Department of Neurology, Columbia University Irving Medical Center, New York, NY, 10032, USA.
Department of Neurology, Aiginition Hospital, National and Kapodistrian University of Athens, Athens, 11528, Greece.
Behav Genet. 2024 Sep;54(5):398-404. doi: 10.1007/s10519-024-10194-x. Epub 2024 Aug 20.
Although the impact of occupation on cognitive skills has been extensively studied, there is limited research examining if genetically predicted cognitive score may influence occupation. We examined the association between Cognitive Polygenic Index (PGI) and occupation, including the role of brain measures. Participants were recruited for the Reference Ability Neural Network and the Cognitive Reserve studies. Occupational complexity ratings for Data, People, or Things came from the Dictionary of Occupational Titles. A previously-created Cognitive PGI and linear regression models were used for the analyses. Age, sex, education, and the first 20 genetic Principal Components (PCs) of the sample were covariates. Total cortical thickness and total gray matter volume were further covariates. We included 168 white-ethnicity participants, 20-80 years old. After initial adjustment, higher Cognitive PGI was associated with higher Data complexity (B=-0.526, SE = 0.227, Beta= -0.526 p = 0.022, R = 0.259) (lower score implies higher complexity). Associations for People or Things were not significant. After adding brain measures, association for Data remained significant (B=-0.496, SE: 0.245, Beta= -0.422, p = 0.045, R = 0.254). Similarly, for a further, fully-adjusted analysis including all the three occupational complexity measures (B=-0.568, SE = 0.237, Beta= -0.483, p = 0.018, R = 0.327). Cognitive genes were associated with occupational complexity over and above brain morphometry. Working with Data occupational complexity probably acquires higher cognitive status, which can be significantly genetically predetermined.
虽然职业对认知技能的影响已经得到了广泛的研究，但目前关于遗传预测的认知分数是否会影响职业的研究还很有限。我们研究了认知多基因指数（PGI）与职业之间的关系，包括大脑测量指标的作用。参与者是为参考能力神经网络和认知储备研究招募的。数据、人员或事物的职业复杂性评级来自职业名称词典。之前创建的认知 PGI 和线性回归模型用于分析。年龄、性别、教育程度以及样本的前 20 个遗传主成分（PC）是协变量。总皮质厚度和总灰质体积是进一步的协变量。我们纳入了 168 名白种人参与者，年龄在 20-80 岁之间。在初步调整后，较高的认知 PGI 与较高的数据复杂性相关（B=-0.526，SE=0.227，β=-0.526，p=0.022，R=0.259）（分数越低表示复杂性越高）。对于人员或事物的关联则不显著。在加入大脑测量指标后，数据的关联仍然显著（B=-0.496，SE：0.245，β=-0.422，p=0.045，R=0.254）。同样，在进一步的完全调整分析中，包括所有三种职业复杂性指标（B=-0.568，SE=0.237，β=-0.483，p=0.018，R=0.327）。认知基因与职业复杂性相关，超出了大脑形态测量的范围。从事数据相关职业的复杂性可能需要更高的认知状态，而这种状态可能在很大程度上是由遗传决定的。
