General cognitive function is substantially heritable across the human life course from adolescence to old age. We investigated the genetic contribution to variation in this important, health- and well-being-related trait in middle-aged and older adults. We conducted a meta-analysis of genome-wide association studies of 31 cohorts (N=53 949) in which the participants had undertaken multiple, diverse cognitive tests. A general cognitive function phenotype was tested for, and created in each cohort by principal component analysis. We report 13 genome-wide significant single-nucleotide polymorphism (SNP) associations in three genomic regions, 6q16.1, 14q12 and 19q13.32 (best SNP and closest gene, respectively: rs10457441, P=3.93 × 10−9, MIR2113; rs17522122, P=2.55 × 10−8, AKAP6; rs10119, P=5.67 × 10−9, APOE/TOMM40). We report one gene-based significant association with the HMGN1 gene located on chromosome 21 (P=1 × 10−6). These genes have previously been associated with neuropsychiatric phenotypes. Meta-analysis results are consistent with a polygenic model of inheritance. To estimate SNP-based heritability, the genome-wide complex trait analysis procedure was applied to two large cohorts, the Atherosclerosis Risk in Communities Study (N=6617) and the Health and Retirement Study (N=5976). The proportion of phenotypic variation accounted for by all genotyped common SNPs was 29% (s.e.=5%) and 28% (s.e.=7%), respectively. Using polygenic prediction analysis, ~1.2% of the variance in general cognitive function was predicted in the Generation Scotland cohort (N=5487; P=1.5 × 10−17). In hypothesis-driven tests, there was significant association between general cognitive function and four genes previously associated with Alzheimer's disease: TOMM40, APOE, ABCG1 and MEF2C.
We used a sub-sample from the Older Australian Twins Study to estimate the heritability of performance on three tests of language ability: Boston Naming Test (BNT), Letter/Phonemic Fluency (FAS) and Category/Semantic Fluency (CFT) Tests. After adjusting for age, sex, education, mood, and global cognition (GC), heritability estimates obtained for the three tests were 0.35, 0.59, and 0.20, respectively. Multivariate analyses showed that the genetic correlation were high for BNT and CFT (0.61), but low for BNT and FAS (0.17), and for FAS and CFT (0.28). Genetic modelling with Cholesky decomposition indicated that the covariation between the three measures could be explained by a common genetic factor. Environmental correlations between the language ability measures were low, and there were considerable specific environmental influences for each measure. Future longitudinal studies with language performance and neuroimaging data can further our understanding of genetic and environmental factors involved in the process of cognitive aging.
Remote sensing image classification with the maximum-likelihood decision rule leads to a computational cost that depends quadratically on the number of bands in the data. Moreover, the data has to be modelled beforehand by sets of multivariate normal distributions if acceptable classification accuracies are to be obtained. A newalgorithm is presented with a cost linearlyproportional to the number of bands. Being based upon a combination of linear classifiers it is not dependent upon a priori parametric modellingof the data. Instead it partitions the measurement space in a piecewise-linear fashion leading to high accuracies at low cost, particularly for multitemporal data.
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