A. Tsapanou , N. Mourtzi , Y. Gu , C. Habeck , D. Belsky , Y. Stern
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We explored whether these associations were accounted for by magnetic resonance imaging (MRI) measures of brain-aging phenotypes: total gray matter volume (GM), cortical thickness (CT), and white matter hyperintensities burden (WMH).</p></div><div><h3>Results</h3><p>Participants with higher PGI values performed better on cognitive tests (B = 0.627, SE = 0.196, <em>p</em> = 0.002) (age, sex, and principal components as covariates). Associations remained significant with inclusion of covariates for MRI measures of brain aging; B = 0.439, SE: 0.198, <em>p</em> = 0.028). PGI associations were stronger in young and middle-aged (age<65) as compared to older adults. For further validation, linear regression for Cog PGI and cognition in the fully adjusted model and adding the interaction between age group and Cog PGI, showed significant results (B = 0.892, SE: 0.325, <em>p</em> = 0.007) driven by young and middle-aged adults (B = −0.403, SE: 0.193, <em>p</em> = 0.039). In ancillary analysis, the Cognitive PGI was not associated with any of the brain measures.</p></div><div><h3>Conclusions</h3><p>Genetics discovered in GWAS of cognition are associated with cognitive performance in healthy adults across age, but most strongly in young and middle-aged adults. Associations were not explained by brain-structural markers of brain aging. Genetics uncovered in GWAS of cognitive performance may contribute to individual differences established relatively early in life and may not reflect genetic mechanisms of cognitive aging.</p></div>","PeriodicalId":74277,"journal":{"name":"Neuroimage. Reports","volume":"3 1","pages":"Article 100153"},"PeriodicalIF":0.0000,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/ce/26/nihms-1883031.PMC10038095.pdf","citationCount":"1","resultStr":"{\"title\":\"Polygenic indices for cognition in healthy aging; the role of brain measures\",\"authors\":\"A. Tsapanou , N. Mourtzi , Y. Gu , C. Habeck , D. Belsky , Y. Stern\",\"doi\":\"10.1016/j.ynirp.2022.100153\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><h3>Background</h3><p>Genome-wide association studies (GWAS) have identified large numbers of genetic variants associated with cognition. However, little is known about how these genetic discoveries impact cognitive aging.</p></div><div><h3>Methods</h3><p>We conducted polygenic-index (PGI) analysis of cognitive performance in n = 168 European-ancestry adults aged 20–80. We computed PGIs based on GWAS of cognitive performance in young/middle-aged and older adults. We tested associations of the PGI with cognitive performance, as measured through neuropsychological evaluation. We explored whether these associations were accounted for by magnetic resonance imaging (MRI) measures of brain-aging phenotypes: total gray matter volume (GM), cortical thickness (CT), and white matter hyperintensities burden (WMH).</p></div><div><h3>Results</h3><p>Participants with higher PGI values performed better on cognitive tests (B = 0.627, SE = 0.196, <em>p</em> = 0.002) (age, sex, and principal components as covariates). Associations remained significant with inclusion of covariates for MRI measures of brain aging; B = 0.439, SE: 0.198, <em>p</em> = 0.028). PGI associations were stronger in young and middle-aged (age<65) as compared to older adults. For further validation, linear regression for Cog PGI and cognition in the fully adjusted model and adding the interaction between age group and Cog PGI, showed significant results (B = 0.892, SE: 0.325, <em>p</em> = 0.007) driven by young and middle-aged adults (B = −0.403, SE: 0.193, <em>p</em> = 0.039). In ancillary analysis, the Cognitive PGI was not associated with any of the brain measures.</p></div><div><h3>Conclusions</h3><p>Genetics discovered in GWAS of cognition are associated with cognitive performance in healthy adults across age, but most strongly in young and middle-aged adults. Associations were not explained by brain-structural markers of brain aging. Genetics uncovered in GWAS of cognitive performance may contribute to individual differences established relatively early in life and may not reflect genetic mechanisms of cognitive aging.</p></div>\",\"PeriodicalId\":74277,\"journal\":{\"name\":\"Neuroimage. 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Polygenic indices for cognition in healthy aging; the role of brain measures
Background
Genome-wide association studies (GWAS) have identified large numbers of genetic variants associated with cognition. However, little is known about how these genetic discoveries impact cognitive aging.
Methods
We conducted polygenic-index (PGI) analysis of cognitive performance in n = 168 European-ancestry adults aged 20–80. We computed PGIs based on GWAS of cognitive performance in young/middle-aged and older adults. We tested associations of the PGI with cognitive performance, as measured through neuropsychological evaluation. We explored whether these associations were accounted for by magnetic resonance imaging (MRI) measures of brain-aging phenotypes: total gray matter volume (GM), cortical thickness (CT), and white matter hyperintensities burden (WMH).
Results
Participants with higher PGI values performed better on cognitive tests (B = 0.627, SE = 0.196, p = 0.002) (age, sex, and principal components as covariates). Associations remained significant with inclusion of covariates for MRI measures of brain aging; B = 0.439, SE: 0.198, p = 0.028). PGI associations were stronger in young and middle-aged (age<65) as compared to older adults. For further validation, linear regression for Cog PGI and cognition in the fully adjusted model and adding the interaction between age group and Cog PGI, showed significant results (B = 0.892, SE: 0.325, p = 0.007) driven by young and middle-aged adults (B = −0.403, SE: 0.193, p = 0.039). In ancillary analysis, the Cognitive PGI was not associated with any of the brain measures.
Conclusions
Genetics discovered in GWAS of cognition are associated with cognitive performance in healthy adults across age, but most strongly in young and middle-aged adults. Associations were not explained by brain-structural markers of brain aging. Genetics uncovered in GWAS of cognitive performance may contribute to individual differences established relatively early in life and may not reflect genetic mechanisms of cognitive aging.
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