scholarly journals General dimensions of human brain morphometry inferred from genome-wide association data

2021 ◽  
Author(s):  
Anna Elisabeth Fürtjes ◽  
Ryan Arathimos ◽  
Jonathan RI Coleman ◽  
James H Cole ◽  
Simon R Cox ◽  
...  
Keyword(s):  
Human Brain ◽  
Cognitive Ability ◽  
Brain Networks ◽  
Genetic Correlations ◽  
Correlation Structure ◽  
Phenotypic Correlation ◽  
General Factor ◽  
Whole Brain ◽  
Brain Morphometry ◽  
Brain Ageing

The human brain is organised into networks of interconnected regions that have highly correlated volumes. In this study, we aim to triangulate insights into brain organisation and its relationship with cognitive ability and ageing, by analysing genetic data. We estimated general genetic dimensions of human brain morphometry within the whole brain, and nine predefined canonical brain networks of interest. We did so based on principal components analysis (PCA) of genetic correlations among grey-matter volumes for 83 cortical and subcortical regions (Nparticipants = 36,778). We found that the corresponding general dimension of brain morphometry accounts for 40% of the genetic variance in the individual brain regions across the whole brain, and 47-65% within each network of interest. This genetic correlation structure of regional brain morphometry closely resembled the phenotypic correlation structure of the same regions. Applying a novel multivariate methodology for calculating SNP effects for each of the general dimensions identified, we find that general genetic dimensions of morphometry within networks are negatively associated with brain age (rg = -0.34) and profiles characteristic of age-related neurodegeneration, as indexed by cross-sectional age-volume correlations (r = -0.27). The same genetic dimensions were positively associated with a genetic general factor of cognitive ability (rg = 0.17-0.21 for different networks). We have provided a statistical framework to index general dimensions of shared genetic morphometry that vary between brain networks, and report evidence for a shared biological basis underlying brain morphometry, cognitive ability, and brain ageing, that are underpinned by general genetic factors.

scholarly journals Altered Weibull Degree Distribution in Resting-State Functional Brain Networks Is Associated With Cognitive Decline in Mild Cognitive Impairment

2021 ◽  
Vol 12 ◽  
Author(s):  
Yifei Zhang ◽  
Xiaodan Chen ◽  
Xinyuan Liang ◽  
Zhijiang Wang ◽  
Teng Xie ◽  
...  
Keyword(s):  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Human Brain ◽  
Cognitive Performance ◽  
Power Law ◽  
Resting State ◽  
Brain Networks ◽  
Functional Networks ◽  
Whole Brain ◽  
Connectivity Degree

The topological organization of human brain networks can be mathematically characterized by the connectivity degree distribution of network nodes. However, there is no clear consensus on whether the topological structure of brain networks follows a power law or other probability distributions, and whether it is altered in Alzheimer's disease (AD). Here we employed resting-state functional MRI and graph theory approaches to investigate the fitting of degree distributions of the whole-brain functional networks and seven subnetworks in healthy subjects and individuals with amnestic mild cognitive impairment (aMCI), i.e., the prodromal stage of AD, and whether they are altered and correlated with cognitive performance in patients. Forty-one elderly cognitively healthy controls and 30 aMCI subjects were included. We constructed functional connectivity matrices among brain voxels and examined nodal degree distributions that were fitted by maximum likelihood estimation. In the whole-brain networks and all functional subnetworks, the connectivity degree distributions were fitted better by the Weibull distribution [f(x)~x(β−1)e(−λxβ)] than power law or power law with exponential cutoff. Compared with the healthy control group, the aMCI group showed lower Weibull β parameters (shape factor) in both the whole-brain networks and all seven subnetworks (false-discovery rate-corrected, p < 0.05). These decreases of the Weibull β parameters in the whole-brain networks and all subnetworks except for ventral attention were associated with reduced cognitive performance in individuals with aMCI. Thus, we provided a short-tailed model to capture intrinsic connectivity structure of the human brain functional networks in health and disease.

scholarly journals Genetic Covariation Among Facets of Openness to Experience and General Cognitive Ability

2008 ◽  
Vol 11 (3) ◽  
pp. 275-286 ◽  
Author(s):  
Mark A. Wainwright ◽  
Margaret J. Wright ◽  
Michelle Luciano ◽  
Gina M. Geffen ◽  
Nicholas G. Martin
Keyword(s):  
Cognitive Ability ◽  
Structural Equation ◽  
Genetic Factor ◽  
Genetic Correlations ◽  
Inspection Time ◽  
Equation Modeling ◽  
General Factor ◽  
General Cognitive Ability ◽  
Cognitive Measures ◽  
Sibling Pairs

AbstractGenetic and environmental sources of covariation among cognitive measures of verbal IQ, performance IQ (PIQ), academic achievement, 2-choice reaction time (CRT), inspection time (IT) and the 6 Openness facets of the NEO Personality Inventory-Revised (NEO PI-R) were examined. The number of twin and twin–sibling pairs ranged from 432 (182 MZ, 350 DZ/sibling) to 1023 (273 MZ, 750 DZ/sibling) for cognitive measures, and between 432 (90 MZ, 342 DZ/sibling) — 437 (91 MZ, 346 DZ/sibling) for Openness facets. Structural equation modeling best supported a model with a 3-factor additive genetic structure. A genetic general factor subsumed the 5 cognitive measures and 5 of the 6 Openness facets (Actions did not load significantly). A second additive genetic factor incorporated the 6 Openness facets, and a third additive genetic factor incorporated the 5 cognitive measures. Specific additive and dominance genetic effects were also evident, as were shared common and shared unique environmental influences, and specific unique environmental effects. The Openness facets of Ideas and Values evidenced the strongest phenotypic correlations with cognitive indices, particularly verbal measures. The genetic correlations among Openness facets and cognitive measures ranged from −.06 to .79. Results were interpreted as suggesting that Openness is related to general cognitive ability (g) through a genetic mechanism and thatgengenders a minor but discernable disposition towards Openness for the majority of facets.

scholarly journals Realized Sampling Variances of Estimates of Genetic Parameters and the Difference Between Genetic and Phenotypic Correlations

Genetics ◽  
1996 ◽  
Vol 143 (3) ◽  
pp. 1409-1416 ◽  
Author(s):  
Kenneth R Koots ◽  
John P Gibson
Keyword(s):  
Genetic Correlation ◽  
Genetic Parameters ◽  
Genetic Parameter ◽  
Genetic Correlations ◽  
Phenotypic Correlation ◽  
Parameter Estimates ◽  
Sampling Variance ◽  
Phenotypic Correlations ◽  
Genetic And Phenotypic Correlations ◽  
Heritability Estimates

Abstract A data set of 1572 heritability estimates and 1015 pairs of genetic and phenotypic correlation estimates, constructed from a survey of published beef cattle genetic parameter estimates, provided a rare opportunity to study realized sampling variances of genetic parameter estimates. The distribution of both heritability estimates and genetic correlation estimates, when plotted against estimated accuracy, was consistent with random error variance being some three times the sampling variance predicted from standard formulae. This result was consistent with the observation that the variance of estimates of heritabilities and genetic correlations between populations were about four times the predicted sampling variance, suggesting few real differences in genetic parameters between populations. Except where there was a strong biological or statistical expectation of a difference, there was little evidence for differences between genetic and phenotypic correlations for most trait combinations or for differences in genetic correlations between populations. These results suggest that, even for controlled populations, estimating genetic parameters specific to a given population is less useful than commonly believed. A serendipitous discovery was that, in the standard formula for theoretical standard error of a genetic correlation estimate, the heritabilities refer to the estimated values and not, as seems generally assumed, the true population values.

scholarly journals Genetic Influences on the Covariance and Genetic Correlations in a Bivariate Twin Model: An Application to Well-Being

2021 ◽  
Author(s):  
Lianne P. de Vries ◽  
Toos C. E. M. van Beijsterveldt ◽  
Hermine Maes ◽  
Lucía Colodro-Conde ◽  
Meike Bartels
Keyword(s):  
Complex Traits ◽  
Educational Achievement ◽  
Genetic Correlations ◽  
Well Being ◽  
Genetic Influences ◽  
Phenotypic Correlation ◽  
Twin Model ◽  
Twin Models ◽  
Adolescent Twins ◽  
Depressed Symptoms

AbstractThe distinction between genetic influences on the covariance (or bivariate heritability) and genetic correlations in bivariate twin models is often not well-understood or only one is reported while the results show distinctive information about the relation between traits. We applied bivariate twin models in a large sample of adolescent twins, to disentangle the association between well-being (WB) and four complex traits (optimism, anxious-depressed symptoms (AD), aggressive behaviour (AGG), and educational achievement (EA)). Optimism and AD showed respectively a strong positive and negative phenotypic correlation with WB, the negative correlation of WB and AGG is lower and the correlation with EA is nearly zero. All four traits showed a large genetic contribution to the covariance with well-being. The genetic correlations of well-being with optimism and AD are strong and smaller for AGG and EA. We used the results of the models to explain what information is retrieved based on the bivariate heritability versus the genetic correlations and the (clinical) implications.

scholarly journals Relationship between Temperament and Stage of Lactation, Productivity and Milk Composition of Dairy Cows

Animals ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. 1840
Author(s):  
Ramūnas Antanaitis ◽  
Vida Juozaitienė ◽  
Vesta Jonike ◽  
Vytenis Čukauskas ◽  
Danguolė Urbšienė ◽  
...  
Keyword(s):  
Milk Fat ◽  
Genetic Correlations ◽  
Subclinical Mastitis ◽  
Milk Composition ◽  
Cow Milk ◽  
Phenotypic Correlation ◽  
Protein Ratio ◽  
Stage Of Lactation ◽  
Milk Urea ◽  
Milk Performance

The aim of this study was to assess the relationship between temperament and milk performance in cows at different stages of lactation, describing their productivity, metabolic status and resistance to mastitis. This study showed that with increasing lactation, cows’ temperament indicators decreased (p < 0.001) and they became calmer. The highest temperament score on a five-point scale was found in cows between 45 and 100 days of lactation. In the group of pregnant cows, we found more cows (p = 0.005) with a temperament score of 1–2 compared with non-pregnant cows A normal temperament was usually detected in cows with lactose levels in milk of 4.60% or more and when the somatic cell count (SCC) values in cow milk were <100,000/mL and 100,000–200,000/mL, with a milk fat-to-protein ratio of 1.2. A larger number of more sensitive and highly aggressive cows was detected at a low milk urea level. In contrast to a positive phenotypic correlation (p < 0.05), this study showed a negative genetic correlation between the temperament of cows and milk yield (p < 0.001). Positive genetic correlations between temperament scores and milk somatic cells (p < 0.001) and milk fat-to-protein ratio (p < 0.05) were found to indicate a lower genetic predisposition in cows with a calmer temperament to subclinical mastitis and ketosis. On the other hand, the heritability of temperament (h2 = 0.044–0.100) showed that only a small part of the phenotypic changes in this indicator is associated with genetic factors.

scholarly journals The Right Hemisphere Is Responsible for the Greatest Differences in Human Brain Response to High-Arousing Emotional versus Neutral Stimuli: A MEG Study

2021 ◽  
Vol 11 (8) ◽  
pp. 960
Author(s):  
Mina Kheirkhah ◽  
Philipp Baumbach ◽  
Lutz Leistritz ◽  
Otto W. Witte ◽  
Martin Walter ◽  
...  
Keyword(s):  
Human Brain ◽  
Right Hemisphere ◽  
Emotional Stimuli ◽  
Brain Response ◽  
Whole Brain ◽  
Brain Responses ◽  
Cortical Regions ◽  
The Right ◽  
Healthy Participants

Studies investigating human brain response to emotional stimuli—particularly high-arousing versus neutral stimuli—have obtained inconsistent results. The present study was the first to combine magnetoencephalography (MEG) with the bootstrapping method to examine the whole brain and identify the cortical regions involved in this differential response. Seventeen healthy participants (11 females, aged 19 to 33 years; mean age, 26.9 years) were presented with high-arousing emotional (pleasant and unpleasant) and neutral pictures, and their brain responses were measured using MEG. When random resampling bootstrapping was performed for each participant, the greatest differences between high-arousing emotional and neutral stimuli during M300 (270–320 ms) were found to occur in the right temporo-parietal region. This finding was observed in response to both pleasant and unpleasant stimuli. The results, which may be more robust than previous studies because of bootstrapping and examination of the whole brain, reinforce the essential role of the right hemisphere in emotion processing.

scholarly journals Capturing the non-stationarity of whole-brain dynamics underlying human brain states

NeuroImage ◽  
2021 ◽  
pp. 118551
Author(s):  
J.A. Galadí ◽  
S. Silva Pereira ◽  
Y. Sanz Perl ◽  
M.L. Kringelbach ◽  
I. Gayte ◽  
...  
Keyword(s):  
Human Brain ◽  
Brain Dynamics ◽  
Whole Brain ◽  
Brain States

A Proteome-Wide Association Study Identified Candidate Human Brain Proteins Associated with Coffee Consumption

2021 ◽  
Author(s):  
Chun'e Li ◽  
Xiao Liang ◽  
Yumeng Jia ◽  
Yan Wen ◽  
Huijie Zhang ◽  
...  
Keyword(s):  
Human Brain ◽  
Association Study ◽  
Plasma Proteins ◽  
Genome Wide Association Study ◽  
Genetic Correlations ◽  
Coffee Consumption ◽  
Genetic Associations ◽  
Brain Proteins ◽  
Genome Wide ◽  
Brain Proteome

Abstract Background Increasing evidence suggests the association between caffeine and the brain and nervous system. However, there is limited research on the genetic associations between coffee consumption subtypes and brain proteome, plasma proteomes, and peripheral metabolites. Methods First, proteome-wide association study (PWAS) of coffee consumption subtypes was performed by integrating two independent genome-wide association study (GWAS) datasets (91,462–502,650 subjects) with two reference human brain proteomes (ROS/MAP and Banner), by using the FUSION pipeline. Second, transcriptome-wide association study (TWAS) analysis of coffee consumption subtypes was conducted by integrating the two gene expression weight references (RNAseq and splicing) of brain RNA-seq and the two GWAS datasets (91,462–502,650 subjects) of coffee consumption subtypes. Finally, we used the LD Score Regression (LDSC) analysis to evaluate the genetic correlations of coffee consumption subtypes with plasma proteomes and peripheral metabolites. Results For the traits related to coffee consumption, we identified 3 common PWAS proteins, such as MADD (P PWAS−Banner−dis=0.0114, P PWAS−ROS/MAP−rep =0.0489). In addition, 11 common TWAS genes were found in two cohorts, such as ARPC2 (P TWAS−splicing−dis =2063×10− 12, P TWAS−splicing−dis =1.25×10− 10, P TWAS−splicing−dis =1.24e-08, P TWAS−splicing−rep =3.25×10− 9 and P TWAS−splicing−rep =3.42×10− 13). Importantly, we have identified 8 common genes between PWAS and TWAS, such as ALDH2 (P PWAS−banner−rep =1.22×10− 22, PTWAS− splicing−dis = 4.54×10− 92). For the LDSC analysis of human plasma proteome, we identified 11 plasma proteins, such as CHL1 (P dis = 0.0151, P rep =0.0438). For the LDSC analysis of blood metabolites, 5 metabolites have been found, such as myo-inositol (P dis = 0.0073, P dis = 0.0152, P dis =0.0414, P rep =0.0216). Conclusions We identified several brain proteins and genes associated with coffee consumption subtypes. In addition, we also detected several candidate plasma proteins and metabolites related to these subtypes.

scholarly journals Development and Emergence of Individual Variability in the Functional Connectivity Architecture of the Preterm Human Brain

2018 ◽  
Vol 29 (10) ◽  
pp. 4208-4222 ◽  
Author(s):  
Yuehua Xu ◽  
Miao Cao ◽  
Xuhong Liao ◽  
Mingrui Xia ◽  
Xindi Wang ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Human Brain ◽  
Network Architecture ◽  
Brain Networks ◽  
Individual Variability ◽  
Distance Constraint ◽  
Behavioral Differences ◽  
Sensorimotor Network ◽  
Middle Range ◽  
Brain Functional Connectivity

Abstract Individual variability in human brain networks underlies individual differences in cognition and behaviors. However, researchers have not conclusively determined when individual variability patterns of the brain networks emerge and how they develop in the early phase. Here, we employed resting-state functional MRI data and whole-brain functional connectivity analyses in 40 neonates aged around 31–42 postmenstrual weeks to characterize the spatial distribution and development modes of individual variability in the functional network architecture. We observed lower individual variability in primary sensorimotor and visual areas and higher variability in association regions at the third trimester, and these patterns are generally similar to those of adult brains. Different functional systems showed dramatic differences in the development of individual variability, with significant decreases in the sensorimotor network; decreasing trends in the visual, subcortical, and dorsal and ventral attention networks, and limited change in the default mode, frontoparietal and limbic networks. The patterns of individual variability were negatively correlated with the short- to middle-range connection strength/number and this distance constraint was significantly strengthened throughout development. Our findings highlight the development and emergence of individual variability in the functional architecture of the prenatal brain, which may lay network foundations for individual behavioral differences later in life.

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