Disadvantaged social status contributed to sleep disorders: An observational and genome-wide gene-environment interaction analysis.

BACKGROUND: Sleep is a natural and essential physiological need for individuals. Our study aimed to research the associations between accumulated social risks and sleep disorders. METHODS: In this study, we came up with a polysocial risk score (PsRS), which is a cumulative social risk index composed of 13 social determinants of health. This research includes 239,165 individuals with sleep disorders and social determinants of health data from the UK Biobank cohort. First, logistic regression models were performed to examine the associations of social determinants of health and sleep disorders, including chronotype, narcolepsy, insomnia, snoring, short and long sleep duration. Then, PsRS was calculated based on statistically significant social determinants of health for each sleep disorder. Third, a genome-wide gene-environment interaction study was conducted to explore the interactions between single-nucleotide polymorphisms and PsRS in relation to sleep disorders. RESULTS: Higher PsRS scores were associated with worse sleep status, with the adjusted odds ratio (OR) ranging from 1.10 (95% Confidence interval [CI]: 1.09-1.11) to 1.29 (95% CI: 1.27-1.30) for sleep disorders. Emotional stress (OR = 1.36, 95% CI: 1.28-1.43) and not in paid employment (OR = 2.62, 95% CI: 2.51-2.74) were found to have significant contributions for sleep disorders. Moreover, multiple single-nucleotide polymorphisms were discovered to have interactions with PsRS, such as FRAS1 (P = 2.57 × 10-14) and CACNA1A (P = 8.62 × 10-14) for narcolepsy, and ACKR3 (P = 1.24 × 10-8) for long sleep. CONCLUSIONS: Our findings suggested that cumulative social risks was associated with sleep disorders, while the interactions between genetic susceptibility and disadvantaged social status are risk factors for the development of sleep disorders.

Index of multiple deprivation contributed to common psychiatric disorders: A systematic review and comprehensive analysis.

BACKGROUND: Limited studies have been conducted to explore the interaction effects of social environmental and genetic factors on the risks of common psychiatric disorders. METHODS: 56,613-106,695 individuals were collected from the UK Biobank cohort. Logistic or liner regression models were first used to evaluate the associations of index of multiple deprivation (IMD) with bipolar disorder (BD), depression and anxiety in UK Biobank cohort. Then, for the significant IMD associated with BD, depression and anxiety, genome-wide gene-environment interaction study (GWEIS) was performed by PLINK 2.0. RESULT: Totally, the higher levels of IMD were significantly associated with higher risks of BD, depression and anxiety. For BD, GWEIS identified multiple significant SNPs interacting with IMD, such as rs75182167 for income and rs111841503 for education. For depression and anxiety, GWEIS found significant SNPs interacting with income and education, such as rs147013419 for income and rs142366753 for education. CONCLUSION: Social environmental deprivations contributed to the risks of psychiatric disorders. Besides, we reported multiple candidate genetic loci interacting with IMD, providing novel insights into the biological mechanism.

Identification of TCR repertoires in asymptomatic COVID-19 patients by single-cell T-cell receptor sequencing.

The T cell-mediated immune responses associated with asymptomatic infection (AS) of SARS-CoV-2 remain largely unknown. The diversity of T-cell receptor (TCR) repertoire is essential for generating effective immunity against viral infections in T cell response. Here, we performed the single-cell TCR sequencing of the PBMC samples from five AS subjects, 33 symptomatic COVID-19 patients and eleven healthy controls to investigate the size and the diversity of TCR repertoire. We subsequently analyzed the TCR repertoire diversity, the V and J gene segment deference, and the dominant combination of αß VJ gene pairing among these three study groups. Notably, we revealed significant TCR preference in the AS group, including the skewed usage of TRAV1-2-J33-TRBV6-4-J2-2 and TRAV1-2-J33-TRBV6-1-J2-3. Our findings may shed new light on understanding the immunopathogenesis of COVID-19 and help identify optimal TCRs for development of novel therapeutic strategies against SARS-CoV-2 infection.

Identification of Distinct Immune Cell Subsets Associated With Asymptomatic Infection, Disease Severity, and Viral Persistence in COVID-19 Patients.

The cell-mediated protective and pathogenic immune responses to SARS-CoV-2 infection remain largely elusive. Here we identified 76 distinct cell subsets in the PBMC samples that were associated with various clinical presentations of COVID-19 using scRNA-seq technology coupled with a deep and comprehensive analysis of unique cell surface markers and differentially expressed genes. We revealed that (TRAV1-2+CD8+)MAIT cells and (NCAM1hiCD160+)NK cells significantly enriched in the asymptomatic subjects whereas (LAG3+CD160+CD8+)NKT cells increased in the symptomatic patients. We also observed that (CD68-CSF1R-IL1BhiCD14+)classical monocytes were positively correlated with the disease severity. Moreover, (CD33-HLA-DMA-CD14+)classical monocytes and (CLEC10A-S100A9lo)pDC were associated with the viral persistence. The GO and KEGG analyses identified enriched pathways related to immune responses, inflammation, and apoptosis. These findings may enhance our understanding of the immunopathogenesis of COVID-19 and help develop novel strategies against SARS-CoV-2 infection.

The integrative analysis of DNA methylation and mRNA expression profiles confirmed the role of selenocompound metabolism pathway in Kashin-Beck disease.

Kashin-Beck disease (KBD) is an endemic chronic osteochondropathy. The etiology of KBD remains unknown. In this study, we conducted an integrative analysis of genome-wide DNA methylation and mRNA expression profiles between KBD and normal controls to identify novel candidate genes and pathways for KBD. Articular cartilage samples from 17 grade III KBD patients and 17 healthy controls were used in this study. DNA methylation profiling of knee cartilage and mRNA expression profile data were obtained from our previous studies. InCroMAP was performed to integrative analysis of genome-wide DNA methylation profiles and mRNA expression profiles. Gene ontology (GO) enrichment analysis was conducted by online DAVID 6.7. The quantitative real-time polymerase chain reaction (qPCR), Western blot, immunohistochemistry (IHC), and lentiviral vector transfection were used to validate one of the identified pathways. We identified 298 common genes (such as COL4A1, HOXA13, TNFAIP6 and TGFBI), 36 GO terms (including collagen function, skeletal system development, growth factor), and 32 KEGG pathways associated with KBD (including Selenocompound metabolism pathway, PI3K-Akt signaling pathway, and TGF-beta signaling pathway). Our results suggest the dysfunction of many genes and pathways implicated in the pathogenesis of KBD, most importantly, both the integrative analysis and in vitro study in KBD cartilage highlighted the importance of selenocompound metabolism pathway in the pathogenesis of KBD for the first time.

High mobility group box 1 promotes radioresistance in esophageal squamous cell carcinoma cell lines by modulating autophagy.

Resistance to radiotherapy results in relapse and treatment failure in locally advanced esophageal squamous cell carcinoma (ESCC). High mobility group box 1 (HMGB1) is reported to be associated with the radioresistance in bladder and breast cancer. However, the role of HMGB1 in the radiotherapy response in ESCC has not been fully elucidated. Here, we investigated the role of HMGB1 to radioresistance in ESCC clinical samples and cell lines. We found that HMGB1 expression was associated with tumor recurrence after postoperative radiotherapy in locally advanced ESCC patients. HMGB1 knockdown in ESCC cells resulted in increased radiosensitivity both in vitro and in vivo. Autophagy level was found depressed in HMGB1 inhibition cells and activation of autophagy brought back cell's radioresistance. Our results demonstrate that HMGB1 activate autophagy and consequently promote radioresistance. HMGB1 may be used as a predictor of poor response to radiotherapy in ESCC patients. Our finding also highlights the importance of the utility of HMGB1 in ESCC radiosensitization.

eQTLs Weighted Genetic Correlation Analysis Detected Brain Region Differences in Genetic Correlations for Complex Psychiatric Disorders.

BACKGROUND: Psychiatric disorders are usually caused by the dysfunction of various brain regions. Incorporating the genetic information of brain regions into correlation analysis can provide novel clues for pathogenetic and therapeutic studies of psychiatric disorders. METHODS: The latest genome-wide association study (GWAS) summary data of schizophrenia (SCZ), bipolar disorder (BIP), autism spectrum disorder (AUT), major depression disorder (MDD), and attention-deficit/hyperactivity disorder (ADHD) were obtained from the Psychiatric GWAS Consortium (PGC). The expression quantitative trait loci (eQTLs) datasets of 10 brain regions were driven from the genotype-tissue expression (GTEx) database. The PGC GWAS summaries were first weighted by the GTEx eQTLs summaries for each brain region. Linkage disequilibrium score regression was applied to the weighted GWAS summary data to detect genetic correlation for each pair of 5 disorders. RESULTS: Without considering brain region difference, significant genetic correlations were observed for BIP vs SCZ (P = 1.68 × 10-63), MDD vs SCZ (P = 5.08 × 10-45), ADHD vs MDD (P = 1.93 × 10-44), BIP vs MDD (P = 6.39 × 10-9), AUT vs SCZ (P = .0002), and ADHD vs SCZ (P = .0002). Utilizing brain region related eQTLs weighted LD score regression, different strengths of genetic correlations were observed within various brain regions for BIP vs SCZ, MDD vs SCZ, ADHD vs MDD, and SCZ vs ADHD. For example, the most significant genetic correlations were observed at anterior cingulate cortex (P = 1.13 × 10-34) for BIP vs SCZ. CONCLUSIONS: This study provides new clues for elucidating the mechanism of genetic correlations among various psychiatric disorders.

A Genomewide Integrative Analysis of GWAS and eQTLs Data Identifies Multiple Genes and Gene Sets Associated with Obesity.

To identify novel susceptibility genes and gene sets for obesity, we conducted a genomewide expression association analysis of obesity via integrating genomewide association study (GWAS) and expression quantitative trait loci (eQTLs) data. GWAS summary data of body mass index (BMI) and waist-to-hip ratio (WHR) was driven from a published study, totally involving 339,224 individuals. The eQTLs dataset (containing 927,753 eQTLs) was obtained from eQTLs meta-analysis of 5,311 subjects. Integrative analysis of GWAS and eQTLs data was conducted by SMR software. The SMR single gene analysis results were further subjected to gene set enrichment analysis (GSEA) for identifying obesity associated gene sets. A total of 13,311 annotated gene sets were analyzed in this study. SMR single gene analysis identified 20 BMI associated genes (TUFM, SPI1, APOB48R, etc.). Also 3 WHR associated genes were detected (CPEB4, WARS2, and L3MBTL3). The significant association between Chr16p11 and BMI was observed by GSEA (FDR adjusted p value = 0.040). The TGCTGCT, MIR-15A, MIR-16, MIR-15B, MIR-195, MIR-424, and MIR-497 (FDR adjusted p value = 0.049) gene set appeared to be linked with WHR. Our results provide novel clues for the genetic mechanism studies of obesity. This study also illustrated the good performance of SMR for susceptibility gene mapping.

Integrating genome-wide DNA methylation and mRNA expression profiles identified different molecular features between Kashin-Beck disease and primary osteoarthritis.

BACKGROUND: Kashin-Beck disease (KBD) is an endemic osteochondropathy of unknown etiology. Osteoarthritis (OA) is a form of degenerative joint disease sharing similar clinical manifestations and pathological changes to articular cartilage with KBD. METHODS: A genome-wide DNA methylation profile of articular cartilage from five KBD patients and five OA patients was first performed using the Illumina Infinium HumanMethylation450 BeadChip. Together with a previous gene expression profiling dataset comparing KBD cartilage with OA cartilage, an integrative pathway enrichment analysis of the genome-wide DNA methylation and the mRNA expression profiles conducted in articular cartilage was performed by InCroMAP software. RESULTS: We identified 241 common genes altered in both the DNA methylation profile and the mRNA expression profile of articular cartilage of KBD versus OA, including CHST13 (NM_152889, fold-change = 0.5979, P methy = 0.0430), TGFBR1 (NM_004612, fold-change = 2.077, P methy = 0.0430), TGFBR2 (NM_001024847, fold-change = 1.543, P methy = 0.037), TGFBR3 (NM_001276, fold-change = 0.4515, P methy = 6.04 × 10-4), and ADAM12 (NM_021641, fold-change = 1.9768, P methy = 0.0178). Integrative pathway enrichment analysis identified 19 significant KEGG pathways, including mTOR signaling (P = 0.0301), glycosaminoglycan biosynthesis-chondroitin sulfate/dermatan sulfate (P = 0.0391), glycosaminoglycan biosynthesis-keratan sulfate (P = 0.0278), and PI3K-Akt signaling (P = 0.0243). CONCLUSION: This study identified different molecular features between Kashin-Beck disease and primary osteoarthritis and provided novel clues for clarifying the pathogenetic differences between KBD and OA.

Assessing the Associations of Blood Metabolites With Osteoporosis: A Mendelian Randomization Study.

Context: Osteoporosis is a metabolic bone disease. The effect of blood metabolites on the development of osteoporosis remains elusive. Objective: To explore the relationship between blood metabolites and osteoporosis. Design and Methods: We used 2286 unrelated white subjects for the discovery samples and 3143 unrelated white subjects from the Framingham Heart Study (FHS) for the replication samples. The bone mineral density (BMD) was measured using dual-energy X-ray absorptiometry. Genome-wide single nucleotide polymorphism (SNP) genotyping was performed using Affymetrix Human SNP Array 6.0 (for discovery samples) and Affymetrix SNP 500K and 50K array (for FHS replication samples). The SNP sets significantly associated with blood metabolites were obtained from a reported whole-genome sequencing study. For each subject, the genetic risk score of the metabolite was calculated from the genotype data of the metabolite-associated SNP sets. Pearson correlation analysis was conducted to evaluate the potential effect of blood metabolites on the variations in bone phenotypes; 10,000 permutations were conducted to calculate the empirical P value and false discovery rate. Results: We analyzed 481 blood metabolites. We identified multiple blood metabolites associated with hip BMD, such as 1,5-anhydroglucitol (Pdiscovery < 0.0001; Preplication = 0.0361), inosine (Pdiscovery = 0.0018; Preplication = 0.0256), theophylline (Pdiscovery = 0.0048; Preplication = 0.0433, gamma-glutamyl methionine (Pdiscovery = 0.0047; Preplication = 0.0471), 1-linoleoyl-2-arachidonoyl-GPC (18:2/20:4n6; Pdiscovery = 0.0018; Preplication = 0.0390), and X-12127 (Pdiscovery = 0.0002; Preplication = 0.0249). Conclusions: Our results suggest a modest effect of blood metabolites on the variations of BMD and identified several candidate blood metabolites for osteoporosis.

GWAS summary-based pathway analysis correcting for the genetic confounding impact of environmental exposures.

Genome-wide association study (GWAS)-based pathway association analysis is a powerful approach for the genetic studies of human complex diseases. However, the genetic confounding effects of environment exposure-related genes can decrease the accuracy of GWAS-based pathway association analysis of target diseases. In this study, we developed a pathway association analysis approach, named Mendelian randomization-based pathway enrichment analysis (MRPEA), which was capable of correcting the genetic confounding effects of environmental exposures, using the GWAS summary data of environmental exposures. After analyzing the real GWAS summary data of cardiovascular disease and cigarette smoking, we observed significantly improved performance of MRPEA compared with traditional pathway association analysis (TPAA) without adjusting for environmental exposures. Further, simulation studies found that MRPEA generally outperformed TPAA under various scenarios. We hope that MRPEA could help to fill the gap of TPAA and identify novel causal pathways for complex diseases.

Integrating genome-wide association study summaries and element-gene interaction datasets identified multiple associations between elements and complex diseases.

Chemical elements are closely related to human health. Extensive genomic profile data of complex diseases offer us a good opportunity to systemically investigate the relationships between elements and complex diseases/traits. In this study, we applied gene set enrichment analysis (GSEA) approach to detect the associations between elements and complex diseases/traits though integrating element-gene interaction datasets and genome-wide association study (GWAS) data of complex diseases/traits. To illustrate the performance of GSEA, the element-gene interaction datasets of 24 elements were extracted from the comparative toxicogenomics database (CTD). GWAS summary datasets of 24 complex diseases or traits were downloaded from the dbGaP or GEFOS websites. We observed significant associations between 7 elements and 13 complex diseases or traits (all false discovery rate (FDR) < 0.05), including reported relationships such as aluminum vs. Alzheimer's disease (FDR = 0.042), calcium vs. bone mineral density (FDR = 0.031), magnesium vs. systemic lupus erythematosus (FDR = 0.012) as well as novel associations, such as nickel vs. hypertriglyceridemia (FDR = 0.002) and bipolar disorder (FDR = 0.027). Our study results are consistent with previous biological studies, supporting the good performance of GSEA. Our analyzing results based on GSEA framework provide novel clues for discovering causal relationships between elements and complex diseases.

Integrating genome-wide association study and expression quantitative trait locus study identifies multiple genes and gene sets associated with schizophrenia.

Schizophrenia is a serious mental disease with high heritability. To better understand the genetic basis of schizophrenia, we conducted a large scale integrative analysis of genome-wide association study (GWAS) and expression quantitative trait loci (eQTLs) data. GWAS summary data was derived from a published GWAS of schizophrenia, containing 9394 schizophrenia patients and 12,462 healthy controls. The eQTLs dataset was obtained from an eQTLs meta-analysis of 5311 subjects, containing 923,021 cis-eQTLs for 14,329 genes and 4732 trans-eQTLs for 2612 genes. Genome-wide single gene expression association analysis was conducted by SMR software. The SMR analysis results were further subjected to gene set enrichment analysis (GSEA) to identify schizophrenia associated gene sets. SMR detected 49 genes significantly associated with schizophrenia. The top five significant genes were CRELD2 (p value=1.65×10-11), DIP2B (p value=3.94×10-11), ZDHHC18 (p value=1.52×10-10) and ZDHHC5 (p value=7.45×10-10), C11ORF75 (p value=3.70×10-9). GSEA identified 80 gene sets with p values <0.01. The top five significant gene sets were COWLING_MYCN_TARGETS (p value <0.001) and CHR16P11 (p value <0.001), ACTACCT_MIR196A_MIR196B (p value=0.002), CELLULAR_COMPONENT_DISASSEMBLY (p value=0.002) and GRAESSMANN_RESPONSE_TO_MC_AND_DOXORUBICIN_DN (p value=0.002). Our results provide useful information for revealing the genetic basis of schizophrenia.

A Genome-wide Expression Association Analysis Identifies Genes and Pathways Associated with Amyotrophic Lateral Sclerosis.

Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease with strong genetic components. To identity novel risk variants for ALS, utilizing the latest genome-wide association studies (GWAS) and eQTL study data, we conducted a genome-wide expression association analysis by summary data-based Mendelian randomization (SMR) method. Summary data were derived from a large-scale GWAS of ALS, involving 12577 cases and 23475 controls. The eQTL annotation dataset included 923,021 cis-eQTL for 14,329 genes and 4732 trans-eQTL for 2612 genes. Genome-wide single gene expression association analysis was conducted by SMR software. To identify ALS-associated biological pathways, the SMR analysis results were further subjected to gene set enrichment analysis (GSEA). SMR single gene analysis identified one significant and four suggestive genes associated with ALS, including C9ORF72 (P value = 7.08 × 10-6), NT5C3L (P value = 1.33 × 10-5), GGNBP2 (P value = 1.81 × 10-5), ZNHIT3(P value = 2.94 × 10-5), and KIAA1600(P value = 9.97 × 10-5). GSEA identified 7 significant biological pathways, such as PEROXISOME (empirical P value = 0.006), GLYCOLYSIS_GLUCONEOGENESIS (empirical P value = 0.043), and ARACHIDONIC_ACID_ METABOLISM (empirical P value = 0.040). Our study provides novel clues for the genetic mechanism studies of ALS.

Integrating Genome-Wide Association and eQTLs Studies Identifies the Genes and Gene Sets Associated with Diabetes.

AIM: To identify novel candidate genes and gene sets for diabetes. METHODS: We performed an integrative analysis of genome-wide association studies (GWAS) and expression quantitative trait loci (eQTLs) data for diabetes. Summary data was driven from a large-scale GWAS of diabetes, totally involving 58,070 individuals. eQTLs dataset included 923,021 cis-eQTL for 14,329 genes and 4,732 trans-eQTL for 2,612 genes. Integrative analysis of GWAS and eQTLs data was conducted by summary data-based Mendelian randomization (SMR). To identify the gene sets associated with diabetes, the SMR single gene analysis results were further subjected to gene set enrichment analysis (GSEA). A total of 13,311 annotated gene sets were analyzed in this study. RESULTS: SMR analysis identified 6 genes significantly associated with fasting glucose, such as C11ORF10 (p value = 6.04 × 10-8), MRPL33 (p value = 1.24 × 10-7), and FADS1 (p value = 2.39 × 10-7). Gene set analysis identified HUANG_FOXA2_TARGETS_UP (false discovery rate = 0.047) associated with fasting glucose. CONCLUSION: Our study provides novel clues for clarifying the genetic mechanism of diabetes. This study also illustrated the good performance of SMR approach and extended it to gene set association analysis for complex diseases.

Integrating genome-wide association study and expression quantitative trait loci data identifies multiple genes and gene set associated with neuroticism.

Neuroticism is a fundamental personality trait with significant genetic determinant. To identify novel susceptibility genes for neuroticism, we conducted an integrative analysis of genomic and transcriptomic data of genome wide association study (GWAS) and expression quantitative trait locus (eQTL) study. GWAS summary data was driven from published studies of neuroticism, totally involving 170,906 subjects. eQTL dataset containing 927,753 eQTLs were obtained from an eQTL meta-analysis of 5311 samples. Integrative analysis of GWAS and eQTL data was conducted by summary data-based Mendelian randomization (SMR) analysis software. To identify neuroticism associated gene sets, the SMR analysis results were further subjected to gene set enrichment analysis (GSEA). The gene set annotation dataset (containing 13,311 annotated gene sets) of GSEA Molecular Signatures Database was used. SMR single gene analysis identified 6 significant genes for neuroticism, including MSRA (p value=2.27×10-10), MGC57346 (p value=6.92×10-7), BLK (p value=1.01×10-6), XKR6 (p value=1.11×10-6), C17ORF69 (p value=1.12×10-6) and KIAA1267 (p value=4.00×10-6). Gene set enrichment analysis observed significant association for Chr8p23 gene set (false discovery rate=0.033). Our results provide novel clues for the genetic mechanism studies of neuroticism.

Comparison of microRNA expression profiles of Kashin-Beck disease, osteoarthritis and rheumatoid arthritis.

Kashin-Beck disease (KBD) is a chronic osteochondropathy with unclear pathogeny. In this study, we compared the microRNA expression profiles of 16 KBD patients, 16 osteoarthritis (OA) patients and 16 rheumatoid arthritis (RA) patients and 16 healthy controls in their blood specimens. miRNAs expression profiling was performed using Exiqon miRCURY LNATM miRNAs Array. miRNAs target genes were predicted using miRror suite. Another independent mRNA expression profile dataset of 20 KBD patients and 15 healthy controls were integrated with the miRNA expression profiles of KBD. We identified 140 differently expressed miRNAs in KBD vs. CONTROLS: GO enrichment analysis found that hypoxia, Wnt receptor signaling pathway and vitamin B6 biosynthesis related GO terms were significantly overrepresented in the target genes of differently expressed miRNAs in KBD vs. CONTROL: 18 differently expressed common miRNAs were identified in KBD vs. Control, KBD vs. OA and KBD vs. RA. Integrating the lists of differently expressed miRNA target genes and mRNA differently expressed genes detected 6 common genes for KBD. Our results demonstrated the altered miRNAs expression profiles of KBD comparing to healthy controls, OA and RA, which provide novel clues for clarifying the mechanism of KBD.

Genome-wide association study identifies COL2A1 locus involved in the hand development failure of Kashin-Beck disease.

Kashin-Beck disease (KBD) is a chronic osteochondropathy. The pathogenesis of growth and development failure of hand of KBD remains elusive now. In this study, we conducted a two-stage genome-wide association study (GWAS) of palmar length-width ratio (LWR) of KBD, totally including 493 study subjects. Affymetrix Genome Wide Human SNP Array 6.0 was applied for genome-wide SNP genotyping of 90 KBD patients. Association analysis was conducted by PLINK. Imputation analysis was performed by IMPUTE against the reference panel of the 1000 genome project. Two SNPs were selected for replication in an independent validation sample of 403 KBD patients. In the discovery GWAS, significant association was observed between palmar LWR and rs2071358 of COL2A1 gene (P value = 4.68 × 10-8). In addition, GWAS detected suggestive association signal at rs4760608 of COL2A1 gene (P value = 1.76 × 10-4). Imputation analysis of COL2A1 further identified 2 SNPs with association evidence for palmar LWR. Replication study observed significant association signals at both rs2071358 (P value = 0.017) and rs4760608 (P value = 0.002) of COL2A1 gene. Based on previous and our study results, we suggest that COL2A1 was a likely susceptibility gene involved in the hand development failure of KBD.

Tissue-specific pathway association analysis using genome-wide association study summaries.

MOTIVATION: Pathway association analysis has made great achievements in elucidating the genetic basis of human complex diseases. However, current pathway association analysis approaches fail to consider tissue-specificity. RESULTS: We developed a tissue-specific pathway interaction enrichment analysis algorithm (TPIEA). TPIEA was applied to two large Caucasian and Chinese genome-wide association study summary datasets of bone mineral density (BMD). TPIEA identified several significant pathways for BMD [false discovery rate (FDR) < 0.05], such as KEGG FOCAL ADHESION and KEGG AXON GUIDANCE, which had been demonstrated to be involved in the development of osteoporosis. We also compared the performance of TPIEA and classical pathway enrichment analysis, and TPIEA presented improved performance in recognizing disease relevant pathways. TPIEA may help to fill the gap of classic pathway association analysis approaches by considering tissue specificity. AVAILABILITY AND IMPLEMENTATION: The online web tool of TPIEA is available at https://sourceforge.net/projects/tpieav1/files CONTACT: fzhxjtu@mail.xjtu.edu.cnSupplementary information: Supplementary data are available at Bioinformatics online.