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Despite overwhelming evidence that major depression is highly heritable, recent studies have localized only a single depression-related locus reaching genome-wide significance and have yet to identify a causal gene. Focusing on family-based studies of quantitative intermediate phenotypes or endophenotypes, in tandem with studies of unrelated individuals using categorical diagnoses, should improve the likelihood of identifying major depression genes. However, there is currently no empirically derived statistically rigorous method for selecting optimal endophentypes for mental illnesses. Here, we describe the endophenotype ranking value, a new objective index of the genetic utility of endophenotypes for any heritable illness.
Applying endophenotype ranking value analysis to a high-dimensional set of over 11,000 traits drawn from behavioral/neurocognitive, neuroanatomic, and transcriptomic phenotypic domains, we identified a set of objective endophenotypes for recurrent major depression in a sample of Mexican American individuals (n = 1122) from large randomly selected extended pedigrees.
Top-ranked endophenotypes included the Beck Depression Inventory, bilateral ventral diencephalon volume, and expression levels of the RNF123 transcript. To illustrate the utility of endophentypes in this context, each of these traits were utlized along with disease status in bivariate linkage analysis. A genome-wide significant quantitative trait locus was localized on chromsome 4p15 (logarithm of odds = 3.5) exhibiting pleiotropic effects on both the endophenotype (lymphocyte-derived expression levels of the RNF123 gene) and disease risk.
The wider use of quantitative endophenotypes, combined with unbiased methods for selecting among these measures, should spur new insights into the biological mechanisms that influence mental illnesses like major depression.
). In contrast, recent family-based linkage studies of major depression identified a significant quantitative trait locus (QTL) on chromosome 3p25-26 (logarithm of odds [LOD] = 4.0) in a large sample of affected sibling pairs (
). However, the causal gene(s) for this QTL remain to be identified. Given our slow pace of discovery, new approaches may be necessary to improve understanding of specific causal genes influencing risk of mental illness. One possible approach to speed gene localization/identification is the use of informative quantitative intermediate phenotypes or endophenotypes in families (
). Such an approach has strategic benefits (e.g., simultaneous identification of endophenotypes, increased power to identify genes, increased power to detect rare functional variants) over the more common paradigm that has focused on collections of unrelated individuals and relied solely on categorical diagnoses. Endophenotype exploitation should improve the likelihood of identifying major depression genes (
), difficulties choosing appropriate endophenotypes for mental disorders have limited their use in psychiatry, where relatively less is known about the biological mechanisms that predispose illness than in other areas of medicine. While the endophenotype concept is widely espoused in psychiatric genetics (
), a formal or standardized approach for the identification of endophenotypes is lacking. Most studies employ purely phenotypic correlations between disease risk and a quantitative risk factor to define putative endophenotypes. However, the endophenotype concept fundamentally depends on the existence of joint genetic determination of both endophenotype and disease risk (
). This obligatory pleiotropy is most efficiently tested using family-based observations to assess both the heritability of the endophenotype and its genetic correlation with disease liability. To facilitate the identification of optimal endophenotypes, we developed the endophenotype ranking value (ERV), a novel objective index of the genetic utility of endophenotypes for an illness. The ERV provides an unbiased and empirically derived method for choosing appropriate endophenotypes in a manner that balances the strength of the genetic signal for the endophenotype and the strength of its relation to the disorder of interest. It is defined using the square root of the heritability of the illness (hi2), the square root of the heritability of the endophenotype (he2), and their genetic correlation (ρg) and is expressed in the following formula:
Endophenotype ranking values vary between 0 and 1, where higher values indicate that the endophenotype and the illness are more strongly influenced by shared genetic factors. This method necessitates that endophenotypes be heritable and have some level of pleiotropy with the studied illness, reducing the heterogeneity of the disease and focusing on the proportion of shared genetic factors influencing both the endophenotype and the illness. An advantage of the ERV approach is that very large numbers of potential endophenotypes can be efficiently assessed before conducting molecular genetic analyses, analogous to high-throughput screening methods developed for drug discovery. Furthermore, the ERV approach is applicable to any heritable disease and any set of potentially relevant traits.
Applying ERV analysis to a high-dimensional set of traits, we identified a set of significant endophenotypes for recurrent major depression (recurrent major depressive disorder [rMDD]). We focused on recurrent depression to reduce the clinical heterogeneity of the disorder and potentially increase the genetic control over the illness (
). We performed an automated high-dimensional search for endophenotypes via the ranking of 37 behavioral/neurocognitive, 85 neuroanatomic, and 11,337 lymphocyte-based transcriptional candidate endophenotypes for rMDD using data acquired from 1122 Mexican American individuals from large randomly ascertained extended pedigrees who participated in the Genetics of Brain Structure and Function study. Finally, we employed the top-ranked endophentypes in bivariate linkage analysis, localizing a significant QTL exhibiting pleiotropic effects on both endophenotype and disease risk.
Methods and Materials
A total of 1,122 Mexican American individuals from extended pedigrees (71 families, average size 14.9 [1–87] people) were included in the analysis. Participants were 64% female and ranged in age from 18 to 97 (mean ± SD 47.11 ± 14.2) years. Individuals in this cohort have actively participated in research for over 18 years and were randomly selected from the community with the constraints that they are of Mexican American ancestry, part of a large family, and live within the San Antonio region (see  for recruitment details). No other inclusion or exclusion criteria were imposed in the initial study. However, individuals were excluded from scanning for magnetic resonance imaging contraindications. In addition, individuals were excluded from scanning and neurocognitive evaluation for history of neurological illnesses, stroke, or other major neurological event. Reported pedigree relationships were empirically verified with autosomal markers and intrafamilial relationships were edited if necessary (see Table 1 for familial relationships). All participants provided written informed consent on forms approved by the Institutional Review Boards at the University of Texas Health Science Center San Antonio and at Yale University.
), a semistructured interview augmented to include items on lifetime diagnostic history. Masters- and doctorate-level research staff, with established reliability (κ ≥ .85) for affective disorders, conducted all interviews. All subjects with possible psychopathology were discussed in case conferences that included licensed psychologists or psychiatrists. Lifetime consensus diagnoses were determined based on available medical records, the MINI interview, and the interviewer's narrative. Recurrent major depression was defined as two or more distinct episodes of depression meeting DSM-IV criteria.
Behavioral and Neurocognitive Assessment
Each participant received a 90-minute neuropsychological evaluation consisting of standard and computerized measures (
). Thirty-five neurocognitive variables were derived from 17 separate neuropsychological tests, including measures of attention/concentration, executive processing, working memory, declarative memory, language processing, intelligence, and emotional processing. In addition, participants completed two questionnaires indexing depressive mood: the Beck Depression Inventory-II (BDI-II) (
Magnetic resonance imaging data were acquired on a 3T Siemens (Erlangen, Germany) Trio scanner with an 8-channel head coil in the Research Imaging Institute, University of Texas Health Science Center San Antonio. Isotropic anatomic images (800 μm) were acquired for each subject using a retrospective motion-corrected protocol (
). This protocol included the acquisition of seven full-resolution volumes using a T1-weighted, three-dimensional TurboFlash sequence with the following scan parameters: echo time [TE]/repetition time [TR]/inversion time = 3.04/2100/785 milliseconds, flip angle = 13°. Surface-based image analyses were conducted with FreeSurfer (
). T1-weighted images were segmented into gray matter thickness measures for 53 cortical regions and 21 subcortical volumes (averaged across both hemispheres).
T2-weighted imaging data were acquired using a 1-mm isotropic, turbo spin echo FLAIR sequence with the following parameters: TR/TE/inversion time/flip angle/echo train length = 5 seconds/353 milliseconds/1.8 seconds/180°/221. White-matter hyperintensities were manually delineated in three-dimensional space using in-house software by experienced neuroanatomists with high (r2 > .90) test-retest reproducibility (
Diffusion tensor imaging data acquisition used a single-shot single spin echo, echo planar imaging sequence with a spatial resolution of 1.7 × 1.7 × 3.0 mm (TR/TE = 8000/87 milliseconds, field of view = 200 mm, 55 directions, b = 0, and 800 seconds/mm2). Fractional anisotropy values were estimated for each subject on 13 tracts using Tract-Based Spatial Statistics software (
). Total RNA was isolated from lymphocytes and hybridized to Illumina (San Diego, California) Sentrix Human Whole Genome (WG-6) Series 1 BeadChips. These BeadChips simultaneously probe ∼48,000 transcripts, representing more than 25,000 annotated human genes. Although we previously identified 20,413 quantitative transcripts in lymphocytes, we only examined those with heritabilities greater than or equal to .20 (n = 11,337) in the current analysis.
DNA extracted from lymphocytes was used in polymerase chain reactions (PCRs) for the amplification of individual DNA at 432 dinucleotide repeat microsatellite loci (short tandem repeats [STRs]), spaced approximately 10 cM intervals apart across the 22 autosomes, with fluorescently labeled primers from the MapPairs Human Screening Set, Versions 6 and 8 (Research Genetics, Huntsville, Alabama). Polymerase chain reactions were performed separately according to manufacturer specifications in Applied Biosystems 9700 thermocyclers (Applied Biosystems, Foster City, California). For each individual, the products of separate PCRs were pooled using the Robbins Hydra-96 Microdispenser (Robbins Scientific Corporation, Sunnyvale California), and a labeled size standard was added to each pool. The pooled PCR products were loaded into an ABI PRISM 377 or 3100 Genetic Analyzer (Applied Biosystems) for laser-based automated genotyping. The STRs and standards were detected and quantified, and genotypes were scored using the Genotyper software package (Applied Biosystems).
), which employs maximum likelihood variance decomposition methods to determine the relative importance of genetic and environmental influences by modeling the covariance among family members as a function of genetic proximity (see Supplement 1 for detail on variance components methods).
The ERV represents the standardized genetic covariance between the endophenotype (denoted by the subscript, e) and illness (denoted by the subscript, i) and is defined as ERVie = |√hi2√he2ρg|. Heritability (h2) represents the portion of the phenotypic variance accounted for by additive genetic variance (h2 = σ2g/σ2p). Genetic correlation represents the common genetic covariance between two traits or pleiotropy (
). Bivariate quantitative genetic analysis was used to estimate the genetic (ρg) and environmental (ρe) correlations between each potential endophenotype and rMDD. The phenotypic correlation (ρp), which quantifies the overall relationship between the two traits, can be derived from the genetic and environmental correlations as ρp = ρg√(h2eh2i) + ρe√[(1-h2e)(1-h2i)]. These parameters are estimated by jointly utilizing all available pedigree information with a multivariate normal threshold model for combined dichotomous/continuous traits (
). The significance of the ERV was tested by comparing the ln likelihood for the restricted null model (with ρg constrained to equal 0) against the ln likelihood for the alternative model in which the ρg parameter is estimated. The resultant likelihood ratio test is asymptotically distributed as a chi-square with a single degree of freedom. The corresponding p value is identical to that used for genetic correlation. Before analysis, endophenotypes were normalized using an inverse Gaussian transformation. Age, sex, age × sex, age2, and age2 × sex were included as covariates whose effects were simultaneously estimated in all analyses.
Bivariate Linkage Analysis
Bivariate linkage analysis exploits the genetic covariance between the endophenotype and the illness to improve the power to localize QTLs and to detect QTL-specific pleiotropic effects (
), genotype data were used to compute maximum likelihood estimates of allele frequencies. Matrices of empirical estimates of identity-by-descent allele sharing at points throughout the genome for every relative pair were computed using the Loki package (
). For the localization of QTLs, we performed both univariate and bivariate variance components linkage analyses by employing the models for combined analysis of quantitative and dichotomous phenotypes described by Williams et al. (
). Once a genome-wide significant localization was made, formal single degree of freedom likelihood ratio tests for pleiotropy were performed to test the specific hypothesis that a QTL at that location influenced a given endophenotype/rMDD risk (
Two hundred fifteen individuals met criteria for lifetime rMDD (19% of the sample; 73% female subjects). Eighty-six individuals were clinically depressed at the time of the assessment. The estimated heritability for lifetime rMDD was h2 = .463 (standard error ± .12), p = 4.0 × 10−6. We previously demonstrated that this heritability estimate is not significantly influenced by common environmental factors as indexed by shared household (
The 10 top-ranked behavioral/cognitive endophenotypes are presented in Table 2. The BDI-II was the highest ranked endophenotype in this class. Although the BDI-II was developed as an index of mood state, the heritability of this measure was h2 = .254 (± .07), p = 5.6 × 10−5, demonstrating that 25% of the variability on this measure is due to additive genetic factors. The genetic correlation between the BDI-II and the neuroticism questions from the Eysenck Personality Questionnaire, the second best ranked endophenotype in this domain, was ρG = .870 (± .09), p = 3.3 × 10−4, suggesting significant pleiotropy and potential redundancy between these two measures. Top-ranked cognitive measures include indices of working and declarative memory, attention, and emotion recognition.
Table 2Ten Top-Ranked Endophenotypes per Domain for Recurrent Major Depression
Genetic Correlation (ρg)
Beck Depression Inventory II
1.9 × 10−5
1.79 × 10−4
Declarative Memory (CVLT Recognition)
5.49 × 10−2
Working Memory (Digit Span Forward)
5.69 × 10−2
Working Memory (Letter-Number Span)
6.39 × 10−2
Penn Facial Memory (Immediate)
6.99 × 10−2
Penn Facial Memory (Delayed)
8.19 × 10−2
Attention (CPT hits)
8.39 × 10−2
Attention (Trails A)
9.69 × 10−2
Penn Emotion Recognition
1.09 × 10−1
Ventral diencephalon volume
3.99 × 10−3
Parietal hyperintensity volume
7.89 × 10−3
1.29 × 10−2
1.39 × 10−2
Cerebellar white matter volume
1.39 × 10−2
Frontal hyperintensity volume
1.39 × 10−2
Corticospinal tract (FA)
2.19 × 10−2
Subcortical hyperintensity volume
4.19 × 10−2
Superior parietal gyrus thickness
4.59 × 10−2
Thalamus proper volume
4.89 × 10−2
5.29 × 10−6
1.19 × 10−5
2.09 × 10−5
2.39 × 10−5
3.69 × 10−5
3.99 × 10−5
4.09 × 10−5
7.99 × 10−5
7.99 × 10−5
1.19 × 10−4
Ten top-ranked endophenotypes for recurrent major depression in the categories of behavioral/cognitive, neuroimaging, and RNA transcripts (see Supplement 1 for the complete rankings). Genetic correlations are between the respective endophenotypes and lifetime affection status. Endophenotype heritability estimates were estimated as part of bivariate models.
The top-ranked brain region was bilateral ventral diencephalon volume (Table 2), a region primarily comprised of the hypothalamus. As part of the hypothalamic-pituitary-adrenal axis, the hypothalamus mediates neuroendocrine and neurovegetative functions and has been consistently implicated in the neurobiology of depression (
), both regions with reasonably high ERV ranking (3rd and 13th ranked, respectively). Our results suggest that the genetic factors that influence the structure of these subcortical regions (Figure 1) also confer risk for rMDD. Additionally, white-matter hyperintensity measures, which are associated with aging, cerebrovascular dysfunction, smoking, and a host of other depression-related pathologies (
), by suggesting common genetic factors increase risk for rMDD and white-matter hyperintensities.
Potential Transcriptional Endophenotypes
Endophenotype ranking value analyses on 11,337 transcripts are presented in Figure 2 and top-ranked transcriptional endophenotypes for rMDD are presented in Table 2. The top-ranking transcript, RNF123, is a member of the E3 ubiquitin-protein ligase family, which have diverse functions including protein degradation and modulation of protein assembly, structure, function, and localization (
), respectively. Although other identified transcripts are less obvious candidates for rMDD risk, they may represent novel genes whose functions are not fully understood and may extend to depression phenotypes.
Genome-Wide Bivariate Linkage Analyses Using rMDD and Top-Ranked Endophenotypes
We performed a genome-wide linkage-based search for pleiotropic quantitative trait loci influencing disease risk and the top-ranked endophenotype from each class: BDI-II, bilateral ventral diencephalon volume, and the RNF123 transcript. First, standard univariate linkage analyses were performed. Two traits exhibited genome-wide or near genome-wide significance QTLs. The best univariate score for rMDD was found on chromosome 4 at 47 cM (LOD = 2.98, nominal p = .00011). While not reaching traditional genome-wide significance, this result points to a potential disease-related QTL at chromosomal location 4p15. The bilateral ventral diencephalon exhibited an unequivocal genome-wide significant peak on chromosome 7 at 131 cM (LOD = 3.40, nominal p = 3.8 × 10−5). Neither BDI-II nor RNF123 expression levels showed strong evidence for causal QTLs in univariate analysis. Suggestive evidence for a QTL influencing BDI-II was found on chromosome 17 at 98 cM (LOD = 2.57, nominal p = .0003). We found little evidence for a QTL influencing quantitative RNF123 gene expression levels, with the single best univariate QTL location found on chromosome 6 at 53 cM (LOD = 1.81).
Bivariate linkage analyses were performed to determine if QTL localization could be enhanced via simultaneous analysis with rMDD affection status. The most dramatic improvement in localization was seen for rMDD and RNF123 transcription levels. The bivariate analysis of this endophenotype/disease combination substantially improved the evidence for a QTL located at 4p15 seen in the univariate rMDD results. Figure 3 shows the QTL localization results for the bivariate analysis and the two related univariate analyses. The peak bivariate LOD (scaled to a standard single degree of freedom LOD) was 3.51 (nominal p = 3.8 × 10−5) at 45 cM, a marked improvement over that seen for rMDD alone. No other rMDD/endophenotype combination provided genome-wide evidence for QTLs.
Table 3 shows the results of likelihood ratio statistic-based formal tests of pleiotropy at the chromosome 4:45 cM location obtained from the bivariate analysis of RNF123/rMDD. The marginal results are from univariate analysis (technically co-incident linkage ) and the strict test of pleiotropy that can be performed using the bivariate linkage model. The chromosome 4 locus significantly influences rMDD (p = 4.7 × 10−5), RNF123 (p = .0010), and diencephalon volume (p = .0290) and shows a trend for BDI-II (p = .1170). The fact that this QTL influences both risk of rMDD and two of our three best endophenotypes provides additional validation for endophenotype identification, with evidence for rMDD increasing by nearly an order of magnitude. These results strongly support a QTL influencing rMDD and related endophenotypes at chromosome 4p15.
Table 3Tests of Pleiotropy at the Chromosome 4:45 cM Quantitative Trait Locus
Pleiotropy p Value from Bivariate Model
Co-Incident Linkage p Value from Univariate Model
4.7 × 10−5
1.1 × 10−4
Ventral Diencephalon Volume
BDI-II, Beck Depression Inventory-II; MDD, major depressive disorder.
Given evidence for a QTL influencing diencephalon volume on chromosome 7, we tested for pleiotropic effects. As expected, these tests revealed a major effect on diencephalon (pleiotropy p value = 1.6 × 10−5) and rMDD liability (pleiotropy p value = .0437). Both of these results are substantially improved over their univariate analogues and only with bivariate analysis do we detect a significant influence of this QTL on rMDD liability. The other two leading endophenotypes show no pleiotropic effects at this QTL.
Our results demonstrate the utility of the ERV approach for formally identifying endophenotypes in high-dimensional data and provide a novel genome-wide significant QTL for recurrent major depression. Bivariate genetic analyses including a quantitative endophenotype and disease risk significantly improved QTL detection over that observed utilizing diagnosis alone. These results may reflect the improved statistical sensitivity of quantitative over qualitative traits or that endophenotypes index a somewhat less heterogeneous aspect of the pathophysiology associated with mental illnesses (
). In either case, quantitative endophenotypes can significantly improve the potential to localize loci for complex disorders like rMDD, where multiple genes with varying effects and incomplete penetrance are thought to interact with environmental factors to determine illness susceptibility.
The present experiment demonstrates the utility of gene expression measures in peripheral tissues for psychiatric phenotypes. Transcripts can be considered endophenotypes that, while removed from the phenomenology-based diagnosis, are close to gene action and in the case of primary cis-regulation, provide evidence for a gene's involvement in the illness. Although brain tissue is ideal for gene expression studies in psychiatry, difficulty obtaining this tissue in genetically informative samples necessitates the use of a surrogate marker and lymphocytes appear to be good surrogates for detection of mental disease-relevant genes (
). The lymphocyte measures used in the present experiment were collected 12 to 15 years before the current assessments, minimizing the potential that these traits were influenced by acute variation in mood or medication usage (
). It is notable that the top-ranked transcriptional endophenotype for rMDD was ranked higher than any of the behavioral/cognitive or neuroimaging traits, including BDI-II, suggesting that transcripts may provide an important new set of markers for disease risk.
Our single strongest ERV result was observed for quantitative messenger RNA levels of the RNF123 gene with risk for rMDD. This gene (also known as KPC1) encodes ring finger protein 123, which is likely involved in the regulation of neurite outgrowth via its modulation of the degradation of the cyclin-dependent kinase inhibitor p27(Kip1) (
). Cyclin-dependent kinase inhibitor p27(Kip1) is involved in increased hippocampal neuronal differentiation via a glucocorticoid receptor function that is observed upon administration of the antidepressant sertraline (
). Thus, RNF123 appears to be a novel candidate involved in hippocampal neurogenesis of significant relevance to depression risk. We observed a significant negative genetic correlation between RNF123 expression level and disease risk consistent with evidence that RNF123 inhibits p27(Kip1) and depression amelioration. Thus, RNF123 represents a potential drug target for depression.
The dominant paradigm in psychiatric genetic studies focuses on a specific disease itself. However, as with most disease states, this end point is relatively distant from the causal anatomic or physiological disruption. In contrast, we supplement disease status with quantitative endophenotypes, selected through an empirically derived process, to identify and characterize genes that influence rMDD. Since these endophenotypes vary within the normal population, it is possible to localize genes influencing them in samples ascertained without regard to a specific phenotype (illness). The endophenotype and normal variation strategy have been successfully applied to the study of other complex diseases such as heart disease (
). However, this strategy has not been effectively applied in the search for mental illness genes.
There is debate regarding the definition of a good endophenotype or even if endophenotypes will benefit the search for mental illness genes. We propose that endophenotypes that are heritable and genetically correlated with disease liability can facilitate gene identification. Although both disease and endophenotype must be heritable for the ERV approach, there is no requirement that the endophenotype exhibit higher heritability than the disease itself. Higher heritability estimates do not imply a simpler genetic architecture or improve the potential to localize genes (
). A quantitative endophenotype with a low but significant heritability estimate that is genetically correlated with disease still allows one to rank individuals along a continuous liability distribution (
), increasing power to identify genes. The ERV index includes no assumption about the genetic architecture of an endophenotype. While endophenotypes that are closer to gene action are desirable, this is not a requisite of an endophenotype, as information about the genetic simplicity of a particular endophenotype is generally not available or easily quantified. A putative endophenotype with a high ERV value will reflect the genetic component of disease liability better than one with a low ERV. Therefore, even quantitative endophenotypes with complex genetic architectures (involving many genes) can offer major advantages in genetic dissection of disease liability. Indeed, the gold standard endophenotype for heart disease, low-density lipoprotein cholesterol levels, is a complex quantitative trait that is not particularly close to gene action (given that it does not represent a single protein) that was successfully used to find cardiovascular disease risk genes (
The present experiment establishes the value of randomly selected families in the search for common psychiatric illness genes. While we highlight the optimality of large families for the assessment of heritability, genetic correlations, and ERV calculations, we note that modern high-density typing now allows empirical assessment of deep kinship between unrelated individuals that could in principle be used to estimate these parameters (albeit very inefficiently due to the remoteness of relationships). Thus, very large previously collected data sets of unrelateds may be of some future value in ERV estimation.
While we demonstrate the utlitiy of the ERV approach, the current experiment has several limitatons. For example, not all potential candidate ednophentypes for affective disorders were included (
), as this is impractial in large-scale genetic studies. In addition, verification of endophenotypes in independent samples is warranted. However, when the goal of simultaneous evaluation of disease liability and endophenotype is focused on gene discovery, it may be folly to wait for such replication rather than immediately pursuing an independent discovery avenue like deep sequencing of a gene whose expression level is genetically correlated with disease liability. The formal testing and rigorous defining of endophenotypes for a given disease should speed the identification of risk genes and improve our understanding of the underlying pathobiological processes. Endophenotypes identified by emperical approaches like the ERV will likely outperform nonobjective expert-derived putative endophenotypes.
The endophenotype strategy has the potential to significantly improve our understanding of the genetic architecture of psychiatric illnesses (
). However, choosing optimal endophenotypes for brain-related illness is difficult when relying on theoretical factors alone. The ERV approach provides an unbiased method for selecting endophenotypes that is applicable to any heritable disease and should facilitate the use of endophentypes in the search for genes influencing brain-related illnesses. Objective formal identification of endophenotypes using the ERV procedure led to improved power to localize causal QTLs influencing risk of major depression and the identification of a novel potential player in depression risk focused on the RNF123 gene, its products, and its pathway.
Financial support for this study was provided by the National Institute of Mental Health Grants MH0708143 (Principal Investigator [PI]: DCG), MH078111 (PI: JB), and MH083824 (PI: DCG). Theoretical development of the endophenotype ranking value and its implementation in SOLAR is supported by National Institute of Mental Health Grant MH59490 (PI: JI). This investigation was conducted, in part, in facilities constructed with support from Research Facilities Improvement Program Grant Numbers C06 RR13556 and C06 RR017515 from the National Center for Research Resources, National Institutes of Health .
We thank the study participants, our research staffs, and Irving Gottesman for 50 years of championing endophenotypes in psychiatric genetics. Irving Gottesman is the true source of the endophenotype ranking value. We acknowledge the Azar and Shepperd families and ChemGenex Pharmaceuticals for supporting the transcriptional profiling, sequencing, genotyping, and statistical analysis. The supercomputing facilities used for this work at the AT&T Genomics Computing Center were supported, in part, by a gift from the SBC Foundation.
All authors reported no biomedical financial interests or potential conflicts of interest.