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Candidate gene

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The candidate gene approach to conducting genetic association studies focuses on associations between genetic variation within pre-specified genes of interest and phenotypes or disease states. This is in contrast to genome-wide association studies (GWAS), which scan the entire genome for common genetic variation. Candidate genes are most often selected for study based on a priori knowledge of the gene’s biological functional impact on the trait or disease in question.[1][2] The rationale behind focusing on allelic variation in specific, biologically relevant regions of the genome is that certain mutations will directly impact the function of the gene in question, and lead to the phenotype or disease state being investigated. This approach usually uses the case-control study design to try to answer the question, “Is one allele of a candidate gene more frequently seen in subjects with the disease than in subjects without the disease?” [1]

Selecting a candidate gene

Suitable candidate genes are generally selected based on known biological, physiological, or functional relevance to the disease in question. This approach is limited by its reliance on existing knowledge about known or theoretical biology of disease. However, more recently developed molecular tools are allowing insight into disease mechanisms and pinpointing potential regions of interest in the genome. Genome-wide association studies and quantitative trait locus (QTL) mapping examine common variation across the entire genome, and as such can detect a new region of interest that is in or near a potential candidate gene. Microarray data allow researchers to examine differential gene expression between cases and controls, and can help pinpoint new potential genes of interest.[3]

The great variability between organisms can sometimes make it difficult to distinguish normal variation in SNP from a candidate gene from disease-associated variation.[4] In weaving through the large amounts of data, there are several other factors that can help lead to the most probable variant. These factors include priorities in SNPs, relative risk of functional change in genes, and linkage disequilibrium among SNPs.[5]

In addition, the availability of genetic information through online databases enables researchers to mine existing data and web-based resources for new candidate gene targets.[6] Many online databases are available to research genes across species.

  • Gene is one such database that allows access to information about phenotypes, pathways, and variations of many genes across species.
  • When examining functionality between genes in pathways, the Gene Ontology Consortium can help map these relationships. The GO Project describes gene products in three different ways via a species-independent manner: biological processes, cellular components, and molecular functions. Using this information can further a priori knowledge of a pathway and thus help to choose the most likely candidate gene involved.
  • Topp Gene is another useful database that allows users to prioritize candidate genes using functional annotations or network analysis.[7] ToppGene aids researchers in selecting a subset of likely candidate genes from larger sets of candidate genes, likely discovered through high-throughput genome technologies.

Prior to the candidate-gene approach

Before the candidate-gene approach was fully developed, various other methods were used to identify genes linked to disease-states. These methods studied genetic linkage and positional cloning through the use of a genetic screen, and were effective at identifying relative risk genes in Mendelian diseases.[5] However, these methods are not as beneficial when studying complex diseases for several reasons:[5]

1. Complex diseases tend to vary in both age of onset and severity. This can be due to variation in penetrance and expressivity.[8] For most human diseases, variable expressivity of the disease phenotype is the norm. This makes choosing one specific age group or phenotypic marker more difficult to select for study.[5]

2. The origins of complex disease involve many biological pathways, some of which may differ between disease phenotypes.[5]

3. Most importantly, complex diseases often illustrate genetic heterogeneity – multiple genes can be found that interact and produce one disease state. Oftentimes, each single gene is partially responsible for the phenotype produced and overall risk for the disorder.[5][9]

Despite the drawbacks of linkage analysis studies, they are nevertheless useful in preliminary studies to isolate genes linked to disease.[10]

Criticisms to the candidate-gene approach

A study of candidate genes seeks to balance the use of data while attempting to minimize the chance of creating false positive or negative results.[5] Because this balance can oftentimes be difficult, there are several criticisms of the candidate gene approach that are important to understand before beginning such a study.

· One critique is that findings of association within candidate-gene studies have not been easily replicated in follow up studies.[11] Population stratification has often been blamed for this inconsistency; therefore caution must be taken in regards to what criteria define a certain phenotype, as well as other variations in design study.[5]

· Additionally, because these studies incorporate a priori knowledge, some critics argue that our knowledge is not sufficient to make predictions from. Therefore, results gained from these ‘hypothesis-driven’ approaches are dependent on the ability to select plausible candidates from the genome, rather than use an anonymous approach. The limited knowledge of complex disease can result in ‘information bottleneck’, which can be overcome by comparative genomics across different species.[12] This bias can also be overcome by carefully choosing genes based on what factors are most likely to be involved in phenotype.[5]

These critiques are important to remember as one examines their experimental approach. With any other scientific method, the candidate gene approach itself is subject to critique, but still proven to be a powerfully effective tool for studying the genetic makeup of complex traits.[12]

The Use of Candidate Genes in Research Studies

The candidate gene approach is a powerful tool to studying complex diseases, particularly if its limitations are overcome by a wide complementary approach. In a study by Kim et al., genes linked to the obesity trait in both pigs and humans were discovered using comparative genomics and chromosomal heritability.[12] By using these two methods, the researchers were able to overcome the criticism that candidate gene studies are solely focused on prior knowledge. Comparative genomics was completed by examining both human and pig QTL through a method known as GCTA (Genome-wide complex trait analysis), which allowed the researchers to then map genetic variance to specific chromosomes. This allowed the parameter of heritability to provide understanding of where phenotypic variation was on specific chromosomal regions, thus extending to candidate markers and genes within these regions. Other studies may also use computational methods to find candidate genes in a widespread, complementary way, such as one study by Tiffin et al. studying genes linked to type 2 diabetes.[13]

Many studies have similarly used candidate genes as part of a multi-disciplinary approach to examining a trait or phenotype. One example of manipulating candidate genes can be seen in a study completed by Martin E. Feder on heat-shock proteins and their function in Drosophila melanogaster.[14] Feder designed a holistic approach to study Hsp70, a candidate gene that was hypothesized to play a role in how an organism adapted to stress. Drosophila is a highly useful model organism for studying this trait due to the way it can support a diverse number of genetic approaches for studying a candidate gene. The different approaches this study took included both genetically modifying the candidate gene (using site-specific homologous recombination and the expression of various proteins), as well as examining the natural variation of Hsp70. He concluded that the results of these studies gave a multi-faceted view of Hsp70. By engineering and modifying these candidate genes, they were able to confirm the ways in which this gene was linked to a change phenotype. Understanding the natural and historical context in which these phenotypes operate by examining the natural genome structure complemented this.

References

  1. ^ a b "The candidate gene approach" (PDF). Alcohol Res Health. 24 (3): 164–8. 2000. PMID 11199286.
  2. ^ Zhu M, Zhao S. Candidate Gene Identification Approach: Progress and Challenges. Int J Biol Sci 2007; 3(7):420-427. [1]
  3. ^ "Combining mapping and arraying: An approach to candidate gene identification". Proc. Natl. Acad. Sci. U.S.A. 99 (23): 14903–6. November 2002. doi:10.1073/pnas.222549199. PMC 137517. PMID 12415114.
  4. ^ Tiffin, Nicki; Addie, Euan (2006). "Computational disease gene identification: a concert of methods prioritizes type 2 diabetes and obesity candidate genes". Nucleic Acids Research. 34 (10): 3067–3081. doi:10.1093/nar/gkl381.
  5. ^ a b c d e f g h i Tabor, H. K.; Risch, N. J.; Myers, R. M. (May 2002). "Candidate gene approaches for complex traits: practical considerations". Genetics. 3: 1–7.
  6. ^ Zhu, M; Zhao, S. (2007). "Candidate Gene Identification Approach: Progress and Challenges". Int J Biol Sci. 3 (7): 420–427. doi:10.7150/ijbs.3.420.
  7. ^ Chen, J.; Bardes, E. E.; Aronow, B. J.; Jegga, A. G. (2009). "ToppGene Suite for Gene List Enrichment Analysis and Candidate Gene Prioritization". Nucleic Acids Research. 37: 305–311. doi:10.1093/nar/gkp427.
  8. ^ Lobo, I. (2008). "Same genetic mutation, different genetic disease phenotype". Nature Education. 1 (1): 64.
  9. ^ Gizer, I.R.; Ficks, C.; Waldman, I.D. (2009). "Candidate gene studies of ADHD: a meta-analytic review". Human Genetics. 126 (1): 51–90. doi:10.1007/s00439-009-0694-x. PMID 19506906.
  10. ^ Teixeira, L.V.S.; Mandelbaum, K.L. (August 2011). "Candidate gene linkage analysis indicates genetic heterogeneity in Marfan syndrome". Brazilian Journal of Medical and Biological Research. 44 (8): 793–800. doi:10.1590/s0100-879x2011007500095.
  11. ^ Hutchison, Kent E.; Stallings, Michael; McGeary, John; Bryan, Angela (2004). "Population Stratification in the Candidate Gene Study: Fatal Threat or Red Herring?". Psychological Bulletin. 130 (1): 66–79. doi:10.1037/0033-2909.130.1.66.
  12. ^ a b c Kim, Jaemin; Lee, Taeheon (2012). "An integrated approach of comparative genomics and heritability analysis of pig and human on obesity trait: evidence for candidate genes on human chromosome 2". BMC Genomics. 13: 711. doi:10.1186/1471-2164-13-711.{{cite journal}}: CS1 maint: unflagged free DOI (link)
  13. ^ Tiffin, Nicki; Adie, Euan; Turner, Francis; Brunner, Han (2006). "Computational disease gene identification: a concert of methods prioritizes type 2 diabetes and obesity candidate genes". Nucleic Acids Research. 34: 3067–3081. doi:10.1093/nar/gkl381.
  14. ^ Feder, Martin (July 1999). "Engineering Candidate Genes in Studies of Adaptation: The Heat‐Shock Protein Hsp70 in Drosophila melanogaster". The American Naturalist. 154: S55–S66. doi:10.1086/303283.
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