Skip to main content Accessibility help

A critical evaluation of results from genome-wide association studies of micronutrient status and their utility in the practice of precision nutrition

Published online by Cambridge University Press:  31 July 2019

Marie-Joe Dib
Department of Nutritional Sciences, School of Biosciences and Medicine, University of Surrey, Guildford GU2 7XH, UK
Ruan Elliott
Department of Nutritional Sciences, School of Biosciences and Medicine, University of Surrey, Guildford GU2 7XH, UK
Kourosh R. Ahmadi
Department of Nutritional Sciences, School of Biosciences and Medicine, University of Surrey, Guildford GU2 7XH, UK
E-mail address:
Rights & Permissions[Opens in a new window]


Rapid advances in ‘omics’ technologies have paved the way forward to an era where more ‘precise’ approaches – ‘precision’ nutrition – which leverage data on genetic variability alongside the traditional indices, have been put forth as the state-of-the-art solution to redress the effects of malnutrition across the life course. We purport that this inference is premature and that it is imperative to first review and critique the existing evidence from large-scale epidemiological findings. We set out to provide a critical evaluation of findings from genome-wide association studies (GWAS) in the roadmap to precision nutrition, focusing on GWAS of micronutrient disposition. We found that a large number of loci associated with biomarkers of micronutrient status have been identified. Mean estimates of heritability of micronutrient status ranged between 20 and 35 % for minerals, 56–59 % for water-soluble and 30–70 % for fat-soluble vitamins. With some exceptions, the majority of the identified genetic variants explained little of the overall variance in status for each micronutrient, ranging between 1·3 and 8 % (minerals), <0·1–12 % (water-soluble) and 1·7–2·3 % for (fat-soluble) vitamins. However, GWAS have provided some novel insight into mechanisms that underpin variability in micronutrient status. Our findings highlight obvious gaps that need to be addressed if the full scope of precision nutrition is ever to be realised, including research aimed at (i) dissecting the genetic basis of micronutrient deficiencies or ‘response’ to intake/supplementation (ii) identifying trans-ethnic and ethnic-specific effects (iii) identifying gene–nutrient interactions for the purpose of unravelling molecular ‘behaviour’ in a range of environmental contexts.

Full Papers
© The Authors 2019 

There is an urgent need and desire to move away from the currently used trial-and-error or one-size-fits-all approaches in clinical or public health practice, respectively, towards programmes that leverage data on the genomic and phenomic determinants of health/disease to provide more ‘personalised’, ‘stratified’ or ‘precise’ diagnosis, treatment and prevention of common, multifactorial diseases. The flagship UK Biobank programme (, established in 2007, the Genomics England initiative ( in 2013, or the more recent US precision medicine initiative project in 2015 ( all have the common, overarching goal of providing novel insight(s) into disease aetiology and the promise of a new perceived era of ‘better tests, better drugs and above all better, more personalised care to save lives’(1). The reaction to these initiatives has been mixed(Reference Bayer and Galea2, Reference Khoury, Iademarco and Riley3). Most notably, public health experts remain sceptical as they argue an approach that focuses excessively on genes, drugs and disease, whilst ignoring the notion that the major causes of morbidity/mortality in the developed world are potentially preventable – that is, by reducing tobacco or alcohol use, poor diet, physical inactivity and by addressing social differences among populations – remains piecemeal in the case of a majority of non-communicable diseases. They advocate a ‘precision public health’ initiative that moves beyond diagnoses and treatment of individuals to providing the right intervention to the right strata of the population at the right time(Reference Rose4).

Following on from the precision public health-pronged approach, the concept of ‘precision’ nutrition has recently branched out from the ideas underpinning ‘personalised’ nutrition; note that these two terms are often used interchangeably in the literature. Although, there has been little discussion pertaining to the shift from personalised to precision nutrition, from a practical viewpoint the shift broadly describes moving beyond current dietetic-type practice – which bases a dietary plan for a specific disease diagnosis/prognosis, on anthropometric, lifestyle, demographic and psychosocial data – to a setting that also includes genomic data for more targeted and precise adjunct dietary intervention(Reference Lampe, Navarro and Hullar5). This shift within the nutritional sciences has been largely driven by the promises and initial excitement of the genomic medicine revolution, through genome-wide association studies (GWAS), which have been fuelled by the rapid growth in technology offering unprecedented resolution of the human genome at ever decreasing cost. However, putting specific forms of cancer aside, we now know that the genetic variants that have been identified through GWAS explain little of the overall risk or heritability for a majority of the common, multifactorial diseases and are of little practical value for the purposes of ‘prediction’ of individual or group-level outcomes; see Khera et al. (Reference Khera, Chaffin and Aragam6) for an opposing viewpoint. While genetic risk scores (GRS) or polygenic risk scores (PRS) are now being developed in the context of common, multifactorial diseases, their utility in terms of greater predictive performance, or inclusion as part of routine clinical care for better diagnosis, prognosis and monitoring remains largely unknown outside oncology/cancer and warrants formal assessment in pragmatic settings. Furthermore, it is important to state that GRS or PRS have not thus far had any application in micro/macronutrient gene mapping studies. To our knowledge, there are currently no published studies focused on assessing GRS for prediction of micronutrient status. Nevertheless, GRS may play an important role in precision nutrition given that there does seem to be a simpler genetic architecture underpinning population variability in the status of some micronutrients. So, we purport that while personalised nutrition is already commonly used in dietetic practice, and largely without use of genetic information, the move to precision nutrition approaches that advocate the use genetic data is premature. To achieve the full scope of precision nutrition, it is imperative to first review and synthesise the existing evidence – or lack thereof – from large-scale genetic epidemiology studies that have aimed to dissect the complex interplay of diet and genomics before we even contemplate their utility to dissect human health disparities.

To this end, we set out to provide a critical evaluation of findings from GWAS studies of micronutrient status with three overarching aims. First, we aimed to produce a complete catalogue of genetic variants that have been identified through GWAS of micronutrient status. Secondly, we aimed to highlight and critique the principal findings from this catalogue and their utility within clinical or public health practice. Lastly, we aimed to highlight the main gaps that need to be filled to direct future research on the roadmap to precision nutrition. We decided to focus on micronutrients primarily because of their amenability to GWAS studies in terms of function, associations to myriad of common diseases, and their unbiased biochemical assessments.

A catalogue of genes associated with micronutrient status

We used the GWAS catalogue ( and the National Center for Biotechnology Information (NCBI) to identify all GWAS that have been completed to date in relation to micronutrient disposition. We investigated both biomarkers of micronutrient status and biomarkers of micronutrient function, which provide more insight into cellular metabolism and offer more relevant information regarding the biological and physiological function of the micronutrients. Our findings are summarised in Table 1.

Table 1. Summary of genome-wide association studies (GWAS) that have identified genomic loci associated with micronutrient status in Caucasian adults

ACSF3, acyl-CoA synthetase family member 3; ALPL, alkaline phosphatase, biomineralized associated; AMDHD1, amidohydrolase domain containing 1; APOA1/C3/A4/A5, apolipoprotein A1/C3/A4/A5; ARHGEF3, rho guanine nucleotide exchange factor 3; ARL15, ADP ribosylation factor like GTPase 15; ARSB, arylsulfatase B; ATP2B1, ATPase plasma membrane Ca2+ transporting 1; BCMO1, β-carotene oxygenase 1; BHMT, betaine-homocysteine methyltransferase; BHMT2, betaine–homocysteine S-methyltransferase 2; BUD13, BUD13 homolog; CA1, carbonic anhydrase 1; CA13, carbonic anhydrase 13; CA2, carbonic anhydrase 2; CA3, carbonic anhydrase 3; CAPN2, calpain 2; CAPN8, calpain 8; CASR, Ca sensing receptor; CBS, cystathionine-β-synthase; CCDC58, coiled-coil domain containing 58; CCDC27, coiled-coil domain containing 27; CPS1, carbamoyl-phosphate synthase 1; CUBN, cubilin; CYP24A, cytochrome P450 family 24 subfamily A member; CYP24A1, cytochrome P450 family 24 subfamily A member 1; CYP2R1, cytochrome P450 family 2 subfamily R member 1; CYP4F2, cytochrome P450 family 4 subfamily F member 2; DCDC5, double cortin domain containing 5; DGKD, diacylglycerol kinase delta; DHCR7, 7-dehydrocholesterol reductase; DMGDH, dimethylglycine dehydrogenase; DNMT2, DNA methyltransferase-2; DPEP1, dipeptidase 1; FGF23, fibroblast growth factor 23; FGF6, fibroblast growth factor 6; FGFR2, fibroblast growth factor receptor 2; FIGN, Fidgetin; FUT2, fucosyltransferase 2; GATA3, GATA binding protein 3; GC, group-specific component; GCKR, glucokinase regulatory protein; GTPB10, GTP binding protein 10; HBS1L, HBS1 like translational GTPase; HFE, homeostatic Fe regulator; HIBCH, 3-hydroxyisobutyryl-CoA hydrolase; HNF1A, HNF1 homeobox A; ITPR3, inositol 1,4,5-trisphosphate receptor type 3; JMY, junction mediating and regulatory protein, P53 cofactor; KIAA0564, Von Willebrand factor A domain containing 8; KLF8, kruppel like factor 8; LEMD2, LEM domain containing 2; LHFPL2, LHFPL tetraspan subfamily member 2; MCH, mean corpuscular Hb; MCV, mean corpuscular volume; MK167, marker of proliferation Ki-67; MLN, motilin; MMA, methylmalonic acid; MMACHC, metabolism of cobalamin associated C; MPV, mean platelet volume; MTHFR, methylenetetrahydrofolate reductase; MTR, 5-methyltetrahydrofolate-homocysteine methyltransferase; MUL1, mitochondrial E3 ubiquitin protein ligase 1; MUT, methylmalonyl-CoA mutase; MYB, MYB proto-oncogene; NADSYN1, NAD synthetase 1; NBPF3, NBPF member 3; NOX4, NADPH oxidase 4; SLC23A1, solute carrier family 23 member 1; PAPSS2, 30-phosphoadenosine 50-phosphosulfate synthase 2; PDE7B, phosphodiesterase 7B; PIK3CG, phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit Y; PPDC, prepilin peptidase-dependent protein C; PRICKLE, prickle planar cell polarity protein; PRKCE, protein kinase C epsilon; PTPRE, protein tyrosine phosphatase, receptor type E; RAD51AP1, RAD51 associated protein 1; RBC, erythrocyte; RBP4, retinol binding protein 4; SCAMP, secretory carrier membrane protein; SCAMP5, secretory carrier membrane protein 5; SCARB1, scavenger receptor class B member 1; SEC23A, Sec23 homolog A, coat complex II component; SELENBP1, Se binding protein 1; SHROOM3, shroom family member 3; MDS1, myelodysplasia syndrome 1 protein; SLC17A3, solute carrier family 17 member 3; SLC30A10, solute carrier family 30 member 10; SLC34A1, solute carrier family 34 member 1; SLC39A8, solute carrier family 39 member 8; TAOK1, TAO kinase 1; TCN1, transcobalamin 1; TF, transferrin; TFR2, transferrin receptor 2; TMPRSS6, transmembrane serine protease 6; TRPM6, transient receptor potential cation channel subfamily M member 6; TRR, transfer RNA arginine; WDR66, WD repeat domain 66; ZNF259, Zn finger protein 1; ZXDA, Zn finger X-linked duplicated A; ZXDB, Zn finger X-linked duplicated B.

* Results presented in this table are genome-wide significant (P < 10−7), and/or significant at P < 0·05 level in replicated, independent Caucasian cohorts.

Indicates that no results were found.

All heritability results are from twin studies, except serum retinol (family study).

§ Serum 25-hydroxy-vitamin D levels were found to be highly heritable during winter months only, and not heritable at all during summer months(Reference Karohl, Su and Kumari32).

To our surprise, we found that all major micronutrients – including, minerals, fat- and water-soluble vitamins – have been studied through GWAS, and that a varied number of loci associated with status of each micronutrient have been identified. Notably, a majority of the micronutrient-related heritability or GWAS have been carried out using healthy, Caucasian populations, which is why our summary table is limited to these criteria.

Heritability of micronutrient status

A number of important summary statistics can be extracted from Table 1. Estimates of heritability (h2) ranged from 23 % for serum Fe status up to 70 % for 25-hydroxy-vitamin D. In general, mineral statuses were the least heritable (mean h2 = 35 %) followed by water (mean h2 = 48 %) – and fat-soluble (mean h2 = 50 %) vitamin status. We failed to identify a reported h2 for a sizable number of micronutrients. In fact, of the twenty-two micronutrients reported in Table 1, only ten had a reported h2 value and they were predominantly based on twin rather than family studies. There was very good coverage of the minerals with only two – Mn and P – out of the eight minerals not having a reported h2 value. Ca status was by far the most heritable mineral at h2 = 61 % whilst all other minerals exhibited low heritability values. Of the water- or fat-soluble vitamins, for example, only vitamins A (serum retinol measurements), B9 (erythrocyte folate), B12 and D have reported h2 values. Perhaps not surprisingly, in the case of 25-hydroxy-vitamin D, only the winter measurements were heritable and status of 25-hydroxy-vitamin D was not heritable at all during summer months(Reference Karohl, Su and Kumari32). It is important to note that we did not identify any heritability studies in non-Caucasian populations.

Genome-wide association studies of micronutrient status

We identified twenty-two micronutrients that had at least one GWAS reported and of the twenty-two all identified at least one significant (P < 10−7 in discovery and significant at P < 0·05 level in replicated, independent cohorts) associated variant. Those micronutrients missing from the GWAS catalogue were B, Co, Cl, Cr, iodine, Mo, K, Na, vitamins B1, B2, B3, B7 and choline.

The proportion of variance explained by the identified variants – individually or collectively – ranged from <1 % in the case of serum ferritin to 11 % for vitamin C status (l-ascorbic acid concentrations) and in general the majority of the variants explain little of the overall heritability in status for each micronutrient although there are notable exceptions. For example, a single common single nucleotide variant in the genes SLC23A1 and HIBCH explains 11 and 12 % of the inter-individual variability in l-ascorbic acid and methylmalonic acid (MMA – a functional biomarker of vitamin B12 status), respectively(Reference Timpson, Forouhi and Brion41, Reference Molloy, Pangilinan and Mills46). Moreover, in the case of minerals generally, often a small number of variants explain a sizable proportion of the heritability; on average, 14 % (range: 2–28 %) of the heritability in mineral status is explained by the variant. The situation is far less clear for water-/fat-soluble vitamins mainly due to unavailability of adequate data for a majority of these micronutrients. Furthermore, we could not identify reported estimates of the variability of variants identified in a number of GWAS, including those of Mn (the only mineral to not provide this estimate), and vitamins B6, pro-vitamin A serum α-carotene, vitamin D and vitamin K.

Lessons from heritability and genome-wide association studies of micronutrient status

A number of observations and inferences can be made about the available data but four points warrant a particular mention. First, it is important to state that certain aspects of the data from Table 1 stand in direct contrast to findings from GWAS of common traits/diseases where often a very large number of loci explain a small fraction of the inter-individual variation or trait heritabilities. Second, we found a dearth of (discovery) studies undertaken in other ethnic groupings, although a small number of replication studies were undertaken in East Asian and African-American cohorts(Reference Kim, Kwangsik and Ramanana50Reference Li, Lange and Duan54). Third, there seems to be a general lack of utility provided by the identified genetic variants for prediction of individual and/or group-level status although GWAS have provided some novel insight into mechanisms that underpin inter-individual variability in micronutrient status. This is exemplified in the recent study by Molloy et al. (Reference Molloy, Pangilinan and Mills46). Their replicated findings showed that serum MMA levels were unexpectedly largely influenced by a cobalamin-independent pathway involved in valine-catabolism mediated by the HIBCH gene product. Elevated plasma MMA concentrations are clinically used to diagnose individuals with inborn errors of metabolism or with vitamin B12 deficiency (i.e. the elderly). The discovery of this SNP (1) questions the clinical utility of circulating MMA assessment in diagnosing cobalamin deficiency, and (2) may be relevant to study and consider in individuals possessing the variant who are in states of acute disease. Finally, we think the data clearly highlight the current gaps in knowledge and the significant challenges ahead that need to be addressed before we entertain the scope of precision nutritional approaches that leverage genetic information in either clinical or public health settings.

Gaps in knowledge and challenges ahead

In our opinion, there are three overarching challenges that warrant particular attention. First and foremost, most of the data presented in Table 1 are derived from studies based on supposedly ‘healthy’ variability in status. Furthermore, there is a dearth of heritability or GWAS studies that aim to partition the genetic basis of ‘functional’ micronutrient deficiency or studies that have evaluated the role of the identified loci in Table 1 in increasing the risk of functional deficiency. Secondly, we identified only one GWAS of (differential) ‘response’ to micronutrient intervention in either health or disease, although there are some candidate gene/polymorphism studies. This is a particularly important issue as currently the underlying genetic architecture of micronutrient ‘status’ and ‘response’ to intervention remains totally unknown. Indeed, there is little point in identifying strata of the population at elevated, genetically-driven risk of functional deficiency if they will not benefit from supplementation (i.e. due to lack of beneficial response) as a result of an alternative genetic cue. Thirdly, an obvious question remains as to the portability of findings from GWAS across populations, which could hinder the translation of findings into relevant recommendations/applications in different ethnic groups. As shown in Table 1, the current data stem overwhelmingly from Caucasian discovery cohorts motivating future studies that aim to test and identify population-specific and trans-ethnic micronutrient-associated genetic loci. Although questions about the complexity of gene–nutrient interactions and their role in diseases with environmental causes have been previously discussed, it is nonetheless vital to explore in more depth the adaptability of molecular processes modelled by different environments and contexts.

Unravelling the genetic basis of functional micronutrient deficiency

As outlined in Table 1, the application of GWAS has allowed us to identify genetic loci associated with static and, when available, functional biomarkers of micronutrient status in healthy subjects. We would like to highlight a general lack of adequate functional biomarker(s) of micronutrient status that are sensitive in distinguishing between healthy variability, insufficiency and overt or severe deficiency, particularly at different levels of dietary exposure. Notably, there seems to be a gulf of either unavailability of a functional biomarker per se, as is the case for Cu and Zn, to unavailability of biomarkers for assessing status in replete, healthy subjects.

The lack of adequate functional biomarkers thus represents a major Achilles heel when trying to unravel the contribution of genetic variation to the full spectrum of micronutrient status. This has been discussed in depth by Combs in the case of Se status but the topic warrants a more general discussion across all the micronutrients, which is beyond the scope of the current commentary(Reference Combs55). Perhaps a good case example that highlights the complexities associated with genetic studies of static and functional biomarkers of micronutrient status is represented by a recent GWAS of vitamin B12 status. For instance, there are now a number of GWAS studies of serum vitamin B12 levels as well as a recent GWAS of serum MMA – a functional early and specific indicator of (mitochondrial) vitamin B12 status/deficiency. As can be seen from Table 1, there are five independent loci that have been shown to be significantly associated with serum vitamin B12 concentrations in healthy adults, including common genetic variants in genes FUT2, TCN1, DNMT2, CUBN and MUT which collectively explain approximately 3 % of the heritability in serum vitamin B12 levels. A notable finding from the recent GWAS of MMA (2016) was the discovery of genetic variants in the gene 3-hydroxyisobutyryl-CoA hydrolase (HIBCH) and acyl-CoA synthetase family member 3 (ACSF3) genes that collectively explain approximately 12 % of the variance in MMA concentration in healthy adults. We have recently shown that the heritability for normal MMA levels is approximately 15 % which means that >80 % of the heritable variation in MMA is accounted for by variation at these two genes (under review). What is important about this finding is that genetic variation at neither of these two genes is associated with serum vitamin B12 concentrations and none of the five genes associated with serum vitamin B12 concentrations are associated with MMA concentration in healthy adults. This highlights the difficulties associated with using biomarkers of deficiency to study health, given previous findings from randomised controlled trials and systematic studies that have shown that plasma vitamin B12 concentrations, MMA and total homocysteine are effective biomarkers of vitamin B12 status(Reference Hoey, Strain and McNulty56).

Our search of both the published literature and the GWAS catalogue, in fact, highlighted a dearth of genetic epidemiology research aimed at unravelling the relative importance and contribution of genetic and environmental factors to the cause of functional micronutrient deficiency. Although inadequate exposure is often cited as the most important cause of deficiency, there are a number of potential factors that may govern micronutrient bioavailability – including absorption, distribution or transport, metabolism, elimination/excretion (ADME) that may manifest over time as micronutrient deficiency. These include negative nutrient–drug interactions(Reference Péter, Navis and de Borst57), physiological changes through ageing, occurrence of disease or pathophysiological changes that occur predisease diagnosis, dietary intervention and genetic factors cross-cutting all these processes that can either affect ADME or increase the risk of the processes affecting aberrant ADME processes.

We note that out of all the micronutrients reported in Table 1, only two, Fe (serum ferritin) and vitamin K (percentage decarboxylated osteocalcin), were studied in deficient subjects(Reference Benjamin, Dupuis and Larson58, Reference Mclaren, Garner and Constantine59) and, to our knowledge, there are currently no reported heritability studies of functional micronutrient deficiency per se. Although not directly comparable, we note that there were no overlaps between findings of GWAS of deficiency in Fe or vitamin K and healthy status. As shown in Table 1, genome-wide significant loci were mapped to the IGL and to PTRPE and MK167 loci in the case of Fe and vitamin K deficiency, respectively. None of these loci were shown to be significant in GWAS of Fe or vitamin K in non-deficient, healthy subjects(Reference Benjamin, Dupuis and Larson58). This may indeed be a function of the GWAS approach and the difficulty in detecting associated loci at genome-wide significance(Reference Marini, Yang and Asrani60). On this point, we would advocate the use of a combination of genetic approaches (candidate polymorphism, gene and pathway) and would like to draw attention to two alternative gene mapping efforts that we believe warrant particular attention for precision nutrition approaches. First, a recent case finding from the 100 000 genomes project illustrates the use of large-scale sequencing approaches for precision nutrition. A newborn with fluctuating neurology and immunodeficiency was one of the participants of the 100 000 genomes project. Although sadly the child passed away, deep sequencing led to the identification of mutations in the TCN2 gene, which lead to defective vitamin B12 transport. Case reports had suggested high doses of vitamin B12 might overcome this and indeed it seems this has helped a second child in the family. The scope of this finding is best illustrated by the study of Marini et al., which has shown that the effects of some of the functional mutations in the MTHFR gene, which impact activity of the methyl transferase enzyme, can be remedied through folic acid supplementation(Reference Marini, Gin and Ziegle61), further supporting the idea that the identification of polymorphisms that have subtle effects on functional micronutrient status could provide us with further insights for the remedial of dysfunctional enzymes, and could potentially be of use in providing corrective measures to allele carriers on a larger scale.

Deciphering the genetic basis of variable response to dietary micronutrient intervention/exposure

A major gap in the roadmap to precision nutrition is the lack of knowledge, appreciation and, worryingly, dearth of research into unravelling the basis and relative contribution of acquired and/or inherited factors to the observed variability in response to dietary micronutrient exposure or intervention. Consequently, the underlying genetic basis, and similarities and/or differences in the genetic basis of ‘status’ and ‘response’ to intervention remain largely unexplored.

There are only a handful of studies that have attempted to (i) partition the relative importance of genetic and environmental factors in response to dietary interventions; or (ii) identify genetic variants associated with response to dietary intervention. To our knowledge, only one classical twin study (n 101 twin pairs), using a ‘before and after’ intervention trial of 400 µg of folic acid per d for a period of 6 weeks, has calculated heritabilities of plasma folate status and total homocysteine (tHcy) concentrations at baseline, post-dose and response to intervention. Furthermore, they tested whether the MTHFR C677T polymorphism, which is known to be associated with steady-state – that is, baseline levels – plasma folate or tHcy concentrations, is also associated with differential response to folic acid intervention. The results showed that both steady-state levels (h2 = 59 %) and ‘response’ to folate intervention were both highly heritable (h2 = 64 %) with 22 % of the cohort failing to respond folate supplementation – that is, no change in tHcy – although most participants had up to a 3-fold increase in folate status. Notably, the MTHFR C677T polymorphism was not associated with response to intervention although it showed association with baseline levels of tHcy(Reference Cotlarciuc, Andrew and Dew62). In support of this observation, two separate GWAS on the Beta-Carotene Cancer Prevention Study cohort (2011 and 2012) sought to identify common genetic variants associated with both vitamin E status(Reference Major, Yu and Wheeler63) and serological response to vitamin E supplementation(Reference Major, Yu and Chung64). In line with findings from Cotlarciuc et al. (Reference Cotlarciuc, Andrew and Dew62), given power and design constraints, the genetic architecture of status and response in the case of vitamin E seem to be largely discordant with no genes and variants therein associated with both outcomes. The present study also highlights a lack of clarity as to what ‘response’ would entail. For example, in the studies by Major et al. (Reference Major, Yu and Wheeler63, Reference Major, Yu and Chung64) a lack of serological response to vitamin E supplementation could either mean that the vitamin E was not absorbed or that it may simply be cleared from the circulation very quickly in some people as a result of genetic factors.

Finally, Mao et al. (Reference Mao, Bath and Vanderlelie65) carried out a candidate polymorphism association study, using the Selenium in Pregnancy Intervention (SPRINT) study of 227 pregnant women, to test for associations between common genetic variation in four genes (DMGDH, SEPP1, GPx1, and GPx4), previously associated with Se status in non-pregnant populations, to Se status in the first trimester of pregnancy, its longitudinal change across the three trimesters, and response to Se supplementation of 60 µg per d. Briefly, the study replicated the known association between the common, candidate polymorphism in the gene DMGDH and Se status (based on both toenail and whole-blood) explaining approximately 2 % of the variance in whole-blood Se concentration at 12 weeks of gestation. In unsupplemented women, the candidate polymorphism in the SEPP1 gene was significantly (P ≤ 0·005) associated with the longitudinal change in whole-blood Se levels from 12 to 20 through to 35 weeks of gestation, explaining 8 % of the variance in this healthy change in Se status across the three trimesters. The same polymorphism in the SEPP1 gene was associated with adequate response to Se supplementation, as measured by the change in GPx3 activity from 12 to 35 weeks of gestation, explaining >5 % of the variance in response to intervention(Reference Mao, Bath and Vanderlelie65). The results from the present study highlight that given an appropriate study design and power, genetic variants can explain a sizable fraction of the variability of healthy change in Se status throughout pregnancy as well as beneficial response to intervention in insufficient/deficient pregnant mothers. Importantly, these results highlight the inherent complexities associated with interpretation of gene/nutrient relationships given different contexts – pregnancy, old age and disease status – and motivate systematic studies, using novel designs in a myriad of settings, aimed at unravelling the genetic basis of response due to a change in environmental context or to dietary interventions and the identification of subpopulations of hyper, normal and hypo responders to intervention.

Identifying trans-ethnic and ethnic-specific genetic effects

To date, all micronutrient biomarker status heritability studies have been done on Caucasian population groups, and very few GWAS have investigated genetic association to micronutrient status in other ethnicities. In fact, merely two association studies have been done on vitamin B12 status in Asian populations(Reference Lin, Lu and Gao52, Reference Nongmaithem, Joglekar and Krishnaveni53), and one on Fe status in African Americans(Reference Li, Lange and Duan54). Consequently, there is a major gap and skew in knowledge with regard to understanding the genetic basis of micronutrient status variability in different ethnicities, potentially tempering the portability of GWAS findings within and across populations of different ancestry and the translatability of the latter into relevant public health application.

Several studies have demonstrated the importance of trans-ethnic replication in the field of personalised medicine. For instance, the benefits of trans-ethnic GWAS with regard to both discovery and characterisation of complex trait loci have been exemplified in a trans-ancestry genome-wide meta-analysis, in which trans-ethnic association analysis allowed the identification of additional type 2 diabetes susceptibility loci, thus extending knowledge into the genetic architecture of the disease(Reference Mahajan, Go and Zhang66). Additionally, a study that ran a comprehensive survey of GWAS replicability across twenty-eight diseases found that some SNP–disease associations failed to replicate in East Asians, and that these SNP were mapped to genomic regions where linkage disequilibrium significantly varies between populations(Reference Marigorta and Navarro67). Trans-ethnic mapping is a potentially powerful tool in identifying genetic variants underlying micronutrient status variability, and subsequently improving public health application/intervention for the purposes of disease prevention. More trans-ethnic GWAS are needed to evaluate the genetic architecture differences in linkage disequilibrium patterns that may be driving genetic associations, and to gain a deeper understanding of the role of identified genetic variants in the complex genetic architecture of micronutrient status variability(Reference Li and Keating68).

Findings from our literature search have also highlighted the existence of population/ethnic-specific genetic variants. Most notably, the vitamin B12-associated FUT6 gene was mapped in Asians, and the Fe-associated MAF and HDGFL1 genes were only mapped in African Americans, but not in Caucasians. In contrast, the TF gene, encoding the transferrin protein, was identified in both Caucasians and African Americans. Identifying pan-ethnic and ethnic-specific variants in a wider range of ethnicities therefore remains a major priority in the roadmap to precision nutrition, as they (1) could potentially provide us with the know-how to ethnically stratify populations, and would be a stepping stone in gaining a deeper understanding of the gene–nutrient interactions attributed to such stratification, and (2) are essential for the portability of GWAS results, and for making distinctions between genome-wide associations driven by population-specific variants in multi-ethnic studies.

Context-dependency of molecular behaviour

While the interest in understanding the links between the genome, environment and complex disease risk is growing, questions regarding the importance of unravelling the gene–nutrient interactions for the purpose of personalised or precision nutrition have been brought forward(Reference Khoury and Evans69, Reference Mozaffarian70). In fact, it has been suggested that such efforts may not be needed, and that the use of biochemical and/or anthropometric indices may be enough in formulating personalised dietary interventions, as these are environmental risk factors known to influence disease risk – namely CVD risk(Reference Mozaffarian70). To that, we respond that investigating gene–nutrient interactions should remain a priority for the refinement of dietary interventions and the management of disease risk, as robust evidence now suggests that genetic variants (1) are able to modify the response to interventions(Reference Heianza and Qi71), and (2) can trigger adverse health outcomes when exposed to high-risk environmental factors(Reference Heianza and Qi71), a concept we term ‘context-dependency of molecular behaviour’. Additionally, several studies have shown that gene polymorphisms respond to nutrient interaction, affecting biochemical markers of metabolic and CVD(Reference Tai, Corella and Demissie72Reference Zheng, Parnell and Smith74) and obesity(Reference Heianza and Qi71). However, how these polymorphisms interact with nutrients in different contexts of health, disease, drug interventions and across ethnicities has not been investigated to date, and is needed for consideration in future research.

Further to investigating the molecular behaviour of micronutrient–gene interactions in the abovementioned scenarios, a longer-term goal would be to explore their molecular individuality, aiming to understand how molecular systems change with respect to different environmental contexts. A paper by Thompson et al. (Reference Thompson, Kuttab-Boulos and Witonsky75) showed unusual geographic dependency of the CYP3A*3 polymorphism – a gene encoding monooxygenases that catalyse reactions involved in drug metabolism and steroid synthesis – frequency with relative distance from the equator. Interestingly, CYP3A*3 frequency significantly correlated with that of another variant, M235T, known for its implication in hypertension. Findings also suggested that both variants exhibited strong frequency variations across populations and were significantly influenced by environmental factors correlated with latitude(Reference Thompson, Kuttab-Boulos and Witonsky75). Such shared selective pressure calls for further investigation of molecular behaviour in response to a range of environmental stimuli. Identifying the adaptability of these molecular behaviours in response to both short-term and long-term environmental exposures (e.g. pregnancy, healthy ageing, cross-culturally or different living arrangements) would allow further sub-stratification of population groups, and would open new avenues for a more ‘targeted’ and ‘precise’ public health approach.


Advances in technology, powerful biobanks and implementation of large-scale ‘omics’ initiatives and genomic medicine have quickly transcribed a new era of nutritional risk management based on the concept of precision nutrition, promising the implementation of precise preventative measures to thwart the burden of non-communicable disease in the population. However, if the full scope of precision nutrition is ever to be realised, the rigour and direction of the research needs to be evaluated and re-focused. Our overarching conclusion is that, while GWAS have delivered some significant new mechanistic insights, on their own they will not deliver on the vision of precision nutrition. We believe that this type of analysis highlights the need to pause to consider and refine experimental strategies. In the absence of knowledge about how and why individuals respond so differently to dietary changes and intervention, we believe that there is currently insufficient molecular and physiological knowledge to be able to provide robust guidance on how best to resolve micronutrient deficiencies in an individualised or population-based manner. It is important to use the results from Table 1 as incentives for (i) discovery of functional, robust biomarkers of status; (ii) investigating the underlying mechanisms of the established associations, as this holds better promise in advancing precision nutrition than predicting health/disease/status outcomes does; and (iii) identifying genetic variants associated with functional deficiency, nutritional disease, response to supplementation, trans-ethnic and ethnic-specific effects. Furthermore, gaining a deeper understanding of the behavioural changes of molecular systems in different environmental contexts (e.g. pregnancy, old age, disease) remain important priorities in shifting from the current one-size-fits-all and trial-and-error dietary public health approaches to a more precise population-based approach. Finally, open acknowledgment of how little we currently know per se. For example, who could argue with the notion that the microbiome and the vast genetic variability therein plays major role in governing inter-individual variability in status and response to dietary exposure? There is a growing literature on this exciting area of research but more work is needed to delineate the role of the microbiome and assess their utility in practice(Reference Zmora, Zeevi and Korem76Reference Zeevi, Korem and Zmora79).


Conception and design were by K. R. A.; M.-J. D. and K. R. A. carried out data acquisition and interpretation. M.-J. D., R. E. and K. R. A. all contributed to drafting the manuscript and revision for intellectual content. All authors read and approved the final manuscript.

There were no conflicts of interest.


Genomics England (2013) Genomics England launched, mapping DNA to better understand cancer, rare and infectious diseases. (accessed July 2019).Google Scholar
Bayer, R & Galea, S (2015) Public health in the precision-medicine era. N Engl J Med 373, 499501.CrossRefGoogle ScholarPubMed
Khoury, MJ, Iademarco, MF & Riley, WT (2016) Precision public health for the era of precision medicine. Am J Prev Med 50, 398401.CrossRefGoogle ScholarPubMed
Rose, G (1985) Sick individuals and sick populations. Int J Epidemiol 14, 3238.CrossRefGoogle ScholarPubMed
Lampe, JW, Navarro, SL, Hullar, MAJ, et al. (2013) Inter-individual differences in response to dietary intervention: integrating omics platforms towards personalised dietary recommendations. Proc Nutr Soc 72, 207218.CrossRefGoogle ScholarPubMed
Khera, AV, Chaffin, M, Aragam, KG, et al. (2018) Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations. Nat Genet 50, 12191224.CrossRefGoogle ScholarPubMed
Whitfield, JB & Martin, NG (1984) The effects of inheritance on constituents of plasma: a twin study on some biochemical variables. Ann Clin Biochem 21, 176183.CrossRefGoogle ScholarPubMed
Williams, PD, Puddey, IB, Martin, NG, et al. (1992) Platelet cytosolic free calcium concentration, total plasma calcium concentration and blood pressure in human twins: a genetic analysis. Clin Sci 82, 493504.CrossRefGoogle ScholarPubMed
Hunter, DJ, Lange, MD, Snieder, H, et al. (2002) Genetic contribution to renal function and electrolyte balance: a twin study. Clin Sci (Lond) 103, 259265.CrossRefGoogle ScholarPubMed
O’Seaghdha, CM, Wu, H, Yang, Q, et al. (2013) Meta-analysis of genome-wide association studies identifies six new loci for serum calcium concentrations. PLoS Genet 9, e1003796.CrossRefGoogle ScholarPubMed
O’Seaghdha, CM, Yang, Q, Glazer, NL, et al. (2010) Common variants in the calcium-sensing receptor gene are associated with total serum calcium levels. Hum Mol Genet 19, 42964303.CrossRefGoogle ScholarPubMed
Kapur, K, Johnson, T, Beckmann, ND, et al. (2010) Genome-wide meta-analysis for serum calcium identifies significantly associated SNPs near the calcium-sensing receptor (CASR) gene. PLoS Genet 6, e1001035.CrossRefGoogle ScholarPubMed
Whitfield, JB, Dy, V, McQuilty, R, et al. (2010) Genetic effects on toxic and essential elements in humans: arsenic, cadmium, copper, lead, mercury, selenium, and zinc in erythrocytes. Environ Health Perspect 118, 776782.CrossRefGoogle ScholarPubMed
Evans, DM, Zhu, G, Dy, V, et al. (2013) Genome-wide association study identifies loci affecting blood copper, selenium and zinc. Hum Mol Genet 22, 39984006.CrossRefGoogle ScholarPubMed
Njajou, OT, Alizadeh, BZ, Aulchenko, Y, et al. (2006) Heritability of serum iron, ferritin and transferrin saturation in a genetically isolated population, the Erasmus Rucphen Family (ERF) study. Hum Hered 61, 222228.CrossRefGoogle Scholar
Whitfield, JB, Cullen, LM, Jazwinska, EC, et al. (2000) Effects of HFE C282Y and H63D polymorphisms and polygenic background on iron stores in a large community sample of twins. Am J Hum Genet 66, 12461258.CrossRefGoogle Scholar
Marroni, F, Grazio, D, Pattaro, C, et al. (2008) Estimates of genetic and environmental contribution to 43 quantitative traits support sharing of a homogeneous environment in an isolated population from South Tyrol, Italy. Hum Hered 65, 175182.CrossRefGoogle Scholar
Pichler, I, Minelli, C, Sanna, S, et al. (2011) Identification of a common variant in the TFR2 gene implicated in the physiological regulation of serum iron levels. Hum Mol Genet 20, 12321240.CrossRefGoogle ScholarPubMed
Tanaka, T, Roy, CN, Yao, W, et al. (2010) A genome-wide association analysis of serum iron concentrations. Blood 115, 9496.CrossRefGoogle ScholarPubMed
Chambers, JC, Zhang, W, Li, Y, et al. (2009) Genome-wide association study identifies variants in TMPRSS6 associated with hemoglobin levels. Nat Genet 41, 11701172.CrossRefGoogle ScholarPubMed
Ganesh, SK, Zakai, NA, Van Rooij, FJA, et al. (2009) Multiple loci influence erythrocyte phenotypes in the CHARGE Consortium. Nat Genet 41, 11911198.CrossRefGoogle ScholarPubMed
Benyamin, B, Ferreira, MAR, Willemsen, G, et al. (2009) Common variants in TMPRSS6 are associated with iron status and erythrocyte volume. Nat Genet 41, 11731175.CrossRefGoogle ScholarPubMed
McLaren, CE, Barton, JC, Eckfeldt, JH, et al. (2010) Heritability of serum iron measures in the hemochromatosis and iron overload screening (HEIRS) family study. Am J Hematol 85, 101105.CrossRefGoogle ScholarPubMed
Soranzo, N, Spector, TD, Mangino, M, et al. (2009) A genome-wide meta-analysis identifies 22 loci associated with eight hematological parameters in the HaemGen consortium. Nat Genet 41, 11821190.CrossRefGoogle ScholarPubMed
Meyer, TE, Verwoert, GC, Hwang, SJ, et al. (2010) Genome-wide association studies of serum magnesium, potassium, and sodium concentrations identify six loci influencing serum magnesium levels. PLoS Genet 6, e1001045.CrossRefGoogle ScholarPubMed
Corre, T, Arjona, FJ, Hayward, C, et al. (2017) Genome-wide meta-analysis unravels interactions between magnesium homeostasis and metabolic phenotypes. J Am Soc Nephrol 29, 335348.CrossRefGoogle ScholarPubMed
Ng, E, Lind, PM, Lindgren, C, et al. (2015) Genome-wide association study of toxic metals and trace elements reveals novel associations. Hum Mol Genet 24, 47394745.CrossRefGoogle ScholarPubMed
Kestenbaum, B, Glazer, NL, Kottgen, A, et al. (2010) Common genetic variants associate with serum phosphorus concentration. J Am Soc Nephrol 21, 12231232.CrossRefGoogle ScholarPubMed
Cornelis, MC, Fornage, M, Foy, M, et al. (2015) Genome-wide association study of selenium concentrations. Hum Mol Genet 24, 14691477.CrossRefGoogle ScholarPubMed
Carter, TC, Pangilinan, F, Molloy, AM, et al. (2015) Common variants at putative regulatory sites of the tissue nonspecific alkaline phosphatase gene influence circulating pyridoxal 5’-phosphate concentration in healthy adults. J Nutr 145, 13861393.CrossRefGoogle ScholarPubMed
Tanaka, T, Scheet, P, Giusti, B, et al. (2009) Genome-wide association study of vitamin B6, vitamin B12, folate, and homocysteine blood concentrations. Am J Hum Genet 84, 477482.CrossRefGoogle ScholarPubMed
Karohl, C, Su, S, Kumari, M, et al. (2010) Heritability and seasonal variability of vitamin D concentrations in male twins. Am J Clin Nutr 92, 13931398.CrossRefGoogle ScholarPubMed
Hazra, A, Kraft, P, Lazarus, R, et al. (2009) Genome-wide significant predictors of metabolites in the one-carbon metabolism pathway. Hum Mol Genet 18, 46774687.CrossRefGoogle ScholarPubMed
Nilsson, SE, Read, S, Berg, S, et al. (2009) Heritabilities for fifteen routine biochemical values: findings in 215 Swedish twin pairs 82 years of age or older. Scand J Clin Lab Invest 69, 562569.CrossRefGoogle ScholarPubMed
Hazra, A, Kraft, P, Selhub, J, et al. (2008) Common variants of FUT2 are associated with plasma vitamin B12 levels. Nat Genet 40, 11601162.CrossRefGoogle ScholarPubMed
Dalmia, A, Dib, MJ, Maude, H, et al. (2019) A genetic epidemiological study in British adults and older adults shows a high heritability of the combined indicator of vitamin B12 status (cB12)and connects B12 status with utilisation of mitochondrial substrates and energy metabolism. Nutr Biochem 70, 156163.CrossRefGoogle Scholar
Molloy, AM, Pangilinan, F, Mills, JL, et al. (2016) A common polymorphism in HIBCH influences methylmalonic acid concentrations in blood independently of cobalamin. Am J Hum Genet 98, 869882.CrossRefGoogle ScholarPubMed
Bathum, L, Petersen, I, Christiansen, L, et al. (2007) Genetic and environmental influences on plasma homocysteine: results from a Danish twin study. Clin Chem 53, 971979.CrossRefGoogle ScholarPubMed
Van Meurs, JBJ, Pare, G, Schwartz, SM, et al. (2013) Common genetic loci influencing plasma homocysteine concentrations and their effect on risk of coronary artery disease. Am J Clin Nutr 98, 668676.CrossRefGoogle ScholarPubMed
Paré, G, Chasman, DI, Parker, AN, et al. (2009) Novel associations of CPS1, MUT, NOX4, and DPEP1 with plasma homocysteine in a healthy population a genome-wide evaluation of 13 974 participants in the Women’s Genome Health Study. Circ Cardiovasc Genet 2, 142145.CrossRefGoogle Scholar
Timpson, NJ, Forouhi, NG, Brion, M, et al. (2010) Genetic variation at the SLC23A1 locus is associated with circulating concentrations of L-ascorbic acid (vitamin C): evidence from 5 independent studies with >15,000 participants. Am J Clin Nutr 92, 375382.CrossRefGoogle ScholarPubMed
D’Adamo, CR, Dawson, VJ, Ryan, KA, et al. (2016) The CAPN2/CAPN8 locus on chromosome 1q is associated with variation in serum alpha-carotene concentrations. J Nutrigenet Nutrigenomics 9, 254264.CrossRefGoogle ScholarPubMed
Gueguen, S, Leroy, P, Gueguen, R, et al. (2005) Genetic and environmental contributions to serum retinol and alpha-tocopherol concentrations: the Stanislas Family Study. Am J Clin Nutr 81, 10341044.CrossRefGoogle ScholarPubMed
Mondul, AM, Yu, K, Wheeler, W, et al. (2011) Genome-wide association study of circulating retinol levels. Hum Mol Genet 20, 47244731.CrossRefGoogle ScholarPubMed
Ferrucci, L, Perry, JRB, Matteini, A, et al. (2009) Common variation in the beta-carotene 15, 15’-monooxygenase 1 gene affects circulating levels of carotenoids: a genome-wide association study. Am J Hum Genet 84, 123133.CrossRefGoogle ScholarPubMed
Molloy, AM, Pangilinan, F, Mills, JL, et al. (2016) A common polymorphism in HIBCH influences methylmalonic acid concentrations in blood independently of cobalamin. Am J Hum Genet 98, 869882.CrossRefGoogle ScholarPubMed
Ahn, J, Yu, K, Stolzenberg-Solomon, R, et al. (2010) Genome-wide association study of circulating vitamin D levels. Hum Mol Genet 19, 27392745.CrossRefGoogle ScholarPubMed
Jiang, X, O’Reilly, PF, Aschard, H, et al. (2018) Genome-wide association study in 79,366 European-ancestry individuals informs the genetic architecture of 25-hydroxyvitamin D levels. Nat Commun 9, 260.CrossRefGoogle ScholarPubMed
O’Brien, KM, Sandler, DP, Shi, M, (2018) Genome-wide association study of serum 25-hydroxyvitamin D in US women. Front Genet 9, 67.CrossRefGoogle ScholarPubMed
Kim, S, Kwangsik, Nhoa, Ramanana, VK, et al. (2015) Genetic influences on plasma homocysteine levels in African Americans and Yoruba Nigerians 49, 9911003.CrossRefGoogle Scholar
Zubair, N, Kooperberg, C, Liu, J, et al. (2015) Genetic variation predicts serum lycopene concentrations in a multiethnic population of postmenopausal women. J Nutr 145, 187192.CrossRefGoogle Scholar
Lin, X, Lu, D, Gao, Y, et al. (2012) Genome-wide association study identifies novel loci associated with serum level of vitamin B12 in Chinese men. Hum Mol Genet 21, 26102617.CrossRefGoogle ScholarPubMed
Nongmaithem, SS, Joglekar, CV, Krishnaveni, GV, et al. (2017) Erratum: GWAS identifies population-specific new regulatory variants in FUT6 associated with plasma B12 concentrations in Indians [Human Molecular Genetics (2017)]. Hum Mol Genet 26, 2589.CrossRefGoogle Scholar
Li, J, Lange, LA, Duan, Q, et al. (2015) Genome-wide admixture and association study of serum iron, ferritin, transferrin saturation and total iron binding capacity in African Americans. Hum Mol Genet 24, 572581.CrossRefGoogle ScholarPubMed
Combs, GF (2015) Biomarkers of selenium status. Nutrients 7, 22092236.CrossRefGoogle ScholarPubMed
Hoey, L, Strain, JJ & McNulty, H (2009) Studies of biomarker responses to intervention with vitamin B-12: a systematic review of randomized controlled trials. Am J Clin Nutr 89, 1981S1996S.CrossRefGoogle ScholarPubMed
Péter, S, Navis, G, de Borst, MH, et al. (2017) Public health relevance of drug–nutrition interactions. Eur J Nutr 56, 2336.CrossRefGoogle ScholarPubMed
Benjamin, EJ, Dupuis, J, Larson, MG, et al. (2007) Genome-wide association with select biomarker traits in the Framingham Study. BMC Med Genet 8, Suppl. 1, S11.CrossRefGoogle Scholar
Mclaren, CE, Garner, CP, Constantine, CC, et al. (2011) Genome-wide association study identifies genetic loci associated with iron deficiency. PLoS ONE 6, e17390.CrossRefGoogle ScholarPubMed
Marini, NJ, Yang, W, Asrani, K, et al. (2016) Sequence variation in folate pathway genes and risks of human cleft lip with or without cleft palate. Am J Med Genet A 170, 27772787.CrossRefGoogle ScholarPubMed
Marini, NJ, Gin, J, Ziegle, J, et al. (2008) The prevalence of folate-remedial MTHFR enzyme variants in humans. Proc Natl Acad Sci U S A 105, 80558060.CrossRefGoogle ScholarPubMed
Cotlarciuc, I, Andrew, T, Dew, T, et al. (2011) The basis of differential responses to folic acid supplementation. J Nutrigenet Nutrigenomics 4, 99109.CrossRefGoogle ScholarPubMed
Major, JM, Yu, K, Wheeler, W, et al. (2011) Genome-wide association study identifies common variants associated with circulating vitamin E levels. Hum Mol Genet 20, 38763883.CrossRefGoogle ScholarPubMed
Major, JM, Yu, K, Chung, CC, et al. (2012) Genome-wide association study identifies three common variants associated with serologic response to vitamin E supplementation in men. J Nutr 142, 866871.CrossRefGoogle ScholarPubMed
Mao, J, Bath, SC, Vanderlelie, JJ, et al. (2016) No effect of modest selenium supplementation on insulin resistance in UK pregnant women, as assessed by plasma adiponectin concentration. Br J Nutr 115, 3238.CrossRefGoogle ScholarPubMed
Mahajan, A, Go, MJ, Zhang, W, et al. (2014) Genome-wide trans-ancestry meta-analysis provides insight into the genetic architecture of type 2 diabetes susceptibility. Nat Genet 461, 234244.CrossRefGoogle Scholar
Marigorta, UM & Navarro, A. (2013) High trans-ethnic replicability of GWAS results implies common causal variants. PLoS Genet 9, e1003566.CrossRefGoogle ScholarPubMed
Li, YR & Keating, BJ (2014) Trans-ethnic genome-wide association studies: advantages and challenges of mapping in diverse populations. Genome Med 6, 91.CrossRefGoogle ScholarPubMed
Khoury, MJ & Evans, JP (2015) A public health perspective on a national precision medicine cohort: balancing long-term knowledge generation with early health benefit. JAMA 313, 21172118.CrossRefGoogle ScholarPubMed
Mozaffarian, D (2017) Dietary and policy priorities for cardiovascular disease, diabetes, and obesity – a comprehensive review. Circulation 133, 187225.CrossRefGoogle Scholar
Heianza, Y & Qi, L (2017) Gene–diet interaction and precision nutrition in obesity. Int J Molec Sci 18, 787.CrossRefGoogle ScholarPubMed
Tai, ES, Corella, D, Demissie, S, et al. (2005) Polyunsaturated fatty acids interact with the PPARA -L162V polymorphism to affect plasma triglyceride and apolipoprotein C-III concentrations in the Framingham Heart Study 1. J Nutr 135, 397403.CrossRefGoogle Scholar
Smith, CE & Ordovas, JM. (2011) Fatty acid interactions with genetic polymorphisms for cardiovascular disease. Curr Opin Clin Nutr Metab Care 13, 139144.CrossRefGoogle Scholar
Zheng, J, Parnell, LD, Smith, CE, et al. (2014) Circulating 25-hydroxyvitamin D, IRS1 variant rs2943641, and insulin resistance: replication of a gene–nutrient interaction in 4 populations of different ancestries. Clin Chem 60, 186196.CrossRefGoogle ScholarPubMed
Thompson, EE, Kuttab-Boulos, H, Witonsky, D, et al. (2004) CYP3A variation and the evolution of salt-sensitivity variants. Am J Hum Genet 75, 10591069.CrossRefGoogle ScholarPubMed
Zmora, N, Zeevi, D, Korem, T, et al. (2016) Taking it personally: personalized utilization of the human microbiome in health and disease. Cell Host and Microbe 19, 1220.CrossRefGoogle ScholarPubMed
Von Schwartzenberg, RJ & Turnbaugh, PJ (2015) Siri, what should I eat? Cell 163, 10511052.CrossRefGoogle Scholar
Vanamala, JKP, Knight, R & Spector, TD (2015) Can your microbiome tell you what to eat? Cell Metab 22, 960961.CrossRefGoogle Scholar
Zeevi, D, Korem, T, Zmora, N, et al. (2015) Personalized nutrition by prediction of glycemic responses. Cell 163, 10791095.CrossRefGoogle ScholarPubMed

Altmetric attention score

Full text views

Full text views reflects PDF downloads, PDFs sent to Google Drive, Dropbox and Kindle and HTML full text views.

Total number of HTML views: 66
Total number of PDF views: 263 *
View data table for this chart

* Views captured on Cambridge Core between 31st July 2019 - 21st January 2021. This data will be updated every 24 hours.

Hostname: page-component-76cb886bbf-rm8z7 Total loading time: 0.782 Render date: 2021-01-21T00:50:37.085Z Query parameters: { "hasAccess": "1", "openAccess": "0", "isLogged": "0", "lang": "en" } Feature Flags: { "shouldUseShareProductTool": true, "shouldUseHypothesis": true, "isUnsiloEnabled": true, "metricsAbstractViews": false, "figures": false, "newCiteModal": false }

Send article to Kindle

To send this article to your Kindle, first ensure is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about sending to your Kindle. Find out more about sending to your Kindle.

Note you can select to send to either the or variations. ‘’ emails are free but can only be sent to your device when it is connected to wi-fi. ‘’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

A critical evaluation of results from genome-wide association studies of micronutrient status and their utility in the practice of precision nutrition
Available formats

Send article to Dropbox

To send this article to your Dropbox account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your <service> account. Find out more about sending content to Dropbox.

A critical evaluation of results from genome-wide association studies of micronutrient status and their utility in the practice of precision nutrition
Available formats

Send article to Google Drive

To send this article to your Google Drive account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your <service> account. Find out more about sending content to Google Drive.

A critical evaluation of results from genome-wide association studies of micronutrient status and their utility in the practice of precision nutrition
Available formats

Reply to: Submit a response

Your details

Conflicting interests

Do you have any conflicting interests? *