A phenomics-based approach for the detection and interpretation of shared genetic influences on 29 biochemical indices in Chinese south men

doi:10.21203/rs.2.12060/v2

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A phenomics-based approach for the detection and interpretation of shared genetic influences on 29 biochemical indices in Chinese south men

https://doi.org/10.21203/rs.2.12060/v2

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published 16 Dec, 2019

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Background: Phenomics provides a new technologies and platforms as a systematic phenome-genome approach. However, few studies have reported on the system mining of shared genetics among clinical biochemical indices based on Phenomics methods, especially in China. This study aimed to apply phenomics to systematically explore shared genetics among 29 biochemical indices based on the Fangchenggang Area Male Health and Examination Survey cohort. Result: A total of 1,999 subjects with 29 biochemical indices and 709,211 single nucleotide polymorphisms were subjected to phenomics analysis. Three bioinformatics methods, namely, Pearson test, Jaccard index, and linkage disequilibrium score regression , were used. Results showed that 29 biochemical indices were from a network. IgA, IgG, IgE, IgM, HCY, AFP and B12 were in the central community of 29 biochemical indices. Key genes and loci associated with metabolism traits were further identified, shared-genetics analysis showed that 29 SNPs (P < 10 -4 ) were associated with three or more traits. After integrating the SNPs related to two or more traits with the GWAS catalog, 31 SNPs were found to be associated with several diseases (P < 10 -8 ). Taking ALDH2 as an example to preliminarily explore its biological function, we also confirmed that rs671 (ALDH2) polymorphism affected multiple traits of osteogenesis and adipogenesis differentiation in 3T3-L1 preadipocytes. Conclusion: All these findings indicated a network of shared genetics and 29 biochemical indices, which will helpfully understand the genetics participated in biochemical metabolism.

Epigenetics & Genomics

phenomics

FAMHES cohort

biochemical indices

shared genetics

lipid metabolism

Complex traits are the product of various biological signals and some intermediate traits that may be affected either directly or indirectly by these signals [1]. A phenome is the sum of many phenotypic characteristics (phenomics traits) that signifies the expression of the whole genome, proteome and metabolome under a specific environmental influence [2, 3]. The study of phenomes (called phenomics) provides a suite of new technologies and platforms that have enabled a transition from focused phenotype-genotype studies to a systematic phenome-genome approach [4]. Many recent studies have found that, compared to consider only the binary patients vs. healthy controls, mapping intermediate steps in disease processes, such as various disease-related clinical quantitative traits or gene expression, are more informative[5, 6].

Pleiotropy, which is a DNA variant or mutation that can affect multiple traits, is a common phenomenon in genetics [7]. For example, Joseph Pickrell and colleagues [8] performed genome-wide association studies (GWAS) of 42 traits or diseases to compare the genetic variants associated with multiple phenotypes, and identified 341 loci associated with multiple traits. Heid IM et al[9] performed a GWAS of fasting insulin, high-density lipoprotein cholesterol (HDL-C) and triglyceride (TG) levels to identify 53 loci associated with a limited capacity to store fat in a healthy way, and this multi-trait approach could increase the power to gain insights into an otherwise difficult-to-grasp phenotype. Furthermore, many evidences have found that diseases or clinically quantitative traits can be interconnected. For example, the increasing of circulating fatty acids (Fas) could lead to the development of obesity-associated metabolic complications, such as insulin resistance [10]. Goh et al [11] found that essential human genes tended to encode hub proteins and widely expressed in multiple tissues. Many shared genetics variants are identified in linkage disequilibrium with variants associated with other human traits or diseases, and these pleiotropic connections make the human traits connect together [8, 12]. Therefore, understanding the complex relationships among human traits and diseases is important for learning about the molecular function of hub genes.

The Fangchenggang Area Male Health and Examination (FAMHES) cohort, which was initiated in 2009 in Fangchenggang City, Guangxi, China. It is a comprehensive demographic and health survey focus on investigating the interaction between environment and genetic factors on man’s health. In previously study, we had reported biochemical indices are closely associated with disease. For example, higher complement 3 (C3) and complement 4 (C4) were associated with an increase in metabolic syndrome (MetS) [13]. Low serum osteocalcin level was a potential marker for MetS [14] and impaired glucose tolerance [15]. Uric acid (UA) was positively correlated with the prevalence of MetS [16]. Additionally, genome-wide assay indicated that gene or loci associated with lipid traits would related with biochemical indices. For example, alcohol consumption and ALDH2 rs671 polymorphism affected serum triglyceride (TG) levels [17]. Although the role of genetic factor and gene polymorphisms lead to affect biochemical indices are reported, the network of biochemical indices themselves, biochemical indices and genetic type are still puzzling. As the rapid advances in bioinformatics techniques, clarifying the biochemical indices network with genetic types becomes feasible.

The aim of this study was to identify the shared genetics responsible for 29 biochemical indices in the FAMHES cohort, using a phenomics approach. shed light on the relationships between these 29 biochemical indices, including their shared genetic basis and genetic risk loci.

Genetic and trait-based characteristics of 1,999 samples

A total of 1,999 subjects with 29 biochemical indices that passed the QC call rate of 95% were analyzed, and a total of 709,211 SNPs in these subjects were subjected to the subsequent genetic analysis. The average GWAS inflation factor for all 29 biochemical indices was 1.029 (range: 0.975-1.060), suggesting that the stratification correlation worked well (Table S1). The heatmaps based on the Pearson correlation coefficient showed that 106 correlated pairs were found among these 29 traits (correlation coefficient was over 0.3 or less than -0.3 and the P value was less than 0.01) (Figure 1). In addition, cluster analysis with the hclust package in R package could be classified these 29 biochemical indices into 2 groups, one group including blood urea nitrogen (BUN), Cholesterol, Glucose, testosterone (TE), follicle-stimulating hormone (FSH), Insulin, immunoglobulin G (IgG), homocysteine (HCY), folate (FOL), alpha-fetoprotein (AFP), immunoglobulin A (IgA), low-density lipoprotein cholesterol (LDL-C), immunoglobulin M (IgM), C3, how-density lipoprotein cholesterol (HDL), Triglyce, C-reactive protein (CRP). The other group including vitamin B12 (B12), Ferritin (FRRR), Uricacid, immunoglobulin E (IgE), anti-streptococcus hemolysin "O" (ASO), Creatinine, osteocalcin (OSTEOC), Estradiol, sex hormone binding globulin (SHBG), alanine transaminase (ALT) (Figure S1). All of each group contained common lipid metabolism indices, suggesting that these traits were correlated with lipid metabolism.

Correlation analysis based on network medicine

For each trait, we used a linear mixed model estimate fixed value, adjusted with PC1 and PC2 of population stratification and age, respectively, to perform GWAS. A total of 86,556 SNPs (p-value<1×10^-3) associated with all 29 biochemical indices were obtained and then annotated using the SNP Function database with default parameters and the south Asian population option[18]. A total of 12,521 genes were obtained, and protein-protein interactions were determined using the BioGRID database [19]. A total of 5,313 genes with known proteins were obtained, and the interactional network was built with Cytoscape 3[20]. The topological coefficient, clustering coefficient and degree distribution were important indices to evaluate network nodes. Details of these three factors for 5,313 genes are shown in Figure S2 (A, B, C, D).

The Jaccard correlation matrix heatmaps showed that there were 63 correlated pairs among 435 pairwise combinations among these 29 traits indices with MCI were over 0.6 (Figure 2). In these pairs, HCY, IgG, SHBG, B12, IgA and C4 were closely related with more than other six traits. However, in view of the information regarding gene/protein interactions in public databases is limited, interaction information for most of the genes/proteins in this study could not be obtained, and the Jaccard index was computed based on a small number of genes/proteins.

Correlation analysis based on linkage disequilibrium score regression (LDSC)

Genetics can help to elucidate cause and effect. However, single variants tend to have minor effects, and reverse causation involves an even smaller list of confounding factors. Therefore, interrogating genetic overlap via GWAS that focuses on genome-wide significant SNPs is predicted to be an effective means of mining the correlation between different phenotypes. The GWAS effect size estimate for a given SNP will capture information about SNPs near the linkage disequilibrium [21]. The correlations based on GWAS of the 29 quantitative clinical traits were estimated using cross-trait LDSC. The genetic correlation estimates for all 435 pairwise combinations among these 29 traits. After removing the outlier values, 68 significant correlated pairs (p<0.05) were found (Figure 3). The details for these 68 selected pairs of traits are shown in Table S2.

Integration and interpretation of important pairs identified by these three methods

To identify the correlation pairs among these three methods, we integrated the correlated traits fitting at least one of the following: Pearson coefficient was greater than 0.3 or less than -0.3 and P value less than 0.01, Jaccard coefficient was greater than 0.6, or P value of LDSC was less than 0.05. Totally 208 correlated pairs among biochemical indices were found, among them 106, 63, 68 correlated pairs were found by Pearson coefficient , Jaccard coefficient , LDSC, respectively. Only 1 correlated pairs was found by all three methods. 10 correlated pairs both by Pearson coefficient and LDSC, 10 correlated pairs were found both by Pearson coefficient and LDSC, 15 by Pearson and Jaccard coefficient, and 5 by Jaccard coefficient and LDSC. (Figure S3, A). The related traits were integrated if they fulfilled the following conditions: the Pearson coefficient was greater than 0.3 and p-value less than 0.01, the Jaccard coefficient was greater than 0.6, or the LDSC p value was less than 0.05. Six traits (IgA, IgG, HCY, AFP, IgE and B12) were the first top factors in the network of these 29, and related to more than 20 traits. Additionally, IgM, CRP, C4, BUN, TG, Creatinine and FSH were the second top factors and connected with more than 15-20 traits, and OSTEOC, Estradiol, Glucose, FOL, TE, SHBG, FERR, BMI, ALT and HDL were the third top traits which correlated with more than 10 traits (Figure S3, B).

Genes and SNPs those are potentially important across multiple traits

We selected SNPs with P<10^-3 for each trait, resulting in a total of 60,644 SNPs for all 27 traits. The essential genes have a tendency to express in multiple tissues, and are topologically and functionally central [12]. After integrating all 5,313 genes and removing the free notes in the total network among 29 biochemical indices, 427 genes (with P<10^-3 at least one SNP) were correlated with more than 5 traits. After filtering the genes with SNPs (P<10^-4), there were 71 genes correlated with more than or equal to 3 traits, especially aldehyde dehydrogenase 2 family member (ALDH2), BRCA1 associated protein (BRAP), cadherin 13(CDH13) and CUB and Sushi multiple domains 1 (CSMD1) which was related with more than 5 traits. In these 71 genes, 38 genes were found to connect more than 5 other genes in the interactional network annotated from BioGRID database [19] (Table S3). This showed that essential genes related with multiple traits were sure to locate in the central gene interactional network.

Among all the genome wide variations SNPs, 481 (P<1✕10^-3) were associated with three or more clinical biochemical quantitative traits, and 13 of these 481 SNPs were related with more than 5 traits. In these SNPs, rs12229654 (near cut like homeobox 2 (CUX2)), rs2188380 (locates in CUX2)， rs3809297 (locates in CUX2) and rs3782886 (locates in BRAP) was related with more than 10 traits. Six SNPs in CUX2 were correlated with more than 5 traits, which denote that CUX2 should play an important role on this net. In addition, for all the SNPs with P<1✕10^-4, 29 SNPs were related with three or more biochemical indices (Figure 4). After annotated 29 SNPs with P<1✕10^-4 using the HaploReg database[22], we found that almost all these SNPs were related to enhancer histone binding, promoter DNase binding and transcript binding, which affected protein binding or the presence of eQTL (Table S4).

After integrating the SNPs associated with more than 2 traits（P<1✕10-4）with GWAS catalog[23], we found that 31 SNPs in 18 genes were found in GWAS catalog (Table S5). Among those SNPs, five SNPs (rs579459, rs649129, rs507666, rs495828, and rs651007) in ABO were associated with more than 10 quantitative traits and diseases. One SNP (rs671) in ALDH2 was related to 21 traits, six SNPs (rs10519302, rs16964211, rs2305707, rs2414095, rs6493487 and rs727479) in or near CYP19A1 were associated with mainly with hormone measurements. This finding supports the idea that shared genetics on traits can produce correlations among these traits.

The rs671 polymorphism in ALDH2 affects osteogenic and adipogenic differentiation of 3T3-L1 preadipocytes

An interaction between SNP (rs671) in ALDH2 was related to 13 traits were found in this study. The relations between rs671 and lipid metabolism or osteocalcin have been found in some literatures [24, 25], however, their function need to invest. Rs671 is a nonsynonymous (ns) SNP (G504L) in the ALDH2 gene, which is located on chromosome 12. To evaluate the effects of the rs671 polymorphism on osteogenic and adipogenic differentiation of 3T3-L1 preadipocytes, a lentivirus vector was used to overexpress ALDH2-WT or ALDH2-G504L-mut in 3T3-L1 preadipocytes (Figure S4). The cell growth curve of ALDH2-G504L-mut showed no obvious change compared with that of the control, but expression of ALDH2-WT induced a significant increase in cell proliferation (Figure 5A). The cell apoptosis results were consistent with this finding; overexpression of ALDH2-WT resulted in a 3.935-fold decrease in late apoptotic cells in comparison to that of ALDH2-G504L-mut or control cells (Figure 5B, C). We next investigated the impact of the ALDH2 G504L mutation on osteogenic and adipogenic differentiation of 3T3-L1 preadipocytes. At 7 days after osteoblast induction, cells were subjected to Alizarin red-S staining. ALDH2-WT cells showed more mineralized nodules than the control cells or those expressing ALDH2-G504L-mut (Figure 5D, E). In addition, mRNA expression of osteoblast-related genes, such as alkaline phosphatase (AKP), osteocalcin, RUNX family transcription factor 2 (Runx2), and collagen type I (Col1), was significantly higher in ALDH2-WT cells than in ALDH2-G504L-mut or control cells (Figure 5F). After 7 days of adipogenic induction, the ALDH2-WT cells displayed accumulation of lipid vacuoles, as detected by oil red O staining, compared with ALDH2-G504L-mut or control cells (Figure 5G, H). The expression levels of adipogenesis-related proteins, such as adiponectin, C/EBPα (CCAAT/enhancer binding protein α), C/EBPβ, adipocyte fatty acid-binding protein (Fabp4), and Pparγ (peroxisome proliferator-activated receptor), were much higher in ALDH2-WT cells than in ALDH2-G504L-mut or control cells (Figure 5I). Taken together, these results suggest that ALDH2-G504L-mut affected the osteogenic and adipogenic differentiation of 3T3-L1 preadipocytes.

Immunoglobulin is produced by plasma cells and lymphocytes and characteristic of these types of cells, and plays an essential role in the body’s immune system. In this study, we found that IgG, IgA, IgE and IgM were the central traits in biochemical indices network, and these traits could link to 19 and more traits. HCY, a naturally occurring amino acid found in blood plasma, plays the central role in biochemical indices by connecting with 23 traits. High levels of HCY have proved to associate with several body dysfunction, such as the vasculature[26] and endothelial injury [27]. Interesting, Vitamin B12 was identified its central role in biochemical indices network through correlating to 21 other traits. Which is similar to previous studies, Vitamin B12 correlates with several quantitative traits, such as bone mineral density, FOL and FERR [28-30].

Pleiotropy refers that some genes or loci have the ability to affect multiple distinct phenotypic traits. After integrating all the related genes among 29 biochemical indices, it is surprised that ALDH2 and BRAP can be related with 9 traits, and connected with 19 and 13 genes, respectively. ALDH2 belongs to the aldehyde dehydrogenase family of proteins, which is the second enzyme of the major oxidative pathway of alcohol metabolism. ALDH2 dysfunction will lead to several disease, such as cancer [27, 31], alcoholic fatty liver [32], cardiovascular diseases [33]. BRAP is a cytoplasmic protein which can bind to the nuclear localization signal of BRCA1 and other proteins [34]. The polymorphisms in this gene associated with myocardial infarction [35], metabolic syndrome [36]. Additionally, the common CSMD1 was related to 8 traits. CSMD1 is a large (~390 kDa) membrane-bound complement inhibitor [37]. Mutations of this gene participate in complement activation and inflammation in central nervous system, which leads to Parkinson disease [38]. This denotes that these three genes may be the hub genes in biochemical indices networks.

If the SNPs locate sites related to promoter DNase binding, enhancer histone binding and transcript binding, the marginally significant SNPs work as regulatory roles affecting protein binding or the presence of eQTL [39, 40]. In this research, 29 SNPs (P < 10-4) were associated with three or more traits and correlated with each other. These results revealed that the shared regulatory genetics are most likely to drive association signals and play important roles in clinical biological function. This phenomenon may provide important “scaffolding” to support a framework to explore the basic mechanism of biochemical indices.

Shared genetics are commonly applied to build disease-diseased relationship and mine the common disorder of diseases [41, 42]. An important general insight from this study was that associated genes across traits tend to gather in trait specific network modules, we had found 31 SNPs in 18 genes were associated with several traits and diseases, such as five SNPs (rs579459, rs649129, rs507666, rs495828 and rs651007) of ABO were associated with cholesterol, LDL levels. Five SNPs (rs10519302, rs16964211, rs2305707, rs2414095, rs6493487, rs727479) of CYP19A1 associated with estradiol levels. Rs671 in ALDH2 associated with glucose, OSTEOC, SHBG levels. These founding suggest that shared genetics on traits can produce correlations between different traits of disease. For example, ABO gene located around 9q34.2, encodes glycosyltransferases related to the first discovered ABO blood group system [43]. The abnormal expression or polymorphism of this gene is correlated with several body dysfunctions, such as ischemic stroke [44], large artery atherosclerotic stroke [45] and pancreatic cancer [46]. CYP19A1 gene located on 15q21.2, encodes key enzyme for estrogen biosynthesis. SNP in CYP19A1 might affect the aromatase activity the influence the estradiol levels then impacts on human health. Previously research reported that correlation with SNPs of CYP19 and disease, such as polycystic ovarian syndrome [47], coronary heart disease[48] , and its correlation with coronary artery disease (CAD). ALDH2 gene located on 12q24.12, encode aldehyde dehydrogenase, the second enzyme of the major oxidative pathway of alcohol metabolism. Rs671 is nonsynonymous mutation sit on exon 12. rs671 mutation was found to be associated with several traits (BMI, osteocalcin, renal function-related traits[49], response to alcohol consumption[50, 51], triglyceride[17], hematological and biochemical traits[52], intracranial aneurysm [53], mean corpuscular hemoglobin [17]).

Taking ALDH2 as an example to preliminarily explore its biological function, the in vitro function testing of rs671 played a role in the proliferation and osteogenic and adipogenic differentiation of 3T3-L1 preadipocytes. Compared with that of ALDH2-G504L-mut, ALDH2-WT overexpression led to 3T3-L1 preadipocytes transdifferentiating into osteoblasts and adipocytes, as evidenced by Alizarin Red S and oil Red O staining. Adipocyte and osteoblast genes expression was upregulated in ALDH2-WT overexpressing cells when differentiation was induced. These findings proved that one SNP might affect different traits.

We investigated the correlations among 29 biochemical indices through three biological information methods. Firstly, we found that IgA, IgG, IgE, IgM, HCY, AFP and B12 were in the central community of 29 biochemical indices. Secondly, the shared genetics analysis showed that 29 SNPs (P<10^-4) were associated with more than 3 traits. 31 SNPs can be found to associate with several diseases (P<10^-8) by integrating the SNPs related with 2 or more traits with the GWAS catalog. Thirdly, taking ALDH2 as an example to preliminarily explore its biological function, we found that the rs671 (ALDH2) polymorphism could affect the osteogenic and adipogenic differentiation of 3T3-L1 preadipocytes. We clarified 29 biochemical indices were from a network, hub variations/genes that played a vital role in biological processes, these findings highlight a network of shared genetics and 29 biochemical indices.

Study sample

Our study included 2,012 unrelated healthy Chinese men aged 20-69 years from the FAMHES[14, 15], which was conducted among non-institutionalized Chinese men in Guangxi and was designed to investigate the effects of environmental and genetic factors and their interaction with the development of age-related chronic diseases. Men aged ≥18 years were requested to participate in the study upon large-scale physical examination at the Medical Center of Fangchenggang First People’s Hospital from September 2009 to December 2009. The included participants all self-reported that they were free of hyperthyroidism, diabetes mellitus, stroke, coronary heart disease, rheumatoid arthritis, impaired hepatic or renal function, and tumors. Our study research protocol was approved by the Guangxi medical university Ethics Committee, and each participant provided written informed consent.

Measurements of 29 Biochemical indices

Overnight (≥8 h) fasting venous blood specimens were obtained between 7:00 am and 10:00 am, and serum samples were extracted and stored at -80°C. Triglyceride, cholesterol, HDL-C, LDL-C, glucose, ALT, BUN, uric acid and creatinine were measured enzymatically on a Dimension-RxL Chemistry Analyzer (Dade Behring, Newark, DE) in the Department of Clinical Laboratory Science at the Fangchenggang First People's Hospital. CRP, C3, C4, IgA, IgE, IgG, IgM, ASO were measured with immunoturbidimetric methods on a HITACHI 7600 biochemistry analyzer (Hitachi Corp, Tokyo, Japan). Ferritin, folate and vitamin B12, TE, estradiol, FSH, SHBG, insulin, AFP and OSTEOC were measured with the same batch of reagents by electrochemiluminescence immunoassay and HCY assayed by enzyme cycle method using a COBAS 6000 system E601 (Elecsys module) immunoassay analyzer (Roche Diagnostics, GmbH, Mannheim, Germany).

SNP genotyping and quality control (QC) analysis

Genome-wide SNP genotyping was performed with an Illumina Omni 1 M chip (Illumina, San Diego, USA). Among 2,012 genotyped subjects, 1,999 passed the QC call rate of 95% and were included in the final data analysis. A total of 709,211 SNPs in these subjects passed the QC criteria as follows: the P value for the Hardy-Weinberg Equilibrium (HWE) test was greater than 0.001, minor allele frequency (MAF) was greater than 0.01, and genotype call rate was greater than 95%. The inferred genotypes of SNPs in the genome that were not directly genotyped were computed by the IMPUTE program[54] (e.g., SNPs catalogued in HapMap Phase II CHB population release #24). All genotypes with a posterior probability of >90% based on IMPUTE software imputation were retained.

Jaccard coefficient

Phenotypes are linked if they share alterations in genetics. The pathobiology of human diseases might be understood by creating molecular and phenotypic networks [55, 56]. We used SNP function[18] (https://snpinfo.niehs.nih.gov/) tool to identify the genes containing all of the SNPs which the p-value for the GWAS was less P<1×10^-3. The human interactome was obtained by combining protein-protein interaction (PPI) information from the BioGRID database [19].

We built the correlations among 29 clinical phenomes based on the common genes/proteins between two traits. To minimize the bias in estimating of the correlation between two given traits, we calculated the molecular comorbidity index (MCI) by adapting the formula from Grosdidier S[57] to further consider the different coefficients of distance between the two diseases. The MCI was defined as follows:

Where and are the proteins related to clinical traits 1 and 2, respectively. are those proteins related to trait 1 that interact with the proteins associated with trait 2 (and vice versa ). The two operators ∩ and ∪ denote the intersection and union between the two sets of elements ( and ), respectively.

Correlation analysis by LDSC

The genetic correlations derived from the summary statistics are evaluated by the GWAS effect size for a given SNP and integrated the effects of all SNPs which are in linkage disequilibrium (LD) with that SNP. The LDSC (which targets genetic correlation) uses variants across the whole genome and is a symmetrical (i.e., nondirectional) analysis for the risk factor and the outcomes [21]. In short, LDSC assumes that, for polygenic traits, SNPs will also capture information about SNPs near the LD. This relationship between the LD and the associated signal can also be used to test the relationship between the two traits for all SNPs in the genome. To further elucidate the correlations of these 29 biochemical indices in FAMHES from the genetic architecture, we applied LDSC to estimate the correlation of these 29 traits.

Osteogenic and adipogenic differentiation of 3T3-L1 preadipocytes

Full-length ALDH2-WT and ALDH2-G504L-mut cDNA were cloned into the pTSBOE-CMV-MSC-3flag-EF1-tRFP-F2A-Puro lentivirus vector (Quanyang, Shanghai). The 3T3-L1 preadipocytes were cultured in Dulbecco’s modified Eagle’s medium (DMEM) with 10% fetal bovine serum (FBS) at 37°C in a humidified atmosphere with 5% CO₂. The osteoblast-inducing medium used was α-MEM (α-minimum Eagle’s medium) containing 10% FBS（fatal bovine serum）, 100 nM dexamethasone, 5 mM β-phosphoglyceride and 5 μg/mL vitamin C. The adipogenesis-inducing medium included A and B medium. A medium was DMEM containing 10% FBS, 100 nM dexamethasone, 0.5 mM 3-isobutyl-1-methylxanthine and 5 μg/mL insulin. B medium was DMEM containing 10% FBS and 5 μg/mL insulin. For adipocyte induction, cells were cultured for two cycles of A medium for 2 days, then B medium for 1 day. Cell proliferation was measured by a CCK-8 assay according to the manufacturer’s instructions (DOJINDO, Japan). Cell apoptosis was examined by Annexin V-APC/7-AAD staining followed by flow cytometry detection. For Oil Red O or Alizarin Red S staining, cells were fixed with 4% paraformaldehyde for 30 min and stained with 4% Oil Red O solution or 0.4% Alizarin Red S. Lipid droplets and calcium nodules were quantified using Image J software. Cellular RNA was extracted using an RNA extraction kit (Promega, China). Reverse transcription was performed with a Transcriptor Reverse Transcriptase kit (Kangwei, China). Quantitative reverse transcriptase-PCR was performed using a Roche Light Cycler 480 and KANGWEI qPCR Kit (KANGWEI, China). Per primer sequences were listed in Table S6.

Statistical analysis

The correlations among the 29 biochemical indices were computed by the CORR procedure using SAS 9.0 and defined as the Pearson correlation coefficient between the rank variables. With the exception of BUN, HCY, B12, FERR, OSTEOC, creatinine, Uricacid, cholesterol, HDL, LDL, TE and C3, 17 traits without normal distribution were logarithmically transformed to normalize the distribution. The association of the SNPs with 29 clinical quantitative traits was evaluated using a linear regression adjusted for population stratification factors (PC1 and PC2) and age, respectively. Population stratification was evaluated by a principal component approach with EIGENSTRAT software[58].

AFP: alpha-fetoprotein

ALDH2: aldehyde dehydrogenase 2 family member

ALT: alanine transaminase

ASO: anti-streptococcus hemolysin "O"

BUN: blood urea nitrogen

CRP: C-reactive protein

C3: complement 3

C4: complement 4

FAMHES: The Fangchenggang Area Male Health and Examination cohort

Fas: fatty acids

FOL: folate

FRRR: Ferritin

FSH: follicle-stimulating hormone

GWAS: genome-wide association studies

HCY: homocysteine

HDL-C: high-density lipoprotein cholesterol

IgA: immunoglobulin A

IgE: immunoglobulin E

IgG: immunoglobulin G

IgM: immunoglobulin M

LDL-C: low-density lipoprotein cholesterol

LDSC: linkage disequilibrium score regression

MetS: metabolic syndrome

OSTEOC: osteocalcin

SNPs: single nucleotide polymorphism

TE: testosterone

TG: triglyceride

UA: Uric acid

Ethics approval and consent to participate

The study was approved by the Ethical Committee of Guangxi Medical University.

Consent for publication

All the authors are aware of and approve the manuscript as submitted to BMC genomics.

Availability of data and materials

All data generated or analyzed during this study are included in this published article [and its supplementary information files].

Competing interests

The authors declare that they have no competing interests.

Funding

This work was supported by the National Natural Science Foundation of China (No. 81770759, 81472414, 81560608 and 81460388), Guangxi Natural Science Foundation (No. 2015GXNSFBB139008), Guangxi Medical University Training Program for Distinguished Young Scholars, Youth Science Foundation of Guangxi Medical University (No. GXMUYSF201603) and the Guangxi Colleges and Universities Key Laboratory of Biological Molecular Medicine Research Foundation (No. GXBMR201603).

Author’s contributions

Yanling Hu, Yonghua Jiang, Zengnan Mo conceived and designed the experiments. Aihua Tan, Haiying Zhang, Xiaobo Yang, Yong Gao, Tianyu Li, Zengnan Mo performed epidemiologic study, Yonghua Jiang performed Cell biology experiment. Lei Yu, Chenyang Hou, Haofa Kuang, Qunying Wu, Jinghan Su,Qingniao Zhou, Yuanyuan Zhu, Chenqi Zhang, Wei Wei, Lianfeng Li, Weidong Li, Yuanjie Huang, Hongli Huang, Xing Xie, Tingxi Lu performed bioinformatic analysis experiments. Yanling Hu, Yonghua Jiang wrote the manuscript.

Acknowledgements

Not applicable

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Additional files 1: Figure S1. The cluster dendrogram for the 29 Biochemical indices from the FAMHES cohort created with the hclust win R package. In this analysis, two main clusters were produced among these 29 traits. FERR (ferritin), CRP(C-Reactive Protein), C3 (complement 3), C4 (complement4), AFP (serum alphafetoprotein), TG (triglycerides), LDL ( low density lipoprotein), ALT (Alanine transaminase), BMI (body mass index), ASO (anti streptolysin) (anti-streptolysin “O”), IgG(immunoglobulin G), IgA (immunoglobulin A), IgM (immunoglobulin M), BUN (blood urea nitrogen), FSH (follicle-Stimulating Hormone), HDL ( high density lipoprotein), TE (Testosterone), SHBG (sex Hormone Binding Globulin), IgE (immunoglobulin E), B12(Vitamin B12), HCY(homocysteine).

Additional files 2: Figure S2. Network characteristics of 5,313 associated genes for 29 Biochemical indices in individuals from the FAMHES cohort were analyzed by Cytoscape. (A) topological coefficient, (B) degree, (C) clustering coefficient, (D) closeness centrality.

Additional files 3: Figure S3. The integration of correlated traits from three methods. (A)Venn diagram of the integration of correlated traits from three methods. (B) The related traits were integrated if they fulfilled the following conditions: the Pearson coefficient was greater than 0.3 and p-value less than 0.01, the Jaccard coefficient was greater than 0.6, or the LDSC p value was less than 0.05. Each testing method was denoted by a specific color, green for Jaccard, blue for LDSC.

Additional files 4: Figure S4. A lentiviral vector was used to overexpress ALDH2-WT or ALDH2-G504L-mut in 3T3-L1 preadipocytes. (A) Localization of the Glu504Lys substitution mutation in ALDH2. Ex: exon. (B) The plasmid used to express the ALDH2-Gluc504Lys mutant protein in 3T3-L1, ALDH2-WT was expressed using the same plasmid backbone. (C) Sequencing analysis of the ALDH2 gene exogenously expressed in 3T3-L1 cells infected with ALDH2-WT (top) or ALDH2-G504L-mut (bottom). (D) Expression of the transfected ALDH2 protein in 3T3-L1 cells was indirectly assessed by the detection of RFP expression from the lentiviral vector. An RFP signal was detected by fluorescence microscopy at 48 h after infection in both 3T3-L1 cells infected with ALDH2-WT and ALDH2-G504L-mut. RFP control means 3T3-L1 cells infected with plasmid backbone.

Additional files 5: Table S1. The information of the 27 clinic quantitative traits from 1,999 populations.

Additional files 6: Table S2. Genetic correlation estimates, standard errors and P values for selected pairs of traits.

Additional files 7: Table S3. The information on essential genes correlated with more than 3 traits.

Additional files 8: Table S4. 29 SNPs (P<1✕10^-4) related to more than 3 traits were annotated in the HaploReg database.

Additional files 9: Table S5. The annotation of 31(P<1✕10^-4) SNPs associated with more than 1 trait.

Additional files 10: Table S6. The primer sequences of osteogenic and adipogenic differentiation in 3T3-L1 cells.

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A phenomics-based approach for the detection and interpretation of shared genetic influences on 29 biochemical indices in Chinese south men

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