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Genes to predict VO2max trainability: a systematic review Williams, Camilla J; Williams, Mark G; Eynon, Nir; Ashton, Kevin J; Little, Jonathan P; Wisloff, Ulrik; Coombes, Jeff S Nov 14, 2017

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REVIEW Open AccessGenes to predict VO2max trainability: asystematic reviewCamilla J. Williams1, Mark G. Williams2, Nir Eynon3*, Kevin J. Ashton4, Jonathan P. Little5, Ulrik Wisloff1,6and Jeff S. Coombes1From 34th FIMS World Sports Medicine CongressLjubljana, Slovenia. 29th September – 2nd October 2016AbstractBackground: Cardiorespiratory fitness (VO2max) is an excellent predictor of chronic disease morbidity and mortalityrisk. Guidelines recommend individuals undertake exercise training to improve VO2max for chronic disease reduction.However, there are large inter-individual differences between exercise training responses. This systematic review isaimed at identifying genetic variants that are associated with VO2max trainability.Methods: Peer-reviewed research papers published up until October 2016 from four databases were examined.Articles were included if they examined genetic variants, incorporated a supervised aerobic exercise intervention;and measured VO2max/VO2peak pre and post-intervention.Results: Thirty-five articles describing 15 cohorts met the criteria for inclusion. The majority of studies used a cross-sectional retrospective design. Thirty-two studies researched candidate genes, two used Genome-Wide AssociationStudies (GWAS), and one examined mRNA gene expression data, in addition to a GWAS. Across these studies, 97genes to predict VO2max trainability were identified. Studies found phenotype to be dependent on several of thesegenotypes/variants, with higher responders to exercise training having more positive response alleles than lowerresponders (greater gene predictor score). Only 13 genetic variants were reproduced by more than two authors.Several other limitations were noted throughout these studies, including the robustness of significance foridentified variants, small sample sizes, limited cohorts focused primarily on Caucasian populations, and minimalbaseline data. These factors, along with differences in exercise training programs, diet and other environmentalgene expression mediators, likely influence the ideal traits for VO2max trainability.Conclusion: Ninety-seven genes have been identified as possible predictors of VO2max trainability. To verify thestrength of these findings and to identify if there are more genetic variants and/or mediators, further tightly-controlled studies that measure a range of biomarkers across ethnicities are required.Keywords: Cardiorespiratory fitness, VO2max, Predictor genes, TrainingBackgroundThe worldwide prevalence of chronic diseases, such ascardiovascular disease, cancers, stroke and diabetes isrising [1]. Low cardiorespiratory fitness is strongly asso-ciated with chronic diseases and premature mortality[2–7]. To alleviate the health and economic burden as-sociated with low cardiorespiratory fitness, healthguidelines across the world recommend individualsundertake regular exercise [1].Exercise training can increase cardiorespiratory fitnessand decrease chronic disease via a number of mecha-nisms [7]. Adaptations include improvements to cardiacsize, stroke volume (increase in volume of bloodpumped from the left ventricle), cardiac output (volumeof blood pumped from the heart per minute), pulmonaryblood flow and respiratory function, supply of oxygen-rich blood to working muscles (increased number of* Correspondence: Nir.Eynon@vu.edu.au3Institute of Sport, Exercise and Active Living (ISEAL), Victoria University,Melbourne 8001, AustraliaFull list of author information is available at the end of the article© The Author(s). 2017 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, andreproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link tothe Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.The Author(s) BMC Genomics 2017, 18(Suppl 8):831DOI 10.1186/s12864-017-4192-6capillaries and blood volume), muscle mitochondrialfunction and content, oxidative enzyme capacity, vascu-lar wall health and function, and biomechanical effi-ciency [2, 7]. It has been suggested that improvementsin cardiorespiratory fitness in response to exercise train-ing varies greatly between individuals, with some peopleresponding well or very well (‘responders’ or ‘high-re-sponders’) to exercise training, whereas others only havemild increases in their cardiorespiratory fitness followingsimilar exercise training (‘low-responders’) [4, 5, 8–11].Importantly, these responses need to be compared towithin-subject random variation to ascertain true inter-individual differences [12]. The ability to change cardio-respiratory fitness is a multifactorial trait influenced byenvironmental factors (such as exercise training) andgenetic factors [4, 5, 11]. Considering cardiorespiratoryfitness is one of the best integrative predictors of mor-bidity and mortality risk, it may be important to under-stand how genetics predict the variability in response toexercise training. This knowledge could lead to targetedpersonalised exercise therapy to decrease the burden ofchronic disease.The gold standard measure for cardiorespiratory fit-ness is maximal oxygen uptake (VO2max), which is quan-tified as the maximal amount of oxygen the body canuse in 1 min, during dynamic work with large musclemass [13]. Research into human variation of VO2max wasfirst undertaken over forty years ago, with several au-thors identifying a strong genetic influence on VO2maxin twins [14, 15]. Subsequent studies have identified sig-nificant familial aggregation for VO2max trainability. Forexample, authors have found greater variance betweenpairs of monozygotic (MZ; identical) twins than withinpairs of twins for VO2max training response after stan-dardized aerobic training interventions [16, 17]. Thestrongest evidence to date on this topic was found in theHEalth, Risk factors, exercise training And GEnetics(HERITAGE) family study [18]. Four hundred seventy-three Caucasian adults from 99 nuclear families com-pleted 20 weeks of Moderate Intensity ContinuousTraining (MICT). The average increase in VO2max was400 mL O2/min, with a range from − 114 to + 1097 mL/min. This difference was two and half times greater be-tween families than within families, with a 47% heritabil-ity estimate for VO2max training response [18]. A majorlimitation from these findings, however, is there was nocomparator control group.Since this familial longitudinal research, the HumanGenome Project completed sequencing of the humangenome resulting in significant advancements in geneticanalysis capabilities. This led to a better understandingof genetic variations of large populations. Analyzing gen-etic variants on a population level using techniques suchas candidate gene analysis, GWAS, whole genome andexome sequencing and RNA expression analysis (RNA-seq, or microarrays) has resulted in the possibility ofdeveloping ‘personalized genomics’. This aims for bio-logical profiling to provide more effective health man-agement and treatment [5]. However, research in thefield of exercise genomics it still in its infancy and muchwork is needed before genomic tools could be utilized topersonalize exercise training programs [19].The aim of this study was to systematically review theliterature and identify genetic variants that have beenassociated with VO2max trainability following an aerobicexercise training intervention. Given the infancy of thisresearch field, results should only be used to provide thebasis for future research. This research should aim toconfirm previous findings and investigate mediators thatcan influence gene expression. Importantly, future gen-etic studies in this area should attempt to investigate thephysiological functions that contribute to improvingVO2max training response and overall health outcomes.Findings from ongoing research may assist clinical pro-fessionals to provide personalized evidenced-basedmedicine centered on phenotype, contributing to thefight against chronic disease.MethodsA comprehensive search of four databases (PubMed,Embase, Cinahl, Cochrane) was completed from theirinception until October 2016. Studies focusing on genesand their VO2max/VO2peak response to supervised aer-obic training were sought with the following searchterms: genetic profiling, polymorphism, single nucleo-tide polymorphisms, SNPs, genetic variants, predictorgenes, trainability, endurance training, cardiovascularfitness, cardiorespiratory fitness, VO2max, VO2peak, aerobicpower, aerobic fitness, aerobic capacity. A full list of searchterms can be found at the end of this review.Two authors (CW and JC) agreed on the criteria forinclusion. Articles were incorporated if they were: ori-ginal, peer-reviewed research; included an aerobic inter-vention, with minimum 75% supervision; included geneticvariant testing; included a maximal VO2max/peak usingdirect gas analysis from an incremental test (pre and postintervention); conducted on humans; and written inEnglish.Using an extraction grid, one author (CW) conductedthe initial screening analysis. After removing duplicatesand scanning the titles and abstract of articles, thosemeeting the inclusion criteria were reviewed. Data re-corded from the review consisted of the author’s nameand place of study, study design, study sample, tissuesource, genotyping method used, gene and variant exam-ined, genotype, gene expression (if examined), interven-tion used, possible mediators (such as medications andhealth concerns), and the influence of the genetic variantThe Author(s) BMC Genomics 2017, 18(Suppl 8):831 Page 82 of 131investigated on VO2max change. Further articles wereretrieved from snowballing included articles fromtheir reference lists. Articles included in the revieware in Table 1.A summary of key findings from the included articlesis provided in Tables 2 and 3. Limitations were assessedby two authors (CW and JC) based on the intervention,genotyping method used, study design and sample used.Table 4 was developed to highlight which predictorgenes for VO2max trainability merited further explo-ration. A third author (MW) examined Tables 1, 2, 3and 4 to ensure all genetic variants, genomic coordi-nates and genotypes, were described with a consistentannotation.ResultsOf the 1635 articles identified, 35 met the inclusion cri-teria (see Fig. 1). A summary of these articles is providedin Tables 1, 2 and 3. From the 35 articles, 97 genetic var-iants were identified as being significantly associatedwith VO2max trainability (Table 4).Study characteristicsAcross the studies DNA samples from 4212 individualswere used. Tissue sources were predominantly bloodleucocytes, lymphoblastoid cell lines and buccal cells.Genotype was primarily identified through PCR-RFLP(polymerase chain reaction restriction fragment lengthpolymorphism based analysis) for candidate genes andIllumina Human CV370-Quad Bead Chips for GWASanalysis (which can capture over 370,000 SNPs perparticipant).Overall, 68% of participants in the reviewed studieswere men, and ages ranged from 17 to 75 years. Theaverage BMI of participants was 25.3 kg/m2 (SD 2.36).Where detailed, DNA samples were taken from a varietyof ethnicities, including Caucasian (74.5%), Asian(13.5%), African-American (7.5%), Hispanic (4.3%) andNative American (0.2%).The 35 included articles described 15 cohorts, withthree cohorts providing subject data for 19 articles (seeTable 1 for details). Nine articles [20–28] used datafrom the HERITAGE study and five [29–33] reviewedCaucasian participant data from the Cardiac Rehabilita-tion and Genetics of Exercise Performance and Train-ing Effect (CARAGENE) study. Five studies examinedclinical data from 102 young male and apparently healthypolice recruits in China [34–38]. The remaining samplescame from independent clinical studies focusing on appar-ently healthy but sedentary adults from a variety of ethnic-ities including Caucasians, Asians, African-Americans,Native American and Hispanics [13, 39–53].Most reviewed studies (n = 32) used a single-grouplongitudinal design. However, one study compared threegroups using a longitudinal design [28]. One study usedretrospective data from two Randomized Controlled Tri-als (RCT) [20]; and one was a double-blind study [39].Twenty-eight studies examined a MICT intervention.Two studies examined protocols using High IntensityInterval Training (HIIT) [28, 40]. The 5 remaining stud-ies trained participants by running at VentilatoryThreshold (VT) [34–38]. Training intensity was mea-sured using a percentage of VO2max, Heart Rate Reserve(HRR), VT, Maximal Power (Pmax) or Maximum HeartRate (HRmax). Intensities varied between 50 and 85%VO2max, 95% -105% VT, 50–85% Pmax, 80–85% HRRand 50–80% HRmax. Training volume varied between 20to 90 min per session (2-4×/week). The period of inter-ventions ranged from 4 weeks to 9 months. Trainingmodalities consisted primarily of cycle ergometers andtreadmills.Only six studies incorporated a standardized dietprior to and during the intervention period [23, 41–45].Three articles included strength training [20, 39, 47]and two studies included military training [39, 47] asthe intervention.Genotyping findings1. Candidate gene studiesThe candidate gene association approach requires aprior hypothesis that the genetic polymorphisms ofinterest are causal variants or in strong linkage disequi-librium (LD) with a causal variant, and would be associ-ated with a particular exercise-related phenotype at asignificantly different rate than predicted by chancealone (may be higher or lower). This approach is effect-ive in detecting genetic variants that are either directlycausative, or belong to a shared haplotype that is causa-tive [54]. Thirty-two candidate gene studies were basedon the gene’s molecular function and possible associ-ation with VO2max trainability (Table 2).Genes associated with muscular subsystemsVO2peak can be influenced by muscle efficiency and ithas been hypothesized that genes encoding muscularsubsystems may contribute to the genetic variability inVO2peak training response [33]. Twelve genes and 21genetic variants related to muscular phenotypes were in-vestigated in 935 (76 female) cardiac patients from theCARAGENE study [33]. Three out of the 21 genetic var-iants were significantly associated (p < 0.05) with an in-crease in VO2peak following 3 months of MICT (2–3 ×90-min sessions per week at 80% HRmax; p < 0.05). Thesevariants included GR:c.68 > A (G/A genotype, number ofpeople with genotype; n = 55) in the glucocorticoid re-ceptor gene (GR; rs6190), CNTF:c.115-6G > A (AAThe Author(s) BMC Genomics 2017, 18(Suppl 8):831 Page 83 of 131Table1SummaryofincludedarticlesAuthor,Year,CountryGene/stestedforVO2maxtrainabilityStudyDesignStudySampleTissuesourceMethodforGenotypingInterventionXu,2015,ChinaALAS2Singlegroup,longitudinal.VO2maxandvenousbloodsamplestakenpre&postintervention.N=244healthyChinesemales;18-22years(20±1.76);wt65.06±9.59kg;ht.174.37±6.16cm.N=72randomlyselectedforHiHiLotraining(69.8±7.8kgand177.93±5.26cm).PeripheralbloodleucocytesPCRprotocol+separationonpolyacrylamidegel4weeks;supervisedHiLotraininginhypoxia-trainingcentre.Hi=bicycleergometerfor30minsat75%VO2max,in15.4%O2concentratedenvironment,3×/weekfor4weeks.Lo=sametrainingbutatlowerelevation.Yu,2014,ChinaAPOESinglegroup,longitudinal.VO2max,anthropometricandserumlevelstestedpre&postintervention.N=360;180Chinesemalesandfemales;age32.8±11.9yrs.;BMI25.4±5.6kg/m2M;BMI26±6.2kg/m2F;nohealthconcerns;inactive.PeripheralbloodleucocytesPCR-(polymerasechainreaction)-RFLP(restrictionfragmentlengthpolymorphism)assay6mths;progressive;supervisedaerobictraining;60–85%VO2max.Zarebska,2014,PolandGSTP1Singlegroup,longitudinal.VO2max,HR max,VE max,ATandbodycompositiontestedpre&postintervention;balanceddietpriortointervention(2000kcal)N=66Polishfemales;19–24yrs.;BMI21.8±2.1kg/m2 ;nohealthconcerns;inactive;nosupplementsormedications;non-smokers.BuccalcellsTaqManallelicdiscriminationassayusingqPCR3mths;supervised;progressiveMICT;3×/wk.;50–75%HR max;30–60min.Ghosh,2013,SingaporeGWASRetrospective,single-grouplongitudinal.V02maxtestedpre&postintervention.HERITAGEWHITES:n=473Caucasians;230male&243females;nomajorhealthconcerns;inactive.LymphoblastoidcelllinesIlluminaHumanCNV370-QuadBeadChipsHERITAGE:20wks;supervised;progressiveMICT;3×/wk.;55–75%VO2max;30–50min.Bouchard,2011,USAGWASRetrospectiveHERITAGE:Singlegroup,longitudinal;VO2maxtestedpre&postintervention.DREW:RCT;VO2maxtestedpre&postintervention.STRRIDE1&2:RCT;VO2maxtestedpre&postintervention.HERITAGEWHITES:n=473Caucasians(252women);17–65yrs.;inactive;nomajorhealthconcernsHERITAGEBLACKS:n=259(177women);17–65years;inactive;nomajorhealthconcernsHERITAGEaverageage=35.7±14.5yrs.,BMI25.8±4.9kg/m2 .DREWstudy:n=464overweightorobesepostmenopausalwomen;inactive;nomajorhealthconcerns.STRRIDE1study:M&F;40–65yrs.;inactive;overweight,dyslipidemicandpostmenopausal(F).STRRIDE2study:18–70yrs.;inactive;overweight,dyslipidemic.N=183forSTRRIDE1&2studies.LymphoblastoidcelllinesIlluminaHumanCNV370-QuadBeadChipsHERITAGE20wks;supervised;progressive,MICT;3×/wk.;55–75%VO2max;30–50min.DREW:6mths;supervised;exercisegroups:4,8or12kcal/kg/week(MICT);3-4×/week;progressivetrainingintensitystartedat50%VO2max.Eachgroupexpended4kcal/kg/weekforfirstweek.Group1:maintained4kcal/kg/weekfor6months.Group2:increasedby1kcal/kg/weekuntil8ckal/weekreached–maintainforremainingtime.Group3:increasedby1kcal/kg/weekuntil8ckal/weekreached–maintainforremainingtime.STRRIDE1:8–9mths;supervisedexercisesessions.Threegroups:1.High-amount/vigorousintensityexercise(170min/week/2000kcal/week)orthecalorieequivalentofjoggingfor~20milesperweekat55–85%VO2max.2.Lowamount/vigorous-intensityexercise/1200kcal/week(~120min/week)ortheequivalentof12miles/weekforjoggingat65–80%.The Author(s) BMC Genomics 2017, 18(Suppl 8):831 Page 84 of 131Table1Summaryofincludedarticles(Continued)Author,Year,CountryGene/stestedforVO2maxtrainabilityStudyDesignStudySampleTissuesourceMethodforGenotypingIntervention3.Lowamount,moderateintensityexercise(1200kcal/week(170min/week)ortheequivalentof12miles/weekat40–55%VO2max.STRRIDE2:8–9mths;supervised;fourgroups:1:Aerobictraining–1300cal–65-80%;2:Resistancetrainingonlywith3setsof12–15reps3x/week.3:Combinationofthefirst2protocols;4:Highanaerobictraining–2200cal–3xweek–65-80%.First2–3months‘rampupperiod’.Following6mthsusingappropriateprotocol.McKenzie,2011,USAAKTSinglegroup,longitudinal.VO2maxtestedpre&postintervention;dietarystabilisation.N=51Mand58FCaucasians;50–75yrs.;nomajorhealthconcerns;non-smoking;BMI<37;haematocrit>35;BPbetween120/80butlessthan160/100mmHg;atleastonelipidabnormality;notanymedicationforbloodpressure,cholesterolorglucose;Fpost-menopausalforatleast2years(stableHRTornonHRT);inactive.PeripheralbloodleucocytesTaqManallelicdiscriminationassayusingqPCR24wks;supervised;progressiveMICT;3×/wk.;50–70%HRR;20–40min.Thomaes,2011,BelgiumAMPD1;GR;CNTFRetrospective,singlegroup,longitudinal.VO2peaktestedpre&postintervention.N=935coronaryarterydiseasepatients(CAD);76females;Caucasian;age56±0.3yrs.;BMI25.8±0.1kg/m2 ;5%smokers;85%cardiacmedications;5%diabetes;27%hypertension.PeripheralbloodleucocytesInvaderTMassay(thirdwavetechnologies)3mths;supervised;2-3×/wk.;80%HR max;90mins/session.Onkelinx,2011,BelgiumNOS3;Catalase;VEGF;Eco-SOD;GPX;P22Phox;PPARGC1;PPARαRetrospective,singlegroup,longitudinal.VO2peaktestedpre&postintervention.N=935coronaryarterydiseasepatients(CAD);76females;Caucasian;age56±0.3yrs.;BMI25.8±0.1kg/m2 ;5%smokers;85%cardiacmedications;5%diabetes;27%hypertension.PeripheralbloodleucocytesInvaderTMAssay(thirdwavetechnologies)CARAGENE:3mths;supervised;3×/week;90mins;~intensity=80%(HR/peakHRx100)Silva,2011,BrazilNOS3Singlegroup,longitudinal.VO2peaktestedpre&postintervention.N=80Portuguesepolicerecruits;20–35years;BMI23.3±3.6kg/m2 ;nohealthconcerns;inactive.PeripheralbloodleucocytesPCR-RFLP18weeks;supervised;3×/week/80mins;intensitygradedtoVTHR.The Author(s) BMC Genomics 2017, 18(Suppl 8):831 Page 85 of 131Table1Summaryofincludedarticles(Continued)Author,Year,CountryGene/stestedforVO2maxtrainabilityStudyDesignStudySampleTissuesourceMethodforGenotypingInterventionTimmons,2010,UKGWAS1:Singlegroup,longitudinal.VO2max&musclebiopsiestestedpre&postintervention;2:Blindtest.VO2max&musclebiopsiestestedpre&postintervention;3:Retrospective:HERITAGEWHITESdata1:N=24sedentaryhealthyCaucasianmen(23±1yrs.,1.82±0.02m,78.6±2.7kg);2:17active&healthyCaucasianmen(29±6yrs.,81.8±9kg,1.8±0.5m);3:HERITAGECaucasians(asdescribedinBouchard2011).LymphoblastoidcelllinesfromvenousbloodIlluminaHumanCNV370-QuadBeadChips1:6weeks;supervisedMICT;4×45mincyclingsessions/week@70%VO2max.2:12weeks;cycleergometer5×/week.PeakpowertestperformedeveryMontodetermineintensityforweek:Tues:3minintervalsat85%.P maxseparatedby3minintervalsat40%P max;Thurs:8minintervalsat85%P maxseparatedby3minintervalsat40%P max;Fri:120minat55%P maxcontinuously;durationincreasedby5%/wk.;last6wksdurationmaintainedbutintensityincreasedby1%/week;3:HERITAGEWHITESStudy(asdescribedinBouchard2011).Jenkins,2010,USAPLINhaplotypesRetrospective,singlegroup,longitudinal.VO2maxtested;bodycomposition;pre&postintervention;dietarystabilisation(AmericanHeartAssociation).N=46M&55FCaucasians(50–75years);inactive;nomajorhealthconcerns;BP<160/99;non-smokers;BMI<37kg/m2 ;nomedsforBP,cholesterolorglucosecontrol;atleastonelipidabnormality.UnknownTaqManallelicdiscriminationassayusingqPCR24weeks;supervised;multi-modalMICT;progressive;3×/wk.;20–40min;upto70%VO2maxreached;60minwalkhomeincludedpost12wks.Alves,2009,BrazilACE&AngiotensinSinglegroup,longitudinal.VO2maxandechocardiographyofleftventriclepreandpostintervention.N=83Brazilianpolicemen;age26years±4.5;BMI24kg/m2±1;healthy;normotensive.UnknownPolymerasechainreactionprotocol.17weeks;supervisedMICT;50–80%VO2peak;60min×3/week.He,2008a,ChinaNRF-1Singlegroup,longitudinal;VO2max,VTandREtestedpre&postintervention.N=102Chinesemalesoldiers;nohealthconcerns;age18.8±0.9yrs.;wt60.3±6.5kg;ht.1.71±5.8m;nomedications;non-smokers.PeripheralbloodleucocytesPCR-RFLPassay18wks;supervised;3×5000mrunningsessions/wk.;95%–105%VT.He,2008b,ChinaPPARGC1Singlegroup,longitudinal;VO2max,VTandREtestedpre&postintervention.N=102Chinesemalesoldiers;nohealthconcerns;age18.8±0.9yrs.;wt60.3±6.5kg;ht.1.71±5.8m;nomedications;non-smokers.PeripheralbloodleucocytesPCR-RFLPassay18wks;supervised;3×5000mrunningsessions/wk.;95%–105%VT.He,2007a,ChinaTFAMSinglegroup,longitudinal.VO2max,VTandREtestedpre&postintervention.N=102Chinesemalesoldiers;nohealthconcerns;age18.8±0.9yrs.;wt60.3±6.5kg;ht.1.71±5.8m;nomedications;non-smokers.PeripheralbloodleucocytesPCR-RFLPassay18wks;supervised;3×5000mrunningsessions/wk.;95%–105%VT.He,2007b,ChinaNRF-2/NFE2L2Singlegroup,longitudinal.VO2max,VTandREtestedpre&postintervention.N=102Chinesemalesoldiers;nohealthconcerns;age18.8±0.9yrs.;wt60.3±6.5kg;ht.1.71±5.8m;nomedications;non-smokers.PeripheralbloodleucocytesPCR-RFLPassay18wks;supervised;3×5000mrunningsessions/wk.;95%–105%VT.The Author(s) BMC Genomics 2017, 18(Suppl 8):831 Page 86 of 131Table1Summaryofincludedarticles(Continued)Author,Year,CountryGene/stestedforVO2maxtrainabilityStudyDesignStudySampleTissuesourceMethodforGenotypingInterventionHautala,2007,USAPPARDRetrospective,singlegroup,longitudinal.VO2max,bodycompositionandlipidstestedpre&postintervention.N=477fromHERITAGECaucasianstudy(183female)N=264fromHERITAGEAfrican-Americanstudy(247female)UnknownSNPscorergenotypingsoftware20wks;supervised;progressive,MICT;3×/wk.;55–75%VO2max;30–50min.Defoor,2006a,BelgiumADRB1Retrospective,singlegroup,longitudinal.VO2peaktestedpre&postintervention.N=935coronaryarterydiseasepatients(CAD);76females;Caucasian;age56±0.3yrs.;BMI25.8±0.1kg/m2 ;5%smokers;85%cardiacmedications;5%diabetes;27%hypertension.PeripheralbloodleucocytesInvaderassayCARAGENE:3mths;supervised;2-3×/wk.;80%HR max;90mins/session.Defoor,2006b,BelgiumACERetrospective,singlegroup,longitudinal.VO2peaktestedpre&postintervention.N=935coronaryarterydiseasepatients(CAD);76females;Caucasian;age56±0.3yrs.;BMI25.8±0.1kg/m2 ;5%smokers;85%cardiacmedications;5%diabetes;27%hypertension.PeripheralbloodleucocytesInvaderassayCARAGENE:3mths;supervised;2-3×/wk.;80%HR max;90mins/session.He,2006,ChinaHBBRetrospective,singlegroup,longitudinal.VO2max,VTandREtestedpre&postintervention.N=102Chinesemalesoldiers;nohealthconcerns;age18.8±0.9yrs.;wt60.3±6.5kg;ht.1.71±5.8m;nomedications;non-smokersPeripheralbloodleucocytesPCR-RFLPassay18wks;supervised;3x5000mrunningsessions/wk.;95%–105%VTDefoor,2005CKMMRetrospective,singlegroup,longitudinal.VO2peaktestedpre&postintervention.N=935coronaryarterydiseasepatients(CAD);76females;Caucasian;age56yrs.±0.3;BMI25.8kg/m2±0.1;5%smokers;85%cardiacmedications;5%diabetes;27%hypertension.PeripheralbloodleucocytesInvaderassayCARAGENE:3mths;supervised;2-3×/wk.;80%HR max;90mins/session.Leon,2004,USAAPOERetrospective,singlegroup,longitudinal.VO2max,bloodlipidstestedpre&postintervention;counsellednottoalterhealthhabits.N=241maleand89femaleHERTIAGECaucasians;17–65years;inactive;nomajorhealthconcernsLymphoblastoidcelllinesfromvenousbloodPCR-RFLPassayHERTIAGE:20wks;supervised;progressiveMICT;3×/wk.;55–75%VO2max;30–50min.Thompson,2004,USAAPOESinglegroup,longitudinal.VO2max,anthropometricdataandlipidlevelscollectedpre&postintervention;dietarycontrol.N=170Caucasians(120completedprogram–60MandF);18–70years(39±11years);consumedlessthan2drinks/day;physicallyinactive;BMI<31;nomajorhealthconcerns.PeripheralbloodleucocytesPCR-RFLPassay6monthssupervisedprogressivetraining;60–80%ofVO2max;increasingfrom15to40minsduringfirst4wks.Onceat40mins,maintainedthisfor4sessionseachweekfor5–6months.Multimodalbuttreadmillprimaryaerobicactivity.Rico-Sanz,2003,CanadaAMPD1Retrospective,singlegroup,longitudinal.VO2max,submaxandsubmaxtomaximaltestedpre&postintervention.N=329HERTAGECaucasiansand90HERITGAEAfrican-Americansmeasuredfortrainingresponse;17–65years;inactive;nomajorhealthconcerns.UnknownPCRprotocol+separationonagarosegelsHERITAGE:20wks;supervised;progressiveMICT;3×/wk.;55–75%VO2max;30–50minThe Author(s) BMC Genomics 2017, 18(Suppl 8):831 Page 87 of 131Table1Summaryofincludedarticles(Continued)Author,Year,CountryGene/stestedforVO2maxtrainabilityStudyDesignStudySampleTissuesourceMethodforGenotypingInterventionPrior,2003,USAHIF1ASinglegroup,longitudinal.VO2maxtestedpre&postintervention.N=101Caucasianand22African-Americansingoodhealth;age57.7±0.91yrs.;BMI29.2±0.64kg/m2PeripheralbloodlymphocytesPCR-RFLPassay24weeks;supervised;progressiveMICT;3×/wk.;20–40min;50–70%VO2maxWoods,2002,UKACESinglegroup,longitudinal.VO2max,andHR/VO2relationshiptestedpre&postintervention.N=59CaucasianswithACEIIalleleand29withoutACEDDallele;~age18.9yrs.;~ht.1.78m;~wt73.4kg;militarycamp.PeripheralbloodleucocytesPCRprotocol+polyacrylamidegelseparation11weeks;supervisedaerobictraining;75%squads;35%adventuroustraining;25%runningandcircuittraining.Murakami,2001,JapanMtDNASinglegroup,longitudinal.VO2maxtestedpre&postinterventionN=41JapaneseM(age20.6±2.2yrs),inactive;nomajorhealthconcerns;wt62.8±7.5kg;ht.171.8±6.7cm.PeripheralbloodleucocytesPCR-RFLPassay8weeks;supervised1×/weekoutof3.5;60min/session;70%VO2maxSonna,2001,USAACEDouble-blindstudy.VO2peak,anthropometricsphysicalfitnessassessmentforactivedutypersonneltestedpreandpostintervention.N=85Fand62M;age21.7±3.6yrs.;84Caucasian,20Hispanic,1NativeAmericans,5Asianand37African-American;nomajorhealthconcerns;BMI23.1±3.1kg/m2 ;BF%27.9±6.1Fand16.4±5.7M.PeripheralbloodleucocytesPCR-RFLPassay8weekssupervised;6days/week;2xaerobic(sprints&3–5miles)&2xstrength.Participantsplacein1of4abilitygroupssoallrunningforsameduration.Participantsalsocompletedroadmarchesandotherdrills.Rankinen,2000a,USANa+−K+ATPaseαRetrospective,singlegroup,longitudinal.VO2maxandmaxpoweroutputtestedpre&postintervention.HERITAGEWHITES:472Caucasians;17–65years;inactive;nomajorhealthconcerns.LympohblastoidcelllinesPCRprotocol+agarosegelseparationHERTIAGE:20wks;supervised;progressiveMICT;3×/wk.;55–75%VO2max;30–50minRankinen,2000b,USAACERetrospective,singlegroup,longitudinal.V02max,VE,VT,bloodlactate,oxygen,strokevolume,carbondioxide,HR,testedpre&postintervention(submaxVO2testforolderpatients).HERITAGEWHITESANDBLACKS:476Caucasian&248Blacks;17–65years;inactive;nomajorhealthconcerns.LympohblastoidcelllinesPCRprotocol+agarosegelseparationHERTIAGE:20wks;supervised;progressiveMICT;3×/wk.;55–75%VO2max;30–50minHagberg,USA,1999APOERetrospective,singlegroup,longitudinal.VO2maxandlipidlevelstestedpreandpost;stabilisedonAmericanHeartAssociationdiet8weekspriortointervention.N=51;40–80-year-oldsedentarymen(61±3yrs);overweightwith~BF%30±3;BP<160/95mmHg;nomajorhealthconcernsormedicationsforbloodlipidsorglucose.PeripheralbloodleucocytesPCR-RFLPassay9months’endurancetraining;multimodal;5–7monthssupervisedandlast2–4monthsusedheartratemonitortoensure70–80%VO2maxintensityand3days/weekfor45minwascompliedwith.Rivera,1999,CanadaCKMMRetrospective,singlegroup,longitudinal.VO2maxtestedpre&postintervention.HERITAGEWHITES:495Caucasiansfrom98families;17–65years;inactive;nomajorhealthconcerns.LympohblastoidcelllinesPCR-RFLPassayHERTIAGE:20wks;supervised;progressiveMICT;3×/wk.;55–75%VO2max;30–50minThe Author(s) BMC Genomics 2017, 18(Suppl 8):831 Page 88 of 131Table1Summaryofincludedarticles(Continued)Author,Year,CountryGene/stestedforVO2maxtrainabilityStudyDesignStudySampleTissuesourceMethodforGenotypingInterventionRivera,1997,CanadaCKMMRetrospective,singlegroup,longitudinal.VO2maxtestedpre&postintervention.HERITAGEWHITES:160Caucasianparentsand80offspring;17–65years;inactive;nomajorhealthconcerns.LympohblastoidcelllinesPCR-RFLPassayHERTIAGE:20wks;supervised;progressiveMICT;3×/wk.;55–75%VO2max;30–50minDionne,1991,CanadamtDNASinglegroup,longitudinal.VO2maxtestedpre&postintervention.N=46MfromQuebec(17–27yrs)&27MfromTempe(24–29yrs);inactivePeripheralbloodleucocytesPCR-RFLPassayQuebec:20weeks;supervised;progressivetraining;Max85%HRR;max45min/session;3×/wk.Tempe:12weeks;supervised;progressivetraining;max70–77%VO2max;max40min/session;3×/wkBouchard,1989,CanadaAK1MCKMRCT.VO2max,totalpoweroutputtestedpre&postintervention.N=295M7F(18–30years);healthyCaucasiansMusclebiopsyandperipheralbloodleucocytesFormazantechnique?Group1:15weeks;supervised;progressiveMICT;30–45min/session;3-5×/wk.;60–85%HRRGroup2:15weeks;supervised;progressiveintervaltraining;1-2×/week;80–85%HRRseparatedby5minrecovery.Mmale,Ffemale,wksweeks,mthsmonths,wtweight,ht.height,yrs.years,BMIbodymasindex,BF%bodyfatpercentage,VO2maxmaximaloxygenuptake/cardiorespiratoryfitness,PCRpolymerasechainreactionprotocol,RFLPrestrictionfragmentlengthpolymorphism,qPCRQuantatitivePolymeraseChainReaction,RCTrandomisedcontrolledtrial,GWASgenomewideassociationstudy,HRThormonereplacementtherapy,SNPsinglenucleotidepolymorphism,ATanaerobicthreshold,MICTmoderateintensityintervaltraining,HRheartrate,HRRheartratereserve,HR maxheartratemaximum,Pmaxmaximalaerobicpower,Submaxsubmaximal,Cal/kcalcalories,mtDNAmitochondrialDNA,BPbloodpressureThe Author(s) BMC Genomics 2017, 18(Suppl 8):831 Page 89 of 131Table2SummaryoffindingsfromcandidategenestudiesGeneVariantChromosomeAuthor&DateRaceAgeSexHealthconcerns(+/−/0)*Genotype&VO2maxtrainingresponseP-value(x)HighesttrainingintensitySessions/weekDurationpersession(min)TrainingperiodTrainingmodalityPPARGC1Intron7G/C22Onkelinx,2011935Caucasian~56M&FY(CAD)GG,CG,CC(0)0.5180%HRmax2–3903monthsAmbulatoryHe,2008b102Chinese~19MNAllvariants(0)>0.0595–105%VT3Timetofinish.18weeks5000mrunningAPOEE2:rs7412(c.526C>T;p.Arg176Cys)E3:WTE4:rs429358(c.388T>C;p.Cys130Arg)E3/E3:WT/WTE2/E3:p.Arg176Cys/WTE4/E3:p.Cys130Arg/WTE2/E2:p.Arg176Cys/p.Arg176CysE2/E4:p.Arg176Cys/p.Cys130ArgE4/E4:p.Cys130Arg/p.Cys130Arg19Yu,2014360Chinese18–40M F M F M&FNE2/E3inM(+)n=20E2/E3F(+)n=25E3/E4M(+)n=31E3/E4F(+)n=29E2/E2;E2/E4;E3/E3;E4/E4inM&F(0)0.040.030.020.02>0.0560–85%VO2max‘Progressive’butdetailsNA‘Progressive’butdetailsNA6monthsTreadmillLeon,2004265Caucasian17–65M&FNAllvariants(0)>0.0575%VO2max330–5020weeksCycleergoThompson,2004170Unknown~39M&FNE3/E3(−)n=43E2/E3(0)n=40E3/E4(0)n=41<0.0160–85%VO2max4Upto50min6monthsTreadmillCKM1170&985+18519Defoor,2005935Caucasian~56M&FY(CAD)AA;GG;A/G(0)>0.0580%HRmax2–3903monthsAmbulatoryRivera,1999240Caucasian17–65M&FNCKMlocus(n=227)<0.0175%VO2max330–5020weeksCycleergoRivera,1997495Caucasian17–65M&FNHomozygotes1170bpaallele(−)n=12<0.0575%VO2max330–5020weeksCycleergoBouchard,1989295Caucasian18–30M&FNAllvariants(0)>0.051.60–85%HRR2:80–85%HRR1:1–22:3–51:Intervals2:30–451:152:151:Cycling2:CyclingACEInsertion(I)orDeletion(D)17Alves,200983Brazilian~26MNAllvariants(0)>0.0550–80%VO2peak2–360min17weeksRunningRankinen,2000b476Caucasian248AA17–65M&FNDDCaucasianoffspring(+)n=810.04275%VO2max330–5020weeksErgocycleDefoor,2006935Caucasian~56M&FY(CAD)II(+)(frequencyof0.3Mand0.36F)Entiregroup:0.047NoAceinhibitors:0.01380%HRmax2–3903monthsAmbulatoryWoods,200259Caucasian~19MNII;I/D;DD(0)>0.22NANANA11weeksSquads,adventuretraining,running,circuitsSonna,2001147Caucasian,37AA,26other19–24M&FNII,DD(0)>0.05NA4–690min8weeksMilitarytrainingThe Author(s) BMC Genomics 2017, 18(Suppl 8):831 Page 90 of 131Table2Summaryoffindingsfromcandidategenestudies(Continued)GeneVariantChromosomeAuthor&DateRaceAgeSexHealthconcerns(+/−/0)*Genotype&VO2maxtrainingresponseP-value(x)HighesttrainingintensitySessions/weekDurationpersession(min)TrainingperiodTrainingmodalityCYBA;P22PhoxA24G–640A>G16Onkelinx,2011935Caucasian~56M&FY(CAD)AA,AG,GG(0)CC,CT,TT(0)0.780.9480%HRmax2–3903monthsAmbulatoryPLINPLIN1(6209T>C)–rs228948715:g.90217096C>TPLIN4(11482G>A)–rs89416015:g.90211823C>TPLIN5(13041A>G–rs230479515:g.90210263A>GPLIN6(149954A>T–rs105270015:g.90208310A>T15Jenkins,2010101CaucasianNAM&FNGenotypesandhaplotypes(0)p>0.05Upto70%VO2max320–40min24weeksMulti-modalAKTrs1130214(4:g.105259734C>A)14McKenzie,2011109Caucasian50–75M FElevatedBP,cholesterol,menopauseAllgenotypessig.Increased,butGT/TTmen(+)n=220.03750–70%HRR320–40min24weeksMulti-modalHIF1AT+140C(rs11549465)A-2500TCh14Prior,2003101Caucasian22AA>60<60M&FNCT&TTinCaucasianover60(−)n=37Allotherages,raceandgenotypes(0)0.03>0.05>0.0550–70%VO2max320–40min24weeks‘Aerobictraining’Na+−K+−ATPaseα2Alpha2exon1Alpha2exon21–2213Rankinen,2000a472Caucasian17–65M&FN3.3/3.3(−)n=510.5/10.5offspring(+)n=140.0180.01755–75%VO2max330–5020weeksCycleergoHBB-551C/T–norsID11:g.5248801T>C+16,intron2-rs1076868311:g.5247791C>G+340–norsID11:g.5246488T>A11He,2006102Chinese~19MNCC,CT,TT(0)CC,CG,GG(0)AA,AT,TT(0)>0.0595–105%VT3Timetofinish.18weeks5000mrunningCNTFrs1800169(11:g.58391501G>A)11Thomaes,2011935Caucasian~56M&FNAA(+)n=210.00280%HRmax2–3903monthsAmbulatoryCAT-262C>T11Onkelinx,2011935Caucasian~56M&FY(CAD)TT(−)n=3420.0280%HRmax2–3903monthsAmbulatoryGSTP1rs1695(11:g67352689A>Gc.313A>Gp.Ile105Val)11Zabreska,201466Polish19–24FNGG&AG(+)n=30Absolute:0.029Relative:0.02550–75%HRmax3603months‘Aerobicroutine’ADRB1Pos.145Pos.116510Defoor,2006935Caucasian~56M&FY(CAD)Ser49Gly49,Ser49Ser49,80%HRmax2–3903monthsAmbulatoryGly49Gly49(0)GLy389Gly389,0.18Gly389Arg389,Arg389Arg389(0)0.75TFAMrs1937(10:g.60145342G>Cc.35G>Cp.Ser12Thr)rs2306604(10:g.60148692A>G)rs1049432(10:g.60155120G>T)10He,2007b102Chinese~19MNGG,CG,CC(0)AA,AG,GG(0)GG,GT,TT(0)>0.0595–105%VT3Timetofinish.18weeks5000mrunningThe Author(s) BMC Genomics 2017, 18(Suppl 8):831 Page 91 of 131Table2Summaryoffindingsfromcandidategenestudies(Continued)GeneVariantChromosomeAuthor&DateRaceAgeSexHealthconcerns(+/−/0)*Genotype&VO2maxtrainingresponseP-value(x)HighesttrainingintensitySessions/weekDurationpersession(min)TrainingperiodTrainingmodalityNOS3T-1495A–NorsID7:g.150689397A>TA-949G–rs18007797:g.150689943G>A-786T>C–rs413220527:g150690106C>TG298A–rs17999837:g.150696111T>Gc.894T>G(p.Asp298Glu))7Onkelinx,2011935Caucasian~56M&FY(CAD)TT,TA,AA(0)AA,AG,GG(0)TT,TC,CC(0)TT,CT,C(0)CC,CT,TT(0)GG,GA,AA(0)0.540.760.690.691.881.0480%HRmax2–3903monthsAmbulatory-786T>C–rs413220527:g150690106C>TIntron4–rs61722009VNTR(repeat)7:g.150694276_150694302AGGGGTG894G>T–rs17999837:g.150696111T>Gc.894T>G(p.Asp298Glu))7Silva,201180 Portuguese20–35MNTT,CC,TC(0)4b4b,4ba4c,4a4a(0)GG,GT,TT(0)*Allgenotypessig.Increased.fitness,thusnodifferencebetweengroups0.001GradedtoVTHR380min18weeksRunningNRF-1C&T-rs24029707:g.80647382G>TA&G-rs105001207:g.129393341A>Grs69491527:g129286436A>G7He,2008a102Chinese~19MNCC,CT,TT(0)AA,AG,GG(0)AA,AG,GG(0)0.380.1100.09495–105%VT3Timetofinish.18weeks5000mrunningAK1Mcommonandrarevariants7Bouchard,1989295Caucasian18–30M&FN(0)>0.051.85%HRR2:85%HRR1:1–22:3–51:Intervals2:30–451:152:151:Cycling2:CyclingPPARDExon4+15Exon7+65Ch6Hautala,2007CaucasianAA17–65M&FNCCgenotypeinAAofExon4+15(−)n=190.00575%VO2max330–5020weeksCycleergoVEGF4054606Onkelinx,2011935Caucasian~56M&FY(CAD)GG,GC,CC(0)CC,CT,TT(0)0.520.5280%HRmax2–3903monthsAmbulatoryGR/NR3C1rs6190(5:g.142780337C>Tc.68G>Ap.Arg23Lys)5Thomaes,2011935Caucasian~56M&FY(CAD)G/A(+)n=55<0.0180%HRmax2–3903monthsAmbulatoryPPARαGly482Ser4Onkelinx,2011935Caucasian~56M&FY(CAD)GG,G,SS(0)0.590.880%HRmax2–3903monthsAmbulatorySOD3C760G4Onkelinx,2011935Caucasian~56M&FY(CAD)CC(0)Gcarrier(0)0.120.1880%HRmax2–3903monthsAmbulatoryGPX197P>L3Onkelinx,2011935Caucasian~56M&FY(CAD)Pro197Pro(0)Leu-carrier(0)0.180.7880%HRmax2–3903monthsAmbulatoryNFE2L2Rs125949Rs8031031Rs7181862He,2007b102Chinese~19MNCC,CA,AA(0)CT,TT,AA(0)AG,GG(0)>0.0595–105%VT3Timetofinish.18weeks5000mrunningAMPD1AMPD1:c.133C(rs17602729)1Thomaes,2011935Caucasian~56M&FNCC(+)n=652<0.0580%HRmax2–3903monthsAmbulatoryRico-Sanz,2003329Caucasian90AA17–65M&FNTT(−)inCaucasians(n=6)<0.00675%VO2max330–5020weeksCyclingThe Author(s) BMC Genomics 2017, 18(Suppl 8):831 Page 92 of 131Table2Summaryoffindingsfromcandidategenestudies(Continued)GeneVariantChromosomeAuthor&DateRaceAgeSexHealthconcerns(+/−/0)*Genotype&VO2maxtrainingresponseP-value(x)HighesttrainingintensitySessions/weekDurationpersession(min)TrainingperiodTrainingmodalitymtDNAMTND5m.13470A>CorA>Gm.12406G>Am.13365C>TmtDNASNPviarestrictionenzymeMurakami,200121Japanese20.6MNAllvariants(0)>0.0570%VO2max3–460min8weeksErgoCyclemtDNAWithinmitochondriaDionne,199153Quebec,Tempe17–27MNmtDNAsubunit5N5(−)n=30.05Quebec:85%HRRTempe:77%VO2maxQuebec:3Tempe:3–5Quebec:45minTempe:40minQuebec:20wksTempe:12wksErgoCycleALAS2≤166bpMitochondriaXu,201572Chinese18–22MN≤166bp(+)n=25<0.05‘High/Lowtraining’330min4weeksErgoCyclewherepossible,genevariantswereannotatedusingthereferencessequence(GRCh37/hg19)CADcoronaryarterydisease,wksweeks,mthsmonths,VO2maxmaximaloxygenuptake/cardiorespiratoryfitness,ATanaerobicthreshold,HRRheartratereserve,HRmaxheartratemaximum,Pmaxmaximalaerobicpower,CaucCaucasian,AAAfrican-American,Mmale,Ffemale**(+)=hightrainingresponse,(−)=lowtrainingresponse,(0)=neutraltrainingresponse(x)=p-valuehasbeenadjustedforcovariatesexceptforarticlebyXuetal.(2015)whereitwasn’tclearifp-valuehadbeenadjusted(ALAS 2)The Author(s) BMC Genomics 2017, 18(Suppl 8):831 Page 93 of 131Table3Summaryofhypothesis-freestudiesGeneVariantChromosomeMapPositionMinorallelefrequency(MAF)frequencyRaceGenderAgeTrainingperiodSessions/wkSessiondurationSessionsintensity(+/−/0)**genotype/expressionandVO2maxresponsetotrainingP-valueAuthor,Date^*CAMTA1intronicrs88473616,937,6920.411.473Caucasian2.259African-AmericanM&FM&F17–6517–6520wks3×/wk30–50min55–75%VO2maxAA(−)1.1.49×10-42.0.033.1.54×10−4Bouchard,2011(1&2)Ghosh,2013(3)+ID3rs11574(1:g.23559007T>Cc.313A>Gp.Thr105Ala)123,758,085NA473CaucasianM&F17–6520wks3×/wk30–50min55–75%VO2maxNA2.1×10−3Timmons,2010*RGS185′upstreamofgene(non-coding)rs10921078(1:g.192059022G>A)1190,325,6450.151.483Caucasian2.259African-AmericanM&F17–6520wks3×/wk30–50min55–75%VO2maxGG(−)n=5671.7.17×10~52.0.032Bouchard,2011^RYR2intronicrs7531957(1:g.237789656T>G)1235,856,2790.08473Caucasian)M&F17–6520wks3×/wk30–50min55–75%VO2maxNA1:6.42×10–52:1.18×10−4Bouchard,2011(1)Ghosh,2013(2)#SCLC45A1NA1NANA473CaucasianM&F17–6520wks3×/wk30–50min55–75%VO2maxNA#89.1Ghosh,2013MAST2rs2236560146,268,021NA41 CaucasianMYoungadults1.6wks2.12wks1.4×/wk.2.3×/wk1.45min2.Progressive1.70%VO2max2.ProgressiveNANATimmons,2010SYPL2rs120493301109,832,711NA41 CaucasianMYoungadults1.6wks2.12wks1.4×/wk.2.3×/wk1.45min2.Progressive1.70%VO2max2.ProgressiveNANATimmons,2010#ACVR1CNA2NANA473CaucasianM&F17–6520wks3×/wk30–50min55–75%VO2maxNA#85.8Ghosh,2013SLC4A5rs828902274,323,642NA473CaucasianM&F17–6520wks3×/wk30–50min55–75%VO2maxNANATimmons,2010KCNF1/NLGN1rs2003298(2:g.11086150T>C)211,003,6010.42473CaucasianM&F17–6520wks3×/wk30–50min55–75%VO2maxNA1.21×10~4Bouchard,2011*FLJ44450rs4952535(2:g.42131523G>A)241,985,0270.41473CaucasianM&F17–6520wks3×/wk30–50min55–75%VO2maxG(+)1.01×10-4Bouchard,2011++TTNrs10497520(2:g.179644855T>Cc3601A>Gp.Lys1201Glu)2175,353,1000.50473CaucasianM&F17–6520wks3×/wk30–50min55–75%VO2maxNA2.5×10−3Timmons,2010++NRP2intronicrs3770991(2:g.206655739A>G)2206,363,984NA473CaucasianM&F17–6520wks3×/wk30–50min55–75%VO2maxNA1.4×10−3Timmons,2010CREB1rs27093562208,120,337NA473CaucasianM&F17–6520wks3×/wk30–50min55–75%VO2maxNANATimmons,2010SCN3Ars75749182165,647,425NA473CaucasianMYoungadults1.6wks2.12wks1.4×/wk.2.3×/wk1.45min2.Progressive1.70%VO2max2.ProgressiveNANATimmons,2010^HCG22rs2517512(6:g.31029685C>T)6NA0.18473CaucasianM&F17–6520wks3×/wk30–50min55–75%VO2maxNA3.09×10−5Ghosh,2013*KCNH8(268kb)rs4973706(3:g.18921772T>C)318,896,7760.24473CaucasianM&F17–6520wks3×/wk30–50min55–75%VO2maxA(+)5.31×10~5Bouchard,2011The Author(s) BMC Genomics 2017, 18(Suppl 8):831 Page 94 of 131Table3Summaryofhypothesis-freestudies(Continued)GeneVariantChromosomeMapPositionMinorallelefrequency(MAF)frequencyRaceGenderAgeTrainingperiodSessions/wkSessiondurationSessionsintensity(+/−/0)**genotype/expressionandVO2maxresponsetotrainingP-valueAuthor,Date*ZIC4(146kb)intronicrs117158293148,439,8560.081.473Caucasian2.183CaucasianM&FM&F17–6540–6520wks6mths3×/wk.3-4×/wk30–50min4-8kcal/kg/week55–75%VO2max+50%VO2maxAA(−)n=48.68×10-60.032Bouchard,2011*NLGN1(110kb)intronicrs2030398(3:g.173005973G>A)3174,488,6670.20473CaucasianM&F17–6520wks3×/wk30–50min55–75%VO2maxA(+)1.32×10~4Bouchard,2011^ADCYNA3NANA473CaucasianM&F17–6520wks3×/wk30–50min55–75%VO2maxNA#86.1Ghosh,2013AMOTL2rs133222693135,569,834NA41 CaucasianMYoungadults1.6wks2.12wks1.4×/wk.2.3×/wk1.45min2.Progressive1.70%VO2max2.ProgressiveNANATimmons,2010CSN1S2Bintronicrs2272040(4:g71007047A>G)471,041,6360.13473CaucasianM&F17–6520wks3×/wk30–50min55–75%VO2maxNA5.05×10-5Bouchard,2011*LOC100289626(134kb)rs2053896(4:g137154796G>A)4137,374,2460.10473CaucasianM&F17–6520wks3×/wk30–50min55–75%VO2maxA(+)6.62×10~5Bouchard,2011^*ACSL1rs6552828(4:g.185725416A>G)4185,962,4100.37473CaucasianM&F17–6520wks3×/wk30–50min55–75%VO2maxAA(−)1:1.31×10–62:3.8×10−6Bouchard,2011(1)Ghosh,2013(2)^SLED1rs65528284NANA473CaucasianM&F17–6520wks3×/wk30–50min55–75%VO2maxNA3.8×10−6Ghosh,2013^C4orf40rs3775758(4:g.71008910C>T)4NANA473CaucasianM&F17–6520wks3×/wk30–50min55–75%VO2maxNA1.09×10−4Ghosh,2013^TECrs13117386(4:g.48252763G>C)4NANA473CaucasianM&F17–6520wks3×/wk30–50min55–75%VO2maxNA7.97×10−5Ghosh,2013#NLNNA5NANA473CaucasianM&F17–6520wks3×/wk30–50min55–75%VO2maxNA#88Ghosh,2013FAABP6rs77346835NANA473CaucasianM&F17–6520wks3×/wk30–50min55–75%VO2maxNA1.44×10−4Ghosh,2013TTC1rs21768305159,380,7140.13473CaucasianM&F17–6520wks3×/wk30–50min55–75%VO2maxNA1.42×10~4Bouchard,2011BTNL9rs8889495180,425,011NA41 CaucasianMYoungadults1.6wks2.12wks1.4×/wk.2.3×/wk1.45min2.Progressive1.70%VO2max2.ProgressiveNANATimmons,2010RTN4IP1/QRSL1rs8988966107,169,855NA41 CaucasianMYoungadults1.6wks2.12wks1.4×/wk.2.3×/wk1.45min2.Progressive1.70%VO2max2.ProgressiveNANATimmons,2010HCG22rs2523849631,133,0300.17473CaucasianM&F17–6520wks3×/wk30–50min55–75%VO2maxNA7.53×10-5Bouchard,2011HCG22rs2523848631,133,0830.17473CaucasianM&F17–6520wks3×/wk30–50min55–75%VO2maxNA7.53×10~5Bouchard,2011HCG22rs2428514631,135,4950.15473CaucasianM&F17–6520wks3×/wk30–50min55–75%VO2maxNA8.22×10-5Bouchard,2011HCG22rs2517518631,136,3240.17473CaucasianM&F17–6520wks3×/wk30–50min55–75%VO2maxNA7.53×10~5Bouchard,2011The Author(s) BMC Genomics 2017, 18(Suppl 8):831 Page 95 of 131Table3Summaryofhypothesis-freestudies(Continued)GeneVariantChromosomeMapPositionMinorallelefrequency(MAF)frequencyRaceGenderAgeTrainingperiodSessions/wkSessiondurationSessionsintensity(+/−/0)**genotype/expressionandVO2maxresponsetotrainingP-valueAuthor,DateHCG22rs2523840631,138,4040.17473CaucasianM&F17–6520wks3×/wk30–50min55–75%VO2maxNA7.53×10-5Bouchard,2011HCG22rs2517506631,139,6590.17473CaucasianM&F17–6520wks3×/wk30–50min55–75%VO2maxNA7.53×10~5Bouchard,2011*PRDM1(287kb)rs104990436106,353,8300.13473CaucasianM&F17–6520wks3×/wk30–50min55–75%VO2maxA(+)3.93×10-6Bouchard,2011*ENPP3(17kb)rs104526216132,127,0940.12473CaucasianM&F17–6520wks3×/wk30–50min55–75%VO2maxA(+)1.23×10~4Bouchard,2011+SLC22A3rs24575716160,754,818NA473CaucasianM&F17–6520wks3×/wk30–50min55–75%VO2maxDownregulatedinhighresponders3.0×10−3Timmons,2010^TMEM181NA6NANA473CaucasianM&F17–6520wks3×/wk30–50min55–75%VO2maxNA#84.5Ghosh,2013^PARK2NA6NANA473CaucasianM&F17–6520wks3×/wk30–50min55–75%VO2maxNA#84.8Ghosh,2013^SNX14NA6NANA473CaucasianM&F17–6520wks3×/wk30–50min55–75%VO2maxNA#86.7Ghosh,2013^BTBD9NA6NANA473CaucasianM&F17–6520wks3×/wk30–50min55–75%VO2maxNA#86Ghosh,2013^KCNQ5NA6NANA473Caucasian1.M&F2.M1.17–652.youngadults1.20wks2.6–12wks1.3×/wk.2.3–4/wk1.30–50min2.45minvsprogressive1.55–75%VO2max2.70%vsprogressiveNANA1:#85.92:NAGhosh,2013(1),Timmons,2010(2)PPARDrs2076167635,499,765NA473CaucasianM&F17–6520wks3×/wk30–50min55–75%VO2maxNANATimmons,2010HDAC9rs3814991718,601,4280.11473CaucasianM&F17–6520wks3×/wk30–50min55–75%VO2maxNA1.46×10-4Bouchard,2011WBSCR17(35kb)rs12538806770,200,7770.30473CaucasianM&F17–6520wks3×/wk30–50min55–75%VO2maxNA1.47×10~4Bouchard,2011WBSCR17(33kb)rs13235325770,202,9430.30473CaucasianM&F17–6520wks3×/wk30–50min55–75%VO2maxNA1.47×10-4Bouchard,2011++CPVLrs4257918729,020,374NA473CaucasianM&F17–6520wks3×/wk30–50min55–75%VO2maxUpregulatedinhighresponders3.1×10−3Timmons,2010^ITGB8rs102651497NANA473CaucasianM&F17–6520wks3×/wk30–50min55–75%VO2maxNA7.04×10−5Timmons,2010LHFPL3NA7NANA473CaucasianM&F17–6520wks3×/wk30–50min55–75%VO2maxNA84.34Ghosh,2013PILRBrs13228694799,778,243NA41 CaucasianYoungadults17–651.6wks2.12wks1.4×/wk.2.3×/wk1.45min2.Progressive1.70%VO2max2.ProgressiveNANATimmons,2010+DEPDC6rs73861398121,096,600NA473CaucasianM&F17–6520wks3×/wk30–50min55–75%VO2maxNA1.85×10−2Timmons,2010#PINX1N/A8NANA473CaucasianM&F17–6520wks3×/wk30–50min55–75%VO2maxNA88.2Ghosh,2013The Author(s) BMC Genomics 2017, 18(Suppl 8):831 Page 96 of 131Table3Summaryofhypothesis-freestudies(Continued)GeneVariantChromosomeMapPositionMinorallelefrequency(MAF)frequencyRaceGenderAgeTrainingperiodSessions/wkSessiondurationSessionsintensity(+/−/0)**genotype/expressionandVO2maxresponsetotrainingP-valueAuthor,Date*GRIN3A(516kb)rs15356289104,056,5700.09473CaucasianM&F17–6520wks3×/wk30–50min55–75%VO2maxNA6.81×10~6Bouchard,2011GRIN3A(540kb)rs9590669104,081,0840.27473CaucasianM&F17–6520wks3×/wk30–50min55–75%VO2maxNA1.35×10-4Bouchard,2011*C9orf27(33kb)rs121154549117,759,8710.11473CaucasianM&F17–6520wks3×/wk30–50min55–75%VO2maxG(+)7.74×10~5Bouchard,2011^TTLL11rs70221039NANA473CaucasianM&F17–6520wks3×/wk30–50min55–75%VO2maxNA1.08×10−4Ghosh,2013KCNT1N/A9NANA473CaucasianM&F17–6520wks3×/wk30–50min55–75%VO2maxNA#86.5Ghosh,2013KLF4rs46315279109,309,857NA41 CaucasianMYoungadults1.6wks2.12wks1.4×/wk.2.3×/wk1.45min2.Progressive1.70%VO2max2.ProgressiveNANATimmons,2010TET1rs124134101070,055,236NA41 CaucasianMYoungadults1.6wks2.12wks1.4×/wk.2.3×/wk1.45min2.Progressive1.70%VO2max2.ProgressiveNANATimmons,2010PRKG1N/A10NANA473CaucasianM&F17–6520wks3×/wk30–50min55–75%VO2maxNA#87.3Ghosh,2013^+SVILrs64816191030,022,960NA473CaucasianM&F17–6520wks3×/wk30–50min55–75%VO2maxNA1.0×10−3Timmons,2010+BTAF1rs27920221093.730,409NA473CaucasianM&F17–6520wks3×/wk30–50min55–75%VO2maxNA1.2×10−2Timmons,2010CASC2rs141318410NANA473CaucasianM&F17–6520wks3×/wk30–50min55–75%VO2maxNA1.65×10−4Ghosh,2013KIF5Brs8068191032,403,990NA473CaucasianM&F17–6520wks3×/wk30–50min55–75%VO2maxNANATimmons,2010+H19rs22551375111,976,072NA473CaucasianM&F17–6520wks3×/wk30–50min55–75%VO2maxUpregulatedinhighresponders4.0×10−4Timmons,2010ACTN3rs18157391066,084,671NA473CaucasianM&F17–6520wks3×/wk30–50min55–75%VO2maxNANATimmons,2010BTAF1rs27920221093,730,409NA41 CaucasianMYoungadults1.6wks2.12wks1.4×/wk.2.3×/wk1.45min2.Progressive1.70%VO2max2.ProgressiveNANATimmons,2010*LOC100130460rs21980091110,360,1530.50473CaucasianM&F17–6520wks3×/wk30–50min55–75%VO2maxA(+)2.28×10-5Bouchard,2011*DBX1(64kb)rs105008721120,202,2990.15473CaucasianM&F17–6520wks3×/wk30–50min55–75%VO2maxA(+)6.49×10~6Bouchard,2011^*CD44rs3536251135,125,1220.32473CaucasianM&F17–6520wks3×/wk30–50min55–75%VO2maxNA1:1.12×10–42:1.64×10−4Bouchard,2011(1)Ghosh,2013(2)CXCR5(36kb)rs493856111118,223,6950.23473CaucasianM&F17–6520wks3×/wk30–50min55–75%VO2maxNA9.29×10~5Bouchard,2011*CXCR5(24kb/)BLR1rs793300711118,235,8790.23473CaucasianM&F17–6520wks3×/wk30–50min55–75%VO2maxNA7.35×10-5Bouchard,2011The Author(s) BMC Genomics 2017, 18(Suppl 8):831 Page 97 of 131Table3Summaryofhypothesis-freestudies(Continued)GeneVariantChromosomeMapPositionMinorallelefrequency(MAF)frequencyRaceGenderAgeTrainingperiodSessions/wkSessiondurationSessionsintensity(+/−/0)**genotype/expressionandVO2maxresponsetotrainingP-valueAuthor,Date^CD6rs17509811NANA473CaucasianM&F17–6520wks3×/wk30–50min55–75%VO2maxNA1.11×10−4Ghosh,2013^SHANK2rs1075130811NANA473CaucasianM&F17–6520wks3×/wk30–50min55–75%VO2maxNA8.11×10−5Ghosh,2013#GRIK4N/A11NANA473CaucasianM&F17–6520wks3×/wk30–50min55–75%VO2maxNA88.32Ghosh,2013H19rs2251375111,976,076NA41 CaucasianMYoungadults1.6wks2.12wks1.4×/wk.2.3×/wk1.45min2.Progressive1.70%VO2max2.ProgressiveNANATimmons,2010FAM19A2rs216845212NANA473CaucasianM&F17–6520wks3×/wk30–50min55–75%VO2maxNA1.34×10−4Ghosh,2013^C12orf36(14kb)rs125804761213,435,3300.14473CaucasianM&F17–6520wks3×/wk30–50min55–75%VO2maxNA1.08×10~42.1.45×10−4Bouchard,2011(1)Ghosh,2013(2)^NALCNN/A13NANA473CaucasianM&F17–6520wks3×/wk30–50min55–75%VO2maxNA#85Ghosh,2013+MIPEPrs73245571323,194,862NA473CaucasianM&F17–6520wks3×/wk30–50min55–75%VO2maxNA5.1×10−3Timmons,2010^EEF1DP3rs277396813NANA473CaucasianM&F17–6520wks3×/wk30–50min55–75%VO2maxNA3.67×10−6Ghosh,2013^CLYBLN/A13NANA473CaucasianM&F17–6520wks3×/wk30–50min55–75%VO2maxNA#85.4Ghosh,2013*TTC6rs128967901437,343,6730.09473CaucasianM&F17–6520wks3×/wk30–50min55–75%VO2maxNA3.59×10-5Bouchard,2011METTL3rs12638091421,058,740NA41 CaucasianMYoungadults1.6wks2.12wks1.4×/wk.2.3×/wk1.45min2.Progressive1.70%VO2max2.ProgressiveNANATimmons,2010TTC6rs80188891437,353,3420.09473CaucasianM&F17–6520wks3×/wk30–50min55–75%VO2maxNA5.25×10~5Bouchard,2011*DAAM1rs1956197(14:g.59477414C>T)1458,547,1670.161.473Caucasian2.464Caucasian1.M2.F17–65Postmenopause20wks6mths3×/wk.120-170min/wk30–50min120–170min/wk55–75%VO2max+50%VO2maxAA(−)n=841.43×10-5Bouchard,2011*NDN(75kb)DownstreamofNDNrs8242051521,559,1640.151.473Caucasian2.464Caucasian1.M2.F17–65Postmenopause20wks9mths3×/wk.120-170min/wk30–50min120-170min/wk55–75%VO2max40–85%VO2maxGG(−)n=5213.45×10~50.05Bouchard,2011+DIS3Lrs15465701564,382,829NA473CaucasianM&F17–6520wks3×/wk30–50min55–75%VO2maxNA2.3×10−2Timmons,2010UNKLrs3751894161,426,876NA473CaucasianMYoungadults1.6wks2.12wks1.4×/wk.2.3×/wk1.45min2.Progressive1.70%VO2max2.ProgressiveNANATimmons,2010IL32rs13335163,052,198NA473CaucasianMYoungadults1.6wks2.12wks1.4×/wk.2.3×/wk1.45min2.Progressive1.70%VO2max2.ProgressiveNANATimmons,2010The Author(s) BMC Genomics 2017, 18(Suppl 8):831 Page 98 of 131Table3Summaryofhypothesis-freestudies(Continued)GeneVariantChromosomeMapPositionMinorallelefrequency(MAF)frequencyRaceGenderAgeTrainingperiodSessions/wkSessiondurationSessionsintensity(+/−/0)**genotype/expressionandVO2maxresponsetotrainingP-valueAuthor,Date#RPTORN/A17NANA473CaucasianM&F17–6520wks3×/wk30–50min55–75%VO2maxNA#89Ghosh,2013#VPS53N/A17NANA473CaucasianM&F17–6520wks3×/wk30–50min55–75%VO2maxNA#84Ghosh,2013ACEDI1758,919,622NA473CaucasianM&F17–6520wks3×/wk30–50min55–75%VO2maxNANATimmons,2010SMTNL2rs7217556174,425,585NA41 CaucasianMYoungadults1.6wks2.12wks1.4×/wk.2.3×/wk1.45min2.Progressive1.70%VO2max2.ProgressiveNANATimmons,2010ZSWIM7R211715,825,286NA41 CaucasianMYoungadults1.6wks2.12wks1.4×/wk.2.3×/wk1.45min2.Progressive1.70%VO2max2.ProgressiveNANATimmons,2010ENOSF1rs378635518671,962NA41 CaucasianMYoungadults1.6wks2.12wks1.4×/wk.2.3×/wk1.45min2.Progressive1.70%VO2max2.ProgressiveNANATimmons,2010EMR4rs7256163196,909,1340.31473CaucasianM&F17–6520wks3×/wk30–50min55–75%VO2maxNA1.13×10-4Bouchard,2011IER2rs8920201913,8185NA41 CaucasianMYoungadults1.6wks2.12wks1.4×/wk.2.3×/wk1.45min2.Progressive1.70%VO2max2.ProgressiveNANATimmons,2010DNAJB1rs49262221914,488,050NA41 CaucasianMYoungadults1.6wks2.12wks1.4×/wk.2.3×/wk1.45min2.Progressive1.70%VO2max2.ProgressiveNANATimmons,2010g.63226200G>Ars60903142061,327,9970.16473CaucasianM&F17–6520wks3×/wk30–50min55–75%VO2maxA(+)1:6.48×10~52:6.24×10−5Bouchard,2011(1)Ghosh,2013(2)^YTHDF1rs612240320NANA473CaucasianM&F17–6520wks3×/wk30–50min55–75%VO2maxNA6.24×10−5Ghosh,2013^MACROD2N/A20NANA473CaucasianM&F17–6520wks3×/wk30–50min55–75%VO2maxNA#86.6Ghosh,2013^HLS21N/A21NANA473CaucasianM&F17–6520wks3×/wk30–50min55–75%VO2maxNA#84.7Ghosh,2013*MN1(14kb)rs7383532226,460,0720.35473CaucasianM&F17–6520wks3×/wk30–50min55–75%VO2maxA(+)1.23×10–4Bouchard,2011LOC731789rs11015207NANANA473CaucasianM&F17–6520wks3×/wk30–50min55–75%VO2maxNA1.61×10−4Ghosh,2013Therewerenootherpossiblemediators(suchasmedications,healthconcerns)orothersignificantfindingsnotedintheabovethreestudies.Wherepossible,genevariantswereannotatedusingthereferencessequence(GRCh37/hg19)*Outofthe39SNPsidentifiedviaGWAS,21(*)explained49%oftheVO2maxtrainabilityvariance(afterregressionanalysis).The15mostsignificantwerethenexaminedusingdatafromthefollowingstudies:HERITAGEAfrican-Americans,DREWstudy,STRRIDEstudy.Thevariantsreplicatedareinitalics+11SNPsfromaregressionanalysisexplained~23%oftheestimatedVO2maxvariance.90%RNAexpressionremainedunchangedbyexercisetraining.(++)werefoundinstudybyBouchard(2011)butweren’tincludedintheregressionanalysisbecausetheyweren’tconsideredsignificantatthe0.00015level^Top20GWASassociatedgenesbasedonsecond-bestSNP-Pvalues#CandidategenesidentifiedthroughCANDIDsoftwarebasedonliteraturesearch;GWASassociationdata;sequenceconversion&geneexpression.Thisequatestoa‘finalscore’ratherthanp-value.Boldedtextindicatesmoderate-strongrelatedbiologicalmechanismsthatinfluenceVO2maxtrainability**(+)=significantlyhighertrainingresponse(0)=nosignificantdifferenceintrainingresponsebetweengenotypes(−)=significantlylowertrainingresponseThe Author(s) BMC Genomics 2017, 18(Suppl 8):831 Page 99 of 131Table 4 Predictor genes that may influence VO2max training responseNumber Chromosome Gene Variant Race Genotype/expression andVO2max training response(+/−/0)**Author, Date(x = candidate gene study)1 1 AMPD1 rs17602729 Caucasian TT and CT (−) Thomaes, 2011 (x);Rico-Sanz, 2003 (x)2 1 CAMTA1 rs884736 CaucasianAfrican-AmericanAA (−) Bouchard, 2011;Ghosh, 20133 1 ID3 rs11574 Caucasian TBC Timmons, 20104 1 RGS18 rs10921078 CaucasianAfrican-AmericanGG (−) Bouchard, 20115 1 RYR2 rs7531957 Caucasian TBC Bouchard, 2011;Ghosh, 20136 1 SLC45A1 TBC Caucasian TBC Ghosh, 20137 2 ACVR1C TBC Caucasian TBC Ghosh, 20138 2 KCNF1 rs2003298 Caucasian TBC Bouchard, 20119 2 FLJ44450 rs4952535 Caucasian G (+) Bouchard, 201110 2 TTN rs10497520 Caucasian TBC Timmons, 201011 2 NRP2 rs3770991 Caucasian TBC Timmons, 201012 3 KCNH8 rs4973706 Caucasian A (+) Bouchard, 201113 3 ZIC4 rs11715829 Caucasian AA (−) Bouchard, 201114 3 NLGN1 rs2030398 Caucasian A (+) Bouchard, 201115 3 ADCY5 TBC Caucasian TBC Ghosh, 201316 4 CSN1S2B rs2272040 Caucasian TBC Bouchard, 201117 4 LOC100289626 rs2053896 Caucasian A (+) Bouchard, 201118 4 ACSL1 rs6552828 Caucasian AA (−) Bouchard, 2011;Ghosh, 201319 4 SLED1 rs6552828 Caucasian TBC Ghosh, 201320 4 PRR27; C4orf40 rs3775758 Caucasian TBC Ghosh, 201321 4 TEC rs13117386 Caucasian TBC Ghosh, 201322 5 NR3C1 rs6190 Caucasian GG (−) Thomaes, 201123 5 NLN TBC Caucasian TBC Ghosh, 201324 5 FABP6 rs7734683 Caucasian TBC Ghosh, 201325 5 TTC1 rs2176830 Caucasian TBC Bouchard, 201126 6 PPARD Exon 4 + 15Exon 7 + 65African-American CC (−) Hautala, 2007 (x)27 6 HCG22 rs2517512 Caucasian TBC Ghosh, 201328 6 HCG22 rs2523849 Caucasian TBC Bouchard, 201129 6 HCG22 rs2523848 Caucasian TBC Bouchard, 201130 6 HCG22 rs2428514 Caucasian TBC Bouchard, 201131 6 HCG22 rs2517518 Caucasian TBC Bouchard, 201132 6 HCG22 rs2523840 Caucasian TBC Bouchard, 201133 6 HCG22 rs2517506 Caucasian TBC Bouchard, 201134 6 PRDM1 rs10499043 Caucasian A (+) Bouchard, 201135 6 ENPP3 rs10452621 Caucasian A (+) Bouchard, 201136 6 SLC22A3 rs2457571 Caucasian Downregulated inhigh respondersTimmons, 201037 6 TMEM181 TBC Caucasian TBC Ghosh, 201338 6 PARK2 TBC Caucasian TBC Ghosh, 2013The Author(s) BMC Genomics 2017, 18(Suppl 8):831 Page 100 of 131Table 4 Predictor genes that may influence VO2max training response (Continued)Number Chromosome Gene Variant Race Genotype/expression andVO2max training response(+/−/0)**Author, Date(x = candidate gene study)39 6 SNX14 TBC Caucasian TBC Ghosh, 201340 6 BTBD9 TBC Caucasian TBC Ghosh, 201341 6 KCNQ5 TBC Caucasian TBC Ghosh, 201342 7 HDAC9 rs3814991 Caucasian TBC Bouchard, 201143 7 WBSCR17 rs12538806 Caucasian TBC Bouchard, 201144 7 WBSCR17 rs13235325 Caucasian TBC Bouchard, 201145 7 CPVL rs4257918 Caucasian TBC Timmons, 201046 7 ITGB8 rs10265149 Caucasian TBC Ghosh, 201347 7 LHFPL3 TBC Caucasian TBC Ghosh, 201348 8 DEPDC6 rs7386139 Caucasian TBC Timmons, 201049 8 PINX1 TBC Caucasian TBC Ghosh, 201350 9 GRIN3A rs1535628 Caucasian TBC Bouchard, 201151 9 GRIN3A rs959066 Caucasian TBC Bouchard, 201152 9 C9orf27 rs12115454 Caucasian G (+) Bouchard, 201153 9 TTLL11 rs7022103 Caucasian TBC Ghosh, 201354 9 KCNT1 TBC Caucasian TBC Ghosh, 201355 10 FAM238B; LOC731789 rs11015207 Caucasian TBC Ghosh, 201356 10 PRKG1 TBC Caucasian TBC Ghosh, 201357 10 SVIL rs6481619 Caucasian TBC Timmons, 201058 10 BTAF1 rs2792022 Caucasian TBC Timmons, 201059 10 CASC2 rs1413184 Caucasian TBC Ghosh, 201360 11 H19 rs22551375 Caucasian Upregulated inhigh respondersTimmons, 201061 11 LOC100130460 rs2198009 Caucasian A (+) Bouchard, 201162 11 DBX1 rs10500872 Caucasian A (+) Bouchard, 201163 11 CD44 rs353625 Caucasian TBC Bouchard, 2011;Ghosh, 201364 11 CXCR5 (36 kb) rs4938561 Caucasian TBC Bouchard, 201165 11 CXCR5 (24 kb)/BLR1 rs7933007 Caucasian TBC Bouchard, 201166 11 CD6 rs175098 Caucasian TBC Ghosh, 201367 11 SHANK2 rs10751308 Caucasian TBC Ghosh, 201368 11 GRIK4 TBC Caucasian TBC Ghosh, 201369 11 CNTF rs1800169 Caucasian AA (+) Thomaes, 2011 (x)70 11 CAT -262C > T Caucasian TT (−) Onkelinx, 2011 (x)71 11 GSTP1 c.313A > G (rs1695) Caucasian GG & AG (+) Zarebska, 2014 (x)72 12 FAM19A2 rs2168452 Caucasian TBC Ghosh, 201373 12 C12orf36 rs12580476 Caucasian TBC Bouchard, 2011Ghosh, 201374 13 NALCN TBC Caucasian TBC Ghosh, 201375 13 MIPEP rs7324557 Caucasian TBC Timmons, 201076 13 EEF1DP3 rs2773968 Caucasian TBC Ghosh, 201377 13 CLYBL NA Caucasian TBC Ghosh, 201378 13 Na + −K + −ATPase α2 Alpha2 exon 1Alpha2 exon 21–22Caucasian 3.3/3.3 (−)10.5/10.5 (+)Rankinen, 2000a (x)The Author(s) BMC Genomics 2017, 18(Suppl 8):831 Page 101 of 131genotype, n = 21) in the ciliary neurotrophic factor gene(CNTF; rs1800169) and the AMPD1:c.133C wild type(CC genotype, n = 652) of the adenosine monophosphatedeaminase gene (AMPD1; rs17602729). Furthermore, alarger change in relative VO2peak was reported in pa-tients with a greater number of these variants described(Area Under the Curve (AUC): 0.63; 95% ConfidenceInterval (CI): 0.56–0.7; p < 0.01). More specifically, thosewith a gene predictor score (GPS) of one or less positiveresponse alleles had an average increase in VO2peak of16.7%. Those with four or more positive response alleleshad an average increase of 25%, with each positive re-sponse allele contributing approximately 1% (13.5 mL/min) to the increase in VO2peak.Caucasians aged between 17 and 65 years from theHERITAGE study who were homozygous (TT genotype)for the AMPD1:c.133C > T (p.(Gln45*)) (rs17602729)variant (n = 6), had a lower VO2max training response(<121 mL/min; p = 0.006), compared to the CT and CCgenotypes (n = 497) following 20 weeks of MICT (3 ×50 min per week at 55–75% HRmax) [46].The serine/threonine protein kinase 1 (AKT1) genehas been linked to growth and skeletal muscle differenti-ation [44]. In a study of 109 Caucasians (50–75 yearsold), men (n = 22) with the AKT1:c.-350G > T(rs1130214) variant (TT/GT genotype) significantly in-creased their VO2max compared to men (n = 29) with theGG genotype (fold increase of 1.2 ± 0.02 vs 1.1 ± 0.02, p= 0.037) following 24 weeks of MICT (3 × 20–40 minper week at 50–75% HRR) [44].The glutathione S-transferase P1 (GSTP1) c.313A >Gvariant has been associated with an impaired ability to re-move excess reactive oxygen species. This is hypothesisedto increase the exercise training response by better activa-tion of cell signalling pathways resulting in positive muscleadaptations [45]. While investigating 62 Polish females’(19–24 years-old) response to 12 weeks of MICT (3 ×60 min per week at 50–75% HRmax), participants (n = 30)Table 4 Predictor genes that may influence VO2max training response (Continued)Number Chromosome Gene Variant Race Genotype/expression andVO2max training response(+/−/0)**Author, Date(x = candidate gene study)79 14 HIF1A T + 140C Caucasian (60+ years) C/T (−) Prior, 2003 (x)80 14 AKT1 G205 T (RS1130214) Caucasian men GT & TT (+) McKenzie, 2011 (x)81 14 TTC6 rs12896790 Caucasian C (+) Bouchard, 201182 14 DAAM1 rs1956197 Caucasian AA (−) Bouchard, 201183 15 NDN rs824205 Caucasian GG (−) Bouchard, 201184 15 DIS3L Rs1546570 Caucasian TBC Timmons, 201085 17 ACE Intron 16 Caucasian DD (+)II (+)Rankinen, 2000b (x);Defoor, 2006 (x)86 17 RPTOR NA Caucasian TBC Ghosh, 201387 17 VPS53 NA Caucasian TBC Ghosh, 201388 19 ADGRE3P; EMR4 rs7256163 Caucasian TBC Bouchard, 201189 19 APOE TBC Chinese & unknown E2/E3 (+)E2/E3 (+)E3/E4 (+)E3/E4 (+)E3/E3 (−)Yu, 2014 (x);Thompson, 2004 (x)90 19 CKM Ncol Caucasian Homozygous 1170bp (−); CKM locus (+/−)Rivera, 1999(x);Rivera 1997 (x)91 20 BIRC7 and YTHDF1 rs6090314 Caucasian A (+) Bouchard, 2011Ghosh, 201392 20 YTHDF1 rs6122403 Caucasian TBC Ghosh, 201393 20 MACROD2 NA Caucasian TBC Ghosh, 201394 21 HLCS NA Caucasian TBC Ghosh, 201395 22 MN1 rs738353 Caucasian A (+) Bouchard, 201196 Mitochondria ALAS2 </=166 bp Chinese </=166 bp (+) Xu, 2015 (x)97 Mitochondria mtDNA TBC Quebec, Tempe mtDNA subunit 5 N5 (−) Dionne, 1991 (x)Where possible, gene variants were annotated using the references sequence (GRCh37/hg19)Bolded = genes that have been replicated between or within studies**(+) = high training response, (−) = low training response, (0) = neutral training response, TBC to be confirmed whether variant contributes to a high or lowtraining responseThe Author(s) BMC Genomics 2017, 18(Suppl 8):831 Page 102 of 131with the GSTP1:c.313A >G (GG + GA genotype) demon-strated a 2 mL/kg/min greater improvement in VO2maxcompared to AA genotypes (n = 5) following training (ab-solute p = 0.029, relative p = 0.026, effect size = 0.06) [45].Genes associated with electrolyte balanceThe electrogenic transmembrane ATPase (NA+/K +−ATPase) gene may contribute to VO2max trainability byaffecting the electrolyte balance and membrane excit-ability in working muscles [24]. Examining Caucasiandata from the HERITAGE study, it was found that thosehomozygous for a recurrent 3.3-kb deletion in the exon1 of the ATP1A2 gene (n = 5) had a 41% (45 mL/min)lower training response compared to heterozygotes (n =87) [24]. This exon encodes on part (alpha-2-subunit) ofthe Na+/K + ATPase protein. This genotype also had a48% (197 mL/min) lower VO2max training response thanhomozygotes (n = 380) for a repeated 8.8-kb in the exon1 of the ATP1A2 gene following 20 weeks of MICT(p = 0.018) [24]. VO2max gains were 29% (130 mL/min)and 39% (160 mL/min) greater in offspring homozygousfor a 10.5-kb deletion in exon 21–22 (n = 14) comparedto heterozygotes (n = 93) and homozygotes (n = 187) re-spectively (p = 0.017) [24].The angiotensin-converting enzyme (ACE) gene con-tributes to blood pressure, fluid and salt balance [55].Elite endurance athletes are more likely to have the In-sertion (I) allele [56] which relates to lower ACE activityand reduced blood pressure response during exercise,whereas sprint/power athletes are more likely to havethe Deletion (D) allele and the DD genotype [57] andsubsequently higher ACE activity. Caucasians from theCARAGENE study with the homozygous II genotype(frequency of 0.23 and 0.18 for men and women respect-ively) had a 2.1% greater VO2max training response (p =0.047) compared to the DD genotype (frequency of 0.3and 0.36 for men and women respectively) [31]. Wheneliminating those on ACE inhibitors, the improvementincreased by 3% (p = 0.013) [31]. On the other hand,VO2max trainability was 14–38% greater (p = 0.042) inHERITAGE Caucasian offspring with the DD genotype(n = 81) [25]. Three studies found no association withACE or angiotensinogen genetic variants and VO2maxtraining response in 53 Caucasians (average age 19 years)following 12 weeks of military training [47]; 147 multi-ethnic 19–24 year-old adults following 8 weeks of mili-tary training [39]; and 83 Brazilian policemen (averageage 26 years) following 17 weeks of MICT (3 × 60 minper week at 50–85% VO2peak) [48].Genes associated with lipid metabolismGenotypes of the perilipin (PLIN1) gene may influencetraining response via intracellular lipolysis and energyproduction [43]. In 101 Caucasians (50–75 years old),there were no significant differences between carriersand non-carriers of the PLIN1:c.504 T > A variant(rs1052700) after 24 weeks of MICT (20–40 min, 3 ×per week) [43].Fig. 1 PRISMA flow chart of article selection processThe Author(s) BMC Genomics 2017, 18(Suppl 8):831 Page 103 of 131The peroxisome proliferator activated receptor delta(PPARD) gene affects fatty acid oxidation and energyproduction [22]. African-Americans (n = 19) from theHERITAGE study with the PPARD exon 4 + 15 (CCgenotype) had a significantly lower VO2max training re-sponse (> 50 mL/min lower; p = 0.028) and power out-put (> 15 W lower; p = 0.005) compared to the C/T andTT genotypes (n = 230) [22].Apolipoprotein E (APOE) variants affect the level oflipids in the blood, cell lipid uptake and endothelial vas-cular dilation [23]. APOE has 3 common alleles: E2 (TT/TT), E3 (TT/CC), E4 (CC/CC) at two SNPs (rs429358,rs7412), which can create six possible genotypes (E2/E2,E3/E3, E4/E4, E2/E3, E2/E4, E3/E4) [58]. The APOE E4allele has been associated with Alzheimer’s disease [59],higher levels of low density cholesterol (LDL-C) and agreater risk of coronary heart disease compared to E3(wild-type) and E2 carriers [23]. Chinese men (18–40 years) with the APOE E2/E3 (n = 20) and E3/E4 (n =31) genotypes had a significantly higher VO2max trainingresponse (Odds Ratio (OR) = 0.68 (95% CI (0.04, 1.32); p= 0.04 and OR = 0.60 (95% CI (0.09, 1.11); p = 0.02 re-spectively) compared to other APOE genotypes following6 months of progressive MICT (3 x per week at 60–85%VO2max) [13]. Similarly, Chinese women (18–40 years)with the APOE E2/E3 (n = 25) and E3/E4 (n = 29) geno-types had significantly higher VO2max training responsescompared to other APOE genotypes (OR = 0.62 (95% CI= 0.05, 1.18); p = 0.03 and OR = 0.62(95% CI = 0.09,1.15);p = 0.02 respectively) [13]. Men and women (ethnicityunknown) with the E3/E3 APOE genotype (n = 43) hadan 8% lower training response compared to the E2/E3(n = 40) and E3/E4 genotypes (n = 37) (p < 0.01,Bonferroni-corrected) following 6 months of MICT (4 ×50 min per week at 60–85% VO2max) [42]. However,there was no significant difference in the VO2max train-ing response between APOE genotypes in men andwomen from the HERITAGE study (n = 766) [23]. Simi-larly, in 51 males (40–80 years old, ethnicity not con-firmed) there was no difference in VO2max trainingresponse between genotypes [41].Genes associated with oxidative phosphorylation andenergy productionMitochondrial DNA (mtDNA) encodes several enzymesubunits involved in oxidative phosphorylation, and maybe a key factor in endurance and cardiorespiratory fit-ness [56]. Research of mtDNA variants in 41 inactiveJapanese men (mean age 20.6) failed to find a significantdifference in trainability after 8 weeks of MICT (3–4 × 60 min per week at 70% VO2max) [49]. On the con-trary, 3 men (17–25 years) with the mtDNA variant insubunit 5 of ND5 had a lower VO2max training responsecompared to other mtDNA variants (~ gain 0.22 L/minless, p < 0.05) following 12-weeks of MICT (3–5 ×45 min per week at 85%HRRmax) [50].The creatine kinase muscle (CKM) gene has been as-sociated with reduced fatigue from increased adenosinetriphosphate (ADP) production [26, 27]. Using data fromthe HERITAGE study, parents and offspring homozygotefor the 1170 bp allele (n = 12) had a lower VO2max train-ing response (3 times and 1.5 times lower respectively; p< 0.05) compared to other CKM genotypes (n = 148).This explained 9 and 10% of the inter-individual vari-ation in VO2max change respectively [26]. A nominalgenetic linkage was identified in siblings (n = 277) whoshared two alleles (1170 base pairs or 985 + 185 basepairs) at the CKM locus identical by descent (IBD), withthese siblings having similar changes in VO2max com-pared to siblings with fewer alleles IBD (p = 0.04) [27].In an earlier study focusing on muscle specific inheritedvariations, no association was found in 295 Caucasians(18–30 years old) between CKM or adenylate kinase(AK1) variants after a randomized control trial that in-cluded 15 weeks of endurance training versus maximalpower contraction interval training [40]. Similarly, no as-sociation was found with the CKM gene and VO2maxtrainability in 937 Caucasian patients with coronary ar-tery disease following 3 months of MICT (2–3 × 90 minaerobic sessions per week at 80% HRmax) [29].Nuclear respiratory factor 1 (NRF1) and nuclear factor(erythroid-derived 2)-like 2 (NFE2L2) [36, 37], contrib-ute to mitochondrial biogenesis and oxidative phosphor-ylation [60]. In a study involving 102 physically activeChinese male soldiers (average age 19 years), there wasno association between NRF1 and NFE2L2 genotypes orhaplotypes and VO2max trainability after 18 weeks of 3 ×5000 m runs per week at 95–105% VT [36, 37].Genes associated with oxygen deliveryNitric oxide causes coronary and arterial vasodilation,contributing to oxygen delivery regulation [32]. Datafrom the CARAGENE study was used to investigategenes associated with nitric oxide bioavailability [32].These included nitric oxide synthase 3 (NOS3), cyto-chrome b-245 alpha chain (CYBA, also known as p22-PHOX), glutathione peroxidase (GPX1), catalase (CAT),superoxide dismutase 3 (SOD3), vascular endothelialgrowth factor A (VEGFA), peroxisome proliferator-activated receptor alpha (PPARα) and peroxisomeproliferator-activated receptor gamma coactivator-related 1 (PPARC1) [32]. Participants carrying the C al-lele of the CAT:c.262 T > C variant (n = 342) had up to3.1% greater improvements in VO2max training responsecompared to participants with the TT genotype (n = 521)following MICT (f = 3.6; p = 0.02). Participants with theNOS3 1.4 haplotype combinations (n = 36) had a 6.4%lower training response compared to the 3.3. haplotypeThe Author(s) BMC Genomics 2017, 18(Suppl 8):831 Page 104 of 131combinations (n = 133) (p < 0.05). However, these associ-ations were not significant after Bonferroni correction.No other associations were found with other genes orhaplotypes related to nitric oxide availability and endo-thelial function [32]. Similarly, in a cohort of 80 Portu-guese (20–35 years old) police recruits, there was noassociation between NOS3 genotypes (−786 TT/TC/CC,894 GT/TT/GG) and VO2peak response following18 weeks of 3 × 80-min per week of graded runningtraining [59]. Additionally, no association was foundwith PPARGC1 and VO2max trainability in 102 Chinesemale polices recruits following MICT [36].The beta-2-adrenergic receptor (ADBR2) gene helps tosupport oxygen delivery to working muscles via the adren-ergic receptors [30]. In participants from the CARAGENEstudy, there was no association found between ADBR2 ge-notypes or haplotypes, and VO2max trainability [30].The hypoxia-inducible factor 1 alpha (HIF1A) gene isa transcriptional regulator that controls angiogenesis(blood vessel development) and metabolism by increas-ing the expression of hypoxia-induced genes, such asVEGF [52]. Caucasians 60 years and over with theH1F1A:c.1744C > T (rs11549465; C/T genotype; n = 37)had a significantly lower training response (0.3 mL/kg/min; p = 0.03) compared to those with the CC genotype(n = 64) following 24 weeks of MICT (3 × 20–40 min perweek at 50–70% VO2max) [52].The 5′-aminolevulinate synthase 2 (ALAS2) gene ishighly expressed in erythroid cells and is imperative forhemoglobin and myoglobin synthesis [53]. Seventy-twoChinese participants (18–22 years old) allocated to oneof 13 ALAS2 genotypes with compound dinucleotide re-peats lengths (157 bp −184 bp), were placed in a 4-week‘HiHiLo’ training program (varying between low andhigh altitude training at 75% VO2max) [53]. Baselinehemoglobin levels and change in VO2max with trainingwas significantly higher in subjects (n = 25) with the di-nucleotide repeats ≤ 166 bp (p < 0.05). No significant as-sociations were found between VO2max trainability andother genes related to oxygen transport and utilizationgenotypes in 102 young Chinese soldiers following18 weeks of 3 × 5000 m runs per week [35, 37, 38].These genes include mitochondrial transcription factorA (TFAM) [35] and hemoglobin-beta locus (HBB) [38].2. Hypotheses free studiesOver the last decade, with the advent of technologicaladvances allowing researchers to genotype millions ofgenetic variants (e.g. SNPs) in each individual, the inves-tigation of the contribution of common variants to traitsis now feasible. Unbiased and hypothesis-free genomewide association studies (GWAS) for exercise/health-re-lated traits have emerged.Three studies have used GWAS to identify genes as-sociated with the VO2max response to exercise training[20, 21 28]. These are outlined in Table 3.The first investigated two clinical trials and data fromthe HERITAGE study [28]. RNA expression profilingand VO2max testing was performed on 24 healthy and in-active Caucasian men (average age 24 years) before andafter a 6-week training intervention (4 × 45-min cyclingsessions per week at 70% VO2max). Muscle biopsies fromthe vastus lateralis were collected and the RNA expres-sion of genes was correlated with changes in VO2max byanalysing oligonucleotide arrays. Pearson correlationswere used to identify the relationships between the me-dian logit normalised probe sets and the number oftimes they were selected. In the 24 subjects, using a me-dian correlation cut-off greater than 0.3, 29 genes wereselected greater than 22 out of 24 times. The sum of ex-pression of these 29 genes were found to have a signifi-cant linear relationship with VO2max change followingendurance training (r2 = 0.58, p < 0.00001). Across thegroup, VO2max changes improved on average by 14% andranged from −2.8% to 27.5% (p = 0.0001). More than20% of the group had a response less than 5%. A geneset enrichment analysis found that the oxidative phos-phorylation gene was upregulated (False Discovery Rate(FDR) = 1.1%), which was associated with an increasedreliance on lipids during training (RER decreased onaverage by 10% post training, p < 0.0001). To identify ifthese predictor genes would be similar in a differentsample, a 12-week blind study on 17 young and activeCaucasian men was conducted. Training consisted of 1-day of testing, 2 sessions of interval training (3 × 3-minintervals at 40–85% Pmax) and 2 × 60–120-min cycle ses-sions (55–60% Pmax) each week. The 29 predictor geneswere also significantly associated with VO2max trainabil-ity in this group (p = 0.02). The haplotypes of these pre-dictor genes were then genotyped using candidate genesidentified from the HERITAGE study. Six genetic vari-ants were associated with VO2max trainability: SMTNL2,DEPDC6, SLC22A3, METTL3, ID3 and BTNL9 (p < 0.01each). A stepwise regression model using 25 variantsfrom the predictor set and 10 variants from the HERTI-AGE study (Table 3) found that eleven SNPs (includedin Table 4) contributed to 23% of the differences seen inresidual VO2 max gains, which correlated to approxi-mately 50% of the genetic variability in VO2max trainabil-ity (seven variants from the RNA predictor set and fourfrom the HERITAGE project). Reciprocal RNA expres-sion validation found that three of four HERITAGE can-didate genes enhanced the original RNA transcriptpredictor model. Overall, more than 90% of gene expres-sion did not change. However, OCT3 was downregulatedin high responders and H19 was upregulated in low re-sponders (FDR <5%). BTNL9, KLF4 and SMTNL2 alsoThe Author(s) BMC Genomics 2017, 18(Suppl 8):831 Page 105 of 131had small but inconsistent changes in expression (i.e.dissimilar in high vs low responders) (FDR < 5%).A GWAS examining 324,611 variants from the HERI-TAGE study was completed to identify possible pre-dictor genes associated with VO2peak [20]. Based onsingle-variant analysis, 39 variants (Table 3) were as-sociated with gains in VO2peak although none of theseachieved genome-wide or suggestive significance (p =1.5 × 10−4) [19]. The strongest predictor for trainingresponse was found in the Acyl-CoA synthetase long-chain family member 1 (ACSL1) gene (4:g.185725416A >G; rs6552828) which accounted for 7% of the training re-sponse (p = 1.31 × 10−6). After a stepwise multiple regres-sion analysis of the thirty-nine variants, 21 were suggestedto account for (or at least contribute to) 49% of the vari-ance in VO2max trainability (included in Table 4; p < 0.05).The strongest predictors were found in SNPs associatedwith: PR domain-containing protein 1 (PRDM1); glutam-ate receptor, ionotropic, N-methyl-D-aspartate 3A(GRIN3A); N-methyl-D-aspartate receptor (NMDA); po-tassium voltage-gated channel subfamily H member 8(KCNH8); zinc finger protein of cerebellum 4 (ZIC4); and,ACSL1. An unweighted ‘predictor score’ based on contri-bution to VO2max of these 21 variants was created. A scoreof ‘0’ represented homozygote for the low-response vari-ant; ‘1’ represented heterozygous and ‘2’ representedhomozygous for the high-response allele. Individuals witha score equal to or less than 9 (n = 36) had an averageVO2max score improvement of 221 mL O2/min. Alterna-tively, those (n = 52) with a score equal to or greater than19 had an average VO2max increase of 604 mL/min.The 15 most significant variants were tested for repli-cation in a sample of African-Americans from the HERI-TAGE study, women in the Dose Response to Exercise(DREW) study (n = 112), and the men and women in theStudy of a Targeted Risk Reduction Intervention throughDefined Exercises (STRRIDE) (n = 183) [20]. Variants inthe NDN (15:g.24008071 T > C; rs824205) and DAAM1(14:g.59477414C > T; rs1956197) were replicated in theDREW study, the Z1C4 (3:g.146957166 T > C T;rs11715829) variant was replicated in the STRRIDEstudy and CAMTA1 (7:g.7015105 T > C; rs884736) andRGS18 (1:g.192059022G > A; rs10921078) variants werereplicated in African-Americans from the HERITAGEstudy. Four variants in the genes supervillin (SVIL), neu-ropillin 2 (NRP2), titin (TTN) and carbozypeptidase(CPVL) identified by Timmons et al. [28] were alsofound by Bouchard et al. [20], however, at a significanceof 0.008, these variants were not included in the multi-variate regression analysis.Using the HERITAGE cohort, an extended analysiswas performed, with 2.5 million variants analysed [21].To reduce bias associated with outlier variants, the sec-ond most significant variant p-value was used todetermine genotype and changes in VO2max. Even withan extended analysis, the ACSL1 gene was shown tohave the most significant variant (4:g.185725416A > G;rs6552828), which confirmed findings by Bouchard et al.[20], whom identified the most significant variant ateach gene (Table 3). The following genes and their vari-ants were also replicated in both studies: CAMTA1(rs884736), RYR2 (rs7531957), g.63226200G > A(rs6090314), C12orf36 (rs12580476) and CD44(rs353625) [20, 21].The gene prioritisation tool ‘CANDID’ was then usedto rank candidate genes for changes in VO2max [21]. Thiswas done via: 1) a weighted analysis based on variantgene expression in targeted tissues; 2) GWAS p-valuechange in VO2max; 3) literature related to candidategenes; and 4) ‘cross species sequence conservation’ [21].The top-ranking candidate genes from the GWAS andCANDID tool (Table 1) were then investigated for pos-sible biological mechanisms and changes in VO2max. Asa result, variants were allocated into four groups: 1)broad effects on exercise-related processes (such as theelectron transport chain, physical fitness, skeletal devel-opment and other cardiorespiratory markers); 2) moder-ately strong scores against selective exercise-relatedprocesses; 3) high and low scores across severalexercise-related processes; 4) low scores across allexercise-related processes.Variants and their involvement in pathways relatedto changes in VO2max response were then examined[21]. Out of the sixteen pathways found, variants re-lated to pantothenate and co-enzyme A (CoA) biosyn-thesis, PPAR gene signalling and immune functionsignalling had the highest level of ‘burden’ (variantscontributing to trainability). The variants related tolong-chain fatty acid transport (including ACSL1) andfatty acid oxidation strongly influence VO2max trainingresponse via lipid metabolism process and the tricarb-oxylic acid cycle, both of which affect the availabilityof adenosine triphosphate and subsequently trainingresponse.Predictor genesOut of the 35 articles analysed (candidate genes andGWAS studies), 97 predictor genes were identified aspossible contributors to VO2max trainability (Table 4).These genes were based on what authors deemed sig-nificant, or the most significant, for their particularstudy. Thirteen of these predictor genes were replicatedbetween at least two studies (bolded in Table 4). Thetraits for VO2max trainability (e.g. which genotype wasrelated to the training effect and whether it was a lowor high responding genotype) was not outlined for eachvariant and hence this will require confirmation infuture studies.The Author(s) BMC Genomics 2017, 18(Suppl 8):831 Page 106 of 131DiscussionThis systematic review aimed to summarize genetic vari-ants that have been identified as influencing VO2maxtrainability. We have reviewed 35 studies that have re-ported 97 genes associated with an exercise training-induced improvement in VO2max. It has been estimatedthat VO2max trainability has a significant heritable com-ponent of around 50% [39].There were several studies that identified the samevariant, including: the lipid-related ACSL1:c.-32-716 T > C (rs6552828) [20, 21] and skeletal muscle-related AMPD1:c.133C > T [33, 46]; intra-cellular cal-cium regulator RYR2:c.6166 + 552 T > G; cellular function-related CD44 (rs3653625), transcriptional activatorCAMTA1 (rs884736), non-coding C12orf36 (rs12580476)and apoptotic regulator 20:g.63226200G > A (rs6090314)[20, 21]. Additionally, Bouchard et al. [20] were able to rep-licate the variants in genes from the HERITAGE study,including: growth suppressor NDN, cell cortexfunction-related DAAM1, development-related Z1C4and signal transduction inhibitor RGS18. Numerousidentified variants were found in pathways that contri-bute to training response (e.g. calcium signaling, im-mune function, angiogenesis, mitochondrial biogenesis)with pathways and associated SNPs possibly influencingeach other and overall trainability [21]. Several articlesfound conflicting results with electrolyte balance, lipid pro-duction and energy production genes ACE [25, 31, 47, 48],APOE [13, 23, 41, 42], mtDNA [49, 50] and CKMM vari-ants respectively [26, 27, 29, 40]. All other ‘predictor genes’identified are yet to be replicated.While most of the articles examined in this reviewhave focused on one or a few candidate genes/markers(n = 32), it is noted that exercise-related phenotypes arecomplex traits and are polygenic (i.e. influenced by manygenes working together) with each genetic variant likelyto be contributing a small percentage (typically less than1%) to the overall change in VO2max [33, 39, 61]. Thusrelying on one variant as a predictor is misguided; ratherit has been suggested that a gene predictor score (GPS)based on numerous variants has a greater probability todetermine higher and lower responders for VO2maxtrainability. For example, a score of ‘0’ represents ahomozygote for a low-response variant; ‘1’ representsheterozygous and ‘2’ represents homozygous for a high-response variant [20]. A higher score indicates a greaterpossible VO2max training response (and vice versa). Asimilar model has been suggested in elite athletes aimingto determine the probability of an individual with a the-oretically ‘optimal’ polygenic profile for endurancesports. The ‘optimal’ profile using a so-called ‘total geno-type score’ (TGS, ranging from 0 to 100, with ‘0’ and‘100’ being the worst and best genotype combinations,respectively) was quantified from a simple algorithmresulting from the combination of candidate polymor-phisms [62, 63].These predictor genes, along with muscle RNA and pro-tein expression data provide a sound platform to furtherexplore the cellular mechanisms underlying VO2max train-ability. Further research will need to consider several limi-tations identified from the literature to-date. For example,the lack of replication found between articles and conflict-ing results with certain variants, may be a result of severalmain limitations (typically in study design). Firstly, mostof the articles used a hypotheses-driven candidate geneapproach (n = 32), several articles used retrospective datafrom similar cohorts (n = 19), and many lacked a controlgroup and randomization (n = 31). While it is understand-able that in the past, high-throughput SNP microarray orgene sequencing technology was not available to use, bylooking at one or only a few gene variants (whereas it isestimated that the human genome consists of about 40million common gene variants) it is almost impossible togenerate meaningful information. Similarly, a lack of con-trol group makes it challenging to distinguish between in-dividual response to an intervention and within-subjectrandom variation [64]. Secondly, most of the exercisetraining studies involve a relatively small number of par-ticipants (typically n = 20 to 30; with the exception of theHERITAGE and CARAGENE studies), which results inlack of statistical power when associating genotype with aphenotype. Many of the studies also failed to include a ro-bust significance criterion (p < 0.05 occurs approximately106 times in the genome by chance). Thirdly, a lack of ra-cial diversity (74.5% Caucasian) further reduces the powerof variants detected. Finally, many of the training studieswere not tightly controlled in terms of nutrition, partici-pant baseline data (study entry), physical activity statusand other lifestyle factors.Future research needs to consider epigenetic variationof gene activity that can occur in reaction to externalfactors, such as additional physical activity, drugs, dietand environmental toxins [61, 65]. Such epigeneticmodifications can affect all adaptions to exercise train-ing [10]. For example, in addition to nutrition and base-line physical activity status, there were many otherdifferences in subjects between articles not taken intoconsideration including: age, training duration and vol-ume (MICT vs. HIIT), body weight, body fat percentage,medications, clinical versus healthy populations; sleep,psychological status and the gut microbiome. Together,these are potential epigenetic modifiers (e.g. DNAmethylation and histone acetylation) that can influencegene expression, molecular function and thereby influ-ence VO2max training response [61, 66]. Whether genesor epigenetic modifiers play a larger percentage role inadaptive variability in a specific situation requiresfurther exploration.The Author(s) BMC Genomics 2017, 18(Suppl 8):831 Page 107 of 131To address these limitations, larger-scale studies are re-quired to ascertain if the 97 predictor genes identifiedfrom this review are similar in various cohorts (e.g. severalethnicities, ages, gender). The Athlome Project Consor-tium, which includes the Gene SMART study, is an ex-ample of a current larger-scale investigation examining‘omic markers’ of training response, elite performance andinjury rates/predisposition in variety of populations [67].Ideally, future studies will complement and expand on thisresearch, and consider alternative forms of exercise train-ing intensity and volume, lifestyle factors, general health,diet, medications and health history when implementinginterventions and analyzing data.Furthermore, the role of the gut microbiome, and itsinfluence on metabolism and physiology, needs to be ex-plored. For example, gut microbiota (which has its owngenome) can interact with the tissue cellular environ-ment to regulate gene expression [61]. Poor diet, stress,illness, the use of antibiotics, environmental toxins andpoor lifestyle choices can increase inflammation withinthe gut, causing dysbiosis; this appears to contribute tochronic diseases and other illnesses, irrespective of geno-type, age and gender [68, 69]. Interestingly, VO2max wasrecently shown to be related to gut microbial diversity ina human cross-sectional study [70], suggesting a linkbetween VO2max and gut microbes. Pre- and probiotics,resistant starch and a Mediterranean diet (dietary diver-sification) can alter the gut microbiome [68]. Investiga-ting how the gut and human genome interact topositively influence VO2max is warranted.With these points in mind, the analysis of stool sam-ples, in addition to incorporating epigenetic, transcrip-tion and proteomic analysis, may help to identify thebest aerobic training or lifestyle intervention to upregu-late or downregulate certain genes, signaling pathwaysand molecular responses required for a greater VO2maxtraining response. Implementing tightly-controlled stud-ies examining various mediators (training intervention,diet, lifestyle) and molecular biomarkers across variouspopulations will help to capture accurate information re-lated to ideal traits for VO2max trainability.ConclusionIn total, 97 genes that predicted VO2max trainability wereidentified. Phenotype is dependent on several of thesegenotypes/variants, which may contribute to approxi-mately 50% of an individual’s VO2max trainability. Higherresponders to exercise training have more positive re-sponse alleles (greater gene predictor score) than lowerresponders. Whilst these findings are exciting, furtherrandomized-controlled research with larger and diversecohorts are needed. Additional exploration is required toidentify genetic variants and the mediators (training in-tensity and volume, diet, drugs, other lifestyle factors)that can potentially affect gene expression, molecularfunction and training response. Findings from this re-view and future research may assist clinicians to provideprecision evidence-based medicine centered on pheno-type, contributing to the fight against chronic disease.Pubmed, embase, cinahl and cochrane search termsPubmed searchgene*[ti] OR allele [tiab] OR SNP [tiab] OR genetic pro-filing[tiab] OR genetic variant*[tiab] OR Genomic predic-tor*[tiab] OR polymorphism[tiab] OR heritability[tiab]AND (exercise training [tiab] OR VO2peak[tiab] OR‘cardiorespiratory fitness’[tiab] OR ‘maximal/maximumVO2peak’[tiab] OR maximal/maximum VO2max’[tiab]OR maximal oxygen consumption’[tiab]OR peak oxygenuptake’[tiab] OR interval exercise’[tiab] OR ‘high/low in-tensity exercise’[tiab] OR peak fitness [tiab] OR enduran-ce*[tiab] OR physical fitness[tiab] OR cardiorespiratoryfitness[tiab] OR endurance training [tiab] OR cardiovascu-lar fitness[tiab] OR VO2max[tiab] OR aerobic power[tiab]OR aerobic fitness[tiab] OR exercise capacity[tiab] OR ex-ercise training response[tiab] OR response to exercise trai-ning[tiab]) NOTanimal*.Embasegene:ab,ti OR allele:ab,ti OR snp:ab,ti OR ‘genetic profi-ling’:ab,ti OR ‘genetic variant’:ab,ti OR ‘genomic predic-tor’:ab,ti OR heritability:ab,ti AND (vo2peak:ab,ti ORvo2max:ab,ti OR ‘cardiovascular fitness’:ab,ti OR ‘cardio-respiratory fitness’:ab,ti OR ‘aerobic power’:ab,ti OR ‘aer-obic fitness’:ab,ti OR ‘exercise training response’:ab,ti OR‘physical fitness’:ab,ti).Cinahl(genes OR ‘genetic variant’ OR ‘Genomic predictor’ ORpolymorphism OR ‘genetic profiling’ OR ‘single nucleo-tide polymorphisms’ OR ‘SNPs’ heritability) AND (‘train-ability’ OR’ cardiovascular fitness’ OR ‘interval exercise’OR ‘maximum O2’ OR maximal oxygen consumption’OR ‘peak oxygen consumption’ OR maximal aerobiccapacity’ OR ‘high/low intensity exercise’ OR ‘cardiore-spiratory fitness’ OR ‘aerobic power’ OR ‘response to ex-ercise training’ OR ‘exercise capacity’ OR ‘VO2max’ OR‘VO2peak’ OR endurance).Cochrane database for systematic reviews(genes OR ‘genetic variant’ OR ‘Genomic predictor’ ORpolymorphism OR ‘genetic profiling’ OR ‘single nucleo-tide polymorphisms’ OR ‘SNPs’ OR heritability) AND(‘trainability’ OR’ cardiovascular fitness’ OR ‘interval ex-ercise’ OR ‘maximum O2’ OR maximal oxygen con-sumption’ OR ‘peak oxygen consumption’ OR maximalaerobic capacity’ OR ‘high/low intensity exercise’ OR‘cardiorespiratory fitness’ OR ‘aerobic power’ ORThe Author(s) BMC Genomics 2017, 18(Suppl 8):831 Page 108 of 131‘response to exercise training’ OR ‘exercise capacity’ OR‘VO2max’ OR ‘VO2peak’ OR endurance).Cochrane central register of controlled trial(genes OR ‘genetic variant’ OR ‘Genomic predictor’ ORpolymorphism OR ‘genetic profiling’ OR ‘single nucleo-tide polymorphisms’ OR ‘SNPs’ heritability) AND (‘train-ability’ OR’ cardiovascular fitness’ OR ‘cardiorespiratoryfitness’ OR ‘interval exercise’ OR ‘maximum O2’ ORmaximal oxygen consumption’ OR ‘peak oxygen con-sumption’ OR maximal aerobic capacity’ OR ‘high/lowintensity exercise’ OR ‘aerobic power’ OR ‘response toexercise training’ OR ‘exercise capacity’ OR ‘VO2max’OR ‘VO2peak’ OR endurance).FundingPublication of this manuscript was supported by the Collaborative ResearchNetwork for Advancing Exercise and Sport Science (CRN-AESS).Availability of data and materialsThe datasets supporting the conclusions of this article are included withinthe article.About this supplementThis article has been published as part of BMC Genomics Volume 18Supplement 8, 2017: Proceedings of the 34th FIMS World Sports MedicineCongress. The full contents of the supplement are available online at https://bmcgenomics.biomedcentral.com/articles/supplements/volume-18-supplement-8.Authors’ contributionsCW was the primary author. MW checked the nomenclature of all variantsand terminology used. JC, NE, UW, JL and KA provided expert advice andedits to the manuscript. All authors have read and approved the final manuscript.Ethics approval and consent to participateEthics approval from Bellberry.Consent for publicationWritten informed consent was obtained from the individuals involved inthis study.Competing interestsThe authors declare they have no competing interests.Publisher’s NoteSpringer Nature remains neutral with regard to jurisdictional claims inpublished maps and institutional affiliations.Author details1Centre for Research on Exercise, Physical Activity and Health (CRExPAH),School of Human Movement and Nutrition Sciences, The University ofQueensland, Brisbane, Queensland, Australia. 2Molecular GeneticsDepartment, Mater Pathology, South Brisbane, Queensland, Australia.3Institute of Sport, Exercise and Active Living (ISEAL), Victoria University,Melbourne 8001, Australia. 4Faculty of Health Sciences and Medicine, BondUniversity, Robina, Queensland, Australia. 5School of Health and ExerciseSciences, University of British Columbia, Okanagan, Canada. 6Cardiac K.G.Jebsen Center for Exercise in Medicine at Department of Circulation andMedical Imaging, Norwegian University of Science and Technology,Trondheim, Norway.Published: 14 November 2017References1. 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