Genetic determinants of plasma triglycerides

Plasma triglyceride (TG) concentration is reemerging as an important cardiovascular disease risk factor. More complete understanding of the genes and variants that modulate plasma TG should enable development of markers for risk prediction, diagnosis, prognosis, and response to therapies and might help specify new directions for therapeutic interventions. Recent genome-wide association studies (GWAS) have identified both known and novel loci associated with plasma TG concentration. However, genetic variation at these loci explains only ～10% of overall TG variation within the population. As the GWAS approach may be reaching its limit for discovering genetic determinants of TG, alternative genetic strategies, such as rare variant sequencing studies and evaluation of animal models, may provide complementary information to flesh out knowledge of clinically and biologically important pathways in TG metabolism. Herein, we review genes recently implicated in TG metabolism and describe how some of these genes likely modulate plasma TG concentration. We also discuss lessons regarding plasma TG metabolism learned from various genomic and genetic experimental approaches. Treatment of patients with moderate to severe hypertriglyceridemia with existing therapies is often challenging; thus, gene products and pathways found in recent genetic research studies provide hope for development of more effective clinical strategies.     

Evidence for a complex relationship between apoA-V and apoC-III in patients with severe hypertriglyceridemia

The relevance of apolipoprotein A-V (apoA-V) for human lipid homeostasis is underscored by genetic association studies and the identification of truncation-causing mutations in the APOA5 gene as a cause of type V hyperlipidemia, compatible with an LPL-activating role of apoA-V. An inverse correlation between plasma apoA-V and triglyceride (TG) levels has been surmised from animal data. Recent studies in human subjects using (semi)quantitative immunoassays, however, do not provide unambiguous support for such a relationship. Here, we used a novel, validated ELISA to measure plasma apoA-V levels in patients (n = 28) with hypertriglyceridemia (HTG; 1.8-78.7 mmol TG/l) and normolipidemic controls (n = 42). Unexpectedly, plasma apoA-V levels were markedly increased in the HTG subjects compared with controls (1,987 vs. 258 ng/ml; P < 0.001). In the HTG group, apoA-V and TG were positively correlated (r = +0.44, P = 0.02). In addition, we noted an increased level of the LPL-inhibitory protein apoC-III in the HTG group (45.8 vs. 10.6 mg/dl in controls; P < 0.001). The correlation between apoA-V and TG levels in the HTG group disappeared (partial r = +0.09, P = 0.65) when controlling for apoC-III levels. In contrast, apoC-III and TG remained positively correlated in this group when controlling for apoA-V (partial r = +0.43, P = 0.025). Our findings suggest that in HTG patients, increased TG levels are accompanied by high plasma levels of apoA-V and apoC-III, apolipoproteins with opposite modes of action. This study provides evidence for a complex interaction between apoA-V and apoC-III in patients with severe HTG.     

A systems genetics approach provides a bridge from discovered genetic variants to biological pathways in rheumatoid arthritis

Genome-wide association studies (GWAS) have yielded novel genetic loci underlying common diseases. We propose a systems genetics approach to utilize these discoveries for better understanding of the genetic architecture of rheumatoid arthritis (RA). Current evidence of genetic associations with RA was sought through PubMed and the NHGRI GWAS catalog. The associations of 15 single nucleotide polymorphisms and HLA-DRB1 alleles were confirmed in 1,287 cases and 1,500 controls of Japanese subjects. Among these, HLA-DRB1 alleles and eight SNPs showed significant associations and all but one of the variants had the same direction of effect as identified in the previous studies, indicating that the genetic risk factors underlying RA are shared across populations. By receiver operating characteristic curve analysis, the area under the curve (AUC) for the genetic risk score based on the selected variants was 68.4%. For seropositive RA patients only, the AUC improved to 70.9%, indicating good but suboptimal predictive ability. A simulation study shows that more than 200 additional loci with similar effect size as recent GWAS findings or 20 rare variants with intermediate effects are needed to achieve AUC = 80.0%. We performed the random walk with restart (RWR) algorithm to prioritize genes for future mapping studies. The performance of the algorithm was confirmed by leave-one-out cross-validation. The RWR algorithm pointed to ZAP70 in the first rank, in which mutation causes RA-like autoimmune arthritis in mice. By applying the hierarchical clustering method to a subnetwork comprising RA-associated genes and top-ranked genes by the RWR, we found three functional modules relevant to RA etiology: “leukocyte activation and differentiation”, “pattern-recognition receptor signaling pathway”, and “chemokines and their receptors”.These results suggest that the systems genetics approach is useful to find directions of future mapping strategies to illuminate biological pathways.     

Joint Linkage and Association Analysis with Exome Sequence Data Implicates SLC25A40 in Hypertriglyceridemia

Hypertriglyceridemia (HTG) is a heritable risk factor for cardiovascular disease. Investigating the genetics of HTG may identify new drug targets. There are ∼35 known single-nucleotide variants (SNVs) that explain only ∼10% of variation in triglyceride (TG) level. Because of the genetic heterogeneity of HTG, a family study design is optimal for identification of rare genetic variants with large effect size because the same mutation can be observed in many relatives and cosegregation with TG can be tested. We considered HTG in a five-generation family of European American descent (n = 121), ascertained for familial combined hyperlipidemia. By using Bayesian Markov chain Monte Carlo joint oligogenic linkage and association analysis, we detected linkage to chromosomes 7 and 17. Whole-exome sequence data revealed shared, highly conserved, private missense SNVs in both SLC25A40 on chr7 and PLD2 on chr17. Jointly, these SNVs explained 49% of the genetic variance in TG; however, only the SLC25A40 SNV was significantly associated with TG (p = 0.0001). This SNV, c.374A>G, causes a highly disruptive p.Tyr125Cys substitution just outside the second helical transmembrane region of the SLC25A40 inner mitochondrial membrane transport protein. Whole-gene testing in subjects from the Exome Sequencing Project confirmed the association between TG and SLC25A40 rare, highly conserved, coding variants (p = 0.03). These results suggest a previously undescribed pathway for HTG and illustrate the power of large pedigrees in the search for rare, causal variants.     

Mouse models and the interpretation of human GWAS in type 2 diabetes and obesity

Within the last 3 years, genome-wide association studies (GWAS) have had unprecedented success in identifying loci that are involved in common diseases. For example, more than 35 susceptibility loci have been identified for type 2 diabetes and 32 for obesity thus far. However, the causal gene and variant at a specific linkage disequilibrium block is often unclear. Using a combination of different mouse alleles, we can greatly facilitate the understanding of which candidate gene at a particular disease locus is associated with the disease in humans, and also provide functional analysis of variants through an allelic series, including analysis of hypomorph and hypermorph point mutations, and knockout and overexpression alleles. The phenotyping of these alleles for specific traits of interest, in combination with the functional analysis of the genetic variants, may reveal the molecular and cellular mechanism of action of these disease variants, and ultimately lead to the identification of novel therapeutic strategies for common human diseases. In this Commentary, we discuss the progress of GWAS in identifying common disease loci for metabolic disease, and the use of the mouse as a model to confirm candidate genes and provide mechanistic insights.     

Extreme‐phenotype genome‐wide association study (XP‐GWAS): a method for identifying trait‐associated variants by sequencing pools of individuals selected from a diversity panel

Although approaches for performing genome‐wide association studies (GWAS) are well developed, conventional GWAS requires high‐density genotyping of large numbers of individuals from a diversity panel. Here we report a method for performing GWAS that does not require genotyping of large numbers of individuals. Instead XP‐GWAS (extreme‐phenotype GWAS) relies on genotyping pools of individuals from a diversity panel that have extreme phenotypes. This analysis measures allele frequencies in the extreme pools, enabling discovery of associations between genetic variants and traits of interest. This method was evaluated in maize (Zea mays) using the well‐characterized kernel row number trait, which was selected to enable comparisons between the results of XP‐GWAS and conventional GWAS. An exome‐sequencing strategy was used to focus sequencing resources on genes and their flanking regions. A total of 0.94 million variants were identified and served as evaluation markers; comparisons among pools showed that 145 of these variants were statistically associated with the kernel row number phenotype. These trait‐associated variants were significantly enriched in regions identified by conventional GWAS. XP‐GWAS was able to resolve several linked QTL and detect trait‐associated variants within a single gene under a QTL peak. XP‐GWAS is expected to be particularly valuable for detecting genes or alleles responsible for quantitative variation in species for which extensive genotyping resources are not available, such as wild progenitors of crops, orphan crops, and other poorly characterized species such as those of ecological interest.     

Functional analysis of HapMap SNPs

Genome-wide association studies (GWAS) have successfully identified many genetic variants associated with complex diseases and traits. However, functional consequence of genetic variants studied in GWAS is not yet fully investigated, which would hinder the application of GWAS. We therefore performed a systematic functional analysis of HapMap SNPs, which have been most commonly used as the reference panel for GWAS. Our study highlights several characteristics of HapMap SNPs and identifies subsets of genetic variants with interesting functional implication. The results show that HapMap SNPs have good coverage within RefSeq genes, especially within known disease-related genes. On the other hand, only a small percentage of SNPs are non-synonymous SNPs while many SNPs are actually located at gene deserts. Moreover, many functionally important variants are not yet still interrogated. A redesigned SNP reference panel with additional functionally important variants would be useful to identify disease-causal variants in the future genome-wide studies.     

Genome-wide association studies in multiple sclerosis: lessons and future prospects

Multiple sclerosis (MS) is an inflammatory neurodegenerative disease with complex aetiology. A haplotype within the major histocompatibility region is the major risk factor for MS, but despite clear evidence for a genetic component additional risk variants were not identified until the recent advent of genome-wide association studies (GWAS). At present, 10 GWAS have been conducted in MS, and together with follow-up studies these have confirmed 16 loci with genome-wide significance. Many of these common risk variants are located at or near genes with central immunological functions and the majority are associated with other autoimmune diseases. However, evidence from pathway analyses on more modestly associated variants also supports the involvement of neurological genes. Although the mechanisms by which the associated variants exert their effects are still poorly understood, some have been shown to correlate with expression of nearby genes. Further studies are required to define the functionally relevant variants in the identified regions and to investigate their effects at the molecular and cellular level. Finally, many genetic risk variants for MS remain to be identified. In order to expose some of the loci with more modest effects, a GWAS in nearly 10,000 MS patients has recently been completed.     

Sortilin: an unusual suspect in cholesterol metabolism: from GWAS identification to in vivo biochemical analyses, sortilin has been identified as a novel mediator of human lipoprotein metabolism

The concentration of low-density lipoprotein (LDL) cholesterol (C) in plasma is a key determinant of cardiovascular disease risk and human genetic studies have long endeavoured to elucidate the pathways that regulate LDL metabolism. Massive genome-wide association studies (GWASs) of common genetic variation associated with LDL-C in the population have implicated SORT1 in LDL metabolism. Using experimental paradigms and standards appropriate for understanding the mechanisms by which common variants alter phenotypic expression, three recent publications have presented divergent and even contradictory findings. Interestingly, although these reports each linked SORT1 to LDL metabolism, they did not agree on a mechanism to explain the association. Here, we review recent mechanistic studies of SORT1 - the first gene identified by GWAS as a determinant of plasma LDL-C to be evaluated mechanistically.     

Lipoproteinstoffwechsel und koronare Herzkrankheit

A better understanding of complex diseases and their genetics has been gained by investigating genetic disorders of lipoprotein metabolism. This has resulted in the development of ddrugs to prevent atherosclerosis, the most frequent cause of death in industrialized countries. Thus, analysis of familial hypercholesterinemia (FH), the most frequent cause of which are mutations on the LDLR gene, has contributed to the development of HMG-CoA reductase inhibitors (statins). Meanwhile, in genome-wide association studies (GWAS), variants in over 90 genes have been found to influence the concentration of plasma lipids. However, these explain only a small fraction of the genetic variance of the traits. Taking the classical polymorphism of Apo-E as an example, it is discussed that one possible reason for the ??missing heritability?? may be the selection of the SNPs on the arrays used in the GWAS. Further, this polymorphism demonstrates how interactions may mask a connection between a genotype and a disease. Genetic studies based on the principle of ??Mendelian randomization?? have established the causal role of a high Lp(a) concentration as a risk factor for coronary heart disease (CHD). For patients with end-stage renal disease, however, a polymorphism (KIV-2 CNV) is a better predictor for CHD than Lp(a) concentration.     

Genome-wide association study of circulating estradiol, testosterone, and sex hormone-binding globulin in postmenopausal women

Genome-wide association studies (GWAS) have successfully identified common genetic variants that contribute to breast cancer risk. Discovering additional variants has become difficult, as power to detect variants of weaker effect with present sample sizes is limited. An alternative approach is to look for variants associated with quantitative traits that in turn affect disease risk. As exposure to high circulating estradiol and testosterone, and low sex hormone-binding globulin (SHBG) levels is implicated in breast cancer etiology, we conducted GWAS analyses of plasma estradiol, testosterone, and SHBG to identify new susceptibility alleles. Cancer Genetic Markers of Susceptibility (CGEMS) data from the Nurses' Health Study (NHS), and Sisters in Breast Cancer Screening data were used to carry out primary meta-analyses among ~1600 postmenopausal women who were not taking postmenopausal hormones at blood draw. We observed a genome-wide significant association between SHBG levels and rs727428 (joint β = -0.126; joint P = 2.09 × 10(-16)), downstream of the SHBG gene. No genome-wide significant associations were observed with estradiol or testosterone levels. Among variants that were suggestively associated with estradiol (P<10(-5)), several were located at the CYP19A1 gene locus. Overall results were similar in secondary meta-analyses that included ~900 NHS current postmenopausal hormone users. No variant associated with estradiol, testosterone, or SHBG at P<10(-5) was associated with postmenopausal breast cancer risk among CGEMS participants. Our results suggest that the small magnitude of difference in hormone levels associated with common genetic variants is likely insufficient to detectably contribute to breast cancer risk.     

Mining the LIPG allelic spectrum reveals the contribution of rare and common regulatory variants to HDL cholesterol

Genome-wide association studies (GWAS) have successfully identified loci associated with quantitative traits, such as blood lipids. Deep resequencing studies are being utilized to catalogue the allelic spectrum at GWAS loci. The goal of these studies is to identify causative variants and missing heritability, including heritability due to low frequency and rare alleles with large phenotypic impact. Whereas rare variant efforts have primarily focused on nonsynonymous coding variants, we hypothesized that noncoding variants in these loci are also functionally important. Using the HDL-C gene LIPG as an example, we explored the effect of regulatory variants identified through resequencing of subjects at HDL-C extremes on gene expression, protein levels, and phenotype. Resequencing a portion of the LIPG promoter and 5' UTR in human subjects with extreme HDL-C, we identified several rare variants in individuals from both extremes. Luciferase reporter assays were used to measure the effect of these rare variants on LIPG expression. Variants conferring opposing effects on gene expression were enriched in opposite extremes of the phenotypic distribution. Minor alleles of a common regulatory haplotype and noncoding GWAS SNPs were associated with reduced plasma levels of the LIPG gene product endothelial lipase (EL), consistent with its role in HDL-C catabolism. Additionally, we found that a common nonfunctional coding variant associated with HDL-C (rs2000813) is in linkage disequilibrium with a 5' UTR variant (rs34474737) that decreases LIPG promoter activity. We attribute the gene regulatory role of rs34474737 to the observed association of the coding variant with plasma EL levels and HDL-C. Taken together, the findings show that both rare and common noncoding regulatory variants are important contributors to the allelic spectrum in complex trait loci.     

G = E: What GWAS Can Tell Us about the Environment

As our understanding of genetics has improved, genome-wide association studies (GWAS) have identified numerous variants associated with lifestyle behaviours and health outcomes. However, what is sometimes overlooked is the possibility that genetic variants identified in GWAS of disease might reflect the effect of modifiable risk factors as well as direct genetic effects. We discuss this possibility with illustrative examples from tobacco and alcohol research, in which genetic variants that predict behavioural phenotypes have been seen in GWAS of diseases known to be causally related to these behaviours. This consideration has implications for the interpretation of GWAS findings.     

An Efficient Stepwise Statistical Test to Identify Multiple Linked Human Genetic Variants Associated with Specific Phenotypic Traits

Recent advances in genotyping methodologies have allowed genome-wide association studies (GWAS) to accurately identify genetic variants that associate with common or pathological complex traits. Although most GWAS have focused on associations with single genetic variants, joint identification of multiple genetic variants, and how they interact, is essential for understanding the genetic architecture of complex phenotypic traits. Here, we propose an efficient stepwise method based on the Cochran-Mantel-Haenszel test (for stratified categorical data) to identify causal joint multiple genetic variants in GWAS. This method combines the CMH statistic with a stepwise procedure to detect multiple genetic variants associated with specific categorical traits, using a series of associated I × J contingency tables and a null hypothesis of no phenotype association. Through a new stratification scheme based on the sum of minor allele count criteria, we make the method more feasible for GWAS data having sample sizes of several thousands. We also examine the properties of the proposed stepwise method via simulation studies, and show that the stepwise CMH test performs better than other existing methods (e.g., logistic regression and detection of associations by Markov blanket) for identifying multiple genetic variants. Finally, we apply the proposed approach to two genomic sequencing datasets to detect linked genetic variants associated with bipolar disorder and obesity, respectively.     

Prioritizing GWAS Results: A Review of Statistical Methods and Recommendations for Their Application

Genome-wide association studies (GWAS) have rapidly become a standard method for disease gene discovery. A substantial number of recent GWAS indicate that for most disorders, only a few common variants are implicated and the associated SNPs explain only a small fraction of the genetic risk. This review is written from the viewpoint that findings from the GWAS provide preliminary genetic information that is available for additional analysis by statistical procedures that accumulate evidence, and that these secondary analyses are very likely to provide valuable information that will help prioritize the strongest constellations of results. We review and discuss three analytic methods to combine preliminary GWAS statistics to identify genes, alleles, and pathways for deeper investigations. Meta-analysis seeks to pool information from multiple GWAS to increase the chances of finding true positives among the false positives and provides a way to combine associations across GWAS, even when the original data are unavailable. Testing for epistasis within a single GWAS study can identify the stronger results that are revealed when genes interact. Pathway analysis of GWAS results is used to prioritize genes and pathways within a biological context. Following a GWAS, association results can be assigned to pathways and tested in aggregate with computational tools and pathway databases. Reviews of published methods with recommendations for their application are provided within the framework for each approach.     

Revealing the genetic basis of multiple sclerosis: are we there yet?

For more than 30 years the only genetic factor associated with susceptibility to multiple sclerosis (MS) was the human leukocyte antigen (HLA) region. Recent advancements in genotyping platforms and the development of more effective statistical methods resulted in the identification of 16 more genes by genome-wide association studies (GWAS) in the last three years alone. While the effect of each of these genes is modest compared to that of HLA, this list is expected to grow significantly in the near future, thus defining a complex landscape in which susceptibility may be determined by a combination of allelic variants in different pathways according to ethnic background, disease sub-type, and specific environmental triggers. A considerable overlap of susceptibility genes among multiple autoimmune diseases is becoming evident and integration of these genetic variants with our current knowledge of affected biological pathways will greatly improve our understanding of mechanisms of general autoimmunity and of tissue specificity.     

Where in the genome are significant single nucleotide polymorphisms from genome-wide association studies located?

Recent technological progress has permitted the efficient performance of genome-wide association studies (GWAS) to map genetic variants associated with common diseases. Here, we analyzed 2,893 single nucleotide polymorphisms (SNPs) that have been identified in 593 published GWAS as associated with a disease phenotype with respect to their genomic location. In absolute numbers, most significant SNPs are located in intergenic regions and introns. When compared to their representation on the chips, there is essentially overrepresentation of nonsynonymous coding SNPs (nsSNPs), synonymous coding SNPs, and SNPs in untranscribed regions upstream of genes among the disease associated SNPs. A Gene Ontology term analysis showed that genes putatively causing a phenotype often code for membrane associated proteins or signal transduction genes.     

The IL-23/IL-17 pathway in human chronic inflammatory diseases – new insight from genetics and targeted therapies

Chronic inflammatory diseases such as rheumatoid arthritis, inflammatory bowel disease, spondyloarthritis and psoriasis cause significant morbidity and are a considerable burden for the patients in terms of pain, impaired function and diminished quality of life, as well as for society, because of the associated high health-care costs, and loss of productivity. Our limited understanding of the pathogenic mechanisms involved in these diseases currently hinders early diagnosis and the development of more specific and effective therapies.The past years have been marked by considerable progress in our insight of the genetic basis of many diseases. In particular, genome-wide association studies (GWAS) performed with thousands of patients have provided detailed information about the genetic variants associated with a large number of chronic inflammatory diseases. These studies have brought to the forefront many genes linked to signaling pathways that were not previously known to be involved in pathogenesis, pointing to new directions in the study of disease mechanisms. GWAS also provided fundamental evidence for a key role of the immune system in the pathogenesis of these diseases, because many of the identified loci map to genes involved in different immune processes. However, the mechanisms by which disease-associated genetic variants act on disease development and the targeted cell populations remain poorly understood. The challenge of the post-GWAS era is to understand how these variants affect pathogenesis, to allow translation of genetic data into better diagnostics and innovative treatment strategies.Here, we review recent results that document the importance of the IL-23/IL-17 pathway for the pathogenesis of several chronic inflammatory diseases and summarize data that demonstrate how therapeutic targeting of this pathway can benefit affected patients.