Genetic basis of hereditary colorectal cancers: Hereditary nonpolyposis colorectal cancer and Familial adenomatous polyposis

Hereditary nonpolyposis colorectal cancer (HNPCC) and familial adenomatous polyposis (FAP) are characterized by a high risk and early onset of colorectal cancer (CRC). HNPCC is due to a germline mutation in one of the following MMR genes: MLH1, MSH2, MSH6 and PMS2. A majority of FAP and attenuated FAP (AFAP) cases are due to germline mutations of APC, causing the development of multiple colorectal polyps. To date, over 450 MMR gene mutations and over 800 APC mutations have been identified. Most of these mutations lead to a truncated protein, easily detected by conventional mutation detection methods. However, in about 30% of HNPCC and FAP, and about 90% of AFAP families, mutations remain unknown. We aimed to clarify the genetic basis and genotype-phenotype correlation of mutation negative HNPCC and FAP/AFAP families by advanced mutation detection methods designed to detect large genomic rearrangements, mRNA and protein expression alterations, promoter mutations, phenotype linked haplotypes, and tumoral loss of heterozygosity. We also aimed to estimate the frequency of HNPCC in Uruguayan CRC patients. Our expression based analysis of mutation negative HNPCC divided these families into two categories: 1) 42% of families linked to the MMR genes with a phenotype resembling that of mutation positive, and 2) 58% of families likely to be associated with other susceptibility genes. Unbalanced mRNA expression of MLH1 was observed in two families. Further studies revealed that a MLH1 nonsense mutation, R100X was associated with aberrant splicing of exons not related to the mutation and an MLH1 deletion (AGAA) at nucleotide 210 was associated with multiple exon skipping, without an overall increase in the frequency of splice events. APC mutation negative FAP/AFAP families were divided into four groups according to the genetic basis of their predisposition. Four (14%) families displayed a constitutional deletion of APC with profuse polyposis, early age of onset and frequent extracolonic manifestations. Aberrant mRNA expression of one allele was observed in seven (24%) families with later onset and less frequent extracolonic manifestations. In 15 (52%) families the involvement of APC could neither be confirmed nor excluded. In three (10%) of the families a germline mutation was detected in genes other than APC: AXIN2 in one family, and MYH in two families. The families with undefined genetic basis and especially those with AXIN2 or MYH mutations frequently displayed AFAP or atypical polyposis. Of the Uruguayan CRC patients, 2.6% (12/461) fulfilled the diagnostic criteria for HNPCC and 5.6% (26/461) were associated with increased risk of cancer. Unexpectedly low frequency of molecularly defined HNPCC cases may suggest a different genetic profile in the Uruguayan population and the involvement of novel susceptibility genes. Accurate genetic and clinical characterization of families with hereditary colorectal cancers, and the definition of the genetic basis of "mutation negative" families in particular, facilitate proper clinical management of such families.

A hybrid clustering procedure for concentric and chain-like clusters

K-means algorithm is a well known nonhierarchical method for clustering data. The most important limitations of this algorithm are that: (1) it gives final clusters on the basis of the cluster centroids or the seed points chosen initially, and (2) it is appropriate for data sets having fairly isotropic clusters. But this algorithm has the advantage of low computation and storage requirements. On the other hand, hierarchical agglomerative clustering algorithm, which can cluster nonisotropic (chain-like and concentric) clusters, requires high storage and computation requirements. This paper suggests a new method for selecting the initial seed points, so that theK-means algorithm gives the same results for any input data order. This paper also describes a hybrid clustering algorithm, based on the concepts of multilevel theory, which is nonhierarchical at the first level and hierarchical from second level onwards, to cluster data sets having (i) chain-like clusters and (ii) concentric clusters. It is observed that this hybrid clustering algorithm gives the same results as the hierarchical clustering algorithm, with less computation and storage requirements.

Meta-analysis identifies 13 new loci associated with waist-hip ratio and reveals sexual dimorphism in the genetic basis of fat distribution

Waist-hip ratio (WHR) is a measure of body fat distribution and a predictor of metabolic consequences independent of overall adiposity. WHR is heritable, but few genetic variants influencing this trait have been identified. We conducted a meta-analysis of 32 genome-wide association studies for WHR adjusted for body mass index (comprising up to 77,167 participants), following up 16 loci in an additional 29 studies (comprising up to 113,636 subjects). We identified 13 new loci in or near RSPO3, VEGFA, TBX15-WARS2, NFE2L3, GRB14, DNM3-PIGC, ITPR2-SSPN, LY86, HOXC13, ADAMTS9, ZNRF3-KREMEN1, NISCH-STAB1 and CPEB4 (P = 1.9 × 10−9 to P = 1.8 × 10−40) and the known signal at LYPLAL1. Seven of these loci exhibited marked sexual dimorphism, all with a stronger effect on WHR in women than men (P for sex difference = 1.9 × 10−3 to P = 1.2 × 10−13). These findings provide evidence for multiple loci that modulate body fat distribution independent of overall adiposity and reveal strong gene-by-sex interactions.

A Gaussian quadrature analysis of linear sweep voltammetry

The application of Gaussian Quadrature (GQ) procedures to the evaluation of i—E curves in linear sweep voltammetry is advocated. It is shown that a high degree of precision is achieved with these methods and the values obtained through GQ are in good agreement with (and even better than) the values reported in literature by Nicholson-Shain, for example. Another welcome feature with GQ is its ability to be interpreted as an elegant, efficient analytic approximation scheme too. A comparison of the values obtained by this approach and by a recent scheme based on series approximation proposed by Oldham is made and excellent agreement is shown to exist.

Exclusion of angiotensinogen gene in molecular basis of human hypertension: Sibpair linkage and association analyses in Australian anglo-caucasians

Linkage with essential hypertension has been claimed for a microsatellite marker near the angiotensinogen gene (AGT; chromosome 1q42), as has association for the AGT variants M235T, G(-6)A and A(-20)C. To more rigorously evaluate AGT as a candidate gene for hypertension we performed sibpair analysis with multiple microsatellite markers surrounding this locus and using more sophisticated analysis programs. We also performed an association study of the AGT variants in unrelated subjects with a strong family history (two affected parents). For the linkage study, single and multiplex polymerase chain reaction (PCRs) and automated genescan analysis were conducted on DNA from 175 Australian Anglo-Celtic Caucasian hypertensives for the following markers: D1S2880-(2.1 cM)-D1S213-(2.8 cM)-D1S251-(6.5 cM)-AGT-(2.0 cM) -D1S235. Statistical evaluation of genotype data by nonparametric methods resulted in the following scores: Single-point analysis - SPLINK, P > 0.18; APM method, P > 0.25; ASPEX, MLOD < 0.28; SIB-PAIR, P > 0. 24; Multipoint analysis - MAPMAKER/SIBS, MLOD < 0.24; GENEHUNTER, P > 0.35. Exclusion scores of Lod -4.1 to -5.1 were obtained for these markers using MAPMAKER/SIBS for a lambda(s) of 1.6. The association study of G(-6)A, A(-20)C and M235T variants in 111 hypertensives with strong family history and 190 normotensives with no family history showed significant linkage disequilibrium between particular haplotypes, but we could find no association with hypertension. The present study therefore excludes AGT in the etiology of hypertension, at least in the population of Australian Anglo-Celtic Caucasians studied.

Pathogenicity of Blumeria graminis f. sp. hordei in Australia in 2010 and 2011

A survey of the Australian barley powdery mildew (Blumeria graminis f. sp. hordei) population was conducted in 2010 and 2011. Three hundred and sixty-two isolates of the pathogen were collected from 18 locations across all six states of Australia. Thirty-two barley differentials were used and 11 genotypes were able to differentiate the population with virulence frequencies varying from 14.5 % to 96.6 %. Twenty-seven pathotypes were detected. Fifteen of them were found in both years and they represented 92.0 % of all isolates examined. No virulence was found on a further 16 major genes for resistance (Mla1, Mla3, Mla6, Mla7, Mla9, Mla10, Mla12, Mla13, Mla23, MlaN81, Mlh, MlLa, Mlp1, Ml(IM9), Ml(St) and mlo) indicating a relatively simple population and the ready availability of diverse sources of resistance. This paper reports the powdery mildew virulences present in Australia, provides intelligence for future resistance breeding and sets a basis for further virulence studies.

Analysis-By-Synthesis Based Switched Transform Domain Split Vq Using Gaussian Mixture Model

Using analysis-by-synthesis (AbS) approach, we develop a soft decision based switched vector quantization (VQ) method for high quality and low complexity coding of wideband speech line spectral frequency (LSF) parameters. For each switching region, a low complexity transform domain split VQ (TrSVQ) is designed. The overall rate-distortion (R/D) performance optimality of new switched quantizer is addressed in the Gaussian mixture model (GMM) based parametric framework. In the AbS approach, the reduction of quantization complexity is achieved through the use of nearest neighbor (NN) TrSVQs and splitting the transform domain vector into higher number of subvectors. Compared to the current LSF quantization methods, the new method is shown to provide competitive or better trade-off between R/D performance and complexity.

Differentiation of Indian, East Timorese, Papuan and Floridian Candidatus Liberibacter asiaticus' isolates on the basis of simple sequence repeat and single nucleotide polymorphism profiles at 25 loci

Japanese isolates of Candidatus Liberibacter asiaticus have been shown to be clearly differentiated by simple sequence repeat (SSR) profiles at four loci. In this study, 25 SSR loci, including these four loci, were selected from the whole-genome sequence and were used to differentiate non-Japanese samples of Ca. Liberibacter asiaticus (13 Indian, 3 East Timorese, 1 Papuan and 8 Floridian samples). Out of the 25 SSR loci, 13 were polymorphic. Dendrogram analysis using SSR loci showed that the clusters were mostly consistent with the geographical origins of the isolates. When single nucleotide polymorphisms (SNPs) were searched around these 25 loci, only the upstream region of locus 091 exhibited polymorphism. Phylogenetic tree analysis of the SNPs in the upstream region of locus 091 showed that Floridian samples were clustered into one group as shown by dendrogram analysis using SSR loci. The differences in nucleotide sequences were not associated with differences in the citrus hosts (lime, mandarin, lemon and sour orange) from which the isolates were originally derived.

Components of productivity growth in Finnish agriculture

The objective was to measure productivity growth and its components in Finnish agriculture, especially in dairy farming. The objective was also to compare different methods and models - both parametric (stochastic frontier analysis) and non-parametric (data envelopment analysis) - in estimating the components of productivity growth and the sensitivity of results with respect to different approaches. The parametric approach was also applied in the investigation of various aspects of heterogeneity. A common feature of the first three of five articles is that they concentrate empirically on technical change, technical efficiency change and the scale effect, mainly on the basis of the decompositions of Malmquist productivity index. The last two articles explore an intermediate route between the Fisher and Malmquist productivity indices and develop a detailed but meaningful decomposition for the Fisher index, including also empirical applications. Distance functions play a central role in the decomposition of Malmquist and Fisher productivity indices. Three panel data sets from 1990s have been applied in the study. The common feature of all data used is that they cover the periods before and after Finnish EU accession. Another common feature is that the analysis mainly concentrates on dairy farms or their roughage production systems. Productivity growth on Finnish dairy farms was relatively slow in the 1990s: approximately one percent per year, independent of the method used. Despite considerable annual variation, productivity growth seems to have accelerated towards the end of the period. There was a slowdown in the mid-1990s at the time of EU accession. No clear immediate effects of EU accession with respect to technical efficiency could be observed. Technical change has been the main contributor to productivity growth on dairy farms. However, average technical efficiency often showed a declining trend, meaning that the deviations from the best practice frontier are increasing over time. This suggests different paths of adjustment at the farm level. However, different methods to some extent provide different results, especially for the sub-components of productivity growth. In most analyses on dairy farms the scale effect on productivity growth was minor. A positive scale effect would be important for improving the competitiveness of Finnish agriculture through increasing farm size. This small effect may also be related to the structure of agriculture and to the allocation of investments to specific groups of farms during the research period. The result may also indicate that the utilization of scale economies faces special constraints in Finnish conditions. However, the analysis of a sample of all types of farms suggested a more considerable scale effect than the analysis on dairy farms.

Complete cytogenetic response and major molecular response as surrogate outcomes for overall survival in first-line treatment of chronic myelogenous leukemia: A case study for technology appraisal on the basis of surrogate outcomes evidence

Objectives In 2012, the National Institute for Health and Care Excellence assessed dasatinib, nilotinib, and standard-dose imatinib as first-line treatment of chronic phase chronic myelogenous leukemia (CML). Licensing of these alternative treatments was based on randomized controlled trials assessing complete cytogenetic response (CCyR) and major molecular response (MMR) at 12 months as primary end points. We use this case study to illustrate the validation of CCyR and MMR as surrogate outcomes for overall survival in CML and how this evidence was used to inform National Institute for Health and Care Excellence’s recommendation on the public funding of these first-line treatments for CML. Methods We undertook a systematic review and meta-analysis to quantify the association between CCyR and MMR at 12 months and overall survival in patients with chronic phase CML. We estimated life expectancy by extrapolating long-term survival from the weighted overall survival stratified according to the achievement of CCyR and MMR. Results Five studies provided data on the observational association between CCyR or MMR and overall survival. Based on the pooled association between CCyR and MMR and overall survival, our modeling showed comparable predicted mean duration of survival (21–23 years) following first-line treatment with imatinib, dasatinib, or nilotinib. Conclusions This case study illustrates the consideration of surrogate outcome evidence in health technology assessment. Although it is often recommended that the acceptance of surrogate outcomes be based on randomized controlled trial data demonstrating an association between the treatment effect on both the surrogate outcome and the final outcome, this case study shows that policymakers may be willing to accept a lower level of evidence (i.e., observational association).

Boundedness and convergence of singular integrals on fractal type sets

The topic of this dissertation lies in the intersection of harmonic analysis and fractal geometry. We particulary consider singular integrals in Euclidean spaces with respect to general measures, and we study how the geometric structure of the measures affects certain analytic properties of the operators. The thesis consists of three research articles and an overview. In the first article we construct singular integral operators on lower dimensional Sierpinski gaskets associated with homogeneous Calderón-Zygmund kernels. While these operators are bounded their principal values fail to exist almost everywhere. Conformal iterated function systems generate a broad range of fractal sets. In the second article we prove that many of these limit sets are porous in a very strong sense, by showing that they contain holes spread in every direction. In the following we connect these results with singular integrals. We exploit the fractal structure of these limit sets, in order to establish that singular integrals associated with very general kernels converge weakly. Boundedness questions consist a central topic of investigation in the theory of singular integrals. In the third article we study singular integrals of different measures. We prove a very general boundedness result in the case where the two underlying measures are separated by a Lipshitz graph. As a consequence we show that a certain weak convergence holds for a large class of singular integrals.

Discovering hidden structures in molecular data using a Bayesian partition model approach

Advancements in the analysis techniques have led to a rapid accumulation of biological data in databases. Such data often are in the form of sequences of observations, examples including DNA sequences and amino acid sequences of proteins. The scale and quality of the data give promises of answering various biologically relevant questions in more detail than what has been possible before. For example, one may wish to identify areas in an amino acid sequence, which are important for the function of the corresponding protein, or investigate how characteristics on the level of DNA sequence affect the adaptation of a bacterial species to its environment. Many of the interesting questions are intimately associated with the understanding of the evolutionary relationships among the items under consideration. The aim of this work is to develop novel statistical models and computational techniques to meet with the challenge of deriving meaning from the increasing amounts of data. Our main concern is on modeling the evolutionary relationships based on the observed molecular data. We operate within a Bayesian statistical framework, which allows a probabilistic quantification of the uncertainties related to a particular solution. As the basis of our modeling approach we utilize a partition model, which is used to describe the structure of data by appropriately dividing the data items into clusters of related items. Generalizations and modifications of the partition model are developed and applied to various problems. Large-scale data sets provide also a computational challenge. The models used to describe the data must be realistic enough to capture the essential features of the current modeling task but, at the same time, simple enough to make it possible to carry out the inference in practice. The partition model fulfills these two requirements. The problem-specific features can be taken into account by modifying the prior probability distributions of the model parameters. The computational efficiency stems from the ability to integrate out the parameters of the partition model analytically, which enables the use of efficient stochastic search algorithms.