Background and objective
The study was performed to determine whether catch-up growth is associated with the development of insulin resistance and to explore serum endocrine markers associated with the metabolism of adipose tissue in a Chinese population born small for gestational age(SGA)
Subjects and methods
We recruited 56 children born SGA with catch-up growth and 55 born without catch-up growth, who were further grouped into groups I (with BMI catch-up) and II (without BMI catch-up) respectively, as well as 52 children born appropriate for gestational age (AGA) with normal height. Their serum fasting insulin, fasting glucose, insulin-like growth factor-1, adiponectin, IGFBP-1, triglyceride concentrations, and the homeostasis assessment model for insulin resistance (HOMA-IR) were evaluated.
(1) The HOMA-IR values in SGA-I with catch-up growth group were significantly higher than those in SGA-II with catch-up growth, SGA-I without catch-up growth and AGA children respectively. (2) The serum adiponectin levels of individuals in the SGA-I without catch-up growth and SGA-II with catch-up growth groups were significantly lower than those from the SGA-II without catch-up growth group. There was no difference in triglyceride or IGFBP-1 levels among the groups. (3) The degree of HOMA-IR was positively correlated with age, current BMI and △height SDS in SGA children.
The development of insulin resistance and lower levels of adiponectin were closely correlated with higher BMI and the postnatal height catch-up growth in SGA children.
Small for gestational age; Catch-up growth; Insulin resistance; Adiponectin
The emergence of high-throughput genomic datasets from different sources and platforms (e.g., gene expression, single nucleotide polymorphisms (SNP), and copy number variation (CNV)) has greatly enhanced our understandings of the interplay of these genomic factors as well as their influences on the complex diseases. It is challenging to explore the relationship between these different types of genomic data sets. In this paper, we focus on a multivariate statistical method, canonical correlation analysis (CCA) method for this problem. Conventional CCA method does not work effectively if the number of data samples is significantly less than that of biomarkers, which is a typical case for genomic data (e.g., SNPs). Sparse CCA (sCCA) methods were introduced to overcome such difficulty, mostly using penalizations with l-1 norm (CCA-l1) or the combination of l-1and l-2 norm (CCA-elastic net). However, they overlook the structural or group effect within genomic data in the analysis, which often exist and are important (e.g., SNPs spanning a gene interact and work together as a group).
We propose a new group sparse CCA method (CCA-sparse group) along with an effective numerical algorithm to study the mutual relationship between two different types of genomic data (i.e., SNP and gene expression). We then extend the model to a more general formulation that can include the existing sCCA models. We apply the model to feature/variable selection from two data sets and compare our group sparse CCA method with existing sCCA methods on both simulation and two real datasets (human gliomas data and NCI60 data). We use a graphical representation of the samples with a pair of canonical variates to demonstrate the discriminating characteristic of the selected features. Pathway analysis is further performed for biological interpretation of those features.
The CCA-sparse group method incorporates group effects of features into the correlation analysis while performs individual feature selection simultaneously. It outperforms the two sCCA methods (CCA-l1 and CCA-group) by identifying the correlated features with more true positives while controlling total discordance at a lower level on the simulated data, even if the group effect does not exist or there are irrelevant features grouped with true correlated features. Compared with our proposed CCA-group sparse models, CCA-l1 tends to select less true correlated features while CCA-group inclines to select more redundant features.
Group sparse CCA; Genomic data integration; Feature selection; SNP
The BMP and Wnt/β-catenin signaling pathways cooperatively regulate osteoblast differentiation and bone formation. Although BMP signaling regulates gene expression of the Wnt pathway, much less is known about whether Wnt signaling modulates BMP expression in osteoblasts. Given the presence of putative Tcf/Lef response elements that bind β-catenin/TCF transcription complex in the BMP2 promoter, we hypothesized that the Wnt/β-catenin pathway stimulates BMP2 expression in osteogenic cells. In this study, we showed that Wnt/β-catenin signaling is active in various osteoblast or osteoblast precursor cell lines, including MC3T3-E1, 2T3, C2C12, and C3H10T1/2 cells. Furthermore, crosstalk between the BMP and Wnt pathways affected BMP signaling activity, osteoblast differentiation, and bone formation, suggesting Wnt signaling is an upstream regulator of BMP signaling. Activation of Wnt signaling by Wnt3a or overexpression of β-catenin/TCF4 both stimulated BMP2 transcription at promoter and mRNA levels. In contrast, transcription of BMP2 in osteogenic cells was decreased by either blocking the Wnt pathway with DKK1 and sFRP4, or inhibiting β-catenin/TCF4 activity with FWD1/β-TrCP, ICAT, or ΔTCF4. Using a site-directed mutagenesis approach, we confirmed that Wnt/β-catenin transactivation of BMP2 transcription is directly mediated through the Tcf/Lef response elements in the BMP2 promoter. These results, which demonstrate that the Wnt/β-catenin signaling pathway is an upstream activator of BMP2 expression in osteoblasts, provide novel insights into the nature of functional cross talk integrating the BMP and Wnt/β-catenin pathways in osteoblastic differentiation and maintenance of skeletal homeostasis.
BMP; Wnt/β-catenin; Gene expression; Osteogenesis
Dendritic spine morphology is modulated by protein kinase p38, a mitogen-activated protein (MAPK), in the hippocampus. Protein p38MAPK is a substrate of wip1, a protein phosphatase. The role of wip1 in the central nervous system (CNS) has never been explored. Here, we report a novel function of wip1 in dendritic spine morphology and memory processes. Wip1 deficiency decreases dendritic spine size and density in pyramidal neurons of the hippocampal CA1 region. Simultaneously, impairments in object recognition tasks and contextual memory occur in wip1 deficient mice, but are reversed in wip1/p38 double mutant mice. Thus, our findings demonstrate that wip1 modulates dendritic morphology and memory processes through the p38MAPK signaling pathway. In addition to the well-characterized role of the wip1/p38MAPK in cell death and differentiation, we revealed the novel contribution of wip1 to cognition and dendritic spine morphology, which may suggest new approaches to treating neurodegenerative disorders.
Wip1 phosphatase; p38MAPK; memory; dendritic spine morphology; hippocampus; signaling pathway
The safety and efficacy of a single 1,200-mg dose of the lipoglycopeptide oritavancin are currently being investigated in two global phase 3 studies of acute bacterial skin and skin structure infections. In this study, an in vitro pharmacokinetic/pharmacodynamic model was established to compare the free-drug pharmacodynamics associated with a single 1,200-mg dose of oritavancin to once-daily dosing with daptomycin at 6 mg/kg of body weight and twice-daily dosing with vancomycin at 1,000 mg against three methicillin-resistant Staphylococcus aureus (MRSA) strains over 72 h. The area under the bacterial-kill curve (AUBKC) was used to assess the antibacterial effect of each dosing regimen at 24 h (AUBKC0-24), 48 h (AUBKC0-48), and 72 h (AUBKC0-72). The rapid bactericidal activities of oritavancin and daptomycin contributed to lower AUBKC0-24s for the three MRSA strains than with vancomycin (P < 0.05, as determined by analysis of variance [ANOVA]). Oritavancin exposure also resulted in a lower AUBKC0-48 and AUBKC0-72 against one MRSA strain and a lower AUBKC0-48 for another strain than did vancomycin exposure (P < 0.05). Furthermore, daptomycin exposure resulted in a lower AUBKC0-48 and AUBKC0-72 for one of the MRSA isolates than did vancomycin exposure (P < 0.05). Lower AUBKC0-24s for two of the MRSA strains (P < 0.05) were obtained with oritavancin exposure than with daptomycin. Thus, the antibacterial effect from the single-dose regimen of oritavancin is as effective as that from either once-daily dosing with daptomycin or twice-daily dosing with vancomycin against the MRSA isolates tested in an in vitro pharmacokinetic/pharmacodynamic model over 72 h. These results provide further justification to assess the single 1,200-mg dose of oritavancin for treatment of acute bacterial skin and skin structure infections.
It is usually observed that among genes there exist strong statistical interactions associated with diseases of public health importance. Gene interactions can potentially contribute to the improvement of disease classification accuracy. Especially when gene expression differs across different classes are not great enough, it is more important to take use of gene interactions for disease classification analyses. However, most gene selection algorithms in classification analyses merely focus on genes whose expression levels show differences across classes, and ignore the discriminatory information from gene interactions. In this study, we develop a two-stage algorithm that can take gene interaction into account during a gene selection procedure. Its biggest advantage is that it can take advantage of discriminatory information from gene interactions as well as gene expression differences, by using “Bayes error” as a gene selection criterion. Using simulated and real microarray data sets, we demonstrate the ability of gene interactions for classification accuracy improvement, and present that the proposed algorithm can yield small informative sets of genes while leading to highly accurate classification results. Thus our study may give a novel sight for future gene selection algorithms of human diseases discrimination.
There is growing evidence for a link between energy and bone metabolism. The nuclear receptor subfamily 5 member A2 (NR5A2) is involved in lipid metabolism and modulates the expression of estrogen-related genes in some tissues. The objective of this study was to explore the influence of NR5A2 on bone cells and to determine whether its allelic variations are associated with bone mineral density (BMD).
Analyses of gene expression by quantitative PCR and inhibition of NR5A2 expression by siRNAs were used to explore the effects of NR5A2 in osteoblasts. Femoral neck BMD and 30 single nucleotide polymorphisms (SNPs) were first analyzed in 935 postmenopausal women and the association of NR5A2 genetic variants with BMD was explored in other 1284 women in replication cohorts.
NR5A2 was highly expressed in bone. The inhibition of NR5A2 confirmed that it modulates the expression of osteocalcin, osteoprotegerin, and podoplanin in osteoblasts. Two SNPs were associated with BMD in the Spanish discovery cohort (rs6663479, P=0.0014, and rs2816948, P=0.0012). A similar trend was observed in another Spanish cohort, with statistically significant differences across genotypes in the combined analysis (P=0.03). However, the association in a cohort from the United States was rather weak. Electrophoretic mobility assays and studies with luciferase reporter vectors confirmed the existence of differences in the binding of nuclear proteins and the transcriptional activity of rs2816948 alleles.
NR5A2 modulates gene expression in osteoblasts and some allelic variants are associated with bone mass in Spanish postmenopausal women.
The present study searched for replicable risk genomic regions for alcohol and nicotine co-dependence using a genome-wide association strategy. The data contained a total of 3,143 subjects including 818 European-American (EA) cases with alcohol and nicotine co-dependence, 1,396 EA controls, 449 African-American (AA) cases and 480 AA controls. We performed separate genome-wide association analyses in EAs and AAs and a meta-analysis to derive combined p values, and calculated the genome-wide false discovery rate (FDR) for each SNP. Regions with p<5×10-7 together with FDR<0.05 in the meta-analysis were examined to detect all replicable risk SNPs across EAs, AAs and meta-analysis. These SNPs were followed with a series of functional expression quantitative trait locus (eQTL) analyses. We found a unique genome-wide significant gene region – SH3BP5-NR2C2 – that was enriched with 11 replicable risk SNPs for alcohol and nicotine co-dependence. The distributions of -log(p) values for all SNP-disease associations within this region were consistent across EAs, AAs, and meta-analysis (0.315≤r≤0.868; 8.1×10-52≤p≤3.6×10-5). In the meta-analysis, this region was the only association peak throughout chromosome 3 at p<0.0001. All replicable risk markers available for eQTL analysis had nominal cis- and trans-acting regulatory effects on gene expression. The transcript expression of the genes in this region was regulated partly by several nicotine dependence-related genes and significantly correlated with transcript expression of many alcohol and nicotine dependence-related genes. We concluded that the SH3BP5-NR2C2 region on Chromosome 3 might harbor causal loci for alcohol and nicotine co-dependence.
GWAS; alcohol and nicotine co-dependence
Copy number variation (CNV) is an important structural variation (SV) in human genome. Various studies have shown that CNVs are associated with complex diseases. Traditional CNV detection methods such as fluorescence in situ hybridization (FISH) and array comparative genomic hybridization (aCGH) suffer from low resolution. The next generation sequencing (NGS) technique promises a higher resolution detection of CNVs and several methods were recently proposed for realizing such a promise. However, the performances of these methods are not robust under some conditions, e.g., some of them may fail to detect CNVs of short sizes. There has been a strong demand for reliable detection of CNVs from high resolution NGS data.
A novel and robust method to detect CNV from short sequencing reads is proposed in this study. The detection of CNV is modeled as a change-point detection from the read depth (RD) signal derived from the NGS, which is fitted with a total variation (TV) penalized least squares model. The performance (e.g., sensitivity and specificity) of the proposed approach are evaluated by comparison with several recently published methods on both simulated and real data from the 1000 Genomes Project.
The experimental results showed that both the true positive rate and false positive rate of the proposed detection method do not change significantly for CNVs with different copy numbers and lengthes, when compared with several existing methods. Therefore, our proposed approach results in a more reliable detection of CNVs than the existing methods.
Therapeutic interventions in prediabetes are important in the primary prevention of type 2 diabetes (T2D) and its chronic complications. However, little is known about the pharmacogenetic effect of traditional herbs on prediabetes treatment. A total of 194 impaired glucose tolerance (IGT) subjects were treated with traditional hypoglycemic herbs (Tianqi Jiangtang) for 12 months in this study. DNA samples were genotyped for 184 mutations in 34 genes involved in drug metabolism or transportation. Multinomial logistic regression analysis indicated that rs1142345 (A > G) in the thiopurine S-methyltransferase (TPMT) gene was significantly associated with the hypoglycemic effect of the drug (P = 0.001, FDR P = 0.043). The “G” allele frequencies of rs1142345 in the healthy (subjects reverted from IGT to normal glucose tolerance), maintenance (subjects still had IGT), and deterioration (subjects progressed from IGT to T2D) groups were 0.094, 0.214, and 0.542, respectively. Binary logistic regression analysis indicated that rs1142345 was also significantly associated with the hypoglycemic effect of the drug between the healthy and maintenance groups (P = 0.027, OR = 4.828) and between the healthy and deterioration groups (P = 0.001, OR = 7.811). Therefore, rs1142345 was associated with the clinical effect of traditional hypoglycemic herbs. Results also suggested that TPMT was probably involved in the pharmacological mechanisms of T2D.
Whole genome sequencing studies are essential to obtain a comprehensive understanding of the vast pattern of human genomic variations. Here we report the results of a high-coverage whole genome sequencing study for 44 unrelated healthy Caucasian adults, each sequenced to over 50-fold coverage (averaging 65.8×). We identified approximately 11 million single nucleotide polymorphisms (SNPs), 2.8 million short insertions and deletions, and over 500,000 block substitutions. We showed that, although previous studies, including the 1000 Genomes Project Phase 1 study, have catalogued the vast majority of common SNPs, many of the low-frequency and rare variants remain undiscovered. For instance, approximately 1.4 million SNPs and 1.3 million short indels that we found were novel to both the dbSNP and the 1000 Genomes Project Phase 1 data sets, and the majority of which (∼96%) have a minor allele frequency less than 5%. On average, each individual genome carried ∼3.3 million SNPs and ∼492,000 indels/block substitutions, including approximately 179 variants that were predicted to cause loss of function of the gene products. Moreover, each individual genome carried an average of 44 such loss-of-function variants in a homozygous state, which would completely “knock out” the corresponding genes. Across all the 44 genomes, a total of 182 genes were “knocked-out” in at least one individual genome, among which 46 genes were “knocked out” in over 30% of our samples, suggesting that a number of genes are commonly “knocked-out” in general populations. Gene ontology analysis suggested that these commonly “knocked-out” genes are enriched in biological process related to antigen processing and immune response. Our results contribute towards a comprehensive characterization of human genomic variation, especially for less-common and rare variants, and provide an invaluable resource for future genetic studies of human variation and diseases.
Femoral neck geometric parameters (FNGPs), which include cortical thickness (CT), periosteal diameter (W), buckling ratio (BR), cross-sectional area (CSA), and section modulus (Z), contribute to bone strength and may predict hip fracture risk. Age at menarche (AAM) is an important risk factor for osteoporosis and bone fractures in women. Some FNGPs are genetically correlated with AAM. In this study, we performed a bivariate genome-wide association study (GWAS) to identify new candidate genes responsible for both FNGPs and AAM. In the discovery stage, we tested 760,794 SNPs in 1,728 unrelated Caucasian subject, followed by replication analyses in independent samples of US Caucasians (with 501 subjects) and Chinese (with 826 subjects). We found six SNPs that were associated with FNGPs and AAM. These SNPs are located in three genes (i.e. NRCAM, IDS and LOC148145), suggesting these three genes may co-regulate FNGPs and AAM. Our findings may help improve the understanding of genetic architecture and pathophysiological mechanisms underlying both osteoporosis and AAM.
Copy number variation (CNV) has played an important role in studies of susceptibility or resistance to complex diseases. Traditional methods such as fluorescence in situ hybridization (FISH) and array comparative genomic hybridization (aCGH) suffer from low resolution of genomic regions. Following the emergence of next generation sequencing (NGS) technologies, CNV detection methods based on the short read data have recently been developed. However, due to the relatively young age of the procedures, their performance is not fully understood. To help investigators choose suitable methods to detect CNVs, comparative studies are needed. We compared six publicly available CNV detection methods: CNV-seq, FREEC, readDepth, CNVnator, SegSeq and event-wise testing (EWT). They are evaluated both on simulated and real data with different experiment settings. The receiver operating characteristic (ROC) curve is employed to demonstrate the detection performance in terms of sensitivity and specificity, box plot is employed to compare their performances in terms of breakpoint and copy number estimation, Venn diagram is employed to show the consistency among these methods, and F-score is employed to show the overlapping quality of detected CNVs. The computational demands are also studied. The results of our work provide a comprehensive evaluation on the performances of the selected CNV detection methods, which will help biological investigators choose the best possible method.
Genotype imputation is an important tool in human genetics studies, which uses reference sets with known genotypes and prior knowledge on linkage disequilibrium and recombination rates to infer un-typed alleles for human genetic variations at a low cost. The reference sets used by current imputation approaches are based on HapMap data, and/or based on recently available next-generation sequencing (NGS) data such as data generated by the 1000 Genomes Project. However, with different coverage and call rates for different NGS data sets, how to integrate NGS data sets of different accuracy as well as previously available reference data as references in imputation is not an easy task and has not been systematically investigated. In this study, we performed a comprehensive assessment of three strategies on using NGS data and previously available reference data in genotype imputation for both simulated data and empirical data, in order to obtain guidelines for optimal reference set construction. Briefly, we considered three strategies: strategy 1 uses one NGS data as a reference; strategy 2 imputes samples by using multiple individual data sets of different accuracy as independent references and then combines the imputed samples with samples based on the high accuracy reference selected when overlapping occurs; and strategy 3 combines multiple available data sets as a single reference after imputing each other. We used three software (MACH, IMPUTE2 and BEAGLE) for assessing the performances of these three strategies. Our results show that strategy 2 and strategy 3 have higher imputation accuracy than strategy 1. Particularly, strategy 2 is the best strategy across all the conditions that we have investigated, producing the best accuracy of imputation for rare variant. Our study is helpful in guiding application of imputation methods in next generation association analyses.
This study evaluates 23 (9 Chinese and 14 non-Chinese) randomized controlled trials for efficacy and side effects of Chinese herbal medicine on menopausal symptoms. Menopause was diagnosed according to western medicine criteria in all studies while seven Chinese studies and one non-Chinese study further stratified the participants using traditional Chinese medical diagnosis “Zheng differentiation.” Efficacy was reported by all 9 Chinese and 9/14 non-Chinese papers. Side effects and adverse events were generally mild and infrequent. Only ten severe adverse events were reported, two with possible association with the therapy. CHM did not increase the endometrial thickness, a common side effect of hormone therapy. None of the studies investigated long-term side effects. Critical analysis revealed that (1) high-quality studies on efficacy of Chinese herbal medicine for menopausal syndrome are rare and have the drawback of lacking traditional Chinese medicine diagnosis (Zheng-differentiation). (2) Chinese herbal medicine may be effective for at least some menopausal symptoms while side effects are likely less than hormone therapy. (3) All these findings need to be confirmed in further well-designed comprehensive studies meeting the standard of evidence-based medicine and including Zheng-differentiation of traditional Chinese medicine.
Previous studies using SAGE (the Study of Addiction: Genetics and Environment) and COGA (the Collaborative Study on the Genetics of Alcoholism) genome-wide association study (GWAS) data sets reported several risk loci for alcohol dependence (AD), which have not yet been well replicated independently or confirmed by functional studies. We combined these two data sets, now publicly available, to increase the study power, in order to identify replicable, functional, and significant risk regions for AD. A total of 4116 subjects (1409 European-American (EA) cases with AD, 1518 EA controls, 681 African-American (AA) cases, and 508 AA controls) underwent association analysis. An additional 443 subjects underwent expression quantitative trait locus (eQTL) analysis. Genome-wide association analysis was performed in EAs to identify significant risk genes. All available markers in the genome-wide significant risk genes were tested in AAs for associations with AD, and in six HapMap populations and two European samples for associations with gene expression levels. We identified a unique genome-wide significant gene—KIAA0040—that was enriched with many replicable risk SNPs for AD, all of which had significant cis-acting regulatory effects. The distributions of −log(p) values for SNP-disease and SNP-expression associations for all markers in the TNN–KIAA0040 region were consistent across EAs, AAs, and five HapMap populations (0.369⩽r⩽0.824; 2.8 × 10−9⩽p⩽0.032). The most significant SNPs in these populations were in high LD, concentrating in KIAA0040. Finally, expression of KIAA0040 was significantly (1.2 × 10−11⩽p⩽1.5 × 10−6) associated with the expression of numerous genes in the neurotransmitter systems or metabolic pathways previously associated with AD. We concluded that KIAA0040 might harbor a causal variant for AD and thus might directly contribute to risk for this disorder. KIAA0040 might also contribute to the risk of AD via neurotransmitter systems or metabolic pathways that have previously been implicated in the pathophysiology of AD. Alternatively, KIAA0040 might regulate the risk via some interactions with flanking genes TNN and TNR. TNN is involved in neurite outgrowth and cell migration in hippocampal explants, and TNR is an extracellular matrix protein expressed primarily in the central nervous system.
risk region; alcohol dependence; cis-eQTL; GWAS; alcohol & alcoholism; neurogenetics; addiction & substance abuse; biological psychiatry; GWAScis-eQTL; risk region
It has been a research focus to uncover the genetic determination of complex diseases caused by rare variants. As the vast majority of genomic variants represent background variation, highlighting potentially causal mutations through weighting scheme is critical to the success of rare variants aimed association studies. In this study, we propose a novel Bayesian marker selection approach to perform weighting-based association test. In this approach, individual association signal and its direction are used to weight variants. In addition, the predicted biological function of variants is taken as prior information to direct the selection of likely causal variants. Simulation studies show that the proposed method has improved power over several existing methods in certain conditions. Analyses of two empirical datasets demonstrate its applicability.
weighting; Bayesian marker selection; rare variants; association
Genome-wide pathway association studies provide novel insight into the biological mechanism underlying complex diseases. Current pathway association studies primarily focus on single important disease phenotype, which is sometimes insufficient to characterize the clinical manifestations of complex diseases. We present a multi-phenotypes pathway association study(MPPAS) approach using principle component analysis(PCA). In our approach, PCA is first applied to multiple correlated quantitative phenotypes for extracting a set of orthogonal phenotypic components. The extracted phenotypic components are then used for pathway association analysis instead of original quantitative phenotypes. Four statistics were proposed for PCA-based MPPAS in this study. Simulations using the real data from the HapMap project were conducted to evaluate the power and type I error rates of PCA-based MPPAS under various scenarios considering sample sizes, additive and interactive genetic effects. A real genome-wide association study data set of bone mineral density (BMD) at hip and spine were also analyzed by PCA-based MPPAS. Simulation studies illustrated the performance of PCA-based MPPAS for identifying the causal pathways underlying complex diseases. Genome-wide MPPAS of BMD detected associations between BMD and KENNY_CTNNB1_TARGETS_UP as well as LONGEVITYPATHWAY pathways in this study. We aim to provide a applicable MPPAS approach, which may help to gain deep understanding the potential biological mechanism of association results for complex diseases.
Nasopharyngeal carcinoma (NPC) is an epithelial malignancy facilitated by Epstein-Barr Virus infection. Here we resolve the major genetic influences for NPC incidence using a genome-wide association study (GWAS), independent cohort replication, and high-resolution molecular HLA class I gene typing including 4,055 study participants from the Guangxi Zhuang Autonomous Region and Guangdong province of southern China. We detect and replicate strong association signals involving SNPs, HLA alleles, and amino acid (aa) variants across the major histocompatibility complex-HLA-A, HLA –B, and HLA -C class I genes (PHLA-A-aa-site-62 = 7.4×10−29; P HLA-B-aa-site-116 = 6.5×10−19; P HLA-C-aa-site-156 = 6.8×10−8 respectively). Over 250 NPC-HLA associated variants within HLA were analyzed in concert to resolve separate and largely independent HLA-A, -B, and -C gene influences. Multivariate logistical regression analysis collapsed significant associations in adjacent genes spanning 500 kb (OR2H1, GABBR1, HLA-F, and HCG9) as proxies for peptide binding motifs carried by HLA- A*11:01. A similar analysis resolved an independent association signal driven by HLA-B*13:01, B*38:02, and B*55:02 alleles together. NPC resistance alleles carrying the strongly associated amino acid variants implicate specific class I peptide recognition motifs in HLA-A and -B peptide binding groove as conferring strong genetic influence on the development of NPC in China.
NPC is a deadly throat cancer in China that is dependent on EBV infection. Here, we performed a 1 M SNP genome-wide association study using a large cohort of Chinese study participants at risk for NPC. Although several putative gene regions show significant associations, the strongest statistical signals involved scores of variants within the HLA region on chromosome 6. HLA poses a formidable association-genetics challenge because of extensive linkage disequilibrium, rather low allele frequencies, and multiple physically close interacting genes of diverse function. We examined over 250 NPC-HLA associated variants detected with sequence-based nucleotide alleles and amino acid variants. The multiple associations were collapsed to implicate causal signals by multivariate logistical regression to resolve allele association interaction. One operative variant was identified as the HLA-A*11:01 allele motif, specifically in the peptide binding groove, which recognizes invading antigens; a second involved two aa sites with HLA-B tracking B*13:01 and B*55:02 alleles. We synthesize these new and previous discoveries to help resolve the important gene influences on this disease.
High serum levels of lipopolysaccharide (LPS) with LPS-MD-2/TLR4 complex activated NF-kb and cytokine cause hepatic necrosis in animal models. We investigated the dynamic changes of LPS levels in patients with acute on chronic hepatitis B liver failure (ACHBLF).
We enrolled ACHBLF patients for a 12-week study. Patients’ LPS levels were measured along with 10 healthy controls. Patients on supportive care and recovered without intervention(s) were analyzed. Patients’ LPS levels during the disease progression phase, peak phase, and remission phase were compared with healthy controls.
Among 30 patients enrolled, 25 who received interventions or expired during the study period were excluded from the analysis, five patients on supportive care who completed the study were analyzed. Significant abnormal distributions of LPS levels were observed in patients in different phases (0.0168±0.0101 in progression phase; 0.0960±0.0680 in peak phase; 0.0249±0.0365 in remission phase; and 0.0201±0.0146 in controls; respectively, p<0.05). The highest level of LPS was in the peak phase and significantly elevated when compared to controls (0.0201±0.0146 vs. 0.0960±0.0680, p = 0.007). There were no statistically significant differences in LPS levels between healthy controls and subjects in the progression phase or remission phase. Dynamic changes of LPS were correlated with MELD-Na in the progression phase (p = 0.01, R = 0.876) and in the peak phase (p = 0.000, R = −1.00).
Significant abnormal distributions of LPS levels were observed in ACHBLF with the highest level in the peak phase. The dynamic changes of LPS were correlated with disease severity and suggested LPS causing secondary hepatic injury.
Osteoporosis (OP) is characterized by low bone mineral density (BMD) and has strong genetic determination. However, specific genetic variants influencing BMD and contributing to pathogenesis of osteoporosis are largely uncharacterized. Current genetic studies in bone filed, which aimed at identification of OP risk genes, are mostly focused on DNA, RNA, or protein level individually, lacking integrative evidences from the three levels of genetic information flow to confidently ascertain the significance of genes for osteoporosis. Our previous proteomics study discovered that superoxide dismutase 2 (SOD2) in circulating monocytes (CMCs, i.e., potential osteoclast precursors) was significantly up-regulated at protein level in vivo in Chinese with low vs. high hip BMD. Herein, at mRNA level, we found that SOD2 gene expression was also up-regulated in CMC (p < 0.05) in Chinese with low vs. high hip BMD. At DNA level, in 1,627 unrelated Chinese subjects, we identified eight SNPs at SOD2 gene locus that were suggestively associated with hip BMD (peak signal at rs11968525, p = 0.048). Among the eight SNPs, three SNPs (rs7754103, rs7754295, and rs2053949) were associated with SOD2 mRNA expression level (p < 0.05), suggesting that they are expression quantitative trait locus (eQTL) regulating SOD2 gene expression. In conclusion, the present integrative evidences from DNA, RNA, and protein levels supported SOD2 as a susceptibility gene for osteoporosis.
Osteoporosis; SOD2; eQTL; BMD
Human height is a highly heritable trait considered as an important factor for health. There has been limited success in identifying the genetic factors underlying height variation. We aim to identify sequence variants associated with adult height by a genome-wide association study of copy number variants (CNVs) in Chinese.
Genome-wide CNV association analyses were conducted in 1,625 unrelated Chinese adults and sex specific subgroup for height variation, respectively. Height was measured with a stadiometer. Affymetrix SNP6.0 genotyping platform was used to identify copy number polymorphisms (CNPs). We constructed a genomic map containing 1,009 CNPs in Chinese individuals and performed a genome-wide association study of CNPs with height.
We detected 10 significant association signals for height (p<0.05) in the whole population, 9 and 11 association signals for Chinese female and male population, respectively. A copy number polymorphism (CNP12587, chr18:54081842-54086942, p = 2.41×10−4) was found to be significantly associated with height variation in Chinese females even after strict Bonferroni correction (p = 0.048). Confirmatory real time PCR experiments lent further support for CNV validation. Compared to female subjects with two copies of the CNP, carriers of three copies had an average of 8.1% decrease in height. An important candidate gene, ubiquitin-protein ligase NEDD4-like (NEDD4L), was detected at this region, which plays important roles in bone metabolism by binding to bone formation regulators.
Our findings suggest the important genetic variants underlying height variation in Chinese.
Various types of genomic data (e.g., SNPs and mRNA transcripts) have been employed to identify risk genes for complex diseases. However, the analysis of these data has largely been performed in isolation. Combining these multiple data for integrative analysis can take advantage of complementary information and thus can have higher power to identify genes (and/or their functions) that would otherwise be impossible with individual data analysis. Due to the different nature, structure, and format of diverse sets of genomic data, multiple genomic data integration is challenging. Here we address the problem by developing a sparse representation based clustering (SRC) method for integrative data analysis. As an example, we applied the SRC method to the integrative analysis of 376821 SNPs in 200 subjects (100 cases and 100 controls) and expression data for 22283 genes in 80 subjects (40 cases and 40 controls) to identify significant genes for osteoporosis (OP). Comparing our results with previous studies, we identified some genes known related to OP risk (e.g., ‘THSD4’, ‘CRHR1’, ‘HSD11B1’, ‘THSD7A’, ‘BMPR1B’ ‘ADCY10’, ‘PRL’, ‘CA8’,’ESRRA’, ‘CALM1’, ‘CALM1’, ‘SPARC’, and ‘LRP1’). Moreover, we uncovered novel osteoporosis susceptible genes (‘DICER1’, ‘PTMA’, etc.) that were not found previously but play functionally important roles in osteoporosis etiology from existing studies. In addition, the SRC method identified genes can lead to higher accuracy for the diagnosis/classification of osteoporosis subjects when compared with the traditional T-test and Fisher-exact test, which further validates the proposed SRC approach for integrative analysis.