Analysing landslides in the Three Gorges Region (China) using frequently acquired SAR images

Spaceborne Synthetic Aperture Radar (SAR) sensors obtain regular and frequent radar images from which ground motion can be precisely detected using a variety of different techniques. The ability to remotely measure slope displacements over large regions has many uses and advantages, although the limitations of an increasingly common technique, Differential SAR Interferometry (D-InSAR), must be considered to avoid the misinterpretation of results. Areas of low coherence and the geometrical effects of mountainous terrain in SAR imagery are known to hinder the exploitation of D-InSAR results. A further major limitation for landslide studies is the assumption that variable rates of movement over a given distance cannot exceed a threshold value, dependent upon the SAR image pixel spacing, the radar sensor wavelength and satellite revisit frequency. This study evaluates the use of three SAR image modes from TerraSAR-X and ENVISAT satellites for monitoring slow-moving landslides in the densely vegetated Three Gorges region, China. Low coherence and episodically fast movements are shown to exceed the measureable limit for regular D-InSAR analysis even for the highest resolution, 11-day interferograms. Subsequently, sub-pixel offset time-series techniques applied to corner reflectors and natural targets are developed as a robust method of resolving time-variable displacements. Verifiable offsets are generated with the TerraSAR-X imagery and the precise movement history of landslides is obtained over a period of up to four years. The capability to derive two-dimensional movements from sub-pixel offsets is used to infer a rotational failure mechanism for the most active landslide detected, and a greater understanding of the landslide behaviour is achieved through comparisons with likely triggering factors and 2D limit equilibrium slope stability analysis

Creating the 2011 area classification for output areas (2011 OAC)

This paper presents the methodology that has been used to create the 2011 Area Classification for Output Areas (2011 OAC). This extends a lineage of widely used public domain census only geodemographic classifications in the UK. It provides an update to the successful 2001 OAC methodology, and summarizes the social and physical structure of neighbourhoods using data from the 2011 UK Census. We also present the results of a user engagement exercise that underpinned the creation of an updated methodology for the 2011 OAC. The 2011 OAC comprises 8 Supergroups, 26 Groups and 76 Subgroups. Finally, we present an example of the results of the classification in Southampton

Finnish Parkinson's disease study integrating protein-protein interaction network data with exome sequencing analysis

Variants associated with Parkinson's disease (PD) have generally a small effect size and, therefore, large sample sizes or targeted analyses are required to detect significant associations in a whole exome sequencing (WES) study. Here, we used protein-protein interaction (PPI) information on 36 genes with established or suggested associations with PD to target the analysis of the WES data. We performed an association analysis on WES data from 439 Finnish PD subjects and 855 controls, and included a Finnish population cohort as the replication dataset with 60 PD subjects and 8214 controls. Single variant association (SVA) test in the discovery dataset yielded 11 candidate variants in seven genes, but the associations were not significant in the replication cohort after correction for multiple testing. Polygenic risk score using variants rs2230288 and rs2291312, however, was associated to PD with odds ratio of 2.7 (95% confidence interval 1.4-5.2; p < 2.56e-03). Furthermore, an analysis of the PPI network revealed enriched clusters of biological processes among established and candidate genes, and these functional networks were visualized in the study. We identified novel candidate variants for PD using a gene prioritization based on PPI information, and described why these variants may be involved in the pathogenesis of PD

Allelic heterogeneity and more detailed analyses of known loci explain additional phenotypic variation and reveal complex patterns of association

The identification of multiple signals at individual loci could explain additional phenotypic variance (‘missing heritability’) of common traits, and help identify causal genes. We examined gene expression levels as a model trait because of the large number of strong genetic effects acting in cis. Using expression profiles from 613 individuals, we performed genome-wide single nucleotide polymorphism (SNP) analyses to identify cis-expression quantitative trait loci (eQTLs), and conditional analysis to identify second signals. We examined patterns of association when accounting for multiple SNPs at a locus and when including additional SNPs from the 1000 Genomes Project. We identified 1298 cis-eQTLs at an approximate false discovery rate 0.01, of which 118 (9%) showed evidence of a second independent signal. For this subset of 118 traits, accounting for two signals resulted in an average 31% increase in phenotypic variance explained (Wilcoxon P< 0.0001). The association of SNPs with cis gene expression could increase, stay similar or decrease in significance when accounting for linkage disequilibrium with second signals at the same locus. Pairs of SNPs increasing in significance tended to have gene expression increasing alleles on opposite haplotypes, whereas pairs of SNPs decreasing in significance tended to have gene expression increasing alleles on the same haplotypes. Adding data from the 1000 Genomes Project showed that apparently independent signals could be potentially explained by a single association signal. Our results show that accounting for multiple variants at a locus will increase the variance explained in a substantial fraction of loci, but that allelic heterogeneity will be difficult to define without resequencing loci and functional work

Imputation of variants from the 1000 Genomes Project modestly improves known associations and can identify low-frequency variant-phenotype associations undetected by HapMap based imputation

notes: PMCID: PMC3655956This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.Genome-wide association (GWA) studies have been limited by the reliance on common variants present on microarrays or imputable from the HapMap Project data. More recently, the completion of the 1000 Genomes Project has provided variant and haplotype information for several million variants derived from sequencing over 1,000 individuals. To help understand the extent to which more variants (including low frequency (1% ≤ MAF <5%) and rare variants (<1%)) can enhance previously identified associations and identify novel loci, we selected 93 quantitative circulating factors where data was available from the InCHIANTI population study. These phenotypes included cytokines, binding proteins, hormones, vitamins and ions. We selected these phenotypes because many have known strong genetic associations and are potentially important to help understand disease processes. We performed a genome-wide scan for these 93 phenotypes in InCHIANTI. We identified 21 signals and 33 signals that reached P<5×10(-8) based on HapMap and 1000 Genomes imputation, respectively, and 9 and 11 that reached a stricter, likely conservative, threshold of P<5×10(-11) respectively. Imputation of 1000 Genomes genotype data modestly improved the strength of known associations. Of 20 associations detected at P<5×10(-8) in both analyses (17 of which represent well replicated signals in the NHGRI catalogue), six were captured by the same index SNP, five were nominally more strongly associated in 1000 Genomes imputed data and one was nominally more strongly associated in HapMap imputed data. We also detected an association between a low frequency variant and phenotype that was previously missed by HapMap based imputation approaches. An association between rs112635299 and alpha-1 globulin near the SERPINA gene represented the known association between rs28929474 (MAF = 0.007) and alpha1-antitrypsin that predisposes to emphysema (P = 2.5×10(-12)). Our data provide important proof of principle that 1000 Genomes imputation will detect novel, low frequency-large effect associations

A genome-wide association study identifies protein quantitative trait loci (pQTLs)

There is considerable evidence that human genetic variation influences gene expression. Genome-wide studies have revealed that mRNA levels are associated with genetic variation in or close to the gene coding for those mRNA transcripts - cis effects, and elsewhere in the genome - trans effects. The role of genetic variation in determining protein levels has not been systematically assessed. Using a genome-wide association approach we show that common genetic variation influences levels of clinically relevant proteins in human serum and plasma. We evaluated the role of 496,032 polymorphisms on levels of 42 proteins measured in 1200 fasting individuals from the population based InCHIANTI study. Proteins included insulin, several interleukins, adipokines, chemokines, and liver function markers that are implicated in many common diseases including metabolic, inflammatory, and infectious conditions. We identified eight Cis effects, including variants in or near the IL6R (p = 1.8×10 -57), CCL4L1 (p = 3.9×10-21), IL18 (p = 6.8×10-13), LPA (p = 4.4×10-10), GGT1 (p = 1.5×10-7), SHBG (p = 3.1×10-7), CRP (p = 6.4×10-6) and IL1RN (p = 7.3×10-6) genes, all associated with their respective protein products with effect sizes ranging from 0.19 to 0.69 standard deviations per allele. Mechanisms implicated include altered rates of cleavage of bound to unbound soluble receptor (IL6R), altered secretion rates of different sized proteins (LPA), variation in gene copy number (CCL4L1) and altered transcription (GGT1). We identified one novel trans effect that was an association between ABO blood group and tumour necrosis factor alpha (TNF-alpha) levels (p = 6.8×10-40), but this finding was not present when TNF-alpha was measured using a different assay , or in a second study, suggesting an assay-specific association. Our results show that protein levels share some of the features of the genetics of gene expression. These include the presence of strong genetic effects in cis locations. The identification of protein quantitative trait loci (pQTLs) may be a powerful complementary method of improving our understanding of disease pathways. © 2008 Melzer et al

Defining the causes of sporadic Parkinson's disease in the global Parkinson's genetics program (GP2)

The Global Parkinson's Genetics Program (GP2) will genotype over 150,000 participants from around the world, and integrate genetic and clinical data for use in large-scale analyses to dramatically expand our understanding of the genetic architecture of PD. This report details the workflow for cohort integration into the complex arm of GP2, and together with our outline of the monogenic hub in a companion paper, provides a generalizable blueprint for establishing large scale collaborative research consortia

Clinical, pathological and functional characterization of riboflavin-responsive neuropathy.

Brown-Vialetto-Van Laere syndrome represents a phenotypic spectrum of motor, sensory, and cranial nerve neuropathy, often with ataxia, optic atrophy and respiratory problems leading to ventilator-dependence. Loss-of-function mutations in two riboflavin transporter genes, SLC52A2 and SLC52A3, have recently been linked to Brown-Vialetto-Van Laere syndrome. However, the genetic frequency, neuropathology and downstream consequences of riboflavin transporter mutations are unclear. By screening a large cohort of 132 patients with early-onset severe sensory, motor and cranial nerve neuropathy we confirmed the strong genetic link between riboflavin transporter mutations and Brown-Vialetto-Van Laere syndrome, identifying 22 pathogenic mutations in SLC52A2 and SLC52A3, 14 of which were novel. Brain and spinal cord neuropathological examination of two cases with SLC52A3 mutations showed classical symmetrical brainstem lesions resembling pathology seen in mitochondrial disease, including severe neuronal loss in the lower cranial nerve nuclei, anterior horns and corresponding nerves, atrophy of the spinothalamic and spinocerebellar tracts and posterior column-medial lemniscus pathways. Mitochondrial dysfunction has previously been implicated in an array of neurodegenerative disorders. Since riboflavin metabolites are critical components of the mitochondrial electron transport chain, we hypothesized that reduced riboflavin transport would result in impaired mitochondrial activity, and confirmed this using in vitro and in vivo models. Electron transport chain complex I and complex II activity were decreased in SLC52A2 patient fibroblasts, while global knockdown of the single Drosophila melanogaster riboflavin transporter homologue revealed reduced levels of riboflavin, downstream metabolites, and electron transport chain complex I activity. This in turn led to abnormal mitochondrial membrane potential, respiratory chain activity and morphology. Riboflavin transporter knockdown in Drosophila also resulted in severely impaired locomotor activity and reduced lifespan, mirroring patient pathology, and these phenotypes could be partially rescued using a novel esterified derivative of riboflavin. Our findings expand the genetic, clinical and neuropathological features of Brown-Vialetto-Van Laere syndrome, implicate mitochondrial dysfunction as a downstream consequence of riboflavin transporter gene defects, and validate riboflavin esters as a potential therapeutic strategy