-resolution mass spectrometry (HRMS) combined with genome-wide genotyping allows comprehensive identification of genetically influenced metabolites. As such previous studies of adults discovered and replicated genotype-metabotype associations. However, these associations have not been characterized in children. We conducted the largest genome by metabolome-wide association study to date of children (N = 441) using 619,688 common genetic variants and 14,342 features measured by HRMS. Narrow-sense heritability (h2) estimates of plasma metabolite concentrations using genomic relatedness matrix restricted maximum likelihood (GREML) method showed a bimodal distribution with high h2 (> 0.8) for 15.9% of features and low h2 (< 0.2) for most of features (62.0%). The features with high h2 were enriched for amino acid and nucleic acid metabolism, while carbohydrate and lipid concentrations showed low h2. For each feature, a metabolite quantitative trait loci (mQTL) analysis was performed to identify genetic variants that were potentially associated with plasma levels. Fifty-four associations among 29 features and 43 genetic variants were identified at a genome-wide significance threshold p < 3.5 × 10-12 (= 5 × 10-8/14,342 features). Previously reported associations such as UGT1A1 and bilirubin; PYROXD2 and methyl lysine; and ACADS and butyrylcarnitine were successfully replicated in our pediatric cohort. We found potential candidates for novel associations including CSMD1 and a monostearyl alcohol triglyceride (m/z 781.7483, retention time (RT) 89.3s); CALN1 and Tridecanol (m/z 283.2741, RT 27.6). A gene-level enrichment analysis using MAGMA revealed highly interconnected modules for dADP biosynthesis, sterol synthesis, and long-chain fatty acid transport in the gene-feature network. Comprehensive profiling of plasma metabolome across age groups combined with genome-wide genotyping revealed a wide range of genetic influence on diverse chemical species and metabolic pathways. The developmental trajectory of a biological system is shaped by gene-environment interaction especially in early life. Therefore, continuous efforts on generating metabolomics data in diverse human tissue types across age groups are required to understand gene-environment interaction toward healthy aging trajectories.

Integrative Analysis of Transcriptome-Wide Association Study and Gene-Based Association Analysis Identifies In Silico Candidate Genes Associated with Juvenile Idiopathic Arthritis.

Genome-wide association study (GWAS) of Juvenile idiopathic arthritis (JIA) suffers from low power due to limited sample size and the interpretation challenge due to most signals located in non-coding regions. Gene-level analysis could alleviate these issues. Using GWAS summary statistics, we performed two typical gene-level analysis of JIA, transcriptome-wide association studies (TWAS) using FUnctional Summary-based ImputatiON (FUSION) and gene-based analysis using eQTL Multi-marker Analysis of GenoMic Annotation (eMAGMA), followed by comprehensive enrichment analysis. Among 33 overlapped significant genes from these two methods, 11 were previously reported, including TYK2 (PFUSION = 5.12 × 10-6, PeMAGMA = 1.94 × 10-7 for whole blood), IL-6R (PFUSION = 8.63 × 10-7, PeMAGMA = 2.74 × 10-6 for cells EBV-transformed lymphocytes), and Fas (PFUSION = 5.21 × 10-5, PeMAGMA = 1.08 × 10-6 for muscle skeletal). Some newly plausible JIA-associated genes are also reported, including IL-27 (PFUSION = 2.10 × 10-7, PeMAGMA = 3.93 × 10-8 for Liver), LAT (PFUSION = 1.53 × 10-4, PeMAGMA = 4.62 × 10-7 for Artery Aorta), and MAGI3 (PFUSION = 1.30 × 10-5, PeMAGMA = 1.73 × 10-7 for Muscle Skeletal). Enrichment analysis further highlighted 4 Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways and 10 Gene Ontology (GO) terms. Our findings can benefit the understanding of genetic determinants and potential therapeutic targets for JIA.

Association analysis of risk genes identified by SCHEMA with schizophrenia in the Chinese Han population.

Schizophrenia is a chronic brain disorder. Previously, the Schizophrenia Exome Sequencing Meta-analysis consortium identified 10 highest risk genes related to schizophrenia. This study aimed to analyze the relationship between the 10 highest risk genes identified by the SCHEMA and schizophrenia in a Chinese population. A total of 225 variants in 10 genes were screened in a Chinese population of 6836 using a customized array. All variants were annotated through the Variant Effect Predictor tool, and the functional impacts of missense variants were assessed based on sorting intolerant from tolerant and PolyPhen-2 scores. The SHEsisPlus tool was used to analyze the association between risk genes and schizophrenia at the locus and gene levels. At the locus level, no missense variants significantly related to schizophrenia were found, but we detected three missense variants that appeared only in cases, including TRIO p. Arg1185Gln, RB1CC1 p. Arg1514Cys, and HERC1 p. Val4517Leu. At the gene level, five genes (TRIO, RB1CC1, HERC1, GRIN2A, and CACAN1G) with more than one variant analyzed were kept for the gene-level association analysis. Only the association between RB1CC1 and schizophrenia reached a significant level (OR = 1.634; 95% CI, 1.062-2.516; P = 0.025). In this study, we determined that RB1CC1 might be a risk gene for schizophrenia in the Chinese population. Our results provide new evidence for recognizing the correlation of these risk genes with the Chinese schizophrenia population.

Comprehensive analysis of full-length transcripts reveals novel splicing abnormalities and oncogenic transcripts in liver cancer.

Genes generate transcripts of various functions by alternative splicing. However, in most transcriptome studies, short-reads sequencing technologies (next-generation sequencers) have been used, leaving full-length transcripts unobserved directly. Although long-reads sequencing technologies would enable the sequencing of full-length transcripts, the data analysis is difficult. In this study, we developed an analysis pipeline named SPLICE and analyzed cDNA sequences from 42 pairs of hepatocellular carcinoma (HCC) and matched non-cancerous livers with an Oxford Nanopore sequencer. Our analysis detected 46,663 transcripts from the protein-coding genes in the HCCs and the matched non-cancerous livers, of which 5,366 (11.5%) were novel. A comparison of expression levels identified 9,933 differentially expressed transcripts (DETs) in 4,744 genes. Interestingly, 746 genes with DETs, including the LINE1-MET transcript, were not found by a gene-level analysis. We also found that fusion transcripts of transposable elements and hepatitis B virus (HBV) were overexpressed in HCCs. In vitro experiments on DETs showed that LINE1-MET and HBV-human transposable elements promoted cell growth. Furthermore, fusion gene detection showed novel recurrent fusion events that were not detected in the short-reads. These results suggest the efficiency of full-length transcriptome studies and the importance of splicing variants in carcinogenesis.

Gene Expression Profiling between Patient Groups with High and Low Ki67 Levels after Short-term Preoperative Aromatase Inhibitor Treatment for Breast Cancer.

According to a recent report, a low Ki67 level after short-term preoperative hormone therapy (post-Ki67) might suggest a more favorable prognosis compared with a high post-Ki67 level in patients with hormone receptorpositive/human epidermal growth factor 2-negative (HR+/HER2-) breast cancer with high levels of Ki67. This study aimed to evaluate the pre-treatment genetic differences between these two patient groups. Forty-five luminal B-like patients were stratified into two groups, namely, a group with high (H→H) and one with low (H→L) Ki67 levels after short-term preoperative aromatase inhibitor (AI) treatment. We compared pre-treatment gene expression profiles between the two groups. In gene level analysis, there was no significant difference between the two groups by the class comparison test. In pathway analysis, five metabolism-related gene sets were significantly upregulated in the H→L group (p≤0.05). In the search for novel targets, five genes (PARP, BRCA2, FLT4, CDK6, and PDCD1LG2) showed significantly higher expression in the H→H group (p≤0.05). Several metabolism-related pathways were associated with sensitivity to AI. In the future, it will be necessary to seek out new therapeutic strategies for the poor prognostic group with high post-Ki67.

PanTools v3: functional annotation, classification and phylogenomics.

SummaryThe ever-increasing number of sequenced genomes necessitates the development of pangenomic approaches for comparative genomics. Introduced in 2016, PanTools is a platform that allows pangenome construction, homology grouping and pangenomic read mapping. The use of graph database technology makes PanTools versatile, applicable from small viral genomes like SARS-CoV-2 up to large plant or animal genomes like tomato or human. Here, we present our third major update to PanTools that enables the integration of functional annotations and provides both gene-level analyses and phylogenetics.Availability and implementationPanTools is implemented in Java 8 and released under the GNU GPLv3 license. Software and documentation are available at https://git.wur.nl/bioinformatics/pantoolsSupplementary information Supplementary data are available at Bioinformatics online.

Complete Genomes of Theileria orientalis Chitose and Buffeli Genotypes Reveal within Species Translocations and Differences in ABC Transporter Content.

Theileria orientalis causes losses to cattle producers in Eastern Asia, Oceania and, more recently, North America. One pathogenic genotype (Ikeda) has been sequenced to the chromosomal level, while only draft genomes exist for globally distributed Chitose and Buffeli genotypes. To provide an accurate comparative gene-level analysis and help further understand their pathogenicity, we sequenced isolates of the Chitose and Buffeli genotypes of T. orientalis using long-read sequencing technology. A combination of several long-read assembly methods and short reads produced chromosomal-level assemblies for both Fish Creek (Chitose) and Goon Nure (Buffeli) isolates, including the first complete and circular apicoplast genomes generated for T. orientalis. Comparison with the Shintoku (Ikeda) reference sequence showed both large and small translocations in T. orientalis Buffeli, between chromosomes 2 and 3 and chromosomes 1 and 4, respectively. Ortholog clustering showed expansion of ABC transporter genes in Chitose and Buffeli. However, differences in several genes of unknown function, including DUF529/FAINT-domain-containing proteins, were also identified and these genes were more prevalent in Ikeda and Chitose genotypes. Phylogenetics and similarity measures were consistent with previous short-read genomic analysis. The generation of chromosomal sequences for these highly prevalent T. orientalis genotypes will also support future studies of population genetics and mixed genotype infections.

Genome-wide meta-analysis and omics integration identifies novel genes associated with diabetic kidney disease

Aims/hypothesisDiabetic kidney disease (DKD) is the leading cause of kidney failure and has a substantial genetic component. Our aim was to identify novel genetic factors and genes contributing to DKD by performing meta-analysis of previous genome-wide association studies (GWAS) on DKD and by integrating the results with renal transcriptomics datasets.MethodsWe performed GWAS meta-analyses using ten phenotypic definitions of DKD, including nearly 27,000 individuals with diabetes. Meta-analysis results were integrated with estimated quantitative trait locus data from human glomerular (N=119) and tubular (N=121) samples to perform transcriptome-wide association study. We also performed gene aggregate tests to jointly test all available common genetic markers within a gene, and combined the results with various kidney omics datasets.ResultsThe meta-analysis identified a novel intronic variant (rs72831309) in the TENM2 gene associated with a lower risk of the combined chronic kidney disease (eGFR<60 ml/min per 1.73 m2) and DKD (microalbuminuria or worse) phenotype (p=9.8×10−9; although not withstanding correction for multiple testing, p>9.3×10−9). Gene-level analysis identified ten genes associated with DKD (COL20A1, DCLK1, EIF4E, PTPRN–RESP18, GPR158, INIP–SNX30, LSM14A and MFF; p<2.7×10−6). Integration of GWAS with human glomerular and tubular expression data demonstrated higher tubular AKIRIN2 gene expression in individuals with vs without DKD (p=1.1×10−6). The lead SNPs within six loci significantly altered DNA methylation of a nearby CpG site in kidneys (p<1.5×10−11). Expression of lead genes in kidney tubules or glomeruli correlated with relevant pathological phenotypes (e.g. TENM2 expression correlated positively with eGFR [p=1.6×10−8] and negatively with tubulointerstitial fibrosis [p=2.0×10−9], tubular DCLK1 expression correlated positively with fibrosis [p=7.4×10−16], and SNX30 expression correlated positively with eGFR [p=5.8×10−14] and negatively with fibrosis [p<2.0×10−16]).Conclusions/interpretationAltogether, the results point to novel genes contributing to the pathogenesis of DKD.Data availabilityThe GWAS meta-analysis results can be accessed via the type 1 and type 2 diabetes (T1D and T2D, respectively) and Common Metabolic Diseases (CMD) Knowledge Portals, and downloaded on their respective download pages (https://t1d.hugeamp.org/downloads.html; https://t2d.hugeamp.org/downloads.html; https://hugeamp.org/downloads.html).Graphical abstract

Pinpointing novel risk loci for Lewy body dementia and the shared genetic etiology with Alzheimer’s disease and Parkinson’s disease: a large-scale multi-trait association analysis

BackgroundThe current genome-wide association study (GWAS) of Lewy body dementia (LBD) suffers from low power due to a limited sample size. In addition, the genetic determinants underlying LBD and the shared genetic etiology with Alzheimer’s disease (AD) and Parkinson’s disease (PD) remain poorly understood.MethodsUsing the largest GWAS summary statistics of LBD to date (2591 cases and 4027 controls), late-onset AD (86,531 cases and 676,386 controls), and PD (33,674 cases and 449,056 controls), we comprehensively investigated the genetic basis of LBD and shared genetic etiology among LBD, AD, and PD. We first conducted genetic correlation analysis using linkage disequilibrium score regression (LDSC), followed by multi-trait analysis of GWAS (MTAG) and association analysis based on SubSETs (ASSET) to identify the trait-specific SNPs. We then performed SNP-level functional annotation to identify significant genomic risk loci paired with Bayesian fine-mapping and colocalization analysis to identify potential causal variants. Parallel gene-level analysis including GCTA-fastBAT and transcriptome-wide association analysis (TWAS) was implemented to explore novel LBD-associated genes, followed by pathway enrichment analysis to understand underlying biological mechanisms.ResultsPairwise LDSC analysis found positive genome-wide genetic correlations between LBD and AD (rg = 0.6603, se = 0.2001; P = 0.0010), between LBD and PD (rg = 0.6352, se = 0.1880; P = 0.0007), and between AD and PD (rg = 0.2136, se = 0.0860; P = 0.0130). We identified 13 significant loci for LBD, including 5 previously reported loci (1q22, 2q14.3, 4p16.3, 4q22.1, and 19q13.32) and 8 novel biologically plausible genetic associations (5q12.1, 5q33.3, 6p21.1, 8p23.1, 8p21.1, 16p11.2, 17p12, and 17q21.31), among which APOC1 (19q13.32), SNCA (4q22.1), TMEM175 (4p16.3), CLU (8p21.1), MAPT (17q21.31), and FBXL19 (16p11.2) were also validated by gene-level analysis. Pathway enrichment analysis of 40 common genes identified by GCTA-fastBAT and TWAS implicated significant role of neurofibrillary tangle assembly (GO:1902988, adjusted P = 1.55 × 10−2).ConclusionsOur findings provide novel insights into the genetic determinants of LBD and the shared genetic etiology and biological mechanisms of LBD, AD, and PD, which could benefit the understanding of the co-pathology as well as the potential treatment of these diseases simultaneously.

Abstract 3371: Impact of RNA degradation on transcriptomic profiling in tumors samples from syngeneic mouse models

Abstract RNA-Seq is currently the most prevailing method for measuring transcriptional activities in cells and tissues. It relies on high-quality RNA in order to yield reliable and reproducible results, which is often challenging due to RNA degradation during sample collection and processing. Agilent’s RNA Integrity Number (RIN) is a commonly adopted standard for evaluating RNA quality in NGS workflows. However, while most RNA-Seq experiments are geared towards the quantification of mRNA, the RIN metric heavily relies on the amount of 18S and 28S ribosomal RNA and does not directly measure the integrity of mRNA. To overcome this limitation, researchers have proposed several post-alignment measures of transcript integrity, such as mRIN (mRNA Integrity Number), TIN (Transcript Integrity Number, from RSeQC package), and DI (Degradation Index, from DegNorm package), but so far there is no consensus as to which works best. It is also unclear to what extent RNA degradation impacts the results of downstream analysis when samples with suboptimal RNA quality are included. To answer these questions in the context of cancer research, we analyzed 198 RNA-Seq samples from 7 syngeneic mouse tumor models of different cancer types (4T1, CT26, EL4, E.G7-OVA, H22, Hepa1-6, and KLN205) with a wide range of RIN values (2.3 to 9.8). Interestingly, we found a high concordance between RIN and medTIN (median TIN score) in 4T1, E.G7-OVA, Hepa1-6, and KLN205 samples, but a surprisingly low concordance in EL4 and H22 samples. A tentative interpretation is that, depending on the tissue/cell type, it is possible for an RNA sample to have heavily degraded ribosomal RNA (hence a low RIN value), while still retaining relatively intact mRNA (resulting in a decent medTIN value). Principal component analysis (PCA) revealed that both RIN and medTIN are strongly correlated with the strongest explanatory variable (PC1) of the transcriptome across all library types, confirming that RNA degradation can heavily bias the results of transcript-level DE (differential expression) analysis. We then evaluated the performance of TIN correction, a method proposed in conjunction with the TIN metric to correct for RNA-degradation bias, and found it to be largely ineffective on our dataset. Apparently, there is a need for better normalization/correction methods when a dataset consists of samples with wildly varying RNA quality. On the other hand, the impact of RNA degradation on gene-level DE analysis is much smaller. In PCA on gene-level data, the first 4 PCs, which altogether explain &amp;gt; 70% of the variation in the transcriptome, showed weak or no correlation with RIN or medTIN, and the samples cluster according to their respective tissue groups as expected. Our study calls for cautious analysis and interpretation of gene expression data from degraded RNA samples, and highlights a need for more suitable RNA quality metrics and bias correction methods. Citation Format: Yanghui Sheng, Wubin Qian, Xiaobo Chen, Henry Q. Li, Sheng Guo. Impact of RNA degradation on transcriptomic profiling in tumors samples from syngeneic mouse models [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 3371.