期刊
NEUROPSYCHOLOGY
卷 26, 期 2, 页码 238-250出版社
AMER PSYCHOLOGICAL ASSOC
DOI: 10.1037/a0026768
关键词
executive function; genetics; heritability; twins; middle-age; speed; trail making
资金
- U.S. Department of Veterans Affairs
- National Institute on Aging [R01 AG018386, R01 AG018384, R01 AG022381, R01 AG022982]
Objective: To examine how genes and environments contribute to relationships among Trail Making Test (TMT) conditions and the extent to which these conditions have unique genetic and environmental influences. Method: Participants included 1,237 middle-aged male twins from the Vietnam Era Twin Study of Aging. The Delis-Kaplan Executive Function System TMT included visual searching, number and letter sequencing, and set-shifting components. Results: Phenotypic correlations among TMT conditions ranged from 0.29 to 0.60, and genes accounted for the majority (58-84%) of each correlation. Overall heritability ranged from 0.34 to 0.62 across conditions. Phenotypic factor analysis suggested a single factor. In contrast, genetic models revealed a single common genetic factor but also unique genetic influences separate from the common factor. Genetic variance (i.e., heritability) of number and letter sequencing was completely explained by the common genetic factor while unique genetic influences separate from the common factor accounted for 57% and 21% of the heritabilities of visual search and set shifting, respectively. After accounting for general cognitive ability, unique genetic influences accounted for 64% and 31% of those heritabilities. Conclusion: A common genetic factor, most likely representing a combination of speed and sequencing, accounted for most of the correlation among TMT 1-4. Distinct genetic factors, however, accounted for a portion of variance in visual scanning and set shifting. Thus, although traditional phenotypic shared variance analysis techniques suggest only one general factor underlying different neuropsychological functions in nonpatient populations, examining the genetic underpinnings of cognitive processes with twin analysis can uncover more complex etiological processes.
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