Estimating the contributions of rare and common genetic variations and clinical measures to a model trait: adiponectin

Identification of Influential Variants in Significant Aggregate Rare Variant Tests

INTRODUCTION

Studies that examine the role of rare variants in both simple and complex disease are increasingly common. Though the usual approach of testing rare variants in aggregate sets is more powerful than testing individual variants, it is of interest to identify the variants that are plausible drivers of the association. We present a novel method for prioritization of rare variants after a significant aggregate test by quantifying the influence of the variant on the aggregate test of association.

METHODS

In addition to providing a measure used to rank variants, we use outlier detection methods to present the computationally efficient Rare Variant Influential Filtering Tool (RIFT) to identify a subset of variants that influence the disease association. We evaluated several outlier detection methods that vary based on the underlying variance measure: interquartile range (Tukey fences), median absolute deviation, and SD. We performed 1,000 simulations for 50 regions of size 3 kb and compared the true and false positive rates. We compared RIFT using the Inner Tukey to 2 existing methods: adaptive combination of p values (ADA) and a Bayesian hierarchical model (BeviMed). Finally, we applied this method to data from our targeted resequencing study in idiopathic pulmonary fibrosis (IPF).

RESULTS

All outlier detection methods observed higher sensitivity to detect uncommon variants (0.001 < minor allele frequency, MAF > 0.03) compared to very rare variants (MAF <0.001). For uncommon variants, RIFT had a lower median false positive rate compared to the ADA. ADA and RIFT had significantly higher true positive rates than that observed for BeviMed. When applied to 2 regions found previously associated with IPF including 100 rare variants, we identified 6 polymorphisms with the greatest evidence for influencing the association with IPF.

DISCUSSION

In summary, RIFT has a high true positive rate while maintaining a low false positive rate for identifying polymorphisms influencing rare variant association tests. This work provides an approach to obtain greater resolution of the rare variant signals within significant aggregate sets; this information can provide an objective measure to prioritize variants for follow-up experimental studies and insight into the biological pathways involved.

Translating the biology of adipokines in atherosclerosis and cardiovascular diseases: Gaps and open questions

AIM

Critically discuss the available data, to identify the current gaps and to provide key concepts that will help clinicians in translating the biology of adipokines in the context of atherosclerosis and cardio-metabolic diseases.

DATA SYNTHESIS

Adipose tissue is nowadays recognized as an active endocrine organ, a function related to the ability to secrete adipokines (such as leptin and adiponectin) and pro-inflammatory cytokines (tumor necrosis factor alpha and resistin). Studies in vitro and in animal models have observed that obesity status presents a chronic low-grade inflammation as the consequence of the immune cells infiltrating the adipose tissue as well as adipocytes. This inflammatory signature is often related to the presence of cardiovascular diseases, including atherosclerosis and thrombosis. These links are less clear in humans, where the role of adipokines as prognostic marker and/or player in cardiovascular diseases is not as clear as that observed in experimental models. Moreover, plasma adipokine levels might reflect a condition of adipokine-resistance in which adipokine redundancy occurs. The investigation of the cardio-metabolic phenotype of carriers of single nucleotide polymorphisms affecting the levels or function of a specific adipokine might help determine their relevance in humans. Thus, the aim of the present review is to critically discuss the available data, identify the current gaps and provide key concepts that will help clinicians translate the biology of adipokines in the context of atherosclerosis and cardio-metabolic diseases.

A genome-wide linkage and association analysis of imputed insertions and deletions with cardiometabolic phenotypes in Mexican Americans: The Insulin Resistance Atherosclerosis Family Study

Insertions and deletions (INDELs) represent a significant fraction of interindividual variation in the human genome yet their contribution to phenotypes is poorly understood. To confirm the quality of imputed INDELs and investigate their roles in mediating cardiometabolic phenotypes, genome-wide association and linkage analyses were performed for 15 phenotypes with 1,273,952 imputed INDELs in 1,024 Mexican-origin Americans. Imputation quality was validated using whole exome sequencing with an average kappa of 0.93 in common INDELs (minor allele frequencies [MAFs] ≥ 5%). Association analysis revealed one genome-wide significant association signal for the cholesterylester transfer protein gene (CETP) with high-density lipoprotein levels (rs36229491, P = 3.06 × 10^-12 ); linkage analysis identified two peaks with logarithm of the odds (LOD) > 5 (rs60560566, LOD = 5.36 with insulin sensitivity (S_I ) and rs5825825, LOD = 5.11 with adiponectin levels). Suggestive overlapping signals between linkage and association were observed: rs59849892 in the WSC domain containing 2 gene (WSCD2) was associated and nominally linked with S_I (P = 1.17 × 10^-7 , LOD = 1.99). This gene has been implicated in glucose metabolism in human islet cell expression studies. In addition, rs201606363 was linked and nominally associated with low-density lipoprotein (P = 4.73 × 10^-4 , LOD = 3.67), apolipoprotein B (P = 1.39 × 10^-3 , LOD = 4.64), and total cholesterol (P = 1.35 × 10^-2 , LOD = 3.80) levels. rs201606363 is an intronic variant of the UBE2F-SCLY (where UBE2F is ubiquitin-conjugating enzyme E2F and SCLY is selenocysteine lyase) fusion gene that may regulate cholesterol through selenium metabolism. In conclusion, these results confirm the feasibility of imputing INDELs from array-based single nucleotide polymorphism (SNP) genotypes. Analysis of these variants using association and linkage replicated previously identified SNP signals and identified multiple novel INDEL signals. These results support the inclusion of INDELs into genetic studies to more fully interrogate the spectrum of genetic variation.

CPT1A methylation is associated with plasma adiponectin

BACKGROUND AND AIMS

Adiponectin, an adipose-secreted protein that has been linked to insulin sensitivity, plasma lipids, and inflammatory patterns, is an established biomarker for metabolic health. Despite clinical relevance and high heritability, the determinants of plasma adiponectin levels remain poorly understood.

METHODS AND RESULTS

We conducted the first epigenome-wide cross-sectional study of adiponectin levels using methylation data on 368,051 cytosine-phosphate-guanine (CpG) sites in CD4+ T-cells from the Genetics of Lipid Lowering Drugs and Diet Network (GOLDN, n = 991). We fit linear mixed models, adjusting for age, sex, study site, T-cell purity, and family. We have identified a positive association (regression coefficient ± SE = 0.01 ± 0.001, P = 3.4 × 10^-13) between plasma adiponectin levels and methylation of a CpG site in CPT1A, a key player in fatty acid metabolism. The association was replicated (n = 474, P = 0.0009) in whole blood samples from the Amish participants of the Heredity and Phenotype Intervention (HAPI) Heart Study as well as White (n = 592, P = 0.0005) but not Black (n = 243, P = 0.18) participants of the Bogalusa Heart Study (BHS). The association remained significant upon adjusting for BMI and smoking in GOLDN and HAPI but not BHS. We also identified associations between methylation loci in RNF145 and UFM1 and plasma adiponectin in GOLDN and White BHS participants, although the association was not robust to adjustment for BMI or smoking.

CONCLUSION

We have identified and replicated associations between several biologically plausible loci and plasma adiponectin. These findings support the importance of epigenetic processes in metabolic traits, laying the groundwork for future translational applications.

Rare Variants in Transcript and Potential Regulatory Regions Explain a Small Percentage of the Missing Heritability of Complex Traits in Cattle

The proportion of genetic variation in complex traits explained by rare variants is a key question for genomic prediction, and for identifying the basis of “missing heritability”–the proportion of additive genetic variation not captured by common variants on SNP arrays. Sequence variants in transcript and regulatory regions from 429 sequenced animals were used to impute high density SNP genotypes of 3311 Holstein sires to sequence. There were 675,062 common variants (MAF>0.05), 102,549 uncommon variants (0.01<MAF<0.05), and 83,856 rare variants (MAF<0.01). We describe a novel method for estimating the proportion of the rare variants that are sequencing errors using parent-progeny duos. We then used mixed model methodology to estimate the proportion of variance captured by these different classes of variants for fat, milk and protein yields, as well as for fertility. Common sequence variants captured 83%, 77%, 76% and 84% of the total genetic variance for fat, milk, and protein yields and fertility, respectively. This was between 2 and 5% more variance than that captured from 600k SNPs on a high density chip, although the difference was not significant. Rare variants captured 3%, 0%, 1% and 14% of the genetic variance for fat, milk and protein yields, and fertility respectively, whereas pedigree explained the remaining amount of genetic variance (none for fertility). The proportion of variation explained by rare variants is likely to be under-estimated due to reduced accuracies of imputation for this class of variants. Using common sequence variants slightly improved accuracy of genomic predictions for fat and milk yield, compared to high density SNP array genotypes. However, including rare variants from transcript regions did not increase the accuracy of genomic predictions. These results suggest that rare variants recover a small percentage of the missing heritability for complex traits, however very large reference sets will be required to exploit this to improve the accuracy of genomic predictions. Our results do suggest the contribution of rare variants to genetic variation may be greater for fitness traits.

Empirical characteristics of family-based linkage to a complex trait: the ADIPOQ region and adiponectin levels

We previously identified a low-frequency (1.1 %) coding variant (G45R; rs200573126) in the adiponectin gene (ADIPOQ) which was the basis for a multipoint microsatellite linkage signal (LOD = 8.2) for plasma adiponectin levels in Hispanic families. We have empirically evaluated the ability of data from targeted common variants, exome chip genotyping, and genome-wide association study data to detect linkage and association to adiponectin protein levels at this locus. Simple two-point linkage and association analyses were performed in 88 Hispanic families (1,150 individuals) using 10,958 SNPs on chromosome 3. Approaches were compared for their ability to map the functional variant, G45R, which was strongly linked (two-point LOD = 20.98) and powerfully associated (p value = 8.1 × 10(-50)). Over 450 SNPs within a broad 61 Mb interval around rs200573126 showed nominal evidence of linkage (LOD > 3) but only four other SNPs in this region were associated with p values < 1.0 × 10(-4). When G45R was accounted for, the maximum LOD score across the interval dropped to 4.39 and the best p value was 1.1 × 10(-5). Linked and/or associated variants ranged in frequency (0.0018-0.50) and type (coding, non-coding) and had little detectable linkage disequilibrium with rs200573126 (r (2) < 0.20). In addition, the two-point linkage approach empirically outperformed multipoint microsatellite and multipoint SNP analysis. In the absence of data for rs200573126, family-based linkage analysis using a moderately dense SNP dataset, including both common and low-frequency variants, resulted in stronger evidence for an adiponectin locus than association data alone. Thus, linkage analysis can be a useful tool to facilitate identification of high-impact genetic variants.

Genetic analysis of adiponectin variation and its association with type 2 diabetes in African Americans

OBJECTIVE

Adiponectin is an adipocytokine that has been implicated in a variety of metabolic disorders, including T2D and cardiovascular disease. Studies evaluating genetic variants in ADIPOQ have been contradictory when testing association with T2D in different ethnic groups.

DESIGN AND METHODS

In this study, 18 SNPs in ADIPOQ were tested for association with plasma adiponectin levels and diabetes status. SNPs were examined in two independent African-American cohorts (nmax = 1,116) from the Insulin Resistance Atherosclerosis Family Study (IRASFS) and the African American-Diabetes Heart Study (AA-DHS).

RESULTS

Five polymorphisms were nominally associated with plasma adiponectin levels in the meta-analysis (P = 0.035-1.02 × 10(-6) ) including a low frequency arginine to cysteine mutation (R55C) which reduced plasma adiponectin levels to <15% of the mean. Variants were then tested for association with T2D in a meta-analysis of these and the Wake Forest T2D case-control study (n = 3,233 T2D, 2645 non-T2D). Association with T2D was not observed (P ≥ 0.08), suggesting limited influence of ADIPOQ variants on T2D risk.

CONCLUSIONS

Despite identification of variants associated with adiponectin levels, a detailed genetic analysis of ADIPOQ revealed no association with T2D risk. This puts into question the role of adiponectin in T2D pathogenesis: whether low adiponectin levels are truly causal for or rather a consequence.