Genomic imprinting in diabetes

Genomic imprinting refers to a class of transmissible genetic effects in which the expression of the phenotype in the offspring depends on the parental origin of the transmitted allele. The DNA from one parent may be epigenetically modified so that only a single allele of the imprinted gene is expressed in the offspring. Although imprinting has an important role in the regulation of growth and development through its role in regulating gene expression, its contribution to susceptibility to common complex disorders is not well understood. We summarize current views on the role of imprinting in diabetes and in particular chromosome 6q24-related transient neonatal diabetes mellitus, the best known example of an imprinted genetic disorder that leads to diabetes

Identity-by-Descent Mapping Identifies Major Locus for Serum Triglycerides in Amerindians Largely Explained by an APOC3 Founder Mutation

Background—Identity-by-descent (IBD) mapping using empirical estimates of IBD allele sharing may be useful for studies of complex traits in founder populations, where hidden relationships may augment the inherent genetic information that can be used for localization. Methods and Results—Through IBD mapping, using ~400,000 SNPs, of serum lipid profiles we identified a major linkage signal for triglycerides (TG) in 1,007 Pima Indians (LOD=9.23, p=3.5×10−11 on chromosome 11q). In subsequent fine-mapping and replication association studies in ~7,500 Amerindians, we determined that this signal reflects effects of a loss-of-function Ala43Thr substitution in APOC3 (rs147210663) and 3 established functional SNPs in APOA5. The association with rs147210663 was particularly strong; each copy of the Thr allele conferred 42% lower TG (β=−0.92±0.059 SD unit, p=9.6×10−55 in 4,668 Pimas and 2,793 Southwest Amerindians combined). The Thr allele is extremely rare in most global populations, but has a frequency of 2.5% in Pimas. We further demonstrated that 3 APOA5 SNPs with established functional impact could explain the association with the most well-replicated SNP (rs964184) for TG identified by genome-wide association studies (GWAS). Collectively these 4 SNPs account for 6.9% of variation in TG in Pimas (and 4.1% in Southwest Amerindians), and their inclusion in the original linkage model reduced the linkage signal to virtually null. Conclusions—APOC3/APOA5 constitutes a major locus for serum triglycerides in Amerindians, especially the Pimas, and these results provide an empirical example for the concept that population-based linkage analysis is a useful strategy to identify complex trait variants

Common Variants in 40 Genes Assessed for Diabetes Incidence and Response to Metformin and Lifestyle Intervention in the Diabetes Prevention Program

OBJECTIVE: Genome-wide association studies have begun to elucidate the genetic architecture of type 2 diabetes. We examined whether single nucleotide polymorphisms (SNPs) identified through targeted complementary approaches affect diabetes incidence in the at-risk population of the Diabetes Prevention Program (DPP) and whether they influence a response to preventive interventions. RESEARCH DESIGN AND METHODS: We selected SNPs identified by prior genome-wide association studies for type 2 diabetes and related traits, or capturing common variation in 40 candidate genes previously associated with type 2 diabetes, implicated in monogenic diabetes, encoding type 2 diabetes drug targets or drug-metabolizing/transporting enzymes, or involved in relevant physiological processes. We analyzed 1,590 SNPs for association with incident diabetes and their interaction with response to metformin or lifestyle interventions in 2,994 DPP participants. We controlled for multiple hypothesis testing by assessing false discovery rates. RESULTS: We replicated the association of variants in the metformin transporter gene $SLC47A1$ with metformin response and detected nominal interactions in the AMP kinase (AMPK) gene $STK11$, the AMPK subunit genes $PRKAA1$ and $PRKAA2$, and a missense SNP in $SLC22A1$, which encodes another metformin transporter. The most significant association with diabetes incidence occurred in the AMPK subunit gene $PRKAG2$ (hazard ratio 1.24, 95% CI 1.09–1.40, P = 7 × 10$^{−4}$). Overall, there were nominal associations with diabetes incidence at 85 SNPs and nominal interactions with the metformin and lifestyle interventions at 91 and 69 mostly nonoverlapping SNPs, respectively. The lowest $P$ values were consistent with experiment-wide 33% false discovery rates. CONCLUSIONS: We have identified potential genetic determinants of metformin response. These results merit confirmation in independent samples

Genetic modulation of lipid profiles following lifestyle modification or metformin treatment: The Diabetes Prevention Program

Weight-loss interventions generally improve lipid profiles and reduce cardiovascular disease risk, but effects are variable and may depend on genetic factors. We performed a genetic association analysis of data from 2,993 participants in the Diabetes Prevention Program to test the hypotheses that a genetic risk score (GRS) based on deleterious alleles at 32 lipid-associated single-nucleotide polymorphisms modifies the effects of lifestyle and/or metformin interventions on lipid levels and nuclear magnetic resonance (NMR) lipoprotein subfraction size and number. Twenty-three loci previously associated with fasting LDL-C, HDL-C, or triglycerides replicated (P = 0.04–1×10−17). Except for total HDL particles (r = −0.03, P = 0.26), all components of the lipid profile correlated with the GRS (partial |r| = 0.07–0.17, P = 5×10−5–1×10−19). The GRS was associated with higher baseline-adjusted 1-year LDL cholesterol levels (β = +0.87, SEE±0.22 mg/dl/allele, P = 8×10−5, Pinteraction = 0.02) in the lifestyle intervention group, but not in the placebo (β = +0.20, SEE±0.22 mg/dl/allele, P = 0.35) or metformin (β = −0.03, SEE±0.22 mg/dl/allele, P = 0.90; Pinteraction = 0.64) groups. Similarly, a higher GRS predicted a greater number of baseline-adjusted small LDL particles at 1 year in the lifestyle intervention arm (β = +0.30, SEE±0.012 ln nmol/L/allele, P = 0.01, Pinteraction = 0.01) but not in the placebo (β = −0.002, SEE±0.008 ln nmol/L/allele, P = 0.74) or metformin (β = +0.013, SEE±0.008 nmol/L/allele, P = 0.12; Pinteraction = 0.24) groups. Our findings suggest that a high genetic burden confers an adverse lipid profile and predicts attenuated response in LDL-C levels and small LDL particle number to dietary and physical activity interventions aimed at weight loss

Common Variants in 40 Genes Assessed for Diabetes Incidence and Response to Metformin and Lifestyle Intervention in the Diabetes Prevention Program

OBJECTIVE: Genome-wide association studies have begun to elucidate the genetic architecture of type 2 diabetes. We examined whether single nucleotide polymorphisms (SNPs) identified through targeted complementary approaches affect diabetes incidence in the at-risk population of the Diabetes Prevention Program (DPP) and whether they influence a response to preventive interventions. RESEARCH DESIGN AND METHODS: We selected SNPs identified by prior genome-wide association studies for type 2 diabetes and related traits, or capturing common variation in 40 candidate genes previously associated with type 2 diabetes, implicated in monogenic diabetes, encoding type 2 diabetes drug targets or drug-metabolizing/transporting enzymes, or involved in relevant physiological processes. We analyzed 1,590 SNPs for association with incident diabetes and their interaction with response to metformin or lifestyle interventions in 2,994 DPP participants. We controlled for multiple hypothesis testing by assessing false discovery rates. RESULTS: We replicated the association of variants in the metformin transporter gene SLC47A1 with metformin response and detected nominal interactions in the AMP kinase (AMPK) gene STK11, the AMPK subunit genes PRKAA1 and PRKAA2, and a missense SNP in SLC22A1, which encodes another metformin transporter. The most significant association with diabetes incidence occurred in the AMPK subunit gene PRKAG2 (hazard ratio 1.24, 95% CI 1.09-1.40, P = 7 × 10(-4)). Overall, there were nominal associations with diabetes incidence at 85 SNPs and nominal interactions with the metformin and lifestyle interventions at 91 and 69 mostly nonoverlapping SNPs, respectively. The lowest P values were consistent with experiment-wide 33% false discovery rates. CONCLUSIONS: We have identified potential genetic determinants of metformin response. These results merit confirmation in independent samples

Comprehensive Analysis of Established Dyslipidemia-Associated Loci in the Diabetes Prevention Program

Background-We assessed whether 234 established dyslipidemia-associated loci modify the effects of metformin treatment and lifestyle intervention (versus placebo control) on lipid and lipid subfraction levels in the Diabetes Prevention Program randomized controlled trial. Methods and Results-We tested gene treatment interactions in relation to baseline-adjusted follow-up blood lipid concentrations (high-density lipoprotein [HDL] and low-density lipoprotein-cholesterol, total cholesterol, and triglycerides) and lipoprotein subfraction particle concentrations and size in 2993 participants with pre-diabetes. Of the previously reported single-nucleotide polymorphism associations, 32.5% replicated at PP>1.1×10-16) with their respective baseline traits for all but 2 traits. Lifestyle modified the effect of the genetic risk score for large HDL particle numbers, such that each risk allele of the genetic risk scores was associated with lower concentrations of large HDL particles at follow-up in the lifestyle arm (β=-0.11 μmol/L per genetic risk scores risk allele; 95% confidence interval,-0.188 to-0.033; P=5×10-3; Pinteraction=1×10-3 for lifestyle versus placebo), but not in the metformin or placebo arms (P>0.05). In the lifestyle arm, participants with high genetic risk had more favorable or similar trait levels at 1-year compared with participants at lower genetic risk at baseline for 17 of the 20 traits. Conclusions-Improvements in large HDL particle concentrations conferred by lifestyle may be diminished by genetic factors. Lifestyle intervention, however, was successful in offsetting unfavorable genetic loading for most lipid traits. Clinical Trial Registration-URL: https://www.clinicaltrials.gov. Unique Identifier: NCT00004992

Effects of APOC3 Heterozygous Deficiency on Plasma Lipid and Lipoprotein Metabolism.

Objective- Apo (apolipoprotein) CIII inhibits lipoprotein lipase (LpL)-mediated lipolysis of VLDL (very-low-density lipoprotein) triglyceride (TG) and decreases hepatic uptake of VLDL remnants. The discovery that 5% of Lancaster Old Order Amish are heterozygous for the APOC3 R19X null mutation provided the opportunity to determine the effects of a naturally occurring reduction in apo CIII levels on the metabolism of atherogenic containing lipoproteins. Approach and Results- We conducted stable isotope studies of VLDL-TG and apoB100 in 5 individuals heterozygous for the null mutation APOC3 R19X (CT) and their unaffected (CC) siblings. Fractional clearance rates and production rates of VLDL-TG and apoB100 in VLDL, IDL (intermediate-density lipoprotein), LDL, apo CIII, and apo CII were determined. Affected (CT) individuals had 49% reduction in plasma apo CIII levels compared with CCs ( P<0.01) and reduced plasma levels of TG (35%, P<0.02), VLDL-TG (45%, P<0.02), and VLDL-apoB100 (36%, P<0.05). These changes were because of higher fractional clearance rates of VLDL-TG and VLDL-apoB100 with no differences in production rates. CTs had higher rates of the conversion of VLDL remnants to LDL compared with CCs. In contrast, rates of direct removal of VLDL remnants did not differ between the groups. As a result, the flux of apoB100 from VLDL to LDL was not reduced, and the plasma levels of LDL-cholesterol and LDL-apoB100 were not lower in the CT group. Apo CIII production rate was lower in CTs compared with CCs, whereas apo CII production rate was not different between the 2 groups. The fractional clearance rates of both apo CIII and apo CII were higher in CTs than CCs. Conclusions- These studies demonstrate that 50% reductions in plasma apo CIII, in otherwise healthy subjects, results in a significantly higher rate of conversion of VLDL to LDL, with little effect on direct hepatic uptake of VLDL. When put in the context of studies demonstrating significant protection from cardiovascular events in individuals with loss of function variants in the APOC3 gene, our results provide strong evidence that therapies which increase the efficiency of conversion of VLDL to LDL, thereby reducing remnant concentrations, should reduce the risk of cardiovascular disease

Developing a Common Framework for Evaluating the Implementation of Genomic Medicine Interventions in Clinical Care: The IGNITE Network’s Common Measures Working Group

Purpose Implementation research provides a structure for evaluating the clinical integration of genomic medicine interventions. This paper describes the Implementing GeNomics In PracTicE (IGNITE) Network’s efforts to promote: 1) a broader understanding of genomic medicine implementation research; and 2) the sharing of knowledge generated in the network. Methods To facilitate this goal the IGNITE Network Common Measures Working Group (CMG) members adopted the Consolidated Framework for Implementation Research (CFIR) to guide their approach to: identifying constructs and measures relevant to evaluating genomic medicine as a whole, standardizing data collection across projects, and combining data in a centralized resource for cross network analyses. Results CMG identified ten high-priority CFIR constructs as important for genomic medicine. Of those, eight didn’t have standardized measurement instruments. Therefore, we developed four survey tools to address this gap. In addition, we identified seven high-priority constructs related to patients, families, and communities that did not map to CFIR constructs. Both sets of constructs were combined to create a draft genomic medicine implementation model. Conclusion We developed processes to identify constructs deemed valuable for genomic medicine implementation and codified them in a model. These resources are freely available to facilitate knowledge generation and sharing across the field