American Association for Clinical Chemistry
Better health through laboratory medicine
April 2007 Clinical Laboratory News: Nutritional Genomic Testing

 
April 2007: Volume 33, Number 4

Nutritional Genomic Testing
What Role Will Clinical Labs Play?
By José M. Ordovas, PhD

Dietary recommendations aimed at preventing disease and promoting optimal health at all stages of life represent the most important practical translation of nutrition research to public health. Indeed, the U.S. Department of Agriculture, the Department of Health and Human Services, and various special interest organizations promote dietary guidelines to improve the health of the general population and those at high risk for cardiovascular disease (CVD), cancer, hypertension, and diabetes. But general health and disease-specific dietary guidelines do not take into account physiologic differences in how individuals respond to nutrient intake.

Today, accumulating evidence suggests that an individual’s genetic makeup greatly affects the efficacy of dietary recommendations. Although diet is widely recognized as influencing the health of people who have inborn errors of metabolism, such as phenylketoruria and maple syrup urine disease, scientists have long speculated that other genetic variations affect dietary response in ways that are not as catastophic. Until recently, reseachers lacked the tools necessary for a comprehensive examination of the subtle and complex effects of the individual genetic factors involved in nutrition. Now, however, powered by new genetic technologies and data from the Human Genome Project, researchers can examine nutrient-gene interactions at the molecular level in order to unravel the basis for metabolic alterations that affect the general population. In addition, growing evidence supports the notion that interactions between genes and environmental factors trigger most common diseases.

Driven by these new technologies and paradigms, nutrition scientists have embraced nutritional genomics, or nutrigenomics (See Box, below), in order to address several questions. How does nutrition influence metabolic pathways and homeostatic control? How is this regulation altered in the early phase of a diet-related disease? And to what extent do genetic variations contribute to disease?

What is Nutrigenomics?

The discipline of nutrigenomics applies the sciences of genomics, proteomics, and metabomics to human nutrition and the relationship between nutrition and health. The goal of nutrigenomics is to provide the right dietary advice for an individual and to produce more personalized public health recommendations.


Delivering the Promise of the Human Genome Project

When the Human Genome Project started in 1990, some geneticists raised expectations that the information would quickly revolutionize the practice of medicine. Many newspaper stories, magazine articles, and television reports featured stories on how knowledge of DNA variations among individuals would lead to new and better ways to diagnose, treat, and someday prevent thousands of human disorders. Such stories led the public to believe that the completion of the Human Genome Project would change their lives, and many practicing doctors were convinced that their profession would be quickly transformed by genetics. While these expectations are probably realistic, scientists actively involved in translating data from the Human Genome Project must now confront the complexities of incorporating routine genetic testing into clinical use.

An important finding arising from the Human Genome Project and other studies is the knowledge that genetic disorders are not rare events. Traditionally, while severe inborn errors of metabolism were devastating for the individuals and families affected by them, they received little attention from public health policy makers. Now, however, we know that most morbidity and mortality have a genetic component. This realization has spread genetics beyond its scientific boundaries into the social and ethical landscape, raising potential problems of confidentiality, stigmatization, insurance, as well as legal issues about the balance of responsibilities.

Beyond Genomics: A New List of Vocabulary Words

Now that the Human Genome Project is completed, new fields have emerged that are aimed at understanding the complexity of the whole biological system.

Proteomics—The study of the proteome, the complete set of proteins produced by a species, using the technologies of large-scale protein separation and identification. It includes how proteins are modified, when and where they’re expressed, how they’re involved in metabolic pathways, and how they interact with one another.

Metabonomics—The study of metabolic responses to drugs, environmental changes and diseases. It is an extension of genomics and proteomics.

Bioinformatics—The analysis of biological information using computers and statistical techniques, the science of developing and utilizing computer databases and algorithms to accelerate and enhance biological research. It is used in analyzing genomes, proteomes, and three-dimensional modeling of biomolecules and biologic systems.

Chemometrics—The field of extracting information from multivariate chemical data using tools of statistics and mathematics. It is typically used to explore patterns of association in data.


Advances in Genetic Technologies

One of the factors bringing clinical genome analysis closer to reality is a considerable drop in the cost of DNA sequencing and genotyping. In fact, many genotyping technologies are now available. Not too long ago, large high-throughput genetic studies averaged about $0.50 per base. However, current estimates for very large scale studies are in the range of $0.01 per base, and this cost is expected to fall even further, by as much as one order of magnitude, to $0.001 per base, making completion of large, genome-wide association studies feasible.

Examples of Platforms to Examine DNA Variation of Nutrigenomic Interest

Purpose
Technology
Platforms
~genotypes/day (Samples)
High genomic coverage
Whole Genome Illumina infinium (1)
Affymetrix GeneChip Mapping Arrays (2)
12,000,000
(24–36)
High-throughput survey, large number of candidate genes or linked region survey
Large Multiplex
Illumina GoldenGate (1)
Molecular Inversion Probe Technology (2)
1,000,000
(50–600)
Moderate throughput, single candidate gene or candidate SNPs in pathway
Medium Multiplex
iPLEX, RealSNP (3)
200,000
(3,000–5,000)
Low throughput, candidate SNP or confirmation
Single-plex
TaqMan (4) Open Source GenotypingFragment Sizing (VNTR)
100,000
(96 – 100K)
Methylation variable positions (MVPs)
Integrated genomics-based technology platform
Bisulphite treatment of DNA, gene-specific bisulphite PCR and large-scale sequencing of PCR amplicons. Analysis and quantification of methylation patterns is achieved by mass spectrometric and microarray assays.
In use by the Human Epigenomic Consortium.* Throughput not reported.
Copy number variations (CNVs)
Whole Genome
GeneChip Human Mapping 500K early access arrays (2) Whole Genome TilePath (WGTP) array representing ~94%% of the euchromatic portion of the human genome.
Throughput not reported**
* www.epigenome.org/
**
www.sanger.ac.uk/humgen/cnv/
1 Illumina Inc, San Diego, Calif.
2 Affymetrix, Santa Clara, Calif.
3 Sequenom, San Diego, Calif.
4 Applied Biosystems, Foster City, Calif.

Along with the decreasing cost of DNA sequencing and genotyping, another important advance facilitating the execution of highly informative genetic studies comes from the characterization of linkage disequilibrium (LD) patterns through the International HapMap project, a multi-country effort to identify and catalog genetic similarities and differences in humans. By making this information freely available, the project aims to help researchers find genes involved in disease and response to therapeutic drugs.

In addition, the technological advances that are driving the genetic revolution parallel a similar revolution in informatics. In the last two decades, we have seen unprecedented changes in our ability to conduct genetic research and to store and retrieve the data resulting from this research. The Single Nucleotide Polymorphism Database (dbSNP), a public-domain archive maintained by the National Center for Biotechnology Information at the National Institutes of Health (Bethesda, Md.), is a broad collection of simple genetic polymorphisms and already contains about 9 million SNPs. This number is close to the target of approximately 11 million SNPs with minor allele frequencies of greater than 1% that are estimated to exist in the human genome.

Beyond Genomics

While these advances have created a tremendous boost for the genetic revolution, the genomic revolution is not all about genes (See Box, right). This new approach of looking beyond genes to understand interactions between genetics and the environment has fostered the development of several complementary technologies that will also greatly benefit the field of nutrition sciences. In addition to genomics, techniques such as proteomics, metabonomics, and bioinformatics are already providing insights about gene-nutrient interactions at the cell, individual, and population levels. Researchers will need to combine all these techniques to understand the influence of both specific nutrients and whole dietary patterns on the metabolic behavior of cells, organs, and organisms.

To advance the science of genomics and allow it to become clinically useful, scientists will need to employ bioinformatics and chemometrics to manage the large and complex data sets obtained from genomic studies. By integrating data from these multiple experimental sources, researchers hope to develop a systems biology or global approach to understanding functional human genetics. In this new era, instead of the traditional reductionist approach of looking at individual gene-nutrition interactions, scientists will be able to simultaneously query a significant fraction of all regulated genes and metabolites. This holistic approach will reveal the dynamic interaction of the parts and allow the development of more individualized dietary guidelines.

Before this science can be translated into public use, however, scientists will need to validate the findings and develop solid scientific evidence for the utility of nutrigenomics. To avoid, or at least minimize, any damage to public confidence in this emerging field, experts must avoid creating unrealistic expectations about the current state of knowledge. In order to advance this field, we and other individuals involved in medical research and care must understand the strengths and limitations of the published evidence. Below are some of the limitations that need to be overcome to make the science of nutrigenomics more sound and reproducible.

Dietary Assessment

Dietary assessment plays a crucial role in our ability to detect relationships between dietary exposure and disease causation. Therefore, one of the keys to establishing causality in nutritional genomics is high-quality dietary information. However, the uncertainties associated with the current instruments for assessing dietary intake have been identified as the Achilles’ heel in the evaluation of gene-diet interactions determined via observational studies of the general population.

The best approach to establish true dietary intake is a prospective dietary intervention study carried out under highly controlled conditions. But well-controlled feeding studies have several logistic limitations including cost, the small number of willing participants, and the brief duration of the interventions.

Therefore, a considerable proportion of our knowledge relating dietary intake to phenotypes and disease risk comes from population studies using self-reported dietary questionnaires. Diet records, diet-history questionnaires, 24-hour recalls, or food-frequency questionnaires (FFQs) are the methods most commonly used to assess individual dietary intake. Each method has strengths and weaknesses. While FFQs are widely used in large-scale studies, critics increasingly question their validity.

Integrative approaches to dietary assessment can be improved by measuring biochemical indicators that represent more objective measures of dietary intake for some specific nutrients. However, we still lack reliable biomarkers for many significant nutrients. This limitation could be successfully resolved with the incorporation of the new analytical techniques of the post-genomic era. Thus, one goal for nutritional genomics and the clinical laboratory is identifing and measuring biomarkers that will provide better guidance on the relationship between nutrition and health.

In addition to developing better tools to measure what people eat, moving forward into the era of nutritional genomics will require addressing some important nutritional epidemilogy questions. For example, which type of dietary information is most relevant: foods, nutrients, or dietary patterns? Food preparation and cooking methods can significantly affect the final nutrient content in foods, and food items contain thousands of specific chemical compounds, some known and well quantified, some poorly characterized, others subject to geographical and seasonal variability, and others still undefined. With the expanding knowledge of the role of nutrients and bioactive compounds in gene expression and cellular response, nutritional genomics needs a new, complementary research methodology in which the study of foods, food patterns, and individual nutrients of food components can come together. This integrative approach will be useful in nutritional genomics and is already being used in ongoing studies, including the Framingham Heart Study.

Genetic Assessment

In addition to a reliable measurement of diet, nutritional genomic studies will also require methods to quickly and cheaply determine an individual’s genotype. For the high-throughput methods currently used for genotyping, quality control procedures in the laboratory become critically important. Misclassifying a genotype can bias measurement of association between genotype and disease, as well as gene-nutrient interactions. Quality control measures must be in place, and they should be reported in the methodology section of published papers. Such measures could include internal validation, blinding, duplicates, test failure rate, inspection of whether genotype frequencies conform to Hardy-Weinberg equilibrium, and blind data entry.

Another area of concern in genomic analysis is the use of haplotypes instead of individual SNPs. Scientists have developed various statistical algorithms to estimate haplotypes from genotypic data in unrelated individuals. However, many limitations and inconsistencies are still present in these estimations. Similar concerns exist about how to use data from expression microarrays. Researchers must address these limitations and others to advance the science of nutritional genetics, as well as other emerging areas like pharmacogenomics.

Finally, in order to study genetic susceptibility, we will also need a more precise definition of genotype. Currently, hundreds of thousands or even millions of SNPs can be easily determined at low cost. However, SNPs represent only one form of variation in the human genome. Insertions, deletions, and large copy-number variations, representing gains or losses of several kilobases of DNA, have been reported to be more common than anticipated in the general population. Research has also shown that epigenetic changes, such as methylation of DNA, are highly relevant for the nutrient-driven regulation of gene expression.

Clinical Labs: Positioned to Make the Transition

Despite the limitations of many current methods, the benefits of harnessing the power of genomics for improving the health of the general population and for preventing disease are huge. While the application of nutrigenomics is still in its infancy, the medical community needs to start considering several important points, including who will provide these testing services, and who will interpret and deliver advice to patients. For the latter, a new generation of health professionals needs to be trained to translate the expected complexity of nutrigenomics into targeted but simple and achievable recommendations for individuals.

While new professionals will be needed for test interpretation, the implementation of nutrigenomic testing services in the clinical laboratory does not require extensive new efforts. The structures, experience, and procedures already in place in clinical labs make them the logical setting for nutrigenomic studies. Furthermore, many labs already have the proper structures in place to provide genetic testing, such as handling patient samples according to rigorous standards and familiarity with guidelines for test standardization and utilization. Presently, many of the large genetic testing efforts being carried out by academic research laboratories have not been subject to the excruciating quality control measures that are standard in clinical labs. This variability in the rigor of quality control is an issue that will need much more attention when genetic assays fully transition from research to diagnosis. Even before this happens, however, the clinical lab can play a crucial role in supplying the accurate biochemical phenotypes required to investigate the genotype-phenotype associations that can transform nutrigenomics into a reliable science that will be clinically useful.

A Healthy Future

In the future, nutritional genomics will be the driving force of nutritional research. This new approach to an individual’s health has the potential to have a major impact on public health by changing dietary disease prevention and therapy. However, the scientific issues that need to be tackled in nutritional genomics are tremendously complex. To move this field forward successfully, scientists will need to break out of the mold of traditional research and seek to integrate their studies with other disciplines, as well as the work of clinical laboratories.

And once some practical applications of nutritional genomics are developed, clinical laboratories will have an excellent opportunity to expand their services. The expertise of laboratorians in providing high quality, reliable results makes them the obvious choice for providing these new tests. Despite the difficulties described here, the preliminary evidence strongly suggests that the concept will work, and by using behavioral tools founded on nutrition, we will be able to harness the information contained in our genomes to live longer, healthier lives.

Importance of Gene-Diet Interactions

Predicted values of high-density lipoprotein cholesterol (HDL-C) are shown for different hepatic lipase (LIPC) genotypes—CC, CT, and TT—at different total levels of dietary fat intake (data from reference 7). Low fat intake combined with the TT genotype results in the highest HDL-C level. For a moderate fat intake, there is no relationship between genotype and HDL-C level. For a high fat intake, the TT genotype has the lowest HDL-C level. The data suggest that gene–diet interactions are important in disease risk based on HDL-C phenotypes and dietary fat intake.

Suggested Reading

Crews D, McLachlan JA. Epigenetics, evolution, endocrine disruption, health, and disease. Endocrinology 2006; 147(6 Suppl): S4–S10.

Hirschhorn JN, Daly MJ. Genome-wide association studies for common diseases and complex traits. Nat Rev Genet 2005; 6(2): 95–108.

Kaput J, Ordovas JM, Ferguson L, et al. The case for strategic international alliances to harness nutritional genomics for public and personal health. Br J Nutr 2005 Nov; 94(5): 623–632.

Kussmann M, Raymond F, Affolter M. OMICS-driven biomarker discovery in nutrition and health. J Biotechnol 2006 Aug 5; 124(4): 758–787.

Ordovas JM, Corella D, Demissie S, et al. Dietary fat intake determines the effect of a common polymorphism in the hepatic lipase gene promoter on high density lipoprotein metabolism: Evidence of a strong dose effect in this gene-nutrient interaction in the Framingham study. Circulation 2002; 106: 2315–2321.

Ordovas JM, Corella D. Nutritional genomics. Annu Rev Genomics Hum Genet 2004; 5: 71–118.

Ordovas JM, Mooser V. Metagenomics: the role of the microbiome in cardiovascular diseases. Curr Opin Lipidol 2006; 17(2): 157–161.

Ordovas JM. Nutrigenetics, plasma lipids, and cardiovascular risk. J Am Diet Assoc 2006 Jul; 106(7): 1074–1081.

Redon R, Ishikawa S, Fitch KR, et al. Global variation in copy number in the human genome. Nature 2006; 444: 444–454.

Trujillo E, Davis C, Milner J. Nutrigenomics, proteomics, metabolomics, and the practice of dietetics. J Am Diet Assoc 2006 Mar; 106(3): 403–413.

José M. Ordovas, PhD, is Senior Scientist and Director of the Nutrition and Genomics Laboratory at the Jean Mayer USDA HNRCA at Tufts University in Boston, Mass. He is also Professor of Nutrition at the Gerald J. and Dorothy R. Friedman School of Nutrition Science and Policy at Tufts University. Dr. Ordovas’ major research interests focus on the genetic factors predisposing to cardiovascular disease and their interaction with the environment and behavioral factors with special emphasis on diet. He also participated in the Framingham Heart Study for 20 years.