Making sense out of GWAS findings – starting from the individualThe genetic overlap between major depression and body mass index

肥胖是世界范围内的主要健康负担，也是抑郁障碍(MDD)的相关共病。定义肥胖的一个常用参数是身体质量指数(BM)。BMI的最新遗传关联研究确定了97个全基因组匹配，其中大多数位于在人脑中高度表达的基因。因此，BMI相关的变体支持中枢神经系统的重要作用，涉及突触功能、谷氨酸信号和中枢食欲调节的关键大脑部位。在PGC最近关于MDD的研究中，发现MDD和BMI之间存在显着的遗传相关性，MDD的两个最重要的命中位于先前与BMI和肥胖相关的基因附近。我们的PGC MDD研究小组的另一项研究发现，报告有“体重增加”症状的MDD患者携带更多BMI的遗传风险变量。这些发现支持肥胖是一种与大脑相关的疾病的假说，以及这种联系背后潜在的共同生物学机制。在这里，我们介绍了我们基于个人的方法，以理清遗传风险变量对MDD中BMI特征的假定影响。这种方法是一种新的方法，从个人特定的遗传和疾病负担出发，从GWAS的发现中获得意义，从而揭示了BMI和MDD共同的潜在生物学。这种方法之前曾在精神分裂症中进行过测试，精神分裂症是一种遗传遗传率很高的疾病，有108个全基因组显著的变异。风险变异的特定成分可归因于自闭症和情感性SCZ亚型。我们的方法将强调MDD和BMI之间的联系，遵循从个体出发的不同分析策略：1.在我们的基因驱动的方法中，我们将选择关于BMI相关变体表现出极端基因星座的MDD受试者。这些受试者将被分析他们特定的MDD症状和亚型，并与他们的BMI遗传因素相关。2.在我们的表型驱动的方法中，我们将选择在MDD方面有显著精神共病的肥胖者，并分析他们共同的BMI遗传危险因素。3.在另一种遗传驱动的方法中，我们将选择两个潜在的感兴趣的群体(高BMI遗传负荷但低BMI/低BMI遗传负荷但高BMI)来分析BMI遗传变异与BMI之间的遗传关联偏离是否由基因与环境交互作用分析中的精神共病驱动。在最好的情况下，特定基因/途径的改变将与MDD亚型和症状联系在一起。这一知识可能对医学目标和干预措施有重大好处。所有假说都将在我们的普通人群研究(SHIP-LEGUS N=2400，SHIP-TREND N=4422)和患者队列GANI_MED(N=4371)中进行验证。将在PGC队列中进行复制。

Obesity is a worldwide major health burden and a relevant comorbid condition of depressive disorder (MDD). A commonly used parameter to define obesity is body mass index (BM). The latest genetic association study of BMI identified 97 genome-wide hits most of them located in genes that are highly expressed in the human brain. Thus, the BMI-associated variants supported the important role of the central nervous system involving pathways for synaptic function, glutamate signaling and key brain sites of central appetite regulation. In the recent study for MDD from the PGC a significant genetic correlation between MDD and BMI was found and the two most significant hits for MDD were located near genes previously associated with BMI and obesity. Another study from our PGC MDD research group found that MDD patients reporting symptoms of “increased weight” carried a higher number of genetic risk variants for BMI. These findings support the hypothesis of obesity as a brain related disorder and the potentially shared biological mechanisms underlying this association.Here we introduce our individual-based approach for disentangling the putative impact of genetic risk variants for BMI features in MDD. This approach is a novel way making sense out of GWAS findings starting from the specific genetic and disease burden of an individual and thus revealing insights to the shared underlying biology of BMI and MDD. This approach was previously tested for schizophrenia, a disorder with a high genetic heritability and 108 genome-wide significant variants. Specific compositions of risk variants could be attributed to an autistic and affective SCZ subtype. Our approach will highlight the association between MDD and BMI following different analytic strategies starting from an individual: 1. We will select MDD subjects that exhibit an extreme genotype constellation regarding the BMI associated variants in our genotype-driven approach. These subjects will be analyzed regarding their specific MDD symptoms and subtypes in association with their genetic factors for BMI. 2. In our phenotype-driven approach we will select obese subjects with a striking psychiatric comorbidity regarding MDD and analyze their common genetic risk factors for BMI. 3. In another genetically-driven approach we will select two potentially interesting groups (high genetic load for BMI but low BMI / low genetic load for BMI but high BMI) to analyze if the deviation from the genetic association between BMI genetic variants and BMI is driven by psychiatric comorbidities in gene by environment interaction analyses. In the best case, alterations in specific genes/pathways would be linked to MDD subtypes and symptoms. This knowledge could be of major benefit for medical targets and interventions. All hypotheses will be tested in our general population studies (SHIP-LEGEND N=2400, SHIP-TREND N=4422) and in the patient cohort GANI_MED (N=4371). Replication will be performed in PGC cohorts.