A fast algorithm for genome-wide haplotype pattern mining

<p>Abstract</p> <p>Background</p> <p>Identifying the genetic components of common diseases has long been an important area of research. Recently, genotyping technology has reached the level where it is cost effective to genotype single nucleotide polymorphism (SNP) markers covering the entire genome, in thousands of individuals, and analyse such data for markers associated with a diseases. The statistical power to detect association, however, is limited when markers are analysed one at a time. This can be alleviated by considering multiple markers simultaneously. The <it>Haplotype Pattern Mining </it>(HPM) method is a machine learning approach to do exactly this.</p> <p>Results</p> <p>We present a new, faster algorithm for the HPM method. The new approach use patterns of haplotype diversity in the genome: locally in the genome, the number of observed haplotypes is much smaller than the total number of possible haplotypes. We show that the new approach speeds up the HPM method with a factor of 2 on a genome-wide dataset with 5009 individuals typed in 491208 markers using default parameters and more if the pattern length is increased.</p> <p>Conclusion</p> <p>The new algorithm speeds up the HPM method and we show that it is feasible to apply HPM to whole genome association mapping with thousands of individuals and hundreds of thousands of markers.</p

Haplotype-based quantitative trait mapping using a clustering algorithm

BACKGROUND: With the availability of large-scale, high-density single-nucleotide polymorphism (SNP) markers, substantial effort has been made in identifying disease-causing genes using linkage disequilibrium (LD) mapping by haplotype analysis of unrelated individuals. In addition to complex diseases, many continuously distributed quantitative traits are of primary clinical and health significance. However the development of association mapping methods using unrelated individuals for quantitative traits has received relatively less attention. RESULTS: We recently developed an association mapping method for complex diseases by mining the sharing of haplotype segments (i.e., phased genotype pairs) in affected individuals that are rarely present in normal individuals. In this paper, we extend our previous work to address the problem of quantitative trait mapping from unrelated individuals. The method is non-parametric in nature, and statistical significance can be obtained by a permutation test. It can also be incorporated into the one-way ANCOVA (analysis of covariance) framework so that other factors and covariates can be easily incorporated. The effectiveness of the approach is demonstrated by extensive experimental studies using both simulated and real data sets. The results show that our haplotype-based approach is more robust than two statistical methods based on single markers: a single SNP association test (SSA) and the Mann-Whitney U-test (MWU). The algorithm has been incorporated into our existing software package called HapMiner, which is available from our website at . CONCLUSION: For QTL (quantitative trait loci) fine mapping, to identify QTNs (quantitative trait nucleotides) with realistic effects (the contribution of each QTN less than 10% of total variance of the trait), large samples sizes (≥ 500) are needed for all the methods. The overall performance of HapMiner is better than that of the other two methods. Its effectiveness further depends on other factors such as recombination rates and the density of typed SNPs. Haplotype-based methods might provide higher power than methods based on a single SNP when using tag SNPs selected from a small number of samples or some other sources (such as HapMap data). Rank-based statistics usually have much lower power, as shown in our study

Whole genome association mapping by incompatibilities and local perfect phylogenies

BACKGROUND: With current technology, vast amounts of data can be cheaply and efficiently produced in association studies, and to prevent data analysis to become the bottleneck of studies, fast and efficient analysis methods that scale to such data set sizes must be developed. RESULTS: We present a fast method for accurate localisation of disease causing variants in high density case-control association mapping experiments with large numbers of cases and controls. The method searches for significant clustering of case chromosomes in the "perfect" phylogenetic tree defined by the largest region around each marker that is compatible with a single phylogenetic tree. This perfect phylogenetic tree is treated as a decision tree for determining disease status, and scored by its accuracy as a decision tree. The rationale for this is that the perfect phylogeny near a disease affecting mutation should provide more information about the affected/unaffected classification than random trees. If regions of compatibility contain few markers, due to e.g. large marker spacing, the algorithm can allow the inclusion of incompatibility markers in order to enlarge the regions prior to estimating their phylogeny. Haplotype data and phased genotype data can be analysed. The power and efficiency of the method is investigated on 1) simulated genotype data under different models of disease determination 2) artificial data sets created from the HapMap ressource, and 3) data sets used for testing of other methods in order to compare with these. Our method has the same accuracy as single marker association (SMA) in the simplest case of a single disease causing mutation and a constant recombination rate. However, when it comes to more complex scenarios of mutation heterogeneity and more complex haplotype structure such as found in the HapMap data our method outperforms SMA as well as other fast, data mining approaches such as HapMiner and Haplotype Pattern Mining (HPM) despite being significantly faster. For unphased genotype data, an initial step of estimating the phase only slightly decreases the power of the method. The method was also found to accurately localise the known susceptibility variants in an empirical data set – the ΔF508 mutation for cystic fibrosis – where the susceptibility variant is already known – and to find significant signals for association between the CYP2D6 gene and poor drug metabolism, although for this dataset the highest association score is about 60 kb from the CYP2D6 gene. CONCLUSION: Our method has been implemented in the Blossoc (BLOck aSSOCiation) software. Using Blossoc, genome wide chip-based surveys of 3 million SNPs in 1000 cases and 1000 controls can be analysed in less than two CPU hours

Gene Expression in a Drosophila Model of Mitochondrial Disease

Background A point mutation in the Drosophila gene technical knockout (tko), encoding mitoribosomal protein S12, was previously shown to cause a phenotype of respiratory chain deficiency, developmental delay, and neurological abnormalities similar to those presented in many human mitochondrial disorders, as well as defective courtship behavior. Methodology/Principal Findings Here, we describe a transcriptome-wide analysis of gene expression in tko25t mutant flies that revealed systematic and compensatory changes in the expression of genes connected with metabolism, including up-regulation of lactate dehydrogenase and of many genes involved in the catabolism of fats and proteins, and various anaplerotic pathways. Gut-specific enzymes involved in the primary mobilization of dietary fats and proteins, as well as a number of transport functions, were also strongly up-regulated, consistent with the idea that oxidative phosphorylation OXPHOS dysfunction is perceived physiologically as a starvation for particular biomolecules. In addition, many stress-response genes were induced. Other changes may reflect a signature of developmental delay, notably a down-regulation of genes connected with reproduction, including gametogenesis, as well as courtship behavior in males; logically this represents a programmed response to a mitochondrially generated starvation signal. The underlying signalling pathway, if conserved, could influence many physiological processes in response to nutritional stress, although any such pathway involved remains unidentified. Conclusions/Significance These studies indicate that general and organ-specific metabolism is transformed in response to mitochondrial dysfunction, including digestive and absorptive functions, and give important clues as to how novel therapeutic strategies for mitochondrial disorders might be developed.Public Library of Scienc

The genetics of chronic obstructive pulmonary disease

Chronic obstructive pulmonary disease (COPD) is a heterogeneous disease caused by the interaction of genetic susceptibility and environmental influences. There is increasing evidence that genes link to disease pathogenesis and heterogeneity by causing variation in protease anti-protease systems, defence against oxidative stress and inflammation. The main methods of genomic research for complex disease traits are described, together with the genes implicated in COPD thus far, their roles in disease causation and the future for this area of investigation

Guidelines for the use and interpretation of assays for monitoring autophagy (4th edition)1.

In 2008, we published the first set of guidelines for standardizing research in autophagy. Since then, this topic has received increasing attention, and many scientists have entered the field. Our knowledge base and relevant new technologies have also been expanding. Thus, it is important to formulate on a regular basis updated guidelines for monitoring autophagy in different organisms. Despite numerous reviews, there continues to be confusion regarding acceptable methods to evaluate autophagy, especially in multicellular eukaryotes. Here, we present a set of guidelines for investigators to select and interpret methods to examine autophagy and related processes, and for reviewers to provide realistic and reasonable critiques of reports that are focused on these processes. These guidelines are not meant to be a dogmatic set of rules, because the appropriateness of any assay largely depends on the question being asked and the system being used. Moreover, no individual assay is perfect for every situation, calling for the use of multiple techniques to properly monitor autophagy in each experimental setting. Finally, several core components of the autophagy machinery have been implicated in distinct autophagic processes (canonical and noncanonical autophagy), implying that genetic approaches to block autophagy should rely on targeting two or more autophagy-related genes that ideally participate in distinct steps of the pathway. Along similar lines, because multiple proteins involved in autophagy also regulate other cellular pathways including apoptosis, not all of them can be used as a specific marker for bona fide autophagic responses. Here, we critically discuss current methods of assessing autophagy and the information they can, or cannot, provide. Our ultimate goal is to encourage intellectual and technical innovation in the field

Expression of the gene for mitoribosomal protein S12 is controlled in human cells at the levels of transcription, RNA splicing, and translation

The human gene RPMS12 encodes a protein similar to bacterial ribosomal protein S12 and is proposed to represent the human mitochondrial orthologue, RPMS12 reporter gene expression in cultured human cells supports the idea that the gene product is mitochondrial and is localized to the inner membrane. Human cells contain at least four structurally distinct RPMS12 mRNAs that differ in their 5'-untranslated region (5'-UTR) as a result of alternate splicing and of 5' end heterogeneity. All of them encode the same polypeptide, The full 5'-UTR contains two types of sequence element implicated elsewhere in translational regulation as follows: a short upstream open reading frame and an oligopyrimidine tract similar to that found at the 5' end of mRNAs encoding other growth-regulated proteins, including those of cytosolic ribosomes, The fully spliced (short) mRNA is the predominant form in all cell types studied and is translationally down-regulated in cultured cells in response to serum starvation, even though it lacks both of the putative translational regulatory elements. By contrast, other splice variants containing one or both of these elements are not translationally regulated by growth status but are translated poorly in both growing and non-growing cells. Reporter analysis identified a 26-nucleotide tract of the 5'-UTR of the short mRNA that is essential for translational down-regulation in growth-inhibited cells. Such experiments also confirmed that the 5'-UTR of the longer mRNA variants contains negative regulatory elements for translation. Tissue representation of RPMS12 mRNA is highly variable, following a typical mitochondrial pattern, but the relative levels of the different splice variants are similar in different tissues. These findings indicate a complex, multilevel regulation of RPMS12 gene expression in response to signals mediating growth, tissue specialization, and probably metabolic needs.status: publishe

Expression of the gene for mitoribosomal protein S12 is controlled in human cells at the levels of transcription, RNA splicing, and translation

The human gene RPMS12 encodes a protein similar to bacterial ribosomal protein S12 and is proposed to represent the human mitochondrial orthologue, RPMS12 reporter gene expression in cultured human cells supports the idea that the gene product is mitochondrial and is localized to the inner membrane. Human cells contain at least four structurally distinct RPMS12 mRNAs that differ in their 5'-untranslated region (5'-UTR) as a result of alternate splicing and of 5' end heterogeneity. All of them encode the same polypeptide, The full 5'-UTR contains two types of sequence element implicated elsewhere in translational regulation as follows: a short upstream open reading frame and an oligopyrimidine tract similar to that found at the 5' end of mRNAs encoding other growth-regulated proteins, including those of cytosolic ribosomes, The fully spliced (short) mRNA is the predominant form in all cell types studied and is translationally down-regulated in cultured cells in response to serum starvation, even though it lacks both of the putative translational regulatory elements. By contrast, other splice variants containing one or both of these elements are not translationally regulated by growth status but are translated poorly in both growing and non-growing cells. Reporter analysis identified a 26-nucleotide tract of the 5'-UTR of the short mRNA that is essential for translational down-regulation in growth-inhibited cells. Such experiments also confirmed that the 5'-UTR of the longer mRNA variants contains negative regulatory elements for translation. Tissue representation of RPMS12 mRNA is highly variable, following a typical mitochondrial pattern, but the relative levels of the different splice variants are similar in different tissues. These findings indicate a complex, multilevel regulation of RPMS12 gene expression in response to signals mediating growth, tissue specialization, and probably metabolic needs

Time-regularized and periodic event-triggered control for linear systems

\u3cp\u3eIn this chapter, we provide an overview of our recent results for the analysis and design of Event-Triggered controllers that are tailored to linear systems as provided in Heemels et al., IEEE Trans Autom Control 58(4):847–861, 2013, Heemels et al., IEEE Trans Autom Control 61(10):2766–2781, 2016, Borgers et al., IEEE Trans Autom Control, 2018. In particular, we discuss two different frameworks for the stability and contractivity analysis and design of (static) periodic Event-Triggered control (PETC) and time-regularized continuous Event-Triggered control (CETC) systems: the lifting-based framework of Heemels et al., IEEE Trans Autom Control 61(10):2766–2781, 2016, which applies to PETC systems, and the Riccati-based framework of Heemels et al., IEEE Trans Autom Control 58(4):847–861, 2013, Borgers et al., IEEE Trans Autom Control (2018), which applies to both PETC systems and time-regularized CETC systems. Moreover, we identify the connections and differences between the two frameworks. Finally, for PETC and time-regularized CETC systems, we show how the Riccati-based analysis leads to new designs for dynamic Event-Triggered controllers, which (for identical stability and contractivity guarantees) lead to a significantly reduced consumption of communication and energy resources compared to their static counterparts.\u3c/p\u3