Genetic effects on the cerebellar role in working memory: Same brain, different genes?

Over the past several years, evidence has accumulated showing that the cerebellum plays a significant role in cognitive function. Here we show, in a large genetically informative twin sample (n= 430; aged 16-30. years), that the cerebellum is strongly, and reliably (n=30 rescans), activated during an n-back working memory task, particularly lobules I-IV, VIIa Crus I and II, IX and the vermis. Monozygotic twin correlations for cerebellar activation were generally much larger than dizygotic twin correlations, consistent with genetic influences. Structural equation models showed that up to 65% of the variance in cerebellar activation during working memory is genetic (averaging 34% across significant voxels), most prominently in the lobules VI, and VIIa Crus I, with the remaining variance explained by unique/unshared environmental factors. Heritability estimates for brain activation in the cerebellum agree with those found for working memory activation in the cerebral cortex, even though cerebellar cyto-architecture differs substantially. Phenotypic correlations between BOLD percent signal change in cerebrum and cerebellum were low, and bivariate modeling indicated that genetic influences on the cerebellum are at least partly specific to the cerebellum. Activation on the voxel-level correlated very weakly with cerebellar gray matter volume, suggesting specific genetic influences on the BOLD signal. Heritable signals identified here should facilitate discovery of genetic polymorphisms influencing cerebellar function through genome-wide association studies, to elucidate the genetic liability to brain disorders affecting the cerebellum.

Automated ventricular mapping with multi-atlas fluid image alignment reveals genetic effects in Alzheimer's disease

We developed and validated a new method to create automated 3D parametric surface models of the lateral ventricles in brain MRI scans, providing an efficient approach to monitor degenerative disease in clinical studies and drug trials. First, we used a set of parameterized surfaces to represent the ventricles in four subjects' manually labeled brain MRI scans (atlases). We fluidly registered each atlas and mesh model to MRIs from 17 Alzheimer's disease (AD) patients and 13 age- and gender-matched healthy elderly control subjects, and 18 asymptomatic ApoE4-carriers and 18 age- and gender-matched non-carriers. We examined genotyped healthy subjects with the goal of detecting subtle effects of a gene that confers heightened risk for Alzheimer's disease. We averaged the meshes extracted for each 3D MR data set, and combined the automated segmentations with a radial mapping approach to localize ventricular shape differences in patients. Validation experiments comparing automated and expert manual segmentations showed that (1) the Hausdorff labeling error rapidly decreased, and (2) the power to detect disease- and gene-related alterations improved, as the number of atlases, N, was increased from 1 to 9. In surface-based statistical maps, we detected more widespread and intense anatomical deficits as we increased the number of atlases. We formulated a statistical stopping criterion to determine the optimal number of atlases to use. Healthy ApoE4-carriers and those with AD showed local ventricular abnormalities. This high-throughput method for morphometric studies further motivates the combination of genetic and neuroimaging strategies in predicting AD progression and treatment response. © 2007 Elsevier Inc. All rights reserved.

Multi-site study of additive genetic effects on fractional anisotropy of cerebral white matter: Comparing meta and megaanalytical approaches for data pooling

Combining datasets across independent studies can boost statistical power by increasing the numbers of observations and can achieve more accurate estimates of effect sizes. This is especially important for genetic studies where a large number of observations are required to obtain sufficient power to detect and replicate genetic effects. There is a need to develop and evaluate methods for joint-analytical analyses of rich datasets collected in imaging genetics studies. The ENIGMA-DTI consortium is developing and evaluating approaches for obtaining pooled estimates of heritability through meta-and mega-genetic analytical approaches, to estimate the general additive genetic contributions to the intersubject variance in fractional anisotropy (FA) measured from diffusion tensor imaging (DTI). We used the ENIGMA-DTI data harmonization protocol for uniform processing of DTI data from multiple sites. We evaluated this protocol in five family-based cohorts providing data from a total of 2248 children and adults (ages: 9-85) collected with various imaging protocols. We used the imaging genetics analysis tool, SOLAR-Eclipse, to combine twin and family data from Dutch, Australian and Mexican-American cohorts into one large "mega-family". We showed that heritability estimates may vary from one cohort to another. We used two meta-analytical (the sample-size and standard-error weighted) approaches and a mega-genetic analysis to calculate heritability estimates across-population. We performed leave-one-out analysis of the joint estimates of heritability, removing a different cohort each time to understand the estimate variability. Overall, meta- and mega-genetic analyses of heritability produced robust estimates of heritability.

The v-MFG test: Investigating maternal, offspring and maternal-fetal genetic incompatibility effects on disease and viability

The MFG test is a family-based association test that detects genetic effects contributing to disease in offspring, including offspring allelic effects, maternal allelic effects and MFG incompatibility effects. Like many other family-based association tests, it assumes that the offspring survival and the offspring-parent genotypes are conditionally independent provided the offspring is affected. However, when the putative disease-increasing locus can affect another competing phenotype, for example, offspring viability, the conditional independence assumption fails and these tests could lead to incorrect conclusions regarding the role of the gene in disease. We propose the v-MFG test to adjust for the genetic effects on one phenotype, e.g., viability, when testing the effects of that locus on another phenotype, e.g., disease. Using genotype data from nuclear families containing parents and at least one affected offspring, the v-MFG test models the distribution of family genotypes conditional on offspring phenotypes. It simultaneously estimates genetic effects on two phenotypes, viability and disease. Simulations show that the v-MFG test produces accurate genetic effect estimates on disease as well as on viability under several different scenarios. It generates accurate type-I error rates and provides adequate power with moderate sample sizes to detect genetic effects on disease risk when viability is reduced. We demonstrate the v-MFG test with HLA-DRB1 data from study participants with rheumatoid arthritis (RA) and their parents, we show that the v-MFG test successfully detects an MFG incompatibility effect on RA while simultaneously adjusting for a possible viability loss.

Mapping genetic influences on brain fiber architecture with high angular resolution diffusion imaging (HARDI)

We report the first 3D maps of genetic effects on brain fiber complexity. We analyzed HARDI brain imaging data from 90 young adult twins using an information-theoretic measure, the Jensen-Shannon divergence (JSD), to gauge the regional complexity of the white matter fiber orientation distribution functions (ODF). HARDI data were fluidly registered using Karcher means and ODF square-roots for interpol ation; each subject's JSD map was computed from the spatial coherence of the ODFs in each voxel's neighborhood. We evaluated the genetic influences on generalized fiber anisotropy (GFA) and complexity (JSD) using structural equation models (SEM). At each voxel, genetic and environmental components of data variation were estimated, and their goodness of fit tested by permutation. Color-coded maps revealed that the optimal models varied for different brain regions. Fiber complexity was predominantly under genetic control, and was higher in more highly anisotropic regions. These methods show promise for discovering factors affecting fiber connectivity in the brain.

The correlation between reading and mathematics ability at age twelve has a substantial genetic component

Dissecting how genetic and environmental influences impact on learning is helpful for maximizing numeracy and literacy. Here we show, using twin and genome-wide analysis, that there is a substantial genetic component to children’s ability in reading and mathematics, and estimate that around one half of the observed correlation in these traits is due to shared genetic effects (so-called Generalist Genes). Thus, our results highlight the potential role of the learning environment in contributing to differences in a child’s cognitive abilities at age twelve.

Genetic influences on handedness: data from 25,732 Australian and Dutch twin families

Handedness refers to a consistent asymmetry in skill or preferential use between the hands and is related to lateralization within the brain of other functions such as language. Previous twin studies of handedness have yielded inconsistent results resulting from a general lack of statistical power to find significant effects. Here we present analyses from a large international collaborative study of handedness (assessed by writing/drawing or self report) in Australian and Dutch twins and their siblings (54,270 individuals from 25,732 families). Maximum likelihood analyses incorporating the effects of known covariates (sex, year of birth and birth weight) revealed no evidence of hormonal transfer, mirror imaging or twin specific effects. There were also no differences in prevalence between zygosity groups or between twins and their singleton siblings. Consistent with previous meta-analyses, additive genetic effects accounted for about a quarter (23.64%) of the variance (95%CI 20.17, 27.09%) with the remainder accounted for by non-shared environmental influences. The implications of these findings for handedness both as a primary phenotype and as a covariate in linkage and association analyses are discussed.

Genes and growth performance in crustacean species : a review of relevant genomic studies in crustaceans and other taxa

Global aquaculture has expanded rapidly to address the increasing demand for aquatic protein needs and an uncertain future for wild fisheries. To date, however, most farmed aquatic stocks are essentially wild and little is known about their genomes or the genes that affect important economic traits in culture. Biologists have recognized that recent technological advances including next generation sequencing (NGS) have opened up the possibility of generating genome wide sequence data sets rapidly from non-model organisms at a reasonable cost. In an era when virtually any study organism can 'go genomic', understanding gene function and genetic effects on expressed quantitative trait locus phenotypes will be fundamental to future knowledge development. Many factors can influence the individual growth rate in target species but of particular importance in agriculture and aquaculture will be the identification and characterization of the specific gene loci that contribute important phenotypic variation to growth because the information can be applied to speed up genetic improvement programmes and to increase productivity via marker-assisted selection (MAS). While currently there is only limited genomic information available for any crustacean species, a number of putative candidate genes have been identified or implicated in growth and muscle development in some species. In an effort to stimulate increased research on the identification of growth-related genes in crustacean species, here we review the available information on: (i) associations between genes and growth reported in crustaceans, (ii) growth-related genes involved with moulting, (iii) muscle development and degradation genes involved in moulting, and; (iv) correlations between DNA sequences that have confirmed growth trait effects in farmed animal species used in terrestrial agriculture and related sequences in crustacean species. The information in concert can provide a foundation for increasing the rate at which knowledge about key genes affecting growth traits in crustacean species is gained.

Comparison of fractional and geodesic anisotropy in diffusion tensor images of 90 monozygotic and dizygotic twins

We used diffusion tensor magnetic resonance imaging (DTI) to reveal the extent of genetic effects on brain fiber microstructure, based on tensor-derived measures, in 22 pairs of monozygotic (MZ) twins and 23 pairs of dizygotic (DZ) twins (90 scans). After Log-Euclidean denoising to remove rank-deficient tensors, DTI volumes were fluidly registered by high-dimensional mapping of co-registered MP-RAGE scans to a geometrically-centered mean neuroanatomical template. After tensor reorientation using the strain of the 3D fluid transformation, we computed two widely used scalar measures of fiber integrity: fractional anisotropy (FA), and geodesic anisotropy (GA), which measures the geodesic distance between tensors in the symmetric positive-definite tensor manifold. Spatial maps of intraclass correlations (r) between MZ and DZ twins were compared to compute maps of Falconer's heritability statistics, i.e. the proportion of population variance explainable by genetic differences among individuals. Cumulative distribution plots (CDF) of effect sizes showed that the manifold measure, GA, comparably the Euclidean measure, FA, in detecting genetic correlations. While maps were relatively noisy, the CDFs showed promise for detecting genetic influences on brain fiber integrity as the current sample expands.

Multivariate variance-components analysis in DTI

Twin studies are a major research direction in imaging genetics, a new field, which combines algorithms from quantitative genetics and neuroimaging to assess genetic effects on the brain. In twin imaging studies, it is common to estimate the intraclass correlation (ICC), which measures the resemblance between twin pairs for a given phenotype. In this paper, we extend the commonly used Pearson correlation to a more appropriate definition, which uses restricted maximum likelihood methods (REML). We computed proportion of phenotypic variance due to additive (A) genetic factors, common (C) and unique (E) environmental factors using a new definition of the variance components in the diffusion tensor-valued signals. We applied our analysis to a dataset of Diffusion Tensor Images (DTI) from 25 identical and 25 fraternal twin pairs. Differences between the REML and Pearson estimators were plotted for different sample sizes, showing that the REML approach avoids severe biases when samples are smaller. Measures of genetic effects were computed for scalar and multivariate diffusion tensor derived measures including the geodesic anisotropy (tGA) and the full diffusion tensors (DT), revealing voxel-wise genetic contributions to brain fiber microstructure.

Genome-wide association study using extreme truncate selection identifies novel genes affecting bone mineral density and fracture risk

Osteoporotic fracture is a major cause of morbidity and mortality worldwide. Low bone mineral density (BMD) is a major predisposing factor to fracture and is known to be highly heritable. Site-, gender-, and age-specific genetic effects on BMD are thought to be significant, but have largely not been considered in the design of genome-wide association studies (GWAS) of BMD to date. We report here a GWAS using a novel study design focusing on women of a specific age (postmenopausal women, age 55-85 years), with either extreme high or low hip BMD (age- and gender-adjusted BMD z-scores of +1.5 to +4.0, n = 1055, or -4.0 to -1.5, n = 900), with replication in cohorts of women drawn from the general population (n = 20,898). The study replicates 21 of 26 known BMD-associated genes. Additionally, we report suggestive association of a further six new genetic associations in or around the genes CLCN7, GALNT3, IBSP, LTBP3, RSPO3, and SOX4, with replication in two independent datasets. A novel mouse model with a loss-of-function mutation in GALNT3 is also reported, which has high bone mass, supporting the involvement of this gene in BMD determination. In addition to identifying further genes associated with BMD, this study confirms the efficiency of extreme-truncate selection designs for quantitative trait association studies. © 2011 Duncan et al.

Susceptibility to ankylosing spondylitis in twins: The role of genes, HLA, and the environment

Objective To determine the relative effects of genetic and environmental factors in susceptibility to ankylosing spondylitis (AS). Methods Twins with AS were identified from the Royal National Hospital for Rheumatic Diseases database. Clinical and radiographic examinations were performed to establish diagnoses, and disease severity was assessed using a combination of validated scoring systems. HLA typing for HLA-B27, HLA-B60, and HLA-DR1 was performed by polymerase chain reaction with sequence- specific primers, and zygosity was assessed using microsatellite markers. Genetic and environmental variance components were assessed with the program Mx, using data from this and previous studies of twins with AS. Results Six of 8 monozygotic (MZ) twin pairs were disease concordant, compared with 4 of 15 B27-positive dizygotic (DZ) twin pairs (27%) and 4 of 32 DZ twin pairs overall (12.5%). Nonsignificant increases in similarity with regard to age at disease onset and all of the disease severity scores assessed were noted in disease-concordant MZ twins compared with concordant DZ twins. HLA-B27 and B60 were associated with the disease in probands, and the rate of disease concordance was significantly increased among DZ twin pairs in which the co- twin was positive for both B27 and DR1. Additive genetic effects were estimated to contribute 97% of the population variance. Conclusion Susceptibility to AS is largely genetically determined, and the environmental trigger for the disease is probably ubiquitous. HLA-B27 accounts for a minority of the overall genetic susceptibility to AS.

The X-chromosome and susceptibility to ankylosing spondylitis

Objective. Ankylosing spondylitis (AS) affects 0.25-1.0% of the population, and its etiology is incompletely understood. Susceptibility to this highly familial disease (λ(s) = 58) is primarily genetically determined. There is a significant sex bias in AS, and there are differences in recurrence risk to the offspring of affected mothers and fathers, suggesting that there may be an X-linked recessive effect. We undertook an X- chromosome linkage study to determine any contribution of the X-chromosome to AS susceptibility. Methods. A linkage study of the X-chromosome using 234 affected sibling pairs was performed to investigate this hypothesis. Results. No linkage of the X-chromosome with susceptibility to AS was found. Model- free multipoint linkage analysis strongly excluded any significant genetic contribution (λ ≥1.5) to AS susceptibility encoded on the X-chromosome (logarithm of odds [LOD] <-2.0). Smaller genetic effects (A ≥1.3) were also found to be unlikely (LOD <-1.0). Conclusion. The sex bias in AS is not explained by X-chromosome-encoded genetic effects. The disease model best explaining the sex bias in occurrence and transmission of AS is a polygenic model with a higher susceptibility threshold in females.

Dissection of class III major histocompatibility complex haplotypes associated with rheumatoid arthritis

Objective. We have previously identified a single-nucleotide polymorphism (SNP) haplotype involving the lymphotoxin α (LTA) and tumor necrosis factor (TNF) loci (termed haplotype LTA-TNF2) on chromosome 6 that shows differential association with rheumatoid arthritis (RA) on HLA-DRB1*0404 and *0401 haplotypes, suggesting the presence of additional non-HLA-DRB1 RA susceptibility genes on these haplotypes. To refine this association, we performed a case-control association study using both SNPs and microsatellite markers in haplotypes matched either for HLA-DRB1*0404 or for HLA-DRB1*0401. Methods. Fourteen SNPs lying between HLA-DRB1 and LTA were genotyped in 87 DRB1*04-positive families. High-density microsatellite typing was performed using 24 markers spanning 2,500 kb centered around the TNF gene in 305 DRB1*0401 or *0404 cases and 400 DRB1*0401 or *0404 controls. Single-marker, 2-marker, and 3-marker minihaplotypes were constructed and their frequencies compared between the DRB1*0401 and DRB1*0404 matched case and control haplotypes. Results. Marked preservation of major histocompatibility complex haplotypes was seen, with chromosomes carrying LTA-TNF2 and either DRB1*0401 or DRB1*0404 both carrying an identical SNP haplotype across the 1-Mb region between TNF and HLA-DRB1. Using microsatellite markers, we observed two 3-marker minihaplotypes that were significantly overrepresented in the DRB1*0404 case haplotypes (P = 0.00024 and P = 0.00097). Conclusion. The presence of a single extended SNP haplotype between LTA-TNF2 and both DRB1*0401 and DRB1*0404 is evidence against this region harboring the genetic effects in linkage disequillbrium with LTA-TNF2. Two RA-associated haplotypes on the background of DRB1*0404 were identified in a 126-kb region surrounding and centromeric to the TNF locus.