Li Liu

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

&NA; 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. HighlightsThe first large scale integrative study of GWAS and eQTLs was conducted for schizophrenia.SMR analysis identified 49 significant genes, whose expression levels were related to schizophrenia.Enrichment analysis detected 80 gene sets significantly associated with schizophrenia.Our results expand the understanding of the genetic mechanism of schizophrenia.

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

ContextnOsteoporosis is a metabolic bone disease. The effect of blood metabolites on the development of osteoporosis remains elusive.nnnObjectivenTo explore the relationship between blood metabolites and osteoporosis.nnnDesign and MethodsnWe 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.nnnResultsnWe 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).nnnConclusionsnOur results suggest a modest effect of blood metabolites on the variations of BMD and identified several candidate blood metabolites for osteoporosis.

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

BackgroundnPsychiatric 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.nnnMethodsnThe 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.nnnResultsnWithout 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.nnnConclusionsnThis study provides new clues for elucidating the mechanism of genetic correlations among various psychiatric disorders.

A genome-wide pathway enrichment analysis identifies brain region related biological pathways associated with intelligence

Intelligence is an important quantitative trait associated with human cognitive ability. The genetic basis of intelligence remains unclear now. Utilizing the latest chromosomal enhancer maps of brain regions, we explored brain region related biological pathways associated with intelligence. Summary data was derived from a large scale genome-wide association study (GWAS) of human, involving 78,308 unrelated individuals from 13 cohorts. The chromosomal enhancer maps of 8 brain regions were then aligned with the GWAS summary data to obtain the association testing results of enhancer regions for intelligence. Gene set enrichment analysis was then conducted to identify the biological pathways associated with intelligence for 8 brain regions, respectively. A total of 178 KEGG pathways was analyzed in this study. We detected multiple biological pathways showing cross brain regions or brain region specific association signals for human intelligence. For instance, KEGG_SYSTEMIC_LUPUS_ERYTHEMATOSUS pathway presented association signals for intelligence across 8 brain regions (all P valueu202f<u202f0.01). KEGG_GLYCOSPHINGOLIPID_BIOSYNTHESIS_GANGLIO_SERIES was detected for 5 brain regions. We also identified several brain region specific pathways, such as AMINO_SUGAR_AND_NUCLEOTIDE_SUGAR_METABOLISM for Germinal Matrix (P valueu202f=u202f0.009) and FRUCTOSE_AND_MANNOSE_METABOLISM for Anterior Caudate (P valueu202f=u202f0.005). Our study results provided novel clues for understanding the genetic mechanism of intelligence.

Integrative analysis of genome-wide association study and brain region related enhancer maps identifies biological pathways for insomnia

&NA; Insomnia is a common sleep disorder whose genetic mechanism remains unknown. The aim of this study is to identify novel genes, gene enrichment sets and enriched tissue/cell types for insomnia considering the differences across different brain regions. We conducted an integrative analysis of genome‐wide association study (GWAS) and brain region related enhancer maps. Summary data was derived from a large‐scale GWAS of insomnia, involving 113,006 unrelated individuals. The chromosomal enhancer maps of 6 brain regions were then aligned with the GWAS summary data to obtain the association testing results of enhancer regions for insomnia. Gene prioritization, tissue/cell and pathway enrichment analysis were implemented by Data‐driven Expression Prioritized Integration for Complex Traits (DEPICT) tool. We identified multiple cross‐brain regions or brain‐region specific prioritized genes for insomnia, such as MADD (P = .0013 in angular gyrus), PPP2R3C (P = .0319 in cingulate gyrus), CASP9 (P = .0066 in angular gyrus and P = .0278 in hippocampus middle), PLEKHM2 (P = .0032 in angular gyrus, P = .0052 in anterior caudate, P = .0385 in cingulate gyrus and P = .0011 in inferior temporal lobe). This study also detected a group of insomnia associated biological pathways within multiple or specific brain regions, such as REACTOME_SIGNALING_BY_NOTCH and KEGG_GLYCEROPHOSPHOLIPID_METABOLISM. Our results showed that insomnia associated genes were significantly enriched in neural stem cells. Our results highlight a set of potential points, particularly neural stem cells, for subsequent biological studies for insomnia. HighlightsThe first large‐scale integrative study of GWAS and brain region related enhancer maps was conducted for insomnia.DEPICT analysis identified multiple cross‐brain regions or brain region specific genes and gene sets for insomnia.Our results showed that insomnia associated genes were significantly enriched in neural stem cells.Our results provide a deeper understanding of the genetic mechanism of insomnia.

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 valuexa0=xa07.08xa0×xa010−6), NT5C3L (P valuexa0=xa01.33xa0×xa010−5), GGNBP2 (P valuexa0=xa01.81xa0×xa010−5), ZNHIT3(P valuexa0=xa02.94xa0×xa010−5), and KIAA1600(P valuexa0=xa09.97xa0×xa010−5). GSEA identified 7 significant biological pathways, such as PEROXISOME (empirical P valuexa0=xa00.006), GLYCOLYSIS_GLUCONEOGENESIS (empirical P valuexa0=xa00.043), and ARACHIDONIC_ACID_ METABOLISM (empirical P valuexa0=xa00.040). Our study provides novel clues for the genetic mechanism studies of ALS.

Assessing the Genetic Correlations Between Blood Plasma Proteins and Osteoporosis: A Polygenic Risk Score Analysis

Osteoporosis is a common metabolic bone disease. The impact of global blood plasma proteins on the risk of osteoporosis remains elusive now. We performed a large-scale polygenic risk score (PRS) analysis to evaluate the potential effects of blood plasma proteins on the development of osteoporosis in 2286 Caucasians, including 558 males and 1728 females. Bone mineral density (BMD) and bone areas at ulna & radius, hip, and spine were measured using Hologic 4500W DXA. BMD/bone areas values were adjusted for age, sex, height, and weight as covariates. Genome-wide SNP genotyping of 2286 Caucasian subjects was performed using Affymetrix Human SNP Array 6.0. The 267 blood plasma proteins-associated SNP loci and their genetic effects were obtained from recently published genome-wide association study (GWAS) using a highly multiplexed aptamer-based affinity proteomics platform. The polygenetic risk score (PRS) of study subjects for each blood plasma protein was calculated from the genotypes data of the 2286 Caucasian subjects by PLINK software. Pearson correlation analysis of individual PRS values and BMD/bone area value was performed using R. Additionally, gender-specific analysis also was performed by Pearson correlation analysis. 267 blood plasma proteins were analyzed in this study. For BMD, we observed association signals between 41 proteins and BMD, mainly including whole body total BMD versus Factor H (p valueu2009=u20099.00u2009×u200910−3), whole body total BMD versus BGH3 (p valueu2009=u20091.40u2009×u200910−2), spine total BMD versus IGF-I (p valueu2009=u20092.15u2009×u200910−2), and spine total BMD versus SAP (p valueu2009=u20093.90u2009×u200910−2). As for bone areas, association evidence was observed between 45 blood plasma proteins and bone areas, such as ferritin versus spine area (p valueu2009=u20091.90u2009×u200910−2), C4 versus hip area (p valueu2009=u20091.25u2009×u200910−2), and hemoglobin versus right ulna and radius area (p valueu2009=u20092.70u2009×u200910−2). Our study results suggest the modest impact of blood plasma proteins on the variations of BMD/bone areas, and identify several candidate blood plasma proteins 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.

Use of integrative epigenetic and mRNA expression analyses to identify significantly changed genes and functional pathways in osteoarthritic cartilage

Aim Osteoarthritis (OA) is caused by complex interactions between genetic and environmental factors. Epigenetic mechanisms control the expression of genes and are likely to regulate the OA transcriptome. We performed integrative genomic analyses to define methylation-gene expression relationships in osteoarthritic cartilage. Patients and Methods Genome-wide DNA methylation profiling of articular cartilage from five patients with OA of the knee and five healthy controls was conducted using the Illumina Infinium HumanMethylation450 BeadChip (Illumina, San Diego, California). Other independent genome-wide mRNA expression profiles of articular cartilage from three patients with OA and three healthy controls were obtained from the Gene Expression Omnibus (GEO) database. Integrative pathway enrichment analysis of DNA methylation and mRNA expression profiles was performed using integrated analysis of cross-platform microarray and pathway software. Gene ontology (GO) analysis was conducted using the Database for Annotation, Visualization and Integrated Discovery (DAVID). Results We identified 1265 differentially methylated genes, of which 145 are associated with significant changes in gene expression, such as DLX5, NCOR2 and AXIN2 (all p-values of both DNA methylation and mRNA expression < 0.05). Pathway enrichment analysis identified 26 OA-associated pathways, such as mitogen-activated protein kinase (MAPK) signalling pathway (p = 6.25 × 10-4), phosphatidylinositol (PI) signalling system (p = 4.38 × 10-3), hypoxia-inducible factor 1 (HIF-1) signalling pathway (p = 8.63 × 10-3 pantothenate and coenzyme A (CoA) biosynthesis (p = 0.017), ErbB signalling pathway (p = 0.024), inositol phosphate (IP) metabolism (p = 0.025), and calcium signalling pathway (p = 0.032). Conclusion We identified a group of genes and biological pathwayswhich were significantly different in both DNA methylation and mRNA expression profiles between patients with OA and controls. These results may provide new clues for clarifying the mechanisms involved in the development of OA. Cite this article: A. He, Y. Ning, Y. Wen, Y. Cai, K. Xu, Y. Cai, J. Han, L. Liu, Y. Du, X. Liang, P. Li, Q. Fan, J. Hao, X. Wang, X. Guo, T. Ma, F. Zhang. Use of integrative epigenetic and mRNA expression analyses to identify significantly changed genes and functional pathways in osteoarthritic cartilage. Bone Joint Res 2018;7:343–350. DOI: 10.1302/2046-3758.75.BJR-2017-0284.R1.

Assessing the genetic correlations between early growth parameters and bone mineral density: A polygenic risk score analysis

OBJECTIVEnThe relationships between early growth parameters and bone mineral density (BMD) remain elusive now. In this study, we performed a large scale polygenic risk score (PRS) analysis to evaluate the potential impact of early growth parameters on the variations of BMD.nnnMETHODSnWe used 2286 Caucasian subjects as cohort 1 and 3404 Framingham Heart Study (FHS) subjects as cohort 2 in this study. BMD at ulna & radius, hip and spine were measured using dual energy X-ray absorptiometry. BMD values were adjusted for age, sex, height and weight as covariates. Genome-wide single-nucleotide polymorphism (SNP) genotyping of the 2286 Caucasian subjects was performed using Affymetrix Human SNP Array 6.0. The GWAS datasets of early growth parameters were driven from the Early Growth Genetics Consortium, including birth weight (BW), birth head circumference (BHC), childhood body mass index (CBMI), pubertal height growth related indexes and tanner stage. Polygenic Risk Score (PRSice) and linkage disequilibrium (LD) score regression analysis were conducted to assess the genetic correlation between early growth parameters and BMD.nnnRESULTSnWe detected significant genetic correlations in cohort 1, such as total spine BMD vs. CBMI (p valueu202f=u202f1.51u202f×u202f10-4, rgu202f=u202f0.4525), right ulna and radius BMD vs. CBMI (p valueu202f=u202f1.51u202f×u202f10-4, rgu202f=u202f0.4399) and total body BMD vs. tanner stage (p valueu202f=u202f7.00u202f×u202f10-4, rgu202f=u202f-0.0721). For cohort 2, significant correlations were observed for total spine BMD vs. height change standard deviation score (SDS) between 8u202fyears and adult (denoted as PGFu202f+u202fPGM) (p valueu202f=u202f3.97u202f×u202f10-4, rgu202f=u202f-0.1425), femoral neck BMD vs. the timing of peak height velocity by looking at the height change SDS between age 14u202fyears and adult (denoted as PTFu202f+u202fPTM) (p valueu202f=u202f7.04u202f×u202f10-4, rgu202f=u202f-0.2185), and total spine BMD vs. PTFu202f+u202fPTM (p valueu202f=u202f6.86u202f×u202f10-4, rgu202f=u202f-0.2180).nnnCONCLUSIONnOur study results suggest that some early growth parameters could affect the variations of BMD.