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. 2012 Jan 1;71(1):6-14.
doi: 10.1016/j.biopsych.2011.08.022. Epub 2011 Oct 7.

High dimensional endophenotype ranking in the search for major depression risk genes

Affiliations

High dimensional endophenotype ranking in the search for major depression risk genes

David C Glahn et al. Biol Psychiatry. .

Abstract

Background: 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.

Methods: 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.

Results: 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.

Conclusions: 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.

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Conflict of interest statement

Conflict of Interests. All authors reported no biomedical financial interests or potential conflicts of interest.

Figures

Figure 1
Figure 1. ERV statistics for subcortical brain regions and recurrent major depression
Volume measurements of subcortical nuclei were found to share genetic variance with liability for recurrent major depression in extended pedigrees selected without regard to phenotype. ERV statistics, which provide an unbiased and empirically derived method for choosing appropriate endophenotypes, were strongest for the ventral diencephalon volume, a region primarily comprised of the hypothalamus. For anatomical reference, in this image the cortex is shown as a semi-transparent structure.
Figure 2
Figure 2. Manhattan plot depicting whole transcriptomic search for expression-based endophenotypes for recurrent major depression
ERV values were calculated for 11,337 detected lymphocyte-based transcripts and recurrent major depression. Dashed lines reflect cutoff points for FDR < 0.10 (13 transcripts) and FDR < 0.25 (29 transcripts).
Figure 3
Figure 3. Detection of a QTL influencing recurrent major depression and RNF123 expression levels on chromosome 4
Multipoint LOD functions for chromosome 4 in 1,122 individuals from large extended pedigrees from the Genetics of Brain Structure and Function study. The black line represents the univariate linkage analysis for RNF123 expression levels alone. The blue line represents the univariate linkage analysis for recurrent major depression alone. The red line represents the bivariate linkage analysis for recurrent major depression and RNF123 and reaches genome-wide significance (LOD=3.5) at 45 cM (chromosomal band 4p15). The vertical axis is in LOD score units, and the horizontal axis is in units of genetic distance (centi-Morgans, cM) from the p arm telomere.

Comment in

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