Rare genomic structural variants in complex disease: lessons from the replication of associations with obesity.

The limited ability of common variants to account for the genetic contribution to complex disease has prompted searches for rare variants of large effect, to partly explain the 'missing heritability'. Analyses of genome-wide genotyping data have identified genomic structural variants (GSVs) as a source of such rare causal variants. Recent studies have reported multiple GSV loci associated with risk of obesity. We attempted to replicate these associations by similar analysis of two familial-obesity case-control cohorts and a population cohort, and detected GSVs at 11 out of 18 loci, at frequencies similar to those previously reported. Based on their reported frequencies and effect sizes (OR≥25), we had sufficient statistical power to detect the large majority (80%) of genuine associations at these loci. However, only one obesity association was replicated. Deletion of a 220 kb region on chromosome 16p11.2 has a carrier population frequency of 2×10(-4) (95% confidence interval [9.6×10(-5)-3.1×10(-4)]); accounts overall for 0.5% [0.19%-0.82%] of severe childhood obesity cases (P = 3.8×10(-10); odds ratio = 25.0 [9.9-60.6]); and results in a mean body mass index (BMI) increase of 5.8 kg.m(-2) [1.8-10.3] in adults from the general population. We also attempted replication using BMI as a quantitative trait in our population cohort; associations with BMI at or near nominal significance were detected at two further loci near KIF2B and within FOXP2, but these did not survive correction for multiple testing. These findings emphasise several issues of importance when conducting rare GSV association, including the need for careful cohort selection and replication strategy, accurate GSV identification, and appropriate correction for multiple testing and/or control of false discovery rate. Moreover, they highlight the potential difficulty in replicating rare CNV associations across different populations. Nevertheless, we show that such studies are potentially valuable for the identification of variants making an appreciable contribution to complex disease.

Imaging of experience-dependent structural plasticity in the mouse neocortex in vivo

The functionality of adult neocortical circuits can be altered by novel experiences or learning. This functional plasticity appears to rely on changes in the strength of neuronal connections that were established during development. Here we will describe some of our studies in which we have addressed whether structural changes, including the remodeling of axons and dendrites with synapse formation and elimination, could underlie experience-dependent plasticity in the adult neocortex. Using 2-photon laser-scanning microscopes and transgenic mice expressing GFP in a subset of pyramidal cells, we have observed that a small subset of dendritic spines continuously appear and disappear on a daily basis, whereas the majority of spines persists for months. Axonal boutons from different neuronal classes displayed similar behavior, although the extent of remodeling varied. Under baseline conditions, new spines in the barrel cortex were mostly transient and rarely survived for more than a week. However, when every other whisker was trimmed, the generation and loss of persistent spines was enhanced. Ultrastructural reconstruction of previously imaged spines and boutons showed that new spines slowly form synapses. New spines persisting for a few days always had synapses, whereas very young spines often lacked synapses. New synapses were predominantly found on large, multi-synapse boutons, suggesting that spine growth is followed by synapse formation, preferentially on existing boutons. Altogether our data indicate that novel sensory experience drives the stabilization of new spines on subclasses of cortical neurons and promotes the formation of new synapses. These synaptic changes likely underlie experience-dependent functional remodeling of specific neocortical circuits.

Neuropathological substrates and structural changes in late-life depression: the impact of vascular burden.

A first episode of depression after 65 years of age has long been associated with both severe macrovascular and small microvascular pathology. Among the three more frequent forms of depression in old age, post-stroke depression has been associated with an abrupt damage of cortical circuits involved in monoamine production and mood regulation. Late-onset depression (LOD) in the absence of stroke has been related to lacunes and white matter lesions that invade both the neocortex and subcortical nuclei. Recurrent late-life depression is thought to induce neuronal loss in the hippocampal formation and white matter lesions that affect limbic pathways. Despite an impressive number of magnetic resonance imaging (MRI) studies in this field, the presence of a causal relationship between structural changes in the human brain and LOD is still controversial. The present article provides a critical overview of the contribution of neuropathology in post-stroke, late-onset, and late-life recurrent depression. Recent autopsy findings challenge the role of stroke location in the occurrence of post-stroke depression by pointing to the deleterious effect of subcortical lacunes. Despite the lines of evidences supporting the association between MRI-assessed white matter changes and mood dysregulation, lacunes, periventricular and deep white matter demyelination are all unrelated to the occurrence of LOD. In the same line, neuropathological data show that early-onset depression is not associated with an acceleration of aging-related neurodegenerative changes in the human brain. However, they also provide data in favor of the neurotoxic theory of depression by showing that neuronal loss occurs in the hippocampus of chronically depressed patients. These three paradigms are discussed in the light of the complex relationships between psychosocial determinants and biological vulnerability in affective disorders.

White matter maturation reshapes structural connectivity in the late developing human brain.

From toddler to late teenager, the macroscopic pattern of axonal projections in the human brain remains largely unchanged while undergoing dramatic functional modifications that lead to network refinement. These functional modifications are mediated by increasing myelination and changes in axonal diameter and synaptic density, as well as changes in neurochemical mediators. Here we explore the contribution of white matter maturation to the development of connectivity between ages 2 and 18 y using high b-value diffusion MRI tractography and connectivity analysis. We measured changes in connection efficacy as the inverse of the average diffusivity along a fiber tract. We observed significant refinement in specific metrics of network topology, including a significant increase in node strength and efficiency along with a decrease in clustering. Major structural modules and hubs were in place by 2 y of age, and they continued to strengthen their profile during subsequent development. Recording resting-state functional MRI from a subset of subjects, we confirmed a positive correlation between structural and functional connectivity, and in addition observed that this relationship strengthened with age. Continuously increasing integration and decreasing segregation of structural connectivity with age suggests that network refinement mediated by white matter maturation promotes increased global efficiency. In addition, the strengthening of the correlation between structural and functional connectivity with age suggests that white matter connectivity in combination with other factors, such as differential modulation of axonal diameter and myelin thickness, that are partially captured by inverse average diffusivity, play an increasingly important role in creating brain-wide coherence and synchrony.

The fine-scale architecture of structural variants in 17 mouse genomes.

BACKGROUND: Accurate catalogs of structural variants (SVs) in mammalian genomes are necessary to elucidate the potential mechanisms that drive SV formation and to assess their functional impact. Next generation sequencing methods for SV detection are an advance on array-based methods, but are almost exclusively limited to four basic types: deletions, insertions, inversions and copy number gains. RESULTS: By visual inspection of 100 Mbp of genome to which next generation sequence data from 17 inbred mouse strains had been aligned, we identify and interpret 21 paired-end mapping patterns, which we validate by PCR. These paired-end mapping patterns reveal a greater diversity and complexity in SVs than previously recognized. In addition, Sanger-based sequence analysis of 4,176 breakpoints at 261 SV sites reveal additional complexity at approximately a quarter of structural variants analyzed. We find micro-deletions and micro-insertions at SV breakpoints, ranging from 1 to 107 bp, and SNPs that extend breakpoint micro-homology and may catalyze SV formation. CONCLUSIONS: An integrative approach using experimental analyses to train computational SV calling is essential for the accurate resolution of the architecture of SVs. We find considerable complexity in SV formation; about a quarter of SVs in the mouse are composed of a complex mixture of deletion, insertion, inversion and copy number gain. Computational methods can be adapted to identify most paired-end mapping patterns.

Structural components in functional data

Analyzing functional data often leads to finding common factors, for which functional principal component analysis proves to be a useful tool to summarize and characterize the random variation in a function space. The representation in terms of eigenfunctions is optimal in the sense of L-2 approximation. However, the eigenfunctions are not always directed towards an interesting and interpretable direction in the context of functional data and thus could obscure the underlying structure. To overcome such difficulty, an alternative to functional principal component analysis is proposed that produces directed components which may be more informative and easier to interpret. These structural components are similar to principal components, but are adapted to situations in which the domain of the function may be decomposed into disjoint intervals such that there is effectively independence between intervals and positive correlation within intervals. The approach is demonstrated with synthetic examples as well as real data. Properties for special cases are also studied.

Identification of structural variation in mouse genomes.

Structural variation is variation in structure of DNA regions affecting DNA sequence length and/or orientation. It generally includes deletions, insertions, copy-number gains, inversions, and transposable elements. Traditionally, the identification of structural variation in genomes has been challenging. However, with the recent advances in high-throughput DNA sequencing and paired-end mapping (PEM) methods, the ability to identify structural variation and their respective association to human diseases has improved considerably. In this review, we describe our current knowledge of structural variation in the mouse, one of the prime model systems for studying human diseases and mammalian biology. We further present the evolutionary implications of structural variation on transposable elements. We conclude with future directions on the study of structural variation in mouse genomes that will increase our understanding of molecular architecture and functional consequences of structural variation.

Altered structural connectivity in patients with medial temporal lobe epilepsy: A Diffusion Spectrum Imaging and Graph Analysis study

In this study we investigated the effect of medial temporal lobe epilepsy (MTLE) on the global characteristics of brain connectivity estimated by topological measures. We used DSI (Diffusion Spectrum Imaging) to construct a connectivity matrix where the nodes represents the anatomical ROIs and the edges are the connections between any pair of ROIs weighted by the mean GFA/FA values. A significant difference was found between the patient group vs control group in characteristic path length, clustering coefficient and small-worldness. This suggests that the MTLE network is less efficient compared to the network of the control group.