Abstract
Purpose
Numerous prospective studies indicate that improved cardiorespiratory fitness reduces type 2 diabetes (T2D) risk and delays disease progression. We hypothesized that genetic variants modify fitness response to an intensive lifestyle intervention (ILI) in the Action for Health in Diabetes (Look AHEAD) randomized clinical trial, aimed to detect whether ILI will reduce cardiovascular events in overweight/obese subjects with T2D compared to a standard of care.
Methods
Polymorphisms in established fitness genes and in all loci assayed on the Illumina CARe iSelect chip were examined as predictors of change in metabolic equivalent (MET) level, estimated using a treadmill test, in response to a one-year intervention in 3,899 participants.
Results
We identified a significant signal in previously reported fitness-related gene RUNX1 that was associated with one-year METs response in ILI (0.19±0.04 MET less improvement per minor allele copy; P=1.9×10−5) and genotype-intervention interaction (P=4.8×10−3). In the chip-wide analysis, FKBP7 rs17225700 showed a significant association with ILI response among subjects not receiving beta-blocker medications (0.47±0.09 METs less improvement; P=5.3×10−7), and genotype-treatment interaction (P=5.3×10−5). GRAIL pathway-based analysis identified connections between associated genes, including those influencing vascular tone, muscle contraction, cardiac energy substrate dynamics, and muscle protein synthesis.
Conclusions
This is the first study to identify genetic variants associated with fitness responses to a randomized lifestyle intervention in overweight/obese diabetic individuals. RUNX1 and FKBP7, involved in erythropoesis and muscle protein synthesis, respectively, are related to change in cardiorespiratory fitness in response to exercise.
Keywords: metabolic equivalent, clinical trial, CARe iSelect IBC chip, genotype-treatment interaction
Introduction
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The prevalence of type 2 diabetes (T2D) is expected to rise sharply over the next 40 years to a level where one in three US adults could be affected (8). Numerous prospective epidemiological studies indicate that regular physical activity is related to a 15-60% reduction in risk of T2D (reviewed in (31)), and that behavioral intervention that promotes physical fitness can reduce progression from pre-diabetes to T2D by up to 58% (22). Cardiorespiratory fitness has been inversely associated with incident T2D (25) and cardiovascular events (23). Exercise programs designed to increase physical fitness are recommended to patients with established T2D. The benefit of exercise can be seen with improved insulin sensitivity, as well as reduced adiposity and adipose tissue inflammation (3).
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Studies in animal models demonstrate a significant role for genetic background in physical endurance (2). Similarly, cardiorespiratory fitness in humans was found to be heritable, with heritability estimates ranging between 25% and 65% (reviewed in (37)). Genome-wide association studies (GWAS) conducted in the Framingham Heart Study and HERITAGE Family Study using large arrays of single nucleotide polymorphisms (SNPs) identified no variants associated with pre-training levels or changes in heart rate or fitness in response to training at the genome-wide significance level (P-value < 5×10−8) (7, 39). Suggestive signals, however, were identified in the ryanodine receptor gene (RYR2), as well as other genes that have a plausible role in fitness including ACE, ADRB1, AGT, AGTR1, KCNH8, and others. In another study, SNPs in three muscle-related genes (CNTF, AMPD1, and NR3C1) predicted whether a patient with coronary artery disease responded to a 3-month ambulatory supervised exercise training regimen (38). Finally, a study using a combination of transcriptomics and genomics demonstrated that about half of the variance of VO2max trainability was accounted for either by the abundance of 29 muscle transcripts or by 11 SNPs (20). While these studies demonstrate that genetic predictors of fitness are starting to emerge, there is currently insufficient evidence to implicate specific genes responsible for the inter-individual variation in fitness. Newer gene-centric array-based genotyping technologies that permit improved coverage of the candidate genes, and potentially deep re-sequencing approaches, that capture genetic diversity across populations may prove more effective in identifying fitness genes. Here, we analyzed data from the ITMAT-Broad-CARe (IBC) chip (19), primarily aimed at assaying SNPs in candidate genes and pathways for cardiovascular, inflammatory and metabolic phenotypes, to better define the complex and poorly-characterized role of genetics in human fitness.
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The Action for Health in Diabetes (Look AHEAD) randomized clinical trial demonstrated that an intensive lifestyle intervention (ILI) including both caloric restriction and physical activity produced significantly greater weight loss and improved measures of glucose control in participants with established T2D after one year, compared with a control intervention of diabetes support and education (DSE) (29). The ILI was also effective in increasing cardiorespiratory fitness in Look AHEAD subjects (16).
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Here, we hypothesized that genetic variants modify the fitness response to ILI, compared with DSE, in the presence of established T2D. To test this hypothesis we analyzed whether SNPs within genes already implicated in physical fitness and present on the IBC chip were associated with changes in fitness in response to one year of intervention in Look AHEAD. A differential response to intervention by genotype would help identify biological pathways involved in fitness.
Material and Methods
Study subjects
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The design and methods of the Look AHEAD trial have been reported elsewhere (36), as have the baseline characteristics of the entire randomized cohort (9). Among 5,145 ethnically diverse overweight and obese Look AHEAD subjects with T2D and aged 45 to 76 years at baseline, 4,041 provided consent and DNA for genetic analysis. The Look AHEAD trial was approved by local Institutional Review Boards, including genetic analyses.
Intervention
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Subjects were randomly assigned to DSE or ILI. DSE received standard-care plus 3 education sessions over the one-year period. ILI included individual and group contact throughout the year focusing on caloric restriction and increased physical activity, with the goal of achieving 10% or greater weight loss. ILI participants were instructed initially to increase their physical activity to at least 50 min/week, progressing to at least 175 min/week by week 26, with the intensity being moderate-to-vigorous (similar to brisk walking). Participants were also encouraged to increase lifestyle forms of physical activity (using stairs rather than elevators, walking rather than riding, and reducing use of labor saving devices).
Assessments
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Subject characteristics including age, sex, medication use and race/ethnicity were collected via questionnaire at baseline. Weight at baseline and one year post-randomization was measured using the standardized methods as described previously (21).
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Cardiorespiratory fitness was assessed using a graded exercise test (GXT) on a calibrated motor-driven treadmill as previously described using a standardized protocol (16). A self-selected walking speed of 1.5, 2.0, 2.5, 3.0, 3.5, or 4.0 mph was used with the speed held constant throughout the test. Grade of the treadmill was initiated at 0% and increased by 1% each minute until test termination. During the last 10 seconds of each minute and at the point to test termination the heart rate was measured from a 12-lead ECG and rating of perceived exertion (RPE) was measured using the Borg 15-category scale (scale ranges from 6 to 20). Blood pressure was assessed during the last 45 seconds of each even minute and at test termination. A maximal graded exercise to the point of volitional fatigue was conducted at baseline. The baseline GXT was considered valid provided that that subject achieved either 85% of age-predicted maximal heart rate (defined as 220-age) computed as if not taking a medication that would affect the heart rate response to exercise or RPE≥18 if the subject was taking a medication that would affect the heart rate response to exercise (e.g., beta blocker). This baseline test was used to exclude individuals for whom exercise may have been contraindicated prior to study randomization. Due to cost constraints associated with the need for physician’s presence for a maximal test regardless of health status, subjects completed a submaximal GXT at 1 year using the same walking speed and grade increments as was used for the baseline test; however, the test was terminated at the point with the participant first exceeded 80% of age-predicted maximal heart rate if not on a beta-blocker at either baseline or Year 1, or first exceeded RPE = 16 if on a beta-blocker at either baseline or Year 1. The workload at test termination at 1 year was compared to the workload from baseline where the same heart rate (80% age-predicted maximal heart rate) or RPE (RPE = 16) was met on the baseline GXT. These workloads were converted to estimated METs using the American College of Sports Medicine’s metabolic calculations for estimating energy expenditure (1), and the change in fitness was computed as the difference in METs at the same submaximal heart rate or RPE between the baseline and 1 year GXT.
Genotyping and Candidate Gene selection
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Genotyping was carried out on leukocyte DNA using the Illumina CARe iSelect (IBC) chip (19), as previously described (28). Briefly, genomic DNA was extracted from whole blood (FlexiGene DNA Kit (Qiagen Inc., Valencia, CA)) and genotyping was carried out at the Children’s Hospital of Philadelphia. SNPs were clustered into genotypes using the Illumina BeadStudio software and subjected to quality control filters. Samples were excluded for individual call rates <90%, sex mismatch, and duplicate discordance. SNPs were removed for call rates <95%. Due to low power for capturing genetic effects of the many low frequency variants included in the design, we filtered out SNPs of minor allele frequency (MAF) < 5%. This left 32,561 SNPs on the IBC chip with MAF≥5%, whose mean genotyping success rate was 99.8%. After excluding individuals that failed the IBC chip genotyping, had low call rate, or had discrepancy between self-reported and X-chromosome-determined sex, the study cohort consisted of 3,899 individuals. We performed a detailed literature review of all available fitness genetic association studies. From our review we selected studies with a substantial sample size (>470 participants) that identified a total of 158 candidate genes previously reported by candidate gene, genetic linkage and GWAS, and gene expression studies to be associated with fitness traits based upon standardized exercise treadmill test traits (Supplemental Table S1); 63 of these genes were represented on the IBC. We then performed gene-level replication by prioritizing the analysis of 1,317 SNPs within the 63 genes included on IBC.
Statistical Analysis
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We conducted a joint analysis of baseline and one-year METs measurements, using an unstructured covariance matrix. Longitudinal models evaluating the effects of time (baseline vs. one year), study arm (ILI vs. DSE), and individual SNP markers (0/1/2 minor allele copies) and their interactions on fitness outcomes were estimated with Splus 8.2 (Tibco Software, Inc., 2010) using restricted maximum likelihood. An additive genetic model was assumed for all genetic markers, with regression coefficients interpreted as the effect on METs of each additional copy of the corresponding minor allele.
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After excluding SNPs in linkage disequilibrium (LD, r2 > 0.3), EIGENSTRAT was used to compute principal components (PCs) for use as covariates to control for population admixture in the regression analyses (26).
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Our models additionally adjusted for study site, sex, age, weight, use of beta-blockers, and the first two PCs to control for population admixture, and controlled for the effect on fitness of both baseline values and of change in time-varying covariates allowing these effects to differ by study arm.
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For candidate gene analyses, involving 1,317 SNPs within 63 candidate genes previously reported to be associated with exercise treadmill test traits that were present on IBC (Supplemental Table S1), we determined the number of uncorrelated markers to be 687, after accounting for LD using the Li & Ji approach (24). Therefore, after adjustment for multiple hypothesis testing, a P-value threshold for statistical significance was set at 7.4×10−5 when testing for one-year change in either the ILI or DSE arms. However, since these analyses attempt to replicate the associations with genetic markers previously implicated in METs and/or related treadmill-test traits, we also point out at least nominal (P-value < 0.05) associations.
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For chip-wide analyses, we also calculated the effective number of uncorrelated markers among the 32,561 IBC SNPs under investigation and found it to equal 17,669 after LD correction (24). After controlling for multiple comparisons, this resulted in a chip-wide significance threshold of P = 2.9×10−6. We used a false discovery rate (FDR) approach to guide our reporting of suggestive (FDR<20%) associations, operationalized via a rank ordering of the genetic markers according to their q-values. FDR controls the expected proportion of false negative results among those deemed significant. Q-values are marker-specific quantities that recalibrate the rank ordering of P-values by the probability that they represent a false discovery that were calculated using the Q-value package of Dabney et al. (10).
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Given higher power needed to detect interaction effects, we did not explicitly test for ILI-DSE differences in genetic effects on METs change across the entire marker set. However, we do report these interactions for the subset of markers showing associations with METs change in either study arm, reducing the number of multiple comparisons. However, it may also cause us to miss interactions caused by genetic effects on both ILI and DSE change that are modest in size and of opposite sign.
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In addition to the full-sample analyses, we conducted a sensitivity analysis excluding individuals receiving beta-blocker medications, since their METs phenotype was calculated using different methodology than for the remaining study participants.
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To identify biological associations between the top genes involved in cardiorespiratory fitness, Gene Relationships Among Implicated Loci (GRAIL) was used (33). GRAIL scores association signals by evaluating whether observed genomic regions are non-randomly linked to the other genes through word-similarity metrics (33) in PubMed abstracts. We used the list of SNPs that showed at least nominal associations in the candidate genes and possible associations from the chip-wide analyses (q-value < 0.30) with one-year response to ILI to assess the degree of connectivity between the genes.
Results
Look AHEAD Genetic Study
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At baseline, 3,899 Look AHEAD subjects who participated in this genetic study were evenly distributed between the ILI and DSE intervention arms with regard to age, sex, ethnicity, weight, and baseline fitness (Table 1). At one year, METs levels of study participants that did not use beta-blockers at baseline increased on average by 1.02 units in individuals in ILI vs. 0.23 units in DSE (Table 1). Comparable intervention effects were observed among individuals receiving beta-blockers at baseline, with METs levels increasing by 0.91 units in ILI vs. 0.21 units in DSE. Beta-blocker use itself was stable across time, with only 5.3% of subjects switching regimens from baseline to follow-up.
Table 1.
Characteristics of the Look AHEAD Participants Available for Genetic Study at Baseline and After One Year of Intervention.
| Characteristic | N (%) and Mean (SD) |
||
|---|---|---|---|
| Pooled | ILI | DSE | |
| N | 3899 | 1935 (50) | 1964 (50) |
| Women (%) | 2192 (56) | 1096 (57) | 1096 (56) |
| Ethnicity | |||
| African American (%) | 618 (16) | 313 (16) | 305 (16) |
| American Indian/Alaskan Native (%) | 20 (0.5) | 11 (0.6) | 9 (0.5) |
| Asian/Pacific Islander (%) | 41 (1) | 22 (1) | 19 (1) |
| Hispanic/Latino (%) | 307 (8) | 148 (8) | 159 (8) |
| Non-Hispanic White (%) | 2835 (73) | 1405 (73) | 1430 (73) |
| Other (multiple) (%) | 78 (2) | 36 (2) | 42 (2) |
| Beta-blocker use at baseline (%) | 893 (23) | 470 (24) | 423 (22) |
| Beta-blocker use at 1 year (%) | 877 (25) | 466 (26) | 411 (23) |
| Age (years) | 59.1±6.8 | 59.0±6.9 | 59.2±6.8 |
| Weight (kg) at baseline | |||
| Women | 96.7±17.5 | 96.8±17.7 | 96.6±17.4 |
| Men | 109.6±18.5 | 109.8±19.2 | 109.4±17.8 |
| Weight (kg) at 1 year | |||
| Women | 92.1±17.8 | 88.7±17.3 | 95.6±17.5 |
| Men | 104.1±18.9 | 99.4±18.8 | 108.7±17.9 |
| Fitness (sub-maximal, METs) at baseline* | |||
| Women | 4.7±1.3 | 4.7±1.3 | 4.7±1.4 |
| Men | 5.7±1.6 | 5.8±1.6 | 5.6±1.6 |
| Subjects on beta-blockers | |||
| Women | 5.1±1.2 | 5.1±1.2 | 5.2±1.2 |
| Men | 5.9±1.5 | 5.9±1.5 | 5.9±1.6 |
| Subjects not on beta-blockers | |||
| Women | 4.6±1.4 | 4.6±1.4 | 4.6±1.4 |
| Men | 5.6±1.7 | 5.7±1.7 | 5.5±1.6 |
| Fitness (sub-maximal, METs) at 1 year* | |||
| Women | 5.3±1.6 | 5.6±1.7 | 4.9±1.4 |
| Men | 6.4±2.0 | 6.9±2.1 | 5.9±1.7 |
| Subjects on beta-blockers | |||
| Women | 5.6±1.6 | 5.8±1.6 | 5.3±1.4 |
| Men | 6.6±1.9 | 7.0±1.9 | 6.2±1.7 |
| Subjects not on beta-blockers | |||
| Women | 5.2±1.6 | 5.5±1.7 | 4.8±1.4 |
| Men | 6.3±2.0 | 6.9±2.1 | 5.8±1.7 |
Estimated MET level based upon the treadmill workload at 80% of HR Max in participants not using beta-blockers or at an RPE of 16 in those participants using beta-blockers.
Candidate gene analysis of treatment response
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We analyzed the association of one-year change in METs for 1,317 SNPs (Supplemental Table S1). After the adjustment for multiple hypothesis testing, one significant association was identified between RUNX1 rs9976623 (MAF=0.24) and one-year change in METs in the ILI group (P = 1.9 × 10−5; Table 2, Supplemental Figure S1A). Carriers of the RUNX1 rs9976623 minor allele in the ILI group gained 0.19±0.04 less METs per copy than non-carriers. Carriers in the DSE group showed no significant difference in one-year METs (P = 0.82). These ILI-DSE differences resulted in a nominally significant interaction (P = 4.8 × 10−3, Table 2) between rs9976623 minor allele status and treatment response. Two other SNPs in moderate LD with rs9976623 showed nominal significance as indicated on the regional plot (See Supplemental Figure S1A). Minor alleles of multiple common COL4A1 SNPs, in LD with each other, showed at least nominally significant associations with 0.15±0.04 more METs gain per copy than non-carriers in the ILI arm (P < 2.6 × 10−4; Table 2, Supplemental Figure S1B). No difference was detected in the DSE group (P < 0.18), resulting in at least nominally significant treatment-genotype interactions (P < 1.1 × 10−3, Table 2). In addition, at least nominally significant associations for within-arm change were detected for ACE and AGT in the DSE group and for PRKAG2 in the ILI group (P < 5 × 10−4, Table 2).
Table 2.
Top SNPs in Previously Reported Candidate Genes Associated with One-Year Change in Metabolic Equivalents Levels (METs) in Either the ILI or DSE Arms (N=3,889).*
| SNP | Gene | Chr | Position | Marker Allele** |
MAF | Beta ILI (SE)*** |
P-value ILI |
Beta DSE (SE)*** |
P-value DSE |
P-Value ILI-DSE |
|---|---|---|---|---|---|---|---|---|---|---|
| rs9976623 | RUNX1 | 21 | 35191378 | A/G | 0.24 | −0.19 (0.04) | 1.90E-05 | −0.01 (0.05) | 0.82 | 4.78E-03 |
| rs648705 | COL4A1 | 13 | 109654154 | C/A | 0.40 | 0.15 (0.04) | 1.76E-04 | −0.05 (0.04) | 0.23 | 5.11E-04 |
| rs645098 | COL4A1 | 13 | 109654272 | A/G | 0.33 | 0.15 (0.04) | 2.38E-04 | −0.04 (0.04) | 0.33 | 1.09E-03 |
| rs598893 | COL4A1 | 13 | 109657744 | A/G | 0.40 | 0.15 (0.04) | 2.58E-04 | −0.06 (0.04) | 0.18 | 4.73E-04 |
| rs1860743 | PRKAG2 | 7 | 151050874 | G/A | 0.10 | −0.24 (0.07) | 3.53E-04 | 0.03 (0.07) | 0.62 | 3.78E-03 |
| rs1800764 | ACE | 17 | 58904261 | G/A | 0.48 | −0.01 (0.04) | 0.73 | −0.15 (0.04) | 2.45E-04 | 1.95E-02 |
| rs2148582 | AGT | 1 | 228916422 | G/A | 0.47 | −0.01 (0.04) | 0.88 | 0.14 (0.04) | 4.16E-04 | 9.16E-03 |
ILI, Intensive Lifestyle Intervention; DSE, Diabetes Support & Education.
Ranking based on significance levels for within-arm change (P <5.0E-04).
Marker alleles are presented in Major/Minor allele order, as calculated from the full sample.
Effect per Minor allele (additive genetic model).
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Of the remaining 58 candidate genes on the IBC chip, at least nominally significant associations were observed between one-year change in response to ILI and DSE and multiple SNPs within 29 genes, including RYR2, CASR, and ACE (See Supplemental Table S1). Moreover, the majority of these genes also showed at least nominal genotype-intervention interactions, indicating effect modification in response to ILI and DSE based on the genotype status (data not shown).
Chip-Wide Association analysis of response to lifestyle intervention
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No variants were associated with one-year METs change in response to ILI or DSE at a chip-wide level of significance. However, in addition to RUNX1, 13 SNPs representing 11 independent loci showed genetic associations with ILI treatment response at q-value < 0.20 after the adjustment for covariates (Table 3). The majority also showed nominal genotype-treatment interaction (P < 0.05). These findings included 2 SNPs each at TBC1D1 and MTMR15, and one SNP each at GNAI2, PROP1, FKBP7, SMURF1, NRG3, PLA2G4B, C20orf75, and THBD. An intergenic SNP on chromosome 11p15.5 was not mapped to any known gene (Table 3).
Table 3.
Top Chip-Wide Associations for One-Year Change in Metabolic Equivalents Levels (METs) in Either the ILI or DSE Arm.*
| SNP | Gene | Chr | Position | Marker Allele** |
MAF | Beta ILI (SE)*** |
P-value ILI |
Beta DSE (SE)*** |
P-value DSE |
P-Value ILI-DSE |
|---|---|---|---|---|---|---|---|---|---|---|
| rs17497074 | TBC1D1 | 4 | 37614636 | A/G | 0.10 | 0.30 (0.06) | 4.02E-06 | 0.00 (0.06) | 0.97 | 1.25E-03 |
| rs2735469 | Intergenic | 11 | 1979380 | G/A | 0.13 | −0.26 (0.06) | 7.24E-06 | −0.08 (0.06) | 0.19 | 2.39E-02 |
| rs17225700 | FKBP7 | 2 | 179044846 | A/G | 0.06 | −0.35 (0.08) | 1.54E-05 | 0.03 (0.08) | 0.66 | 6.05E-04 |
| rs2282751 | GNAI2 | 3 | 50266789 | G/A | 0.25 | 0.22 (0.05) | 2.57E-05 | 0.00 (0.05) | 0.97 | 3.01E-03 |
| rs2395018 | SMURF1 | 7 | 98551822 | G/A | 0.22 | 0.23 (0.06) | 4.04E-05 | −0.03 (0.06) | 0.57 | 1.23E-03 |
| rs17579011 | TBC1D1 | 4 | 37613244 | G/A | 0.28 | 0.18 (0.04) | 4.42E-05 | 0.06 (0.04) | 0.18 | 4.85E-02 |
| rs565 | MTMR15 | 15 | 29018482 | G/A | 0.18 | −0.21 (0.05) | 4.52E-05 | −0.06 (0.05) | 0.21 | 4.68E-02 |
| rs6493352 | MTMR15 | 15 | 29021356 | G/A | 0.18 | −0.20 (0.05) | 4.84E-05 | −0.07 (0.05) | 0.15 | 0.07 |
| rs1197687 | PLA2G4B | 15 | 39905943 | G/A | 0.15 | −0.22 (0.06) | 7.11E-05 | −0.02 (0.05) | 0.77 | 8.64E-03 |
| rs2233788 | PROP1 | 5 | 177352193 | A/G | 0.08 | 0.28 (0.07) | 7.12E-05 | −0.01 (0.07) | 0.88 | 3.49E-03 |
| rs1040585 | THBD | 20 | 22988066 | C/A | 0.13 | 0.24 (0.06) | 7.17E-05 | 0.12 (0.06) | 0.04 | 0.16 |
| rs3862551 | NRG3 | 10 | 84088823 | C/A | 0.10 | −0.28 (0.07) | 8.79E-05 | −0.08 (0.07) | 0.26 | 4.17E-02 |
| rs6038334 | C20orf75 | 20 | 5965259 | G/C | 0.32 | −0.16 (0.04) | 8.80E-05 | −0.05 (0.04) | 0.28 | 4.85E-02 |
ILI, Intensive Lifestyle Intervention; DSE, Diabetes Support & Education.
Ranking based on chip-wide false discovery rates for within-arm change (q < 0.20).
Marker alleles are presented in Major/Minor allele order, as calculated from the full sample.
Effect per Minor allele (additive genetic model).
Sensitivity Analysis
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Twenty three percent of the Look AHEAD genetic sample (N=893) received beta-blockers at the time of the treadmill test. As METs were estimated differently among participants receiving beta-blockers (see Methods), we performed a sensitivity analysis excluding such subjects. In candidate gene analysis, multiple COL4A1 SNPs were at least nominally associated with fitness response to intervention with rs11069830 passing the threshold for at least nominal association (Table 4). In chip-wide analyses, the elimination of subjects taking beta-blockers changed the order of some of the top hits in the ILI group, with FKBP7 rs17225700 now showing a stronger effect that passed the chip-wide significance threshold (one-year gain of 0.47±0.09 less METs per allele copy; P = 5.3 × 10−7; Table 4). Specifically, Figure 1 demonstrates a significant effect of ILI on fitness change in FKBP7 rs17225700-A allele carriers that was significantly diminished in GG homozygotes for both men and women. Substantial genotype-treatment interaction was also detected (P = 5.3 × 10−5). The new signals with q-value < 0.2 that were not detected in the pooled analysis included DDN, RGS2, ALCAM, VAX2, and LOC652968 (Table 4).
Table 4.
Top SNPs in Previously Reported Candidate Genes and Chip-Wide Associated with One-Year Change in Metabolic Equivalents Levels (METs) in Either the ILI or DSE Arms in Individuals not Receiving Beta-Blockers (N=3,006). *
| SNP | Gene | Chr | Position | Marker Allele** |
MAF | Beta ILI (SE)*** |
P-value ILI |
Beta DSE (SE)*** |
P-value DSE |
P-Value ILI-DSE |
|---|---|---|---|---|---|---|---|---|---|---|
| Candidate gene analysis | ||||||||||
| rs11069830 | COL4A1 | 13 | 109619133 | C/A | 0.33 | −0.17 (0.05) | 3.84E-04 | 0.08 (0.05) | 0.09 | 2.01E-04 |
| Chip-wide analysis | ||||||||||
| rs17225700 | FKBP7 | 2 | 179044846 | A/G | 0.06 | −0.47 (0.09) | 5.26E-07 | 0.05 (0.09) | 0.58 | 5.25E-05 |
| rs6038334 | C20orf75 | 20 | 5965259 | G/C | 0.32 | −0.22 (0.05) | 6.88E-06 | −0.06 (0.05) | 0.19 | 2.63E-02 |
| rs2735469 | Intergenic | 11 | 1979380 | G/A | 0.13 | −0.30 (0.07) | 1.65E-05 | −0.03 (0.07) | 0.67 | 4.79E-03 |
| rs2811239 | HCRTR2 | 6 | 55229913 | G/A | 0.17 | 0.25 (0.06) | 1.78E-05 | 0.04 (0.06) | 0.48 | 1.48E-02 |
| rs7311091 | DDN | 12 | 47669474 | G/A | 0.06 | −0.37 (0.09) | 2.09E-05 | 0.09 (0.09) | 0.32 | 2.49E-04 |
| rs2746073 | RGS2 | 1 | 191045850 | T/A | 0.24 | 0.22 (0.05) | 2.14E-05 | 0.08 (0.05) | 0.14 | 0.05 |
| rs13418962 | STRN | 2 | 36990142 | G/A | 0.48 | −0.19 (0.05) | 3.52E-05 | −0.02 (0.03) | 0.64 | 9.68E-02 |
| rs3122169 | HCRTR2 | 6 | 55221370 | A/C | 0.18 | 0.23 (0.06) | 4.16E-05 | 0.03 (0.06) | 0.62 | 1.41E-02 |
| rs12214549 | CDKAL1 | 6 | 20670059 | A/G | 0.06 | 0.37 (0.09) | 4.81E-05 | 0.16 (0.09) | 0.07 | 0.11 |
| rs572169 | GHSR | 3 | 173648421 | G/A | 0.27 | −0.21 (0.05) | 5.02E-05 | −0.03 (0.05) | 0.50 | 1.45E-02 |
| rs978436 | ALCAM | 3 | 106712239 | G/A | 0.14 | 0.25 (0.06) | 5.23E-05 | 0.03 (0.06) | 0.59 | 1.45E-02 |
| rs17497197 | STRN | 2 | 37614636 | A/G | 0.21 | 0.22 (0.06) | 5.63E-05 | 0.01 (0.06) | 0.88 | 7.01E-03 |
| rs532625 | COL4A1 | 13 | 109662226 | A/T | 0.45 | −0.18 (0.05) | 6.93E-05 | 0.05 (0.05) | 0.32 | 4.41E-04 |
| rs2233788 | PROP1 | 5 | 177352193 | A/G | 0.08 | 0.33 (0.08) | 7.09E-05 | −0.01 (0.08) | 0.89 | 3.32E-03 |
| rs877549 | LOC652968 | 22 | 29002420 | G/A | 0.11 | 0.27 (0.07) | 9.38E-05 | −0.04 (0.07) | 0.55 | 1.65E-03 |
| rs1981719 | VAX2 | 2 | 71012756 | A/G | 0.38 | 0.18 (0.05) | 1.03E-04 | −0.04 (0.05) | 0.35 | 6.47E-04 |
| rs6576551 | Intergenic | 15 | 24027268 | G/A | 0.25 | 0.24 (0.06) | 1.11E-04 | 0.01 (0.06) | 0.87 | 7.21E-03 |
| rs17497074 | TBC1D1 | 4 | 37614636 | A/G | 0.10 | 0.29 (0.08) | 1.12E-04 | 0.01 (0.07) | 0.86 | 8.33E-03 |
| rs2395018 | SMURF1 | 7 | 98551822 | G/A | 0.22 | 0.25 (0.06) | 1.15E-04 | 0.01 (0.07) | 0.89 | 9.78E-03 |
ILI, Intensive Lifestyle Intervention; DSE, Diabetes Support & Education.
Ranking based on significance levels for within-arm change (P < 5E-04). Bolded are genes that were only detected in the analysis of individuals not receiving beta-blocker treatment.
Marker alleles are presented in Major/Minor allele order, as calculated from the full sample.
Effect per Minor allele (additive genetic model).
Figure 1. One-Year Change in METs in Subjects not Receiving Beta-Blockers by FKBP7 rs17225700 and Sex.
***, corresponds to P < 0.001, **, corresponds to P < 0.01. P-value for interaction = 5.25E-05.
Pathway-Based Analysis
Paragraph Number 23
To identify pathway connections between genes associated with fitness we performed a GRAIL analysis. Strong connectivity between the genes by their enrichment in overlapping pathways was identified between 24 out of 33 previously reported fitness genes that were found to be at least nominally associated with change in METs in Look AHEAD and were in the GRAIL database, including those that influence vascular tone, muscle protein synthesis and contraction, as well as cardiac energy substrate dynamics (Supplemental Figure S2). Genes with the largest number of connections included AGT, CAV1, NOS3, ADRA1A, ADRA1B, COL4A1, COL4A2, and ACE (GRAIL P < 2 × 10−6, data not shown). Of the 35 genes associated with METs response to ILI in the chip-wide analysis at q-value < 0.30, 8 genes, GHSR, HCRTR2, PROP1, VWF, THBD, TGFB3, CORIN, and TIPM1, demonstrated significant connections, with GRAIL P < 0.05 (Figure 2).
Figure 2. Pathway-Based Analysis of the Top Genes Detected in Chip-Wide Screening for One-Year Change in METs in the ILI group.
Thirty five independent genes associated with METs at q-value < 0.30 are arranged along the inner circle using VIZ-GRAIL (32); bold indicates gene with GRAIL connections. The redness and thickness of lines connecting pairs of genes represent the strength of the connections with the thickness of the lines being inversely proportional to the probability that a literature-based connection would be seen by chance. Pathway-related links between 8 of 35 genes scored GRAIL P < 0.05.
Discussion
Paragraph Number 24
This is the largest study to date in a cohort of well-characterized overweight and obese individuals with T2D determining whether genetic variants located at or near genes previously associated with fitness traits or within ~2,100 genes associated with cardiovascular, inflammatory and metabolic traits help explain variation in fitness response to a one-year lifestyle intervention.
Paragraph Number 25
The candidate gene analysis identified a significant signal in RUNX1, previously identified as a fitness gene from the aerobic training-responsive transcriptome (20) that in the present study was associated with one-year METs response in the ILI group and genotype-intervention interaction. RUNX1 is the runt-related transcription factor gene, involved in erythropoesis. Thus, there is a plausible contribution to fitness via its effects on the red blood cell pool and the delivery of oxygen to tissues, including muscle, due to improved oxygenation during times of physical stress. Carriers of the RUNX1 rs9976623 minor allele increased their fitness by 0.19 METs less per copy in response to ILI; there was no association observed in the DSE group. We also detected an at least nominally significant association with COL4A1 and PRKAG2 with METS change in ILI. COL4A1 is the major type IV alpha collagen chain of basement membranes that has been observed to be differentially expressed in human muscle of high responders when compared with low responders following six weeks of aerobic exercise training (20). Variants in PRKAG2, an energy sensor that modulates glucose uptake and glycolysis leading to enhanced glycolytic capacity and protection against hypoxic injury in tissues such as the heart (35), were also identified by the GWAS of treadmill exercise responses in the Framingham Heart Study to be associated with heart rate during the recovery period after exercise (39).
Paragraph Number 26
Variants in angiotensinogen (AGT) and angiotensin converting enzyme (ACE) were suggestively associated with change in fitness, but only in the DSE group. The ACE gene insertion/deletion (I/D), known to affect serum enzyme levels (34), has been associated with exercise response (17) and muscle endurance by some studies, but not others (14). The closest associated coding SNP was located only 140 base pairs away from the I/D variant, whereas rs1800764 has been reportedly associated with diabetic kidney disease (13).
Paragraph Number 27
We also observed nominally significant associations of multiple RYR2 gene variants with fitness response. RYR2 SNPs were found to be associated with heart rate during treadmill test in the Framingham Heart Study (40) and implicated in VO2max training response to a standardized 20-week exercise program in the HERITAGE study GWAS (7). The SNPs identified in our study were found to not be in linkage disequilibrium with the RYR2 SNPs previously reported. RYR2 is a cardiac-type ryanodine receptor that plays a key role in triggering cardiac muscle contraction during cardiac muscle contraction (4). Defects in RYR2 are the cause of familial arrhythmogenic right ventricular cardiomyopathy 2 and of exercise-induced polymorphic ventricular tachyarrhythmias (30).
Paragraph Number 28
Next, in analyses of all SNPs across the IBC chip, no chip-wide significant associations were identified in treatment response that passed the correction for multiple hypothesis testing. The strongest association with regard to ILI response was detected for TBC1D1, with carriers of the minor allele being more likely to gain METs. TBC1D1 is an insulin-sensitive regulator of GLUT4 function in skeletal muscle, suggesting that variation in TBC1D1 may alter glucose uptake, which could have effects on physical fitness. Importantly, a non-synonymous polymorphism in the TBC1D1 gene (R125W, rs35859249), located ~34kb upstream of our top TBC1D1 hit, has been associated with severe familial obesity (27). Interestingly, TBC1D1 variation was also found to be associated with one-year weight loss in the ILI group (26).
Paragraph Number 29
Secondary analysis of data following exclusion of subjects receiving beta blockers revealed a variant in FKBP7 that showed a significant association with ILI response and treatment interaction. Carriers of the minor allele randomized to ILI showed a 0.47±0.09 less MET increase per copy, whereas no difference between the genotypes was detected in the DSE group. Kavanagh et al.(18) reported that each 0.3 MET increase in peak VO2 above a threshold of 3.7 METs associated with a marked benefit in prognosis of cardiovascular and all-cause mortality and conferred a 10% reduction in cardiac mortality in women with known coronary artery disease, indicating that the effect size observed in our study may have clinical implications. FKBP7 is a member of the FKBP-type peptidyl-prolyl cis/trans isomerase family that interacts with FK-506, which is the drug target of rapamycin known to influence muscle protein synthesis in response to exercise (12). The role for the molecular chaperone FKBP7 in cellular signaling is not defined; however, our studies raise the possibility that FKBP7 may modulate cellular signaling processes related to fitness.
Paragraph Number 30
We sought to identify connections between all significant genes and those with the association q-value < 0.20 that influence fitness by applying GRAIL, a program that uses abstracts from the entirety of the published scientific literature to look for relatedness among genes within associated regions that may represent key pathways (33). COL4A1, whose variants we found to be associated with behavioral treatment interaction and fitness response in the ILI group, was linked with integrin beta 1 (ITGB1), along with caveolins (CAV1 and CAV2). These genes were expressed differentially in human muscle from high and low responders to six weeks of aerobic exercise training (20). GRAIL also identified connectivity with nitric oxide synthase 3 (NOS3), or endothelial NOS, which regulates vascular smooth muscle relaxation, as well as ADRA1A and ADRA1B, which are expressed in the heart and play a major role in smooth muscle contraction (Supplemental Figure S2). ADRA1B has been involved in the control of vascular tone linked to cardiomyopathy and heart failure (5). Pathway-based analysis also suggested the involvement of endogenous hormones in cardiorespiratory response to ILI through association signals in GHSR, a growth hormone secretagogue receptor, and PROP1, responsible for pituitary development and hormone expression (Figure 2). Interestingly, these genes were also biologically related to the hypocretin receptor type 2 (HCRTR2) gene, which encodes a G-protein coupled receptor involved in the regulation of feeding behavior.
Paragraph Number 31
Strengths of this study include its randomized assignment of a lifestyle intervention of documented health relevance and the objective measurement of fitness by treadmill testing in the largest sample size to date. Further, the randomized intervention reduces the effect of confounding factors that can have a major in association studies based on cross-sectional and observational data. Finally, the use of GRAIL to identify subsets of genes involved in similar biological processes related to cardiorespiratory fitness increases the confidence in the biological plausibility of our findings, beyond that provided by statistical estimates of false discovery rates.
Paragraph Number 32
Although the IBC chip is a strength of our study, permitting analysis of over 32,000 SNPs in relation to fitness response, we note that this genotyping array is focused on ~2,100 candidate genes previously associated with cardiovascular, inflammatory and metabolic phenotypes. Indeed, of 158 genes selected from the prior literature (Supplemental Table S1), we were able to represent at least one SNP in 63 of these genes. For example, no SNPs were available in PAPSS2, the region identified in a prior GWAS of physical activity participation (11). In several cases, the SNP available was not in close proximity or co-inherited with the previously identified marker. Therefore, although many new regions were queried in the present analysis, this array provided a limited window through which prior candidate gene and GWAS studies associated with fitness could be replicated, leaving the possibility that more direct replication attempts may prove fruitful.
Our intervention is also a strength, given its basis on the intervention deployed in the Diabetes Prevention Program that was successful in reducing diabetes incidence over four years among individuals with impaired glucose tolerance (22). The Look AHEAD intervention also successfully increased cardiorespiratory fitness across over 2,500 overweight individuals with type 2 diabetes. The Look AHEAD intervention, providing physical activity goals and behavioral strategies and counseling to support physical activity uptake and maintenance, nonetheless differs from many prior genetic studies of exercise involving directly supervised exercise. In particular, individuals exposed to the intervention may not have taken up exercise in the same frequency, intensity and duration as would likely occur under supervised exercise training. We believe these approaches are complimentary, as genetic factors that influence uptake of exercise could be equally important as genetic factors that influence physiologic response to a standardize exercise training. Fitness changes resulting from flexible supervised moderate physical activity may be applicable to a larger segment of the population in the community and may elicit different physiologic changes when compared to those observed with standardized supervised exercise training.
Paragraph Number 33
We acknowledge additional limitations in our study, including the use of a submaximal test at year 1 and lack of replication cohort with comparable intervention and outcome measurements. While restricting our analysis to one-year follow-up may mitigate detectable genetic effects associated with the ability to improve cardiorespiratory fitness, we note that the largest change in weight and fitness in the Look AHEAD cohort occurred during the first year of intensive lifestyle intervention (40). Similar to other genetic association studies based upon randomized clinical trials, our results may not be generalizable to the general population and our power to detect modest effects is limited. Due to the strong overlap between body size and fitness, weight change may influence change in physical fitness. To control for the effect of weight on fitness we incorporated weight at baseline and one-year post intervention in our models. Finally, data on physical activity was not included in this study. Self-reported physical activity was available only in a subset of Look AHEAD participants, which would have considerably reduced our active sample size and power. Furthermore, our group has found that self-reported physical activity appears to overestimate exercise behaviors, when compared to objectively-measured physical activity (6).
Paragraph Number 34
In summary, using a gene-centric genotype chip with ~2,100 genes implicated in cardiovascular, inflammatory, and metabolic traits, we identified genetic associations of RUNX1 and FKBP7, involved in erythropoesis and muscle protein synthesis, respectively, with change in cardiorespiratory fitness in response to lifestyle intervention. Despite the substantial sample size and state-of-the-art statistical approaches, we were able to detect a small number of significant associations. Our findings speak to the complex nature of the fitness phenotype, with multiple genes likely to be involved, each of modest effect size. We may have failed to identify important fitness genes or gene variants that were not included on the IBC chip. Future genome-wide association studies or genetic analyses that include rare variants (MAF<5%) may identify fitness gene variants not described here.
Paragraph Number 35
Looking forward, replication of our findings in independent cohorts would allow for a targeted validation of the novel variants in the context of less stringent P-values, such as those used in our study, due to a focused hypothesis testing approach. Similar to the analysis of other common traits, meta-analysis of fitness data from GWAS’s with larger sample sizes can identify novel variants with smaller effects. However, it is important to acknowledge that fitness tests are very labor intensive and may not be readily performed on large epidemiologic cohorts. Nonetheless, standardized measures of fitness and physical activity could be adopted by large studies to allow for more powerful joint analyses. For example, PhenX is a tool designed to build consensus for standard measures of phenotypes and exposures used in genetic studies (15) that could help standardize fitness measures suitable for large genetic studies. In addition, next generation sequencing of DNA from participants with “phenotypic extremes” (i.e., highly physically trained individuals or persons resistant to training) may identify genetic variants responsible for extreme fitness responses. Mendelian randomization studies aimed at determining whether the contribution of the variants in the candidate fitness genes is causal for the development of cardiovascular and metabolic outcomes are warranted. Lastly, it will ultimately be important to integrate multiple genetic variations in the DNA code (e.g., SNPs, copy number variants, methylation patterns) and gene expression to further explore the role of the genome in fitness.
Supplementary Material
Supplemental Table S1.csv
Regional Plots of the SNP P-Values in the Two Top Candidate Genes Associated with One-Year Change in METs. Lead SNP in the ILI group is shown: A. for RUNX1, and B. for COL4A1. The X-axis shows the chromosome and physical distance (kb), the left Y-axis shows the negative base ten logarithm of the p-value and the right Y-axis shows recombination activity (cM/Mb) as a blue line. Linkage disequilibrium of surrounding SNPs with the top SNP is indicated by a scale of intensity of red color filling as shown in the legend at the upper right hand corner of each plot. Positions, recombination rates and gene annotations are according the NCBI’s build 36 (hg 18).
Pathway-Based Analysis of Genes Previously Implicated in Fitness and at Least Nominally Associated with METs in Look AHEAD Subjects. Thirty four independent genes with at least one SNP nominally (P < 0.05) associated with METs are arranged along the inner circle using VIZ-GRAIL (32); bold indicates gene with GRAIL connections. The redness and thickness of lines connecting pairs of genes represent the strength of the connections with the thickness of the lines being inversely proportional to the probability that a literature-based connection would be seen by chance. Pathway-related links between 27 of 34 genes scored GRAIL P < 0.05.
Acknowledgments
Federal Sponsors
National Institute of Diabetes and Digestive and Kidney Diseases: Barbara Harrison, MS; Van S. Hubbard, MD PhD; Susan Z.Yanovski, MD
National Heart, Lung, and Blood Institute: Lawton S. Cooper, MD, MPH; Jeffrey Cutler, MD, MPH; Eva Obarzanek, PhD, MPH, RD
Centers for Disease Control and Prevention: Edward W. Gregg, PhD; David F. Williamson, PhD; Ping Zhang, PhD
Funding and Support
This study is supported by the Department of Health and Human Services through the following cooperative agreements from the National Institutes of Health: DK57136, DK57149, DK56990, DK57177, DK57171, DK57151, DK57182, DK57131, DK57002, DK57078, DK57154, DK57178, DK57219, DK57008, DK57135, and DK56992. The following federal agencies have contributed support: National Institute of Diabetes and Digestive and Kidney Diseases; National Heart, Lung, and Blood Institute; National Institute of Nursing Research; National Center on Minority Health and Health Disparities; NIH Office of Research on Women’s Health; and the Centers for Disease Control and Prevention. This research was supported in part by the Intramural Research Program of the National Institute of Diabetes and Digestive and Kidney Diseases. The Indian Health Service (I.H.S.) provided personnel, medical oversight, and use of facilities. The opinions expressed in this paper are those of the authors and do not necessarily reflect the views of the I.H.S. or other funding sources.
Additional support was received from The Johns Hopkins Medical Institutions Bayview General Clinical Research Center (M01RR02719); the Massachusetts General Hospital Mallinckrodt General Clinical Research Center (M01RR01066); the University of Colorado Health Sciences Center General Clinical Research Center (M01RR00051) and Clinical Nutrition Research Unit (P30 DK48520); the University of Tennessee at Memphis General Clinical Research Center (M01RR0021140); the University of Pittsburgh General Clinical Research Center (M01RR000056 44) and NIH grant (DK 046204); the VA Puget Sound Health Care System Medical Research Service, Department of Veterans Affairs; and the Frederic C. Bartter General Clinical Research Center (M01RR01346), and DK090043 to J.M.M.
The following organizations have committed to make major contributions to Look AHEAD: Federal Express; Health Management Resources; Johnson & Johnson, LifeScan Inc.; Optifast-Novartis Nutrition; Roche Pharmaceuticals; Ross Product Division of Abbott Laboratories; Slim-Fast Foods Company; and Unilever.
The results of the present study do not constitute endorsement by ACSM.
Conflict of Interest
Dr. Unick received an honorarium for a scientific presentation at the International Diabetes Interchange Symposium
Footnotes
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Supplementary Materials
Supplemental Table S1.csv
Regional Plots of the SNP P-Values in the Two Top Candidate Genes Associated with One-Year Change in METs. Lead SNP in the ILI group is shown: A. for RUNX1, and B. for COL4A1. The X-axis shows the chromosome and physical distance (kb), the left Y-axis shows the negative base ten logarithm of the p-value and the right Y-axis shows recombination activity (cM/Mb) as a blue line. Linkage disequilibrium of surrounding SNPs with the top SNP is indicated by a scale of intensity of red color filling as shown in the legend at the upper right hand corner of each plot. Positions, recombination rates and gene annotations are according the NCBI’s build 36 (hg 18).
Pathway-Based Analysis of Genes Previously Implicated in Fitness and at Least Nominally Associated with METs in Look AHEAD Subjects. Thirty four independent genes with at least one SNP nominally (P < 0.05) associated with METs are arranged along the inner circle using VIZ-GRAIL (32); bold indicates gene with GRAIL connections. The redness and thickness of lines connecting pairs of genes represent the strength of the connections with the thickness of the lines being inversely proportional to the probability that a literature-based connection would be seen by chance. Pathway-related links between 27 of 34 genes scored GRAIL P < 0.05.