Breakthroughs in genetic studies of ankylosing spondylitis

Ankylosing spondylitis (AS), the prototypic seronegative arthropathy, is known to be highly heritable, with >90% of the risk of developing the disease determined genetically. As with most common heritable diseases, progress in identifying the genes involved using family-based or candidate gene approaches has been slow. The recent development of the genome-wide association study approach has revolutionized genetic studies of such diseases. Early studies in ankylosing spondylitis have produced two major breakthroughs in the identification of genes contributing roughly one third of the population attributable risk of the disease, and pointing directly to a potential therapy. These exciting findings highlight the potential of future more comprehensive genetic studies of determinants of disease risk and clinical manifestations, and are the biggest advance in our understanding of the causation of the disease since the discovery of the association with HLA-B27.

Effect of an estrogen receptor-α intron 4 polymorphism on fat mass in 11-year-old children

Context: in the ESR1 gene encoding estrogen receptor (ER)-α may be associated with fat mass in adults. Objectives: The objective of the study was to establish whether ESR1 polymorphisms influence fat mass in childhood. Design: This was a cross-sectional analysis after genotyping of rs9340799, rs2234693, and rs7757956 ESR1 polymorphisms. Setting: The Avon Longitudinal Study of Parents and Children (ALSPAC) was a population-based prospective study. Participants: Participants included 3097 11-yr-old children with results for ESR1 genotyping, puberty measures, and dual-energy x-ray absorptiometry results. Outcomes: Relationships between ESR1 polymorphisms and indices of body composition were measured. Results: The rs7757956 polymorphism was associated with fat mass (P = 0.002). Total body fat mass (adjusted for height) was reduced by 6% in children with TA/AA genotypes, and risk of being overweight (≥85th centile of fat mass) was decreased by 20%. This genetic effect appeared to interact with puberty in girls (P = 0.05 for interaction): in those with the TT genotype, total body fat mass (adjusted for height) was 18% higher in Tanner stages 3-5 vs. stages 1-2; the equivalent difference was 7% in those with TA/AA genotypes. Furthermore, the risk of being overweight was 36% lower in girls with TA/AA genotypes in Tanner stages 3-5, but no reduction was seen in those in stages 1-2. Neither rs9340799 nor rs2234693 polymorphisms were associated with body composition measures. Conclusions: Fat mass in 11-yr-old children was related to the rs7757956 ESR1 polymorphism. This association was strongest in girls in more advanced puberty, in whom the risk of being overweight was reduced by 36% in those with the TA/AA genotype.

Genetic, functional, and histopathological evaluation of two C-terminal BRCA1 missense variants

Background: The vast majority of BRCA1 missense sequence variants remain uncharacterised for their possible effect on protein expression and function, and therefore are unclassified in terms of their pathogenicity. BRCA1 plays diverse cellular roles and it is unlikely that any single functional assay will accurately reflect the total cellular implications of missense mutations in this gene. Objective: To elucidate the effect of two BRCA1 variants, 5236G>C (G1706A) and 5242C>A (A1708E) on BRCA1 function, and to survey the relative usefulness of several assays to direct the characterisation of other unclassified variants in BRCA genes. Methods and Results: Data from a range of bioinformatic, genetic, and histopathological analyses, and in vitro functional assays indicated that the 1708E variant was associated with the disruption of different cellular functions of BRCA1. In transient transfection experiments in T47D and 293T cells, the 1708E product was mislocalised to the cytoplasm and induced centrosome amplification in 293T cells. The 1708E variant also failed to transactivate transcription of reporter constructs in mammalian transcriptional transactivation assays. In contrast, the 1706A variant displayed a phenotype comparable to wildtype BRCA1 in these assays. Consistent with functional data, tumours from 1708E carriers showed typical BRCA1 pathology, while tumour material from 1706A carriers displayed few histopathological features associated with BRCA1 related tumours. Conclusions: A comprehensive range of genetic, bioinformatic, and functional analyses have been combined for the characterisation of BRCA1 unclassified sequence variants. Consistent with the functional analyses, the combined odds of causality calculated for the 1706A variant after multifactorial likelihood analysis (1:142) indicates a definitive classification of this variant as "benign". In contrast, functional assays of the 1708E variant indicate that it is pathogenic, possibly through subcellular mislocalisation. However, the combined odds of 262:1 in favour of causality of this variant does not meet the minimal ratio of 1000:1 for classification as pathogenic, and A1708E remains formally designated as unclassified. Our findings highlight the importance of comprehensive genetic information, together with detailed functional analysis for the definitive categorisation of unclassified sequence variants. This combination of analyses may have direct application to the characterisation of other unclassified variants in BRCA1 and BRCA2.

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.

Genetic disorders of the LRP5-Wnt signalling pathway affecting the skeleton

Osteoporosis is a common, increasingly prevalent and potentially debilitating condition of men and women. Genetic factors are major determinants of bone mass and the risk of fracture, but few genes have been definitively demonstrated to be involved. The identification of these factors will provide novel insights into the processes of bone formation and loss and thus the pathogenesis of osteoporosis, enabling the rational development of novel therapies. In this article, we present the extensive genetic and functional data indicating that the LRP5 gene and the Wnt signalling pathway are key players in bone formation and the risk of osteoporosis, and that LRP5 signalling is essential for normal morphology, developmental processes and bone health.

Genetic and genomic studies of PADI4 in rheumatoid arthritis

Objectives. Strong genetic association of rheumatoid arthritis (RA) with PADI4 (peptidyl arginine deiminase) has previously been described in Japanese, although this was not confirmed in a subsequent study in the UK. We therefore undertook a further study of genetic association between PADI4 and RA in UK Caucasians and also studied expression of PADI4 in the peripheral blood of patients with RA. Methods. Seven single-nucleotide polymorphisms (SNP) were genotyped using polymerase chain reaction (PCR)-restriction fragment length polymorphism in 111 RA cases and controls. A marker significantly associated with RA (PADI4_100, rs#2240339) in this first data set (P = 0.03) was then tested for association in a larger group of 439 RA patients and 428 controls. PADI4 transcription was also assessed by real-time quantitative PCR using RNA extracted from peripheral blood mononuclear cells from 13 RA patients and 11 healthy controls. Results. A single SNP was weakly associated with RA (P = 0.03) in the initial case-control study, a single SNP (PADI4_100) and a two marker haplotype of that SNP and the neighbouring SNP (PADI4_04) were significantly associated with RA (P = 0.02 and P = 0.03 respectively). PADI4_100 was not associated with RA in a second sample set. PADI4 expression was four times greater in cases than controls (P = 0.004), but expression levels did not correlate with the levels of markers of inflammation. Conclusion. PADI4 is significantly overexpressed in the blood of RA patients but genetic variation within PADI4 is not a major risk factor for RA in Caucasians.

Genetic studies of disorders of calcium crystal deposition

In summary, although many factors are likely to be involved in regulating calcification and ossification processes, studies of the causation of articular chondrocalcinosis and disorders of spinal ossification, such as DISH and OPLL, implicate control over inorganic pyrophosphate levels as being one of the most important factors in their aetiopathogenesis. The findings of these studies may prove relevant to other rheumatic diseases in which ectopic ossification occurs, such as AS.

Genetic control of bone density and turnover: Role of the collagen 1α1, estrogen receptor, and vitamin D receptor genes

Genetic factors are known to influence both the peak bone mass and probably the rate of change in bone density. A range of regulatory and structural genes has been proposed to be involved including collagen 1α1 (COL1A1), the estrogen receptor (ER), and the vitamin D receptor (VDR), but the actual genes involved are uncertain. We therefore studied the role of the COL1A1 and VDR loci in control of bone density by linkage in 45 dizygotic twin pairs and 29 nuclear families comprising 120 individuals. The influences on bone density of polymorphisms of COL1A1, VDR, and ER were studied by association both cross-sectionally and longitudinally in 193 elderly postmenopausal women (average age, 69 years) over a mean follow-up time of 6.3 years. Weak linkage of the COL1A1 locus with bone density was observed in both twins and families (p = 0.02 in both data sets), confirming previous observations of linkage of this locus with bone density. Association between the MscI polymorphism of COL1A1 and rate of lumbar spine bone loss was observed with significant gene-environment interaction related to dietary calcium intake (p = 0.0006). In the lowest tertile of dietary calcium intake, carriers of "s" alleles lost more bone than "SS" homozygotes (p = 0.01), whereas the opposite was observed in the highest dietary calcium intake (p = 0.003). Association also was observed between rate of bone loss at both the femoral neck and the lumbar spine and the TaqI VDR polymorphism (p = 0.03). This association was strongest in those in the lowest tertile of calcium intake, also suggesting the presence of gene-environment interaction involving dietary calcium and VDR, influencing bone turnover. No significant association was observed between the PvuII ER polymorphism alone or in combination with VDR or COL1A1 genotypes, with either bone density or its rate of change. These data support the involvement of COL1A1 in determination of bone density and the interaction of both COL1A1 and VDR with calcium intake in regulation of change of bone density over time.

Obesity crisis must be owned not only by individuals but governments and industry

FOR the first time in Australia’s history, diet is the No.1 risk factor contributing to our burden of disease. That’s more than smoking, alcohol, injury or anything else. I’m sure some of you are thinking: “Life’s too short to worry about every mouthful you eat. This is such a First World problem.” But it’s not. Around the world, in countries rich and poor, more people die from preventable disease than any other kind, and what we eat is recognised as a major contributor...

Certain Aspects Related To Computation By Modified Crack Closure Integral(MCCI)

This paper presents the proper computational approach for the estimation of strain energy release rates by modified crack closure integral (MCCI). In particular, in the estimation of consistent nodal force vectors used in the MCCI expressions for quarter-point singular elements (wherein all the nodal force vectors participate in computation of strain energy release rates by MCCI). The numerical example of a centre crack tension specimen under uniform loading is presented to illustrate the approach.

Nature and extent of genetic diversity of dengue viruses determined by 454 pyrosequencing

Dengue virus (DENV) populations are characteristically highly diverse. Regular lineage extinction and replacement is an important dynamic DENV feature, and most DENV lineage turnover events are associated with increased incidence of disease. The role of genetic diversity in DENV lineage extinctions is not understood. We investigated the nature and extent of genetic diversity in the envelope (E) gene of DENV serotype 1 representing different lineages histories. A region of the DENV genome spanning the E gene was amplified and sequenced by Roche/454 pyrosequencing. The pyrosequencing results identified distinct sub-populations (haplotypes) for each DENV-1 E gene. A phylogenetic tree was constructed with the consensus DENV-1 E gene nucleotide sequences, and the sequences of each constructed haplotype showed that the haplotypes segregated with the Sanger consensus sequence of the population from which they were drawn. Haplotypes determined through pyrosequencing identified a recombinant DENV genome that could not be identified through Sanger sequencing. Nucleotide level sequence diversities of DENV-1 populations determined from SNP analysis were very low, estimated from 0.009-0.01. There were also no stop codon, frameshift or non-frameshift mutations observed in the E genes of any lineage. No significant correlations between the accumulation of deleterious mutations or increasing genetic diversity and lineage extinction were observed (p>0.5). Although our hypothesis that accumulation of deleterious mutations over time led to the extinction and replacement of DENV lineages was ultimately not supported by the data, our data does highlight the significant technical issues that must be resolved in the way in which population diversity is measured for DENV and other viruses. The results provide an insight into the within-population genetic structure and diversity of DENV-1 populations.

A linear programming approach for estimating the structure of a sparse linear genetic network from transcript profiling data

Background: A genetic network can be represented as a directed graph in which a node corresponds to a gene and a directed edge specifies the direction of influence of one gene on another. The reconstruction of such networks from transcript profiling data remains an important yet challenging endeavor. A transcript profile specifies the abundances of many genes in a biological sample of interest. Prevailing strategies for learning the structure of a genetic network from high-dimensional transcript profiling data assume sparsity and linearity. Many methods consider relatively small directed graphs, inferring graphs with up to a few hundred nodes. This work examines large undirected graphs representations of genetic networks, graphs with many thousands of nodes where an undirected edge between two nodes does not indicate the direction of influence, and the problem of estimating the structure of such a sparse linear genetic network (SLGN) from transcript profiling data. Results: The structure learning task is cast as a sparse linear regression problem which is then posed as a LASSO (l1-constrained fitting) problem and solved finally by formulating a Linear Program (LP). A bound on the Generalization Error of this approach is given in terms of the Leave-One-Out Error. The accuracy and utility of LP-SLGNs is assessed quantitatively and qualitatively using simulated and real data. The Dialogue for Reverse Engineering Assessments and Methods (DREAM) initiative provides gold standard data sets and evaluation metrics that enable and facilitate the comparison of algorithms for deducing the structure of networks. The structures of LP-SLGNs estimated from the INSILICO1, INSILICO2 and INSILICO3 simulated DREAM2 data sets are comparable to those proposed by the first and/or second ranked teams in the DREAM2 competition. The structures of LP-SLGNs estimated from two published Saccharomyces cerevisae cell cycle transcript profiling data sets capture known regulatory associations. In each S. cerevisiae LP-SLGN, the number of nodes with a particular degree follows an approximate power law suggesting that its degree distributions is similar to that observed in real-world networks. Inspection of these LP-SLGNs suggests biological hypotheses amenable to experimental verification. Conclusion: A statistically robust and computationally efficient LP-based method for estimating the topology of a large sparse undirected graph from high-dimensional data yields representations of genetic networks that are biologically plausible and useful abstractions of the structures of real genetic networks. Analysis of the statistical and topological properties of learned LP-SLGNs may have practical value; for example, genes with high random walk betweenness, a measure of the centrality of a node in a graph, are good candidates for intervention studies and hence integrated computational – experimental investigations designed to infer more realistic and sophisticated probabilistic directed graphical model representations of genetic networks. The LP-based solutions of the sparse linear regression problem described here may provide a method for learning the structure of transcription factor networks from transcript profiling and transcription factor binding motif data.

Some aspects of multilevel load-frequency control of a power system

The application of multilevel control strategies for load-frequency control of interconnected power systems is assuming importance. A large multiarea power system may be viewed as an interconnection of several lower-order subsystems, with possible change of interconnection pattern during operation. The solution of the control problem involves the design of a set of local optimal controllers for the individual areas, in a completely decentralised environment, plus a global controller to provide the corrective signal to account for interconnection effects. A global controller, based on the least-square-error principle suggested by Siljak and Sundareshan, has been applied for the LFC problem. A more recent work utilises certain possible beneficial aspects of interconnection to permit more desirable system performances. The paper reports the application of the latter strategy to LFC of a two-area power system. The power-system model studied includes the effects of excitation system and governor controls. A comparison of the two strategies is also made.

A large-scale analysis of genetic variants within putative miRNA binding sites in prostate cancer

Prostate cancer is the second most common malignancy among men worldwide. Genome-wide association studies have identified 100 risk variants for prostate cancer, which can explain approximately 33% of the familial risk of the disease. We hypothesized that a comprehensive analysis of genetic variations found within the 3' untranslated region of genes predicted to affect miRNA binding (miRSNP) can identify additional prostate cancer risk variants. We investigated the association between 2,169 miRSNPs and prostate cancer risk in a large-scale analysis of 22,301 cases and 22,320 controls of European ancestry from 23 participating studies. Twenty-two miRSNPs were associated (P<2.3×10(-5)) with risk of prostate cancer, 10 of which were within 7 genes previously not mapped by GWAS studies. Further, using miRNA mimics and reporter gene assays, we showed that miR-3162-5p has specific affinity for the KLK3 rs1058205 miRSNP T-allele, whereas miR-370 has greater affinity for the VAMP8 rs1010 miRSNP A-allele, validating their functional role. SIGNIFICANCE Findings from this large association study suggest that a focus on miRSNPs, including functional evaluation, can identify candidate risk loci below currently accepted statistical levels of genome-wide significance. Studies of miRNAs and their interactions with SNPs could provide further insights into the mechanisms of prostate cancer risk.

A genetic variant of MDM4 influences regulation by multiple microRNAs in prostate cancer

The oncogene MDM4, also known as MDMX or HDMX, contributes to cancer susceptibility and progression through its capacity to negatively regulate a range of genes with tumour-suppressive functions. As part of a recent genome-wide association study it was determined that the A-allele of the rs4245739 SNP (A>C), located in the 3'-UTR of MDM4, is associated with an increased risk of prostate cancer. Computational predictions revealed that the rs4245739 SNP is located within a predicted binding site for three microRNAs (miRNAs): miR-191-5p, miR-887 and miR-3669. Herein, we show using reporter gene assays and endogenous MDM4 expression analyses that miR-191-5p and miR-887 have a specific affinity for the rs4245739 SNP C-allele in prostate cancer. These miRNAs do not affect MDM4 mRNA levels, rather they inhibit its translation in C-allele-containing PC3 cells but not in LNCaP cells homozygous for the A-allele. By analysing gene expression datasets from patient cohorts, we found that MDM4 is associated with metastasis and prostate cancer progression and that targeting this gene with miR-191-5p or miR-887 decreases in PC3 cell viability. This study is the first, to our knowledge, to demonstrate regulation of the MDM4 rs4245739 SNP C-allele by two miRNAs in prostate cancer, and thereby to identify a mechanism by which the MDM4 rs4245739 SNP A-allele may be associated with an increased risk for prostate cancer.