Genetic architecture of circulating lipid levels

Identifying functional non-coding variants in APOA5/A4/C3/A1 gene cluster associated with coronary heart disease

Recent genome-wide association studies identified several polymorphisms in the APOA5/A4/C3/A1 gene cluster influencing lipids level and risk of coronary heart disease (CHD). However, few studies explored the molecular mechanism. The purposes of this study were to fine-map noncoding region between APOA1 and APOC3 and then explore the clinical relevance in CHD and potential underlying mechanisms. In this study, a 2.7-kb length of the non-coding region between APOA1 and APOC3 was screened and five polymorphisms were investigated in the case-control study. The molecular mechanism was explored. Our data confirmed the association between rs7123454, rs12721030, rs10750098, and rs12721028 with CHD in 828 patients and 828 controls and replicated it in an independent population of 405 patients and 405 controls. In addition, the rs10750098 and rs12721030 are significantly associated with decreased serum APOA1 levels (P = 4.2 × 10^-4 and P = 3.2 × 10^-5, combined analysis), while a significant association was observed between serum APOA1 level and CHD (OR: 0.43, 95% CI: 0.28-0.64, P < .01) with adjustment for clinical covariates and different population sets. In vitro evaluation of potential function of non-coding variants between APOA1 and APOC3 demonstrated that rs10750098 as being the most sufficient to confer the haplotype-specific effect on the regulation of APOs gene transcription. Our results strongly implicate the involvement of common noncoding DNA variants in APOA5/A4/C3/A1 gene cluster in the pathogenesis of dyslipidemia and the risk of CHD.

Full genetic analysis for genome-wide association study of Fangji: a powerful approach for effectively dissecting the molecular architecture of personalized traditional Chinese medicine

Elucidation of the systems biology foundation underlying the effect of Fangji, which are multi-herbal traditional Chinese medicine (TCM) formulas, is one of the major aims in the field. The numerous bioactive ingredients of a Fangji deal with the multiple targets of a complex disease, which is influenced by a number of genes and their interactions with the environment. Genome-wide association study (GWAS) is an unbiased approach for dissecting the genetic variants underlying complex diseases and individual response to a given treatment. GWAS has great potential for the study of systems biology from the point of view of genomics, but the capacity using current analysis models is largely handicapped, as evidenced by missing heritability. Recent development of a full genetic model, in which gene-gene interactions (dominance and epistasis) and gene-environment interactions are all considered, has addressed these problems. This approach has been demonstrated to substantially increase model power, remarkably improving the detection of association of GWAS and the construction of the molecular architecture. This analysis does not require a very large sample size, which is often difficult to meet for a GWAS of treatment response. Furthermore, this analysis can integrate other omic information and allow for variations of Fangji, which is very promising for Fangjiomic study and detection of the sophisticated molecular architecture of the function of Fangji, as well as for the delineation of the systems biology of personalized medicine in TCM in an unbiased and comprehensive manner.

A current snapshot of common genomic variants contribution in psychiatric disorders

In the past decade, numerous advances were achieved in psychiatric genetics. Particularly, the genome wide association studies (GWAS) have contributed to uncovering new genes and pathways associated to psychiatric disorders (PDs). At the same time, with increasing sample sizes in the GWAS, the polygenic risk score (PRS) promoted an additional tool for identification and evaluation the genetic risk quantitatively in PDs. This concept review presents the state of the art GWAS analysis and PRS focusing on the genetic underpinnings of PDs. © 2016 Wiley Periodicals, Inc.

Association of the variants and haplotypes in the DOCK7, PCSK9 and GALNT2 genes and the risk of hyperlipidaemia

Little is known about the association between the single nucleotide polymorphisms (SNPs) and haplotypes of the dedicator of cytokinesis 7 (DOCK7), pro-protein convertase subtilisin/kexin type 9 (PCSK9) and polypeptide N-acetylgalactosaminyltransferase 2 (GALNT2) and serum lipid traits in the Chinese populations. This study was to determine the association between nine SNPs in the three genes and their haplotypes and hypercholesterolaemia (HCH)/hypertriglyceridaemia (HTG), and to identify the possible gene-gene interactions among these SNPs. Genotyping was performed in 733 HCH and 540 HTG participants. The haplotype of C-C-G-C-T-G-C-C-G [in the order of DOCK7 rs1168013 (G>C), rs10889332 (C>T); PCSK9 rs615563 (G>A), rs7552841 (C>T), rs11206517 (T>G); and GALNT2 rs1997947 (G>A), rs2760537 (C>T), rs4846913 (C>A) and rs11122316 (G>A) SNPs] was associated with increased risk of HCH and HTG. The haplotypes of C-C-G-C-T-G-C-C-A and G-C-G-T-T-G-T-C-G were associated with a reduced risk of HCH and HTG. The haplotypes of G-C-G-C-T-G-C-C-A and G-C-G-C-T-G-T-C-G were associated with increased risk of HCH. The haplotypes of C-T-G-C-T-G-C-C-G, G-C-A-C-T-G-C-C-G and G-C-G-C-T-G-C-C-A were associated with an increased risk of HTG. The haplotypes of G-C-G-C-T-G-T-C-A and G-C-G-T-T-G-T-C-G were associated with a reduced risk of HTG. In addition, possible inter-locus interactions among the DOCK7, PCSK9 and GALNT2 SNPs were also noted. However, further functional studies of these genes are still required to clarify which SNPs are functional and how these genes actually affect the serum lipid levels.

A Protein Domain and Family Based Approach to Rare Variant Association Analysis

BACKGROUND

It has become common practice to analyse large scale sequencing data with statistical approaches based around the aggregation of rare variants within the same gene. We applied a novel approach to rare variant analysis by collapsing variants together using protein domain and family coordinates, regarded to be a more discrete definition of a biologically functional unit.

METHODS

Using Pfam definitions, we collapsed rare variants (Minor Allele Frequency ≤ 1%) together in three different ways 1) variants within single genomic regions which map to individual protein domains 2) variants within two individual protein domain regions which are predicted to be responsible for a protein-protein interaction 3) all variants within combined regions from multiple genes responsible for coding the same protein domain (i.e. protein families). A conventional collapsing analysis using gene coordinates was also undertaken for comparison. We used UK10K sequence data and investigated associations between regions of variants and lipid traits using the sequence kernel association test (SKAT).

RESULTS

We observed no strong evidence of association between regions of variants based on Pfam domain definitions and lipid traits. Quantile-Quantile plots illustrated that the overall distributions of p-values from the protein domain analyses were comparable to that of a conventional gene-based approach. Deviations from this distribution suggested that collapsing by either protein domain or gene definitions may be favourable depending on the trait analysed.

CONCLUSION

We have collapsed rare variants together using protein domain and family coordinates to present an alternative approach over collapsing across conventionally used gene-based regions. Although no strong evidence of association was detected in these analyses, future studies may still find value in adopting these approaches to detect previously unidentified association signals.

Bipolar polygenic loading and bipolar spectrum features in major depressive disorder

OBJECTIVES

Family and genetic studies indicate overlapping liability for major depressive disorder and bipolar disorder. The purpose of the present study was to determine whether this shared genetic liability influences clinical presentation.

METHODS

A polygenic risk score for bipolar disorder, derived from a large genome-wide association meta-analysis, was generated for each subject of European-American ancestry (n = 1,274) in the Sequential Treatment Alternatives to Relieve Depression study (STAR*D) outpatient major depressive disorder cohort. A hypothesis-driven approach was used to test for association between bipolar disorder risk score and features of depression associated with bipolar disorder in the literature. Follow-up analyses were performed in two additional cohorts.

RESULTS

A generalized linear mixed model including seven features hypothesized to be associated with bipolar spectrum illness was significantly associated with bipolar polygenic risk score [F = 2.07, degrees of freedom (df) = 7, p = 0.04]. Features included early onset, suicide attempt, recurrent depression, atypical depression, subclinical mania, subclinical psychosis, and severity. Post-hoc univariate analyses demonstrated that the major contributors to this omnibus association were onset of illness at age ≤ 18 years [odds ratio (OR) = 1.2, p = 0.003], history of suicide attempt (OR = 1.21, p = 0.03), and presence of at least one manic symptom (OR = 1.16, p = 0.02). The maximal variance in these traits explained by polygenic score ranged from 0.8% to 1.1%. However, analyses in two replication cohorts testing a five-feature model did not support this association.

CONCLUSIONS

Bipolar genetic loading appeared to be associated with bipolar-like presentation in major depressive disorder in the primary analysis. However, the results were at most inconclusive because of lack of replication. Replication efforts were challenged by different ascertainment and assessment strategies in the different cohorts. The methodological approach described here may prove useful in applying genetic data to clarify psychiatric nosology in future studies.

Mining the human phenome using allelic scores that index biological intermediates

It is common practice in genome-wide association studies (GWAS) to focus on the relationship between disease risk and genetic variants one marker at a time. When relevant genes are identified it is often possible to implicate biological intermediates and pathways likely to be involved in disease aetiology. However, single genetic variants typically explain small amounts of disease risk. Our idea is to construct allelic scores that explain greater proportions of the variance in biological intermediates, and subsequently use these scores to data mine GWAS. To investigate the approach's properties, we indexed three biological intermediates where the results of large GWAS meta-analyses were available: body mass index, C-reactive protein and low density lipoprotein levels. We generated allelic scores in the Avon Longitudinal Study of Parents and Children, and in publicly available data from the first Wellcome Trust Case Control Consortium. We compared the explanatory ability of allelic scores in terms of their capacity to proxy for the intermediate of interest, and the extent to which they associated with disease. We found that allelic scores derived from known variants and allelic scores derived from hundreds of thousands of genetic markers explained significant portions of the variance in biological intermediates of interest, and many of these scores showed expected correlations with disease. Genome-wide allelic scores however tended to lack specificity suggesting that they should be used with caution and perhaps only to proxy biological intermediates for which there are no known individual variants. Power calculations confirm the feasibility of extending our strategy to the analysis of tens of thousands of molecular phenotypes in large genome-wide meta-analyses. We conclude that our method represents a simple way in which potentially tens of thousands of molecular phenotypes could be screened for causal relationships with disease without having to expensively measure these variables in individual disease collections.

Epigenome-wide analysis in familial hypercholesterolemia identified new loci associated with high-density lipoprotein cholesterol concentration

AIM

This study aims to assess whether epigenetic changes may account for high-density lipoprotein cholesterol (HDL-C) level variability in familial hypercholesterolemia (FH), a recognized human model to study cardiovascular disease risk modulators.

MATERIALS & METHODS

A genome-wide DNA methylation analysis (Infinium HumanMethylation27 BeadChip, Illumina) was performed on peripheral blood DNA samples obtained from men with FH with low (n = 10) or high (n = 11) HDL-C concentrations. The initial association with one of the top differentially methylated loci located in the promoter of the TNNT1 gene was replicated in a cohort of 276 FH subjects using pyrosequencing.

RESULTS

According to the Ingenuity Pathway Analysis software, the HDL-C differentially methylated loci identified were significantly associated with pathways related to lipid metabolism and cardiovascular disease. TNNT1 DNA methylation levels were positively correlated with mean HDL particle size, HDL-phospholipid, HDL-apolipoprotein AI, HDL-C and TNNT1 expression levels.

CONCLUSION

These results suggest that epigenome-wide changes account for interindividual variations in HDL particle metabolism and that TNNT1 is a new candidate gene for dyslipidemia.

Familial resemblance of bone turnover rate in men aged 40 and over-the MINOS study

Familial resemblance of bone mineral density (BMD) is well known in both sexes. Fewer data concern the familial resemblance of bone turnover markers (BTMs) and bone size in men. Our aim was to assess the correlation of BMD, bone size, BTM levels and hormones regulating bone turnover in 50 pairs of brothers aged ≥ 40 and 50 pairs of unrelated men matched for age, weight and height. BMD was measured at the lumbar spine, hip, forearm and whole body. We measured serum osteocalcin (OC), bone-specific alkaline phosphatase (bone ALP), N-terminal propeptide of type I procollagen (PINP) and C-terminal telopeptide of type I collagen (CTX-I) as well as urinary free and total deoxypyridinoline (DPD) and CTX-I. After adjustment for age, weight, bioavailable 17β-estradiol, and parathyroid hormone, all the BTMs (except bone ALP) were significantly correlated in the brothers (ICC = 0.36-0.64). Most of these correlations were significantly stronger than in the unrelated men. Bone size correlated significantly between the brothers (ICC = 0.55-0.65). These correlations were significantly stronger than in the unrelated men. BMD correlated between the brothers at most of the skeletal sites and, for some of them, more strongly than in the unrelated men. Serum levels of LDL-cholesterol and triglycerides were significantly correlated in the brothers, but not more strongly than in the unrelated men. BTM levels correlated independently in the brothers aged ≥ 40, when their shared environment was limited. These data suggest a substantial hereditary determinism of the BTM levels in men.

ATHENA: a tool for meta-dimensional analysis applied to genotypes and gene expression data to predict HDL cholesterol levels

Technology is driving the field of human genetics research with advances in techniques to generate high-throughput data that interrogate various levels of biological regulation. With this massive amount of data comes the important task of using powerful bioinformatics techniques to sift through the noise to find true signals that predict various human traits. A popular analytical method thus far has been the genome-wide association study (GWAS), which assesses the association of single nucleotide polymorphisms (SNPs) with the trait of interest. Unfortunately, GWAS has not been able to explain a substantial proportion of the estimated heritability for most complex traits. Due to the inherently complex nature of biology, this phenomenon could be a factor of the simplistic study design. A more powerful analysis may be a systems biology approach that integrates different types of data, or a meta-dimensional analysis. For this study we used the Analysis Tool for Heritable and Environmental Network Associations (ATHENA) to integrate high-throughput SNPs and gene expression variables (EVs) to predict high-density lipoprotein cholesterol (HDL-C) levels. We generated multivariable models that consisted of SNPs only, EVs only, and SNPs + EVs with testing r-squared values of 0.16, 0.11, and 0.18, respectively. Additionally, using just the SNPs and EVs from the best models, we generated a model with a testing r-squared of 0.32. A linear regression model with the same variables resulted in an adjusted r-squared of 0.23. With this systems biology approach, we were able to integrate different types of high-throughput data to generate meta-dimensional models that are predictive for the HDL-C in our data set. Additionally, our modeling method was able to capture more of the HDL-C variation than a linear regression model that included the same variables.

Impact of variants within seven candidate genes on statin treatment efficacy

Statins are the most commonly used drugs in patients with dyslipidemia. Among the patients, a significant inter-individual variability with supposed strong genetic background in statin treatment efficacy has been observed. Genome wide screenings detected variants within the CELSR2/PSRC1/SORT1, CILP2/PBX4, APOB, APOE/C1/C4, HMGCoA reductase, LDL receptor and PCSK9 genes that are among the candidates potentially modifying response to statins. Ten variants (SNPs) within these genes (rs599838, rs646776, rs16996148, rs693, rs515135, rs4420638, rs12654264, rs6511720, rs6235, rs11206510) were analyzed in 895 (46 % men, average age 60.3+/-13.1 years) patients with dyslipidemia treated with equipotent doses of statins (~90 % on simvastatin or atorvastatin, doses 10 or 20 mg) and selected 672 normolipidemic controls (40 % men, average age 46.5 years). Lipid parameters were available prior to the treatment and after 12 weeks of therapy. Statin treatment resulted in a significant decrease of both total cholesterol (7.00+/-1.53-->5.15+/-1.17 mmol/l, P<0.0001) and triglycerides (2.03+/-1.01-->1.65+/-1.23 mmol/l, P<0.0005). Rs599838 variant was not detected in first analyzed 284 patients. After adjustment for multiple testing, there was no significant association between individual SNPs and statin treatment efficacy. Only the rs4420638 (APOE/C1/C4 gene cluster) G allele carriers seem to show more profitable change of HDL cholesterol (P=0.007 without and P=0.06 after adjustment). Results demonstrated that, although associated with plasma TC and LDL cholesterol per se, variants within the CELSR2/PSRC1/SORT1, CILP2/PBX4, APOB, APOE/C1/C4, HMGCoA reductase, LDL receptor and PCSK9 genes do not modify therapeutic response to statins.

Genetic determinants of lipid homeostasis

Circulating levels of blood lipids are heritable risk factors for atherosclerosis and heart disease, and are the target of therapeutic intervention. Studies of monogenic disorders and - more recently - genome-wide association studies have identified several important genetic determinants of blood lipid levels. These have the potential to provide new drug targets to alter blood lipid levels and may improve prediction of cardiovascular disease. Better functional validation of lipid loci is required to clarify the biological role of proteins encoded by specific genomic regions and understand how they influence lipid metabolism and confer disease risk.

Metabolic and cardiovascular genes in polycystic ovary syndrome: a candidate-wide association study (CWAS)

The role of metabolic disturbance in polycystic ovary syndrome (PCOS) has been well established, with insulin resistance and the resulting compensatory hyperinsulinemia thought to promote hyperandrogenemia. Genome-wide association studies (GWAS) have established a large number of loci for metabolic conditions such as type 2 diabetes and obesity. A subset of these loci has been investigated for a role in PCOS; these studies generally have not revealed a confirmed role for these loci in PCOS risk. However, a large scale investigation of genes related to these pathways has not previously been performed. We conducted a two stage case control association study of 121,715 single nucleotide polymorphisms (SNPs) selected to represent susceptibility loci associated with traits such as type 2 diabetes, obesity measures, lipid levels and cardiovascular function using the Cardio-Metabochip in 847 PCOS cases and 845 controls. Several hypothesis-generating associations with PCOS were observed (top SNP rs2129107, P=3.8×10(-6)). We did not find any loci definitively associated with PCOS after strict correction for multiple testing, suggesting that cardio-metabolic loci are not major risk factors underlying the susceptibility to PCOS.

A systems genetic analysis of high density lipoprotein metabolism and network preservation across mouse models

We report a systems genetic analysis of high density lipoprotein (HDL) levels in an F2 intercross between inbred strains CAST/EiJ and C57BL/6J. We previously showed that there are dramatic differences in HDL metabolism in a cross between these strains, and we now report co-expression network analysis of HDL that integrates global expression data from liver and adipose with relevant metabolic traits. Using data from a total of 293 F2 intercross mice, we constructed weighted gene co-expression networks and identified modules (subnetworks) associated with HDL and clinical traits. These were examined for genes implicated in HDL levels based on large human genome-wide associations studies (GWAS) and examined with respect to conservation between tissue and sexes in a total of 9 data sets. We identify genes that are consistently ranked high by association with HDL across the 9 data sets. We focus in particular on two genes, Wfdc2 and Hdac3, that are located in close proximity to HDL QTL peaks where causal testing indicates that they may affect HDL. Our results provide a rich resource for studies of complex metabolic interactions involving HDL. This article is part of a Special Issue entitled Advances in High Density Lipoprotein Formation and Metabolism: A Tribute to John F. Oram (1945-2010).

Disruption of the murine procollagen C-proteinase enhancer 2 gene causes accumulation of pro-apoA-I and increased HDL levels

Given the increased prevalence of cardiovascular disease in the world, the search for genetic variations that impact risk factors associated with the development of this disease continues. Multiple genetic association studies demonstrate that procollagen C-proteinase enhancer 2 (PCPE2) modulates HDL levels. Recent studies revealed an unexpected role for this protein in the proteolytic processing of pro-apolipoprotein (apo) A-I by enhancing the cleavage of the hexapeptide extension present at the N-terminus of apoA-I. To investigate the role of the PCPE2 protein in an in vivo model, PCPE2-deficient (PCPE2 KO) mice were examined, and a detailed characterization of plasma lipid profiles, apoA-I, HDL speciation, and function was done. Results of isoelectric focusing (IEF) electrophoresis together with the identification of the amino terminal peptides DEPQSQWDK and WHVWQQDEPQSQWDVK, representing mature apoA-I and pro-apoA-I, respectively, in serum from PCPE2 KO mice confirmed that PCPE2 has a role in apoA-I maturation. Lipid profiles showed a marked increase in plasma apoA-I and HDL-cholesterol (HDL-C) levels in PCPE2 KO mice compared with wild-type littermates, regardless of gender or diet. Changes in HDL particle size and electrophoretic mobility observed in PCPE2 KO mice suggest that the presence of pro-apoA-I impairs the maturation of HDL. ABCA1-dependent cholesterol efflux is defective in PCPE2 KO mice, suggesting that the functionality of HDL is altered.