Unraveling the underlying mechanisms behind genetically complex traits remains one of the principal goals in psychiatric neurogenetics. The challenges associated with identifying the underlying causes of complex diseases are well illustrated by alcoholism, addictions and other psychiatric diseases. These are complex disorders with moderate to high heritability (approximate range 0.4–0.6) (Goldman et al
). The high incidence and complex inheritance patterns suggest that the elucidation of the roles of common genetic variations in vulnerability might be critical for a better understanding of the pathophysiologies and for the improvement in diagnostic specificity. Whilst several functional loci have been identified (e.g. ADH1B His47Arg and ALDH2 Glu487 in alcoholism (Quertemont, 2004
), the MAOA VNTR in dyscontrol behaviors (Popova, 2006
; Craig, 2007
) and HTTLPR in anxiety/dysphoria (Heinz et al
)), the underlying origins of the genetic variance in vulnerability to addictions and other major psychiatric diseases remain largely unknown.
Analysis of markers throughout the genome has shown that alleles of single nucleotide polymorphisms (SNPs) are often linked to each other in stretches that can range in size from <5 Kb up to >100 Kb (Gabriel et al
). These combinations of linked alleles (haplotypes) allow the entire genome (or portions thereof) to be analyzed using a relatively small number of SNPs. Disease causing SNPs will therefore be linked to other markers and can be identified through their association with other markers even if the causative SNP itself is not assayed (Risch and Merikangas, 1996
; reviewed in Kruglyak, 2008
Until recently researchers were limited in their options for genetic analysis by the limited number of available markers, coupled with comparatively high cost for each genotype obtained. Classical genetic linkage approaches could only be applied when families could be recruited. With the rapid increase in marker information from the HapMap (http://www.hapmap.org/) and GenBank (www.ncbi.nlm.nih.gov/Genbank/) data- bases and the availability of high-density SNP genotyping platforms, researchers now have the possibility of comprehensively interrogating candidate genes and entire biosynthetic/physiological pathways (Perlis et al
) for their genetic contribution to a disorder or phenotype, as well as of performing genome wide scans to identify new candidate genes.
Whole-genome association studies have shown promise in the identification of causative genes in disease (Wellcome Trust Case Control Consortium, 2007
; Easton et al
; Hunter et al
; Frayling et al
; Rioux et al
). However, several problems remain with widespread use of this technology. Published whole-genome association studies have demonstrated that common vulnerability alleles often lead to odds ratios of less than 2, and due to the genome-wide nature of these analyses, and the need for statistical correction (Risch and Merikangas, 1996
; Hirschhorn and Daly, 2005
) (although the required degree of correction for multiple correction remains uncertain), large sample sizes in excess of several thousand cases and controls are needed to detect loci influencing risk (Wang et al
). Furthermore, in the case of bipolar disorder a recent whole-genome association study that compared 2000 cases to 3000 controls identified only a single association signal that survived criteria for genome-wide significance, and this locus accounts for only a small part of the variance in vulnerability attributable to genetic factors. The relatively high per sample cost and the requirement for large numbers of cases and control subjects to identify alleles of modest effect size with associations that are able to withstand correction for multiple testing, make the widespread use of this approach impractical and financially burdensome for many research groups unless pooling approaches are adopted (Shifman et al
; Liu et al
; Johnson et al
The complexity of neuropsychiatric and behavioral disorders coupled with the fact that phenoptype can be modulated by environmental factors and that clinical diagnostic criteria likely miss possible etiological heterogeneity only detectible by biologic measures has promoted researchers to use so-called endophenotypes as surrogates for disease states. These endophenotypes are heritable quantitative measurable traits that are inherited in a stable manner and that are more frequently observed in both cases and their first degree relatives and potentially confer vulnerability to a disorder (Gottesman and Gould, 2003
; Flint and Munafo, 2007
; Frederick and Iacono, 2006
; Enoch et al
). Often these endophenotypes are measured by the use of imaging technologies (MRI and PET) (Martinez et al
; Meyer-Lindenberg and Weinberger, 2006
) or by EEG measures (Yoon et al
), techniques which due to their cost, invasive nature, requirement for expensive, specialized equipment and length of time required for data acquisition are impractical to use on large cohorts. The practicality of the whole-genome association approach to the study of quantitative imaging traits is being assessed, and although no studies are currently published, the data appear to be promising.
Although candidate gene studies have their own inherent limitations (reviewed in Tabor et al
), the use of smaller focused arrays possibly represents a more practical approach for many studies. These focused arrays are able to overcome the issues of inadequate gene coverage and ethnic stratification by providing full coverage for a limited number of candidate genes and by the inclusion of ancestry informative markers (AIMs). Such focused arrays offer the advantages of lower cost and lower false discovery rate, especially in situations where a dataset may have inadequate power for WGA either because of size or other reasons. In the future it also appears likely that such arrays will be required for follow-up on genomic regions identified by linkage and association studies. Studies on individual candidate genes or small groups of such genes have led to the discovery of functional loci such as the ones cited earlier, but on the other hand these studies have been hampered in other ways. Many linkage and association studies on the role of candidate genes in complex disorders have used single non-functional markers that do not capture sufficient information or do not evaluate all genes in the functional domain of interest. In many instances different markers are selected by groups to interrogate a single gene, making the comparison of data difficult. An additional confound in these single gene studies has been the general failure to control for unrecognized ethnic stratification within the cohort that can lead to the generation of both false positive and false negative signals (Schork et al
; Rosenberg and Nordborg, 2006
). Such unrecognized stratification is problematic for genetic studies and can also confound studies relating phenotype to phenotype or risk variable to outcome. In such instances ethnicity can represent a hidden variable.
Recent advances in the neurobiology of addiction, mood disorders and psychoses have established the importance of several mechanisms, including reward, stress resiliency and executive cognitive control (reviewed in Goldman et al
). These studies thereby implicate several molecular networks that are integral to those processes and genes necessary for their function. These molecular pathways include signaling networks, stress/endocrine genes, key neurotransmitter systems including dopamine, serotonin, glutamate, GABA and acetylcholine. In several instances, particular genes and molecules have also been specifically implicated in addiction liability or in addictions-related phenotypes by whole-genome or candidate-gene-focused linkage results.
We have designed a 1536 SNP array, implemented on the Illumina Goldengate assay platform. This array includes 1350 SNPs selected for 130 genes and 186 markers that are highly informative for AIMs. The 130 candidate genes were selected on the basis of their roles in functional domains important in the addictions and in the related phenotypes of anxiety and depression. Figure lists the 130 candidate genes organized into one somewhat arbitrary scheme of functional categorization. The candidate genes included a limited number involved in the pharmacokinetic domain (e.g. several genes in the ADH gene cluster, and ALDH genes). The majority of the genes represent the domains of vulnerability to drug use and pharmacodynamic response. These include dopamine, serotonin, glutamine, GABA, and opioid neurotransmitter genes, signaling genes, and genes modulating stress resiliency and behavioral dyscontrol domains. There is a high degree of overlap between functional gene categories because of pleiotropic actions of molecules on behavior.
Gene content of the Addictions Array.