Gene network effects on brain microstructure and intellectual performance identified in 472 twins

A major challenge in neuroscience is finding which genes affect brain integrity, connectivity, and intellectual function. Discovering influential genes holds vast promise for neuroscience, but typical genome-wide searches assess approximately one million genetic variants one-by-one, leading to intractable false positive rates, even with vast samples of subjects. Even more intractable is the question of which genes interact and how they work together to affect brain connectivity. Here, we report a novel approach that discovers which genes contribute to brain wiring and fiber integrity at all pairs of points in a brain scan. We studied genetic correlations between thousands of points in human brain images from 472 twins and their nontwin siblings (mean age: 23.7 2.1 SD years; 193 male/279 female).Wecombined clustering with genome-wide scanning to find brain systems withcommongenetic determination.Wethen filtered the image in a new way to boost power to find causal genes. Using network analysis, we found a network of genes that affect brain wiring in healthy young adults. Our new strategy makes it computationally more tractable to discover genes that affect brain integrity. The gene network showed small-world and scale-free topologies, suggesting efficiency in genetic interactions and resilience to network disruption. Genetic variants at hubs of the network influence intellectual performance by modulating associations between performance intelligence quotient and the integrity of major white matter tracts, such as the callosal genu and splenium, cingulum, optic radiations, and the superior longitudinal fasciculus.

Accelerating Pseudo-Marginal MCMC using Gaussian Processes

Pseudo-marginal methods such as the grouped independence Metropolis-Hastings (GIMH) and Markov chain within Metropolis (MCWM) algorithms have been introduced in the literature as an approach to perform Bayesian inference in latent variable models. These methods replace intractable likelihood calculations with unbiased estimates within Markov chain Monte Carlo algorithms. The GIMH method has the posterior of interest as its limiting distribution, but suffers from poor mixing if it is too computationally intensive to obtain high-precision likelihood estimates. The MCWM algorithm has better mixing properties, but less theoretical support. In this paper we propose to use Gaussian processes (GP) to accelerate the GIMH method, whilst using a short pilot run of MCWM to train the GP. Our new method, GP-GIMH, is illustrated on simulated data from a stochastic volatility and a gene network model.

Cluster-based network model for time-course gene expression data

We propose a model-based approach to unify clustering and network modeling using time-course gene expression data. Specifically, our approach uses a mixture model to cluster genes. Genes within the same cluster share a similar expression profile. The network is built over cluster-specific expression profiles using state-space models. We discuss the application of our model to simulated data as well as to time-course gene expression data arising from animal models on prostate cancer progression. The latter application shows that with a combined statistical/bioinformatics analyses, we are able to extract gene-to-gene relationships supported by the literature as well as new plausible relationships.

Hierarchical clustering of the genetic connectivity matrix reveals the network topology of gene action on brain microstructure: An N=531 twin study

Genetic correlation (rg) analysis determines how much of the correlation between two measures is due to common genetic influences. In an analysis of 4 Tesla diffusion tensor images (DTI) from 531 healthy young adult twins and their siblings, we generalized the concept of genetic correlation to determine common genetic influences on white matter integrity, measured by fractional anisotropy (FA), at all points of the brain, yielding an NxN genetic correlation matrix rg(x,y) between FA values at all pairs of voxels in the brain. With hierarchical clustering, we identified brain regions with relatively homogeneous genetic determinants, to boost the power to identify causal single nucleotide polymorphisms (SNP). We applied genome-wide association (GWA) to assess associations between 529,497 SNPs and FA in clusters defined by hubs of the clustered genetic correlation matrix. We identified a network of genes, with a scale-free topology, that influences white matter integrity over multiple brain regions.

Membrane associated transporter protein gene (SLC45A2) and the genetic basis of normal human pigmentation variation

This work is concerned with the genetic basis of normal human pigmentation variation. Specifically, the role of polymorphisms within the solute carrier family 45 member 2 (SLC45A2 or membrane associated transporter protein; MATP) gene were investigated with respect to variation in hair, skin and eye colour ― both between and within populations. SLC45A2 is an important regulator of melanin production and mutations in the gene underly the most recently identified form of oculocutaneous albinism. There is evidence to suggest that non-synonymous polymorphisms in SLC45A2 are associated with normal pigmentation variation between populations. Therefore, the underlying hypothesis of this thesis is that polymorphisms in SLC45A2 will alter the function or regulation of the protein, thereby altering the important role it plays in melanogenesis and providing a mechanism for normal pigmentation variation. In order to investigate the role that SLC45A2 polymorphisms play in human pigmentation variation, a DNA database was established which collected pigmentation phenotypic information and blood samples of more than 700 individuals. This database was used as the foundation for two association studies outlined in this thesis, the first of which involved genotyping two previously-described non-synonymous polymorphisms, p.Glu272Lys and p.Phe374Leu, in four different population groups. For both polymorphisms, allele frequencies were significantly different between population groups and the 272Lys and 374Leu alleles were strongly associated with black hair, brown eyes and olive skin colour in Caucasians. This was the first report to show that SLC45A2 polymorphisms were associated with normal human intra-population pigmentation variation. The second association study involved genotyping several SLC45A2 promoter polymorphisms to determine if they also played a role in pigmentation variation. Firstly, the transcription start site (TSS), and hence putative proximal promoter region, was identified using 5' RNA ligase mediated rapid amplification of cDNA ends (RLM-RACE). Two alternate TSSs were identified and the putative promoter region was screened for novel polymorphisms using denaturing high performance liquid chromatography (dHPLC). A novel duplication (c.–1176_–1174dupAAT) was identified along with other previously described single nucleotide polymorphisms (c.–1721C>G and c.–1169G>A). Strong linkage disequilibrium ensured that all three polymorphisms were associated with skin colour such that the –1721G, +dup and –1169A alleles were associated with olive skin in Caucasians. No linkage disequilibrium was observed between the promoter and coding region polymorphisms, suggesting independent effects. The association analyses were complemented with functional data, showing that the –1721G, +dup and –1169A alleles significantly decreased SLC45A2 transcriptional activity. Based on in silico bioinformatic analysis that showed these alleles remove a microphthalmia-associated transcription factor (MITF) binding site, and that MITF is a known regulator of SLC45A2 (Baxter and Pavan, 2002; Du and Fisher, 2002), it was postulated that SLC45A2 promoter polymorphisms could contribute to the regulation of pigmentation by altering MITF binding affinity. Further characterisation of the SLC45A2 promoter was carried out using luciferase reporter assays to determine the transcriptional activity of different regions of the promoter. Five constructs were designed of increasing length and their promoter activity evaluated. Constitutive promoter activity was observed within the first ~200 bp and promoter activity increased as the construct size increased. The functional impact of the –1721G, +dup and –1169A alleles, which removed a MITF consensus binding site, were assessed using electrophoretic mobility shift assays (EMSA) and expression analysis of genotyped melanoblast and melanocyte cell lines. EMSA results confirmed that the promoter polymorphisms affected DNA-protein binding. Interestingly, however, the protein/s involved were not MITF, or at least MITF was not the protein directly binding to the DNA. In an effort to more thoroughly characterise the functional consequences of SLC45A2 promoter polymorphisms, the mRNA expression levels of SLC45A2 and MITF were determined in melanocyte/melanoblast cell lines. Based on SLC45A2’s role in processing and trafficking TYRP1 from the trans-Golgi network to stage 2 melanosmes, the mRNA expression of TYRP1 was also investigated. Expression results suggested a coordinated expression of pigmentation genes. This thesis has substantially contributed to the field of pigmentation by showing that SLC45A2 polymorphisms not only show allele frequency differences between population groups, but also contribute to normal pigmentation variation within a Caucasian population. In addition, promoter polymorphisms have been shown to have functional consequences for SLC45A2 transcription and the expression of other pigmentation genes. Combined, the data presented in this work supports the notion that SLC45A2 is an important contributor to normal pigmentation variation and should be the target of further research to elucidate its role in determining pigmentation phenotypes. Understanding SLC45A2’s function may lead to the development of therapeutic interventions for oculocutaneous albinism and other disorders of pigmentation. It may also help in our understanding of skin cancer susceptibility and evolutionary adaptation to different UV environments, and contribute to the forensic application of pigmentation phenotype prediction.

Multiscale analysis of protein functions and stochastic modelling of gene transcriptional regulatory networks

Genomic and proteomic analyses have attracted a great deal of interests in biological research in recent years. Many methods have been applied to discover useful information contained in the enormous databases of genomic sequences and amino acid sequences. The results of these investigations inspire further research in biological fields in return. These biological sequences, which may be considered as multiscale sequences, have some specific features which need further efforts to characterise using more refined methods. This project aims to study some of these biological challenges with multiscale analysis methods and stochastic modelling approach. The first part of the thesis aims to cluster some unknown proteins, and classify their families as well as their structural classes. A development in proteomic analysis is concerned with the determination of protein functions. The first step in this development is to classify proteins and predict their families. This motives us to study some unknown proteins from specific families, and to cluster them into families and structural classes. We select a large number of proteins from the same families or superfamilies, and link them to simulate some unknown large proteins from these families. We use multifractal analysis and the wavelet method to capture the characteristics of these linked proteins. The simulation results show that the method is valid for the classification of large proteins. The second part of the thesis aims to explore the relationship of proteins based on a layered comparison with their components. Many methods are based on homology of proteins because the resemblance at the protein sequence level normally indicates the similarity of functions and structures. However, some proteins may have similar functions with low sequential identity. We consider protein sequences at detail level to investigate the problem of comparison of proteins. The comparison is based on the empirical mode decomposition (EMD), and protein sequences are detected with the intrinsic mode functions. A measure of similarity is introduced with a new cross-correlation formula. The similarity results show that the EMD is useful for detection of functional relationships of proteins. The third part of the thesis aims to investigate the transcriptional regulatory network of yeast cell cycle via stochastic differential equations. As the investigation of genome-wide gene expressions has become a focus in genomic analysis, researchers have tried to understand the mechanisms of the yeast genome for many years. How cells control gene expressions still needs further investigation. We use a stochastic differential equation to model the expression profile of a target gene. We modify the model with a Gaussian membership function. For each target gene, a transcriptional rate is obtained, and the estimated transcriptional rate is also calculated with the information from five possible transcriptional regulators. Some regulators of these target genes are verified with the related references. With these results, we construct a transcriptional regulatory network for the genes from the yeast Saccharomyces cerevisiae. The construction of transcriptional regulatory network is useful for detecting more mechanisms of the yeast cell cycle.

Mutations in cardiac T-Box Factor gene TBX20 are associated with diverse cardiac pathologies, including defects of septation and valvulogenesis and cardiomyopathy

The T-box family transcription factor gene TBX20 acts in a conserved regulatory network, guiding heart formation and patterning in diverse species. Mouse Tbx20 is expressed in cardiac progenitor cells, differentiating cardiomyocytes, and developing valvular tissue, and its deletion or RNA interference-mediated knockdown is catastrophic for heart development. TBX20 interacts physically, functionally, and genetically with other cardiac transcription factors, including NKX2-5, GATA4, and TBX5, mutations of which cause congenital heart disease (CHD). Here, we report nonsense (Q195X) and missense (I152M) germline mutations within the T-box DNA-binding domain of human TBX20 that were associated with a family history of CHD and a complex spectrum of developmental anomalies, including defects in septation, chamber growth, and valvulogenesis. Biophysical characterization of wild-type and mutant proteins indicated how the missense mutation disrupts the structure and function of the TBX20 T-box. Dilated cardiomyopathy was a feature of the TBX20 mutant phenotype in humans and mice, suggesting that mutations in developmental transcription factors can provide a sensitized template for adult-onset heart disease. Our findings are the first to link TBX20 mutations to human pathology. They provide insights into how mutation of different genes in an interactive regulatory circuit lead to diverse clinical phenotypes, with implications for diagnosis, genetic screening, and patient follow-up.

A transcription factor map as revealed by a genome-wide gene expression analysis of whole-blood mRNA transcriptome in multiple sclerosis

Background Several lines of evidence suggests that transcription factors are involved in the pathogenesis of Multiple Sclerosis (MS) but a complete mapping the whole network has been elusive. One of the reasons is that there are several clinical subtypes of MS and transcription factors which may be involved in one subtype may not be in others. We investigated the possibility that this network could be mapped using microarray technologies and modern bioinformatics methods on a dataset from whole blood in 99 untreated MS patients (36 Relapse Remitting MS, 43 Primary Progressive MS, and 20 Secondary Progressive MS) and 45 age-matched healthy controls, Methodology/Principal Findings We have used two different analytical methodologies: a differential expression analysis and a differential co-expression analysis, which have converged on a significant number of regulatory motifs that seem to be statistically overrepresented in genes which are either differentially expressed (or differentially co-expressed) in cases and controls (e.g. V$KROX_Q6, p-value < 3.31E-6; V$CREBP1_Q2, p-value < 9.93E-6, V$YY1_02, p-value < 1.65E-5). Conclusions/significance: Our analysis uncovered a network of transcription factors that potentially dysregulate several genes in MS or one or more of its disease subtypes. Analysing the published literature we have found that these transcription factors are involved in the early T-lymphocyte specification and commitment as well as in oligodendrocytes dedifferentiation and development. The most significant transcription factors motifs were for the Early Growth response EGR/KROX family, ATF2, YY1 (Yin and Yang 1), E2F-1/DP-1 and E2F-4/DP-2 heterodimers, SOX5, and CREB and ATF families.

An assessment of the geographical scale of recurrent gene flow in wild populations of two species of Mekong River carps (Henicorhynchus spp.)

The Mekong is the most productive river fishery in the world, and such as, the Mekong River Basin (MRB) is very important to very large human populations across the region as a source of revenue (through fishing and marketing of aquatic resources products) and as the major source for local animal protein. Threats to biodiversity in the MRB, either to the fishery sector itself or to other sectors are a major concern, even though currently, fisheries across this region are still very productive. If not managed properly however, fish population declines will cause significant economic impact and affect livelihoods of local people and will have a major impact on food security and nutrition. Biodiversity declines will undoubtedly affect food security, income and socio-economic status of people in the MRB that depend on aquatic resources. This is an indicator of unsustainable development and hence should be avoided. Genetic diversity (biodiversity) that can be measured using techniques based on DNA markers; refers to variation within and among populations within the same species or reproductive units. In a population, new genetic variation is generated by sexual recombination contributed by individuals with mutations in genes and chromosomes. Over time, populations of a species that are not reproducing together will diverge as differential impacts of selection and genetic drift change their genetic attributes. For mud carp (Henicorhynchus spp.), understanding the status of breeding units in the MRB will be important for their long term persistence, sustainability and for implementing effective management strategies. Earlier analysis of stock structure in two economically important mud carp species (Henicorhynchus siamensis and H. lobatus) in the MRB completed with mtDNA markers identified a number of populations of both species where gene flow had apparently been interrupted or reduced but applying these data directly to management unit identification is potentially compromised because information was only available about female dispersal patterns. The current study aimed to address this problem and to fully assess the extent of current gene flow (nDNA) and reproductive exchange among selected wild populations of two species of carp (Henicorhynchus spp.) of high economic importance in the MRB using combined mtDNA and nDNA markers. In combination, the data can be used to define effective management units for each species. In general, nDNA diversity for H. lobatus (with average allelic richness (A) 7.56 and average heterozygosity (Ho) 0.61) was very similar to that identified for H. siamensis (A = 6.81 and Ho = 0.75). Both mud carp species show significant but low FST estimates among populations as a result of lower genetic diversity among sampled populations compared with genetic diversity within populations that may potentially mask any 'real' population structure. Overall, population genetic structure patterns from mtDNA and nDNA in both Henicorhynchus species were largely congruent. Different population structures however, were identified for the two Henicorhynchus species across the same geographical area. Apparent co-similarity in morphology and co-distribution of these two relatively closely related species does not apparently imply parallel evolutionary histories. Differences in each species population structure likely reflect historical drainage rearrangement of the Mekong River. The data indicate that H. siamensis is likely to have occupied the Mekong system for much longer than has H. lobatus in the past. Two divergent stocks were identified for H. lobatus in the MRB below the Khone Falls while a single stock had been evident in the earlier mtDNA study. This suggests that the two Henicorhynchus species may possess different life history traits and that different patterns of gene flow has likely influenced modern genetic structure in these close congeners. In combination, results of the earlier mtDNA and the current study have implications for effective management of both Henicorhynchus species across the MRB. Currently, both species are essentially treated as a single management unit in this region. This strategy may be appropriate for H. lobatus as a single stock was evident in the main stream of the MRB, but may not be appropriate for H. siamensis as more than a single stock was identified across the same range for this species. Management strategies should consider this difference to conserve overall biodiversity (local discrete populations) and this will include maintaining natural habitat and migration pathways, provision of fish sanctuaries (refuges) and may also require close monitoring of any stock declines, a signal that may require effective recovery strategies.

Use of reverse phase protein microarrays and reference standard development for molecular network analysis of metastatic ovarian carcinoma

Cancer can be defined as a deregulation or hyperactivity in the ongoing network of intracellular and extracellular signaling events. Reverse phase protein microarray technology may offer a new opportunity to measure and profile these signaling pathways, providing data on post-translational phosphorylation events not obtainable by gene microarray analysis. Treatment of ovarian epithelial carcinoma almost always takes place in a metastatic setting since unfortunately the disease is often not detected until later stages. Thus, in addition to elucidation of the molecular network within a tumor specimen, critical questions are to what extent do signaling changes occur upon metastasis and are there common pathway elements that arise in the metastatic microenvironment. For individualized combinatorial therapy, ideal therapeutic selection based on proteomic mapping of phosphorylation end points may require evaluation of the patient's metastatic tissue. Extending these findings to the bedside will require the development of optimized protocols and reference standards. We have developed a reference standard based on a mixture of phosphorylated peptides to begin to address this challenge.

Choose wisely: Network, ontology and annotation resources for the analysis of Staphylococcus aureus omics data

Staphylococcus aureus (S. aureus) is a prominent human and livestock pathogen investigated widely using omic technologies. Critically, due to availability, low visibility or scattered resources, robust network and statistical contextualisation of the resulting data is generally under-represented. Here, we present novel meta-analyses of freely-accessible molecular network and gene ontology annotation information resources for S. aureus omics data interpretation. Furthermore, through the application of the gene ontology annotation resources we demonstrate their value and ability (or lack-there-of) to summarise and statistically interpret the emergent properties of gene expression and protein abundance changes using publically available data. This analysis provides simple metrics for network selection and demonstrates the availability and impact that gene ontology annotation selection can have on the contextualisation of bacterial omics data.

Genome-wide scan of healthy human connectome discovers SPON1 gene variant influencing dementia severity

Aberrant connectivity is implicated in many neurological and psychiatric disorders, including Alzheimer's disease and schizophrenia. However, other than a few disease-associated candidate genes, we know little about the degree to which genetics play a role in the brain networks; we know even less about specific genes that influence brain connections. Twin and family-based studies can generate estimates of overall genetic influences on a trait, but genome-wide association scans (GWASs) can screen the genome for specific variants influencing the brain or risk for disease. To identify the heritability of various brain connections, we scanned healthy young adult twins with high-field, highangular resolution diffusion MRI. We adapted GWASs to screen the brain's connectivity pattern, allowing us to discover genetic variants that affect the human brain's wiring. The association of connectivity with the SPON1 variant at rs2618516 on chromosome 11 (11p15.2) reached connectome-wide, genome-wide significance after stringent statistical corrections were enforced, and it was replicated in an independent subsample. rs2618516 was shown to affect brain structure in an elderly population with varying degrees of dementia. Older people who carried the connectivity variant had significantly milder clinical dementia scores and lower risk of Alzheimer's disease. As a posthoc analysis, we conducted GWASs on several organizational and topological network measures derived from the matrices to discover variants in and around genes associated with autism (MACROD2), development (NEDD4), and mental retardation (UBE2A) significantly associated with connectivity. Connectome-wide, genome-wide screening offers substantial promise to discover genes affecting brain connectivity and risk for brain diseases.

Failure to launch: The self-regulating Md -MYB10R6 gene from apple is active in flowers but not leaves of Petunia

Coloured foliage due to anthocyanin pigments (bronze/red/black) is an attractive trait that is often lacking in many bedding, ornamental and horticultural plants. Apples (Malus × domestica) containing an allelic variant of the anthocyanin regulator, Md-MYB10R6, are highly pigmented throughout the plant, due to autoregulation by MYB10 upon its own promoter. We investigated whether Md-MYB10R6 from apple is capable of functioning within the heterologous host Petunia hybrida to generate plants with novel pigmentation patterns. The Md-MYB10R6 transgene (MYB10–R6pro:MYB10:MYB10term) activated anthocyanin synthesis when transiently expressed in Antirrhinumroseadorsea petals and petunia leaf discs. Stable transgenic petunias containing Md-MYB10R6 lacked foliar pigmentation but had coloured flowers, complementing the an2 phenotype of ‘Mitchell’ petunia. The absence of foliar pigmentation was due to the failure of the Md-MYB10R6 gene to self-activate in vegetative tissues, suggesting that additional protein partners are required for Md-MYB10 to activate target genes in this heterologous system. In petunia flowers, where endogenous components including MYB-bHLH-WDR (MBW) proteins were present, expression of the Md-MYB10R6 promoter was initiated, allowing auto-regulation to occur and activating anthocyanin production. Md-MYB10 is capable of operating within the petunia MBW gene regulation network that controls the expression of the anthocyanin biosynthesis genes, AN1 (bHLH) and MYBx (R3-MYB repressor) in petals.

A versatile gene-based test for genome-wide association studies

We have derived a versatile gene-based test for genome-wide association studies (GWAS). Our approach, called VEGAS (versatile gene-based association study), is applicable to all GWAS designs, including family-based GWAS, meta-analyses of GWAS on the basis of summary data, and DNA-pooling-based GWAS, where existing approaches based on permutation are not possible, as well as singleton data, where they are. The test incorporates information from a full set of markers (or a defined subset) within a gene and accounts for linkage disequilibrium between markers by using simulations from the multivariate normal distribution. We show that for an association study using singletons, our approach produces results equivalent to those obtained via permutation in a fraction of the computation time. We demonstrate proof-of-principle by using the gene-based test to replicate several genes known to be associated on the basis of results from a family-based GWAS for height in 11,536 individuals and a DNA-pooling-based GWAS for melanoma in approximately 1300 cases and controls. Our method has the potential to identify novel associated genes; provide a basis for selecting SNPs for replication; and be directly used in network (pathway) approaches that require per-gene association test statistics. We have implemented the approach in both an easy-to-use web interface, which only requires the uploading of markers with their association p-values, and a separate downloadable application.