Linkage studies and genome-wide association studies

A variety of tools and models are available in nutrigenetics research, each with their particular strengths and limitations. Segregation analysis and linkage studies in family groups have led to the identification of the gene loci associated with numerous Mendelian diseases such as Huntington's disease and cystic fibrosis⁽Reference Botstein and Risch^7–Reference Strachan and Read¹⁰⁾ and have also made some contribution to current understanding of the genetic basis of polygenic disorders such as CVD⁽Reference Arnett, Baird and Barkley^11–Reference Helgadottir, Manolescu and Thorleifsson¹³⁾. Over the last 5–10 years the use of genome-wide analysis (GWA) in large cohorts of unrelated individuals, such as the Wellcome Case-Control Consortium, has dramatically increased genomic discoveries. In these studies genetic variation in ⩽80% of the human genome is assessed using typically between 100 000 and 1 000 000 marker SNP and information on linkage disequilibrium from the HapMap project⁽Reference Pearson and Manolio¹⁴⁾. To date, using GWA approaches, ⩽100 susceptibility gene loci for a range of chronic diseases have been confirmed, identified and replicated^(15–Reference Zeggini, Scott and Saxena²¹⁾. Thus far, these studies have contributed little to the understanding of diet–genotype interactions, as most of them have not captured any information on the habitual diet of the study participants. However, current and future application of GWA to cohorts such as the Framingham Heart Study (www.framinghamheartstudy.org), European Prospective Investigation into Cancer and Nutrition (epic.iarc.fr), the Nurses' Health Study (www.channing.harvard.edu/nhs/) and the Health Professionals Follow-up Study (www.hsph.harvard.edu/hpfs/), for which dietary data are available, is likely to result in substantial advancement in current nutrigenetics knowledge.

Candidate-gene studies

Applying candidate-gene approaches to case–control and cohort studies have thus far provided the vast amount of available information on genetic–disease associations⁽Reference Arnett, Baird and Barkley^11, Reference Humphries, Cooper and Talmud^22–Reference Olano-Martin, Abraham and Gill-Garrison²⁸⁾ and how they are influenced by diet and other environmental factors⁽Reference Masson, McNeill and Avenell^29–Reference Stephens, Bain and Humphries³²⁾. In contrast to the GWA studies, which are not hypothesis driven, traditional candidate-gene studies focus on gene loci for which biological function is known and relevant to the phenotype of interest.

Inconsistencies in genotype–phenotype associations

A comprehensive review of the literature based on both the candidate-gene and GWA approaches demonstrates that many reported associations have failed to be consistently replicated in independent studies⁽Reference Hirschhorn, Lohmueller and Byrne^33–Reference Chanock and Manolio³⁵⁾. The reasons for these inter-study inconsistencies are likely to be multifaceted. Some studies may be under-powered, which can lead to a failure to detect the ‘subtle’ physiological impacts of a particular gene variant, or provide an imprecise estimate of the size effect. Furthermore, emerging evidence indicates that an array of physiological, dietary or other lifestyle factors impact on the penetrance of a particular gene variant and result in a change in the size of the genotype effect. Gaining an understanding of these complexities in order to be able to predict the likely impact of genetic variation in particular population subgroups represents a major challenge.

Physiological mediators of apoE genotype–phenotype associations

Although a comprehensive review of the physiological determinants of apoE genotype–disease associations is lacking, there is evidence to suggest that gender, age and body weight may have an impact. Data from the Framingham Offspring Study is suggestive of a divergent penetrance of the apoE2 genotype in females compared with males⁽Reference Elosua, Ordovas and Cupples^48–Reference Wilson, Myers and Larson⁵⁰⁾. In the prospective 15–20-year follow up from examination 1 (1971–8) to examination 5 (1991–4) OR of cardiovascular events of 1·79 (95% CI 1·15, 2·77) 1·63 (95% CI 1·13, 2·34), 0·79 (95% CI 0·42, 1·48) and 1·56 (95% CI 0·99, 2·45) were reported for male E2, male E4, female E2 and female E4 carriers respectively compared with their wild-type E3/E3 reference group⁽Reference Lahoz, Schaefer and Cupples⁴⁹⁾ (Table 1). In a cross-sectional analysis conducted at examination 6 (1995–8) no impact of apoE genotype on carotid stenosis was evident in males. In contrast, a significant protective effect of the ε2 allele was observed in females, with 51% lower incidence rates relative to the wild-type E3/E3 genotype (Table 1)⁽Reference Elosua, Ordovas and Cupples⁴⁸⁾.

Table 1.

The adjusted OR for cardiovascular events in the Framingham Offspring Study according to apoE genotype in males and females (adapted from Elosua et al. ⁽Reference Elosua, Ordovas and Cupples⁴⁸⁾ and Lahoz et al. ⁽Reference Lahoz, Schaefer and Cupples⁴⁹⁾)

E3, E3/E3; E4, E3/E4+E4/E4; E2, E2/E3+E2/E2.

* Events in the subsequent 15–20 years, where events are defined as the presence of CVD, which includes CHD (myocardial infarction, angina pectoris, coronary insufficiency, coronary death), stroke, peripheral vascular disease and congenstive heart failure.

† Adjusted for age only.

‡ Further adjusted for diabetes, smoking, systolic blood pressure, BMI, left ventricular hypertrophy and use of oestrogens in women.

§ Measured in the internal carotid artery.

∥ Adjusted for age and familial correlation only.

¶ Further adjusted for diabetes, smoking, systolic blood pressure, hypertension treatment, BMI, menopausal status and use of oestrogen-replacement therapy for women.

Available data are also highly suggestive of an attenuating effect of apoE genotype according to age, with a lack of association in older cohorts⁽Reference Banares, Peterson and Aguilar^51–Reference Kuusisto, Mykkanen and Kervinen⁵⁵⁾. For example, in the Helsinki Sudden Death Study, which conducted lesion staining of the coronary arteries of 700 individuals, significant age×genotype interactions were observed (P=0·027), with a significant impact of genotype only in the <53 years age-group (P=0·0085)⁽Reference Ilveskoski, Perola and Lehtimaki⁵²⁾. The reason for this reduction in size effect of genotype with age is likely to be because age is associated with an accumulation of environmental influences⁽Reference Zdravkovic, Wienke and Pedersen⁵⁶⁾ and an overall multi-faceted higher-risk phenotype, which may mask the relatively-modest physiological impact of the ε4 allele. Furthermore, the lessening of the genotype size effect with age may reflect the fact that individuals represented in this cohort may be relatively insensitive to the effect of the apoE genotype, with particularly-sensitive individuals suffering from premature mortality not represented.

It is likely that the observation of a greater effect of apoE genotype in normal individuals v. overweight individuals⁽Reference Pardo Silva, Janssens and Hofman⁵⁷⁾ is also attributable to the masking effect of the obesity phenotype on apoE genotype–CHD associations.

Behavioural mediators of apoE genotype–phenotype associations

The apoE genotype interacts with diet and other behavioural factors to influence risk of disease. Numerous environmental factors have been shown to influence the effects of apoE genotype on both coronary risk and risk of age-related cognitive decline, including exercise⁽Reference Corella, Guillen and Saiz^58, Reference Etnier, Caselli and Reiman⁵⁹⁾ alcohol intake⁽Reference Corella, Tucker and Lahoz^60, Reference Djousse, Pankow and Arnett⁶¹⁾ smoking status⁽Reference Stephens, Bain and Humphries^32, Reference Humphries, Talmud and Hawe⁶²⁾ and dietary fat composition⁽Reference Masson, McNeill and Avenell²⁹⁾, and the interactions have been recently reviewed⁽Reference Minihane, Jofre-Monseny and Olano-Martin^30, Reference Jofre-Monseny, Minihane and Rimbach³⁹⁾.

A limited number of epidemiological studies have examined apoE genotype–dietary fat–phenotype associations. In a Costa Rican case–control study genotype–saturated fat interactions were found, with the impact of a high-saturated-fat diet on myocardial infarction risk and LDL-cholesterol more evident in the presence of the ε2 and ε4 alleles⁽Reference Yang, Ruiz-Narvaez and Kraft⁶³⁾. Similar greater responsiveness of LDL-cholesterol to saturated fat in individuals with the apoE4 genotype was evident in an initial analysis of a subsample (n 132) of the EPIC Norfolk cohort⁽Reference Loktionov, Scollen and McKeown⁶⁴⁾; however, this association in E3/E4 and E4/E4 subgroups was not found to be replicated in a subsequent more comprehensive analysis (n 22 915)⁽Reference Wu, Bowman and Welch⁶⁵⁾.

A review has been conducted of a number of intervention trials that have examined the impact of dietary total fat, saturated fat and cholesterol content on blood lipids according to apoE genotype⁽Reference Masson, McNeill and Avenell²⁹⁾. The results from these trials are highly variable, with many failing to report a significant genotype–diet interaction. Variable intervention lengths, dietary manipulations and subject groups are likely to explain, to a large extent, the lack of consistency. Furthermore, the majority of these studies were not designed to examine nutrigenetic interaction, with genotyping being conducted retrospectively as an afterthought. As a result many of the studies were underpowered to detect inter-genotype difference in responses, with the distinct possibility that a failure to detect significance is attributable to small group sizes in the rare allele genotypes rather than a lack of a ‘real’ biological effect.

Of the eleven (of thirty-six studies) that demonstrated significance, six included more than fifty participants, with an additional study that included forty-five participants (fifteen each in E3/E3, E3/E4 and E4/E4 subgroups) prospectively recruited on the basis of apoE genotype⁽Reference Sarkkinen, Korhonen and Erkkila⁶⁶⁾. In this latter study reductions in LDL-cholesterol of 5%, 13% and 16% for E3/E3, E3/E4 and E4/E4 individuals respectively were reported following 8 weeks of intervention with a modified National Cholesterol Education Programme diet.

Thus, available data are suggestive that individuals with the E4 genotype (25% of the UK population) may represent a large population subgroup that is particularly sensitive to dietary total and saturated fat and should be specifically targeted with advice to reduce overall consumption. However, there is great need to consolidate existing analysis using meta-analytical approaches (although variation in study design makes this task difficult) and to conduct adequately-powered trials with prospective recruitment by genotype and detailed analysis of the blood lipid profile in order to conclusively address this wide public health issue.

In addition, recent evidence is suggestive that individuals with the apoE4 genotype are more sensitive to the lipid modulatory effects of the fish oil fatty acids EPA and DHA. Although there are numerous well-described cardioprotective actions of these fatty acids^(67, Reference Kris-Etherton, Harris and Appel⁶⁸⁾, increases in LDL-cholesterol in the 5–10% range are commonly evident following high-dose (>2 g/d) EPA+DHA intakes⁽Reference Balk, Lichtenstein and Chung^69, Reference Harris⁷⁰⁾. In an initial study conducted in individuals with an atherogenic lipoprotein phenotype retrospective genotyping has indicated that the LDL-cholesterol-raising effects observed following supplementation with 3 g EPA+DHA/d were associated with an apoE4 genotype⁽Reference Leigh-Firbank, Minihane and Leake^71, Reference Minihane, Khan and Leigh-Firbank⁷²⁾. Two further studies with prospective recruitment according to genotype have: (a) reconfirmed this association; (b) indicated that it is the DHA rather than EPA that is the hypercholesterolaemic agent; (c) highlighted no genotype×LDL-cholesterol interaction at intakes of <2 g EPA+DHA/d. Modest 3–4% increases in LDL-cholesterol were found to be evident in both E3 and E4 subgroups following supplementation for 8 weeks at doses of 0·7 and 1·8 g EPA+DHA/d⁽Reference Caslake, Miles and Kofler⁷³⁾(E Olano-Martin, E Anil, MJ Caslake, CJ Packard, D Bedford, G Stewart, D Peiris, CM Williams and AM Minihane, unpublished results).

Overall, it appears that with intakes at the current UK recommendation of 450 mg EPA+DHA/d there is little evidence of apoE genotype-mediated differences in the LDL-cholesterol response. However, at intakes in the region of those prescribed as hypotriacylglycerolaemic agents (2–4 g/d⁽Reference Kris-Etherton, Harris and Appel⁷⁴⁾) EPA-rich oil rather than DHA-rich oil may afford greater cardiovascular benefits in individuals with an E4 genotype, as a result of the LDL-cholesterol-raising effect of DHA.

Physiological mediators of perilipin genotype–phenotype associations

A recent review has indicated that gender may be an important determinant of the penetrance of the PLIN genotype⁽Reference Ordovas⁵⁾. In a Spanish adult population OR for obesity of 0·58 and 0·56 were evident in female carriers of the PLIN1 and PLIN4 rare alleles, with no effect of genotype on BMI or waist:hip ratio in men⁽Reference Qi, Corella and Sorli⁸⁰⁾. Although no impact of these particular SNP was evident in a US cohort, PLIN5 and PLIN6 SNP have emerged as significant predictors of percentage body fat and waist circumference in females only, with 7–11% higher percentage body fat and waist circumference in the homozygous rare allele v. wild-type carriers⁽Reference Qi, Shen and Larson⁸¹⁾. In a recently published analysis of the impact of PLIN genotype on postprandial TAG metabolism both PLIN1 and PLIN4 rare alleles were shown to be associated with a more effective postprandial TAG clearance⁽Reference Perez-Martinez, Yiannakouris and Lopez-Miranda⁸²⁾. However, no gender×genotype effects were evident.

Perilipin genotype and response to dietary and pharmacological interventions

Research in this area is currently limited to a small number of studies, with available evidence suggesting that rare allele carriers of PLIN4 are resistant to weight loss following dietary restriction and are less prone to the weight gain typically associated with the administration of the insulin-sensitising glitazones⁽Reference Corella, Qi and Sorli^83, Reference Kang, Cha and Kim⁸⁴⁾. Furthermore, an observational study has indicated that the genotype–diet associations may be gender specific⁽Reference Corella, Qi and Tai⁸⁵⁾. In Asian women, but not men, the PLIN4 genotype was shown to influence the association between saturated fat and insulin-resistance measures (homeostatic model assessment of insulin resistance), with a deleterious impact of high saturated fat only evident in minor allele homozygotes⁽Reference Corella, Qi and Tai⁸⁵⁾. Thus, early indications are that the PLIN4 genotype may be an important determinant of the response of adiposity measures (and its related phenotype) to behavioural changes. However, much more evidence is needed in order to draw firm conclusions.

ApoA5 genotype and postprandial lipaemia

ApoA5 was first identified in 2001⁽Reference Pennacchio, Olivier and Hubacek⁹²⁾ and has been described as a protein involved in both hepatic TAG synthesis and secretion and the hydrolysis and clearance of TAG-rich lipoprotein from the circulation⁽Reference Wong and Ryan⁹³⁾. Over the last 7 years numerous studies have reported associations between the apoA5 T–1131C and S19W rare alleles (which define the apoA5*2 and apoA5*3 haplotypes respectively) and CVD, and metabolic syndrome and diabetes risk⁽Reference Elosua, Ordovas and Cupples^94–Reference Talmud, Martin and Taskinen⁹⁶⁾, and consequently the apo5 gene has emerged as one of the most consistent loci associated with fasting and postprandial TAG levels⁽Reference Olano-Martin, Abraham and Gill-Garrison^28, Reference Tai and Ordovas⁹⁵⁾.

Six studies relating apoA5 genotype and postprandial TAG metabolism have been published thus far, with evidence that the apoA5*2 and apoA5*3 haplotypes are associated with an approximately 30–60% higher postprandial TAG response relative to the wild-type apoA5*1 haplotype, defined by −1131T and S19 alleles⁽Reference Olano-Martin, Abraham and Gill-Garrison^28, Reference Jang, Kim and Kim^97–Reference Moreno-Luna, Perez-Jimenez and Marin¹⁰¹⁾. Five of these investigations have been conducted in young males and ethnically-homogenous cohorts⁽Reference Jang, Kim and Kim^97–Reference Moreno-Luna, Perez-Jimenez and Marin¹⁰¹⁾. Thus, little information on gender, age and ethnicity×genotype interaction can be derived from these studies. However, the most recently published study, which included 153 males and 109 females, provides strong indications of an impact of gender on the association between the −1131C allele and apoA5*2 haplotype and TAG metabolism (Fig. 4)⁽Reference Olano-Martin, Abraham and Gill-Garrison²⁸⁾. A significant impact of genotype was only evident in males (corrected for baseline levels; P=0·007). It is speculated that this finding may be in part attributable to the known impact of oestrogen on many stages of TAG-rich lipoprotein metabolism, which may mask the deleterious impact of the apoA5 mutant.

Fig. 4.

Impact of apoA5 T–1131C genotype on the postprandial plasma TAG response. (a) All subjects: (▪–▪), TT (n 214); (○– –○), TC (n 42); (), CC (n 3); P=0·002. (b), Females: (▪–▪), TT (n 92); (○– –○), TC (n 16); P=0·280. (c) Males (▪–▪), TT (n 122); (○– –○), TC (n 26); P=0·007. Values are means with their standard errors represented by vertical bars. (Adapted from Olano-Martin et al. ⁽Reference Olano-Martin, Abraham and Gill-Garrison²⁸⁾.)

In accordance with other gene variants that modulate lipoprotein metabolism, there is some evidence that dietary fat composition influences the ‘size effect’; of the apoA5 T–1131C variant on TAG metabolism⁽Reference Tai and Ordovas⁹⁵⁾. An analysis of data from the Framingham cohort indicates that the association is modulated by dietary PUFA composition⁽Reference Lai, Corella and Demissie¹⁰²⁾. Fish oil fatty acids are the most potent known dietary hypotriacylglycerolaemic agents, with 20–40% reduction typically observed following high-dose intakes (2–4 g EPA+DHA/d)⁽Reference Minihane, Khan and Leigh-Firbank⁷²⁾. Given that there is considerable mechanistic overlap between fish oil action and apoA5 modulation of TAG metabolism, with fish oils reducing hepatic TAG output and enhancing LPL activity, it is highly likely that EPA+DHA intake would influence apoA5–TAG associations and in part negate the negative effects in those with the apoA5*2 and apoA5*3 haplotypes. This interaction is worthy of investigation.

Investigations of the impact of specific genotypes, haplotypes and genotype combinations on postprandial TAG metabolism are logistically difficult studies to conduct, as they require volunteers to provide regular blood samples typically up to 8 h post test meal. As a result cohorts are often small (<100 individuals), making meaningful analysis of genotype–phenotype associations difficult, in particular for rarer alleles. Furthermore, subgroup analysis according to gender, age etc. is often impossible. Combining existing smaller cohorts may allow more detailed analysis to be conducted. However, this approach is often difficult because of variations in study protocol, in particular test meal composition. There is a strong justification for the standardisation of postprandial protocols in order to allow cross-study comparisons and amalgamation of datasets.