Olin Neuropsychiatry Research Center, Institute of Living, Hartford Hospital, 200 Retreat Avenue, Hartford, CT 06106, USA.
Biol Psychiatry. 2012 Jan 1;71(1):6-14. doi: 10.1016/j.biopsych.2011.08.022. Epub 2011 Oct 7.
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.
尽管有大量证据表明重度抑郁症的遗传性极高，但最近的研究仅确定了一个与抑郁症相关的基因座达到全基因组显著水平，尚未确定因果基因。关注基于家族的定量中间表型或内表型研究，同时对使用分类诊断的无关个体进行研究，应该提高识别重度抑郁症基因的可能性。然而，目前尚无经验推导的、严格的统计学方法来选择精神疾病的最佳内表型。在这里，我们描述了内表型排序值，这是一种新的遗传效用客观指标，用于评估任何遗传性疾病的内表型。
通过对内表型排序值分析的应用，对来自行为/神经认知、神经解剖和转录组学表型领域的超过 11000 个特征的高维数据集进行分析，我们在来自大型随机选择的扩展家系的墨西哥裔美国人个体样本（n=1122）中确定了一组复发性重度抑郁症的客观内表型。
排名靠前的内表型包括贝克抑郁量表、双侧腹侧间脑体积和 RNF123 转录物的表达水平。为了说明内表型在这种情况下的实用性，在双变量连锁分析中，每个特征都与疾病状态一起使用。在染色体 4p15 上定位到一个全基因组显著的数量性状基因座（对数优势=3.5），对该内表型（淋巴细胞衍生的 RNF123 基因表达水平）和疾病风险都表现出多效性效应。
更广泛地使用定量内表型，结合用于在这些措施中进行选择的无偏方法，应该会激发对影响重度抑郁症等精神疾病的生物学机制的新见解。
