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machine learning and snp-snp

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https://www.readbyqxmd.com/read/29065906/biological-function-integrated-prediction-of-severe-radiographic-progression-in-rheumatoid-arthritis-a-nested-case-control-study
#1
Young Bin Joo, Yul Kim, Youngho Park, Kwangwoo Kim, Jeong Ah Ryu, Seunghun Lee, So-Young Bang, Hye-Soon Lee, Gwan-Su Yi, Sang-Cheol Bae
BACKGROUND: Radiographic progression is reported to be highly heritable in rheumatoid arthritis (RA). However, previous study using genetic loci showed an insufficient accuracy of prediction for radiographic progression. The aim of this study is to identify a biologically relevant prediction model of radiographic progression in patients with RA using a genome-wide association study (GWAS) combined with bioinformatics analysis. METHODS: We obtained genome-wide single nucleotide polymorphism (SNP) data for 374 Korean patients with RA using Illumina HumanOmni2...
October 25, 2017: Arthritis Research & Therapy
https://www.readbyqxmd.com/read/28878789/effect-of-co-segregating-markers-on-high-density-genetic-maps-and-prediction-of-map-expansion-using-machine-learning-algorithms
#2
Amidou N'Diaye, Jemanesh K Haile, D Brian Fowler, Karim Ammar, Curtis J Pozniak
Advances in sequencing and genotyping methods have enable cost-effective production of high throughput single nucleotide polymorphism (SNP) markers, making them the choice for linkage mapping. As a result, many laboratories have developed high-throughput SNP assays and built high-density genetic maps. However, the number of markers may, by orders of magnitude, exceed the resolution of recombination for a given population size so that only a minority of markers can accurately be ordered. Another issue attached to the so-called 'large p, small n' problem is that high-density genetic maps inevitably result in many markers clustering at the same position (co-segregating markers)...
2017: Frontiers in Plant Science
https://www.readbyqxmd.com/read/28678713/genome-wide-analysis-of-mdr-and-xdr-tuberculosis-from-belarus-machine-learning-approach
#3
Roman Sergeevich Sergeev, Ivan Kavaliou, Uladzislau Sataneuski, Andrei Gabrielian, Alex Rosenthal, Michael Tartakovsky, Alexander Tuzikov
Emergence of drug-resistant microorganisms has been recognized as a serious threat to public health worldwide. This problem is extensively discussed in the context of tuberculosis treatment. Alterations in pathogen genomes are among the main mechanisms by which microorganisms exhibit drug resistance. Analysis of 144 M. tuberculosis strains of different phenotypes including drug susceptible, MDR and XDR isolated in Belarus was fulfilled in this paper. A wide range of machine learning methods that can discover SNPs related to drug-resistance in the whole bacteria genomes was investigated...
June 27, 2017: IEEE/ACM Transactions on Computational Biology and Bioinformatics
https://www.readbyqxmd.com/read/28655145/partitioned-learning-of-deep-boltzmann-machines-for-snp-data
#4
Moritz Hess, Stefan Lenz, Tamara J Bl├Ątte, Lars Bullinger, Harald Binder
Motivation: Learning the joint distributions of measurements, and in particular identification of an appropriate low-dimensional manifold, has been found to be a powerful ingredient of deep leaning approaches. Yet, such approaches have hardly been applied to single nucleotide polymorphism (SNP) data, probably due to the high number of features typically exceeding the number of studied individuals. Results: After a brief overview of how deep Boltzmann machines (DBMs), a deep learning approach, can be adapted to SNP data in principle, we specifically present a way to alleviate the dimensionality problem by partitioned learning...
June 26, 2017: Bioinformatics
https://www.readbyqxmd.com/read/28651561/the-effect-of-mislabeled-phenotypic-status-on-the-identification-of-mutation-carriers-from-snp-genotypes-in-dairy-cattle
#5
Stefano Biffani, Hubert Pausch, Hermann Schwarzenbacher, Filippo Biscarini
BACKGROUND: Statistical and machine learning applications are increasingly popular in animal breeding and genetics, especially to compute genomic predictions for phenotypes of interest. Noise (errors) in the data may have a negative impact on the accuracy of predictions. The effects of noisy data have been investigated in genome-wide association studies for case-control experiments, and in genomic predictions for binary traits in plants. No studies have been published yet on the impact of noisy data in animal genomics...
June 26, 2017: BMC Research Notes
https://www.readbyqxmd.com/read/28604798/a-bayesian-mathematical-model-of-motor-and-cognitive-outcomes-in-parkinson-s-disease
#6
Boris Hayete, Diane Wuest, Jason Laramie, Paul McDonagh, Bruce Church, Shirley Eberly, Anthony Lang, Kenneth Marek, Karl Runge, Ira Shoulson, Andrew Singleton, Caroline Tanner, Iya Khalil, Ajay Verma, Bernard Ravina
BACKGROUND: There are few established predictors of the clinical course of PD. Prognostic markers would be useful for clinical care and research. OBJECTIVE: To identify predictors of long-term motor and cognitive outcomes and rate of progression in PD. METHODS: Newly diagnosed PD participants were followed for 7 years in a prospective study, conducted at 55 centers in the United States and Canada. Analyses were conducted in 244 participants with complete demographic, clinical, genetic, and dopamine transporter imaging data...
2017: PloS One
https://www.readbyqxmd.com/read/28572842/grid-based-stochastic-search-for-hierarchical-gene-gene-interactions-in-population-based-genetic-studies-of-common-human-diseases
#7
Jason H Moore, Peter C Andrews, Randal S Olson, Sarah E Carlson, Curt R Larock, Mario J Bulhoes, James P O'Connor, Ellen M Greytak, Steven L Armentrout
BACKGROUND: Large-scale genetic studies of common human diseases have focused almost exclusively on the independent main effects of single-nucleotide polymorphisms (SNPs) on disease susceptibility. These studies have had some success, but much of the genetic architecture of common disease remains unexplained. Attention is now turning to detecting SNPs that impact disease susceptibility in the context of other genetic factors and environmental exposures. These context-dependent genetic effects can manifest themselves as non-additive interactions, which are more challenging to model using parametric statistical approaches...
2017: BioData Mining
https://www.readbyqxmd.com/read/28512778/cagi4-crohn-s-exome-challenge-marker-snp-versus-exome-variant-models-for-assigning-risk-of-crohn-disease
#8
Lipika R Pal, Kunal Kundu, Yizhou Yin, John Moult
Understanding the basis of complex trait disease is a fundamental problem in human genetics. The CAGI Crohn's Exome challenges are providing insight into the adequacy of current disease models by requiring participants to identify which of a set of individuals has been diagnosed with the disease, given exome data. For the CAGI4 round, we developed a method that used the genotypes from exome sequencing data only to impute the status of genome wide association studies marker SNPs. We then used the imputed genotypes as input to several machine learning methods that had been trained to predict disease status from marker SNP information...
September 2017: Human Mutation
https://www.readbyqxmd.com/read/28358032/a-hierarchical-feature-and-sample-selection-framework-and-its-application-for-alzheimer-s-disease-diagnosis
#9
Le An, Ehsan Adeli, Mingxia Liu, Jun Zhang, Seong-Whan Lee, Dinggang Shen
Classification is one of the most important tasks in machine learning. Due to feature redundancy or outliers in samples, using all available data for training a classifier may be suboptimal. For example, the Alzheimer's disease (AD) is correlated with certain brain regions or single nucleotide polymorphisms (SNPs), and identification of relevant features is critical for computer-aided diagnosis. Many existing methods first select features from structural magnetic resonance imaging (MRI) or SNPs and then use those features to build the classifier...
March 30, 2017: Scientific Reports
https://www.readbyqxmd.com/read/28060710/a-review-of-machine-learning-and-statistical-approaches-for-detecting-snp-interactions-in-high-dimensional-genomic-data
#10
Suneetha Uppu, Aneesh Krishna, Raj Gopalan
In this era of genome-wide association studies (GWAS), the quest for understanding the genetic architecture of complex diseases is rapidly increasing more than ever before. The development of high throughput genotyping and next generation sequencing technologies enables genetic epidemiological analysis of large scale data. These advances have led to the identification of a number of single nucleotide polymorphisms (SNPs) responsible for disease susceptibility. The interactions between SNPs associated with complex diseases are increasingly being explored in the current literature...
December 2, 2016: IEEE/ACM Transactions on Computational Biology and Bioinformatics
https://www.readbyqxmd.com/read/28046236/su-d-204-06-integration-of-machine-learning-and-bioinformatics-methods-to-analyze-genome-wide-association-study-data-for-rectal-bleeding-and-erectile-dysfunction-following-radiotherapy-in-prostate-cancer
#11
J Oh, S Kerns, H Ostrer, B Rosenstein, J Deasy
PURPOSE: We investigated whether integration of machine learning and bioinformatics techniques on genome-wide association study (GWAS) data can improve the performance of predictive models in predicting the risk of developing radiation-induced late rectal bleeding and erectile dysfunction in prostate cancer patients. METHODS: We analyzed a GWAS dataset generated from 385 prostate cancer patients treated with radiotherapy. Using genotype information from these patients, we designed a machine learning-based predictive model of late radiation-induced toxicities: rectal bleeding and erectile dysfunction...
June 2016: Medical Physics
https://www.readbyqxmd.com/read/27999618/complex-systems-analysis-of-bladder-cancer-susceptibility-reveals-a-role-for-decarboxylase-activity-in-two-genome-wide-association-studies
#12
Samantha Cheng, Angeline S Andrew, Peter C Andrews, Jason H Moore
BACKGROUND: Bladder cancer is common disease with a complex etiology that is likely due to many different genetic and environmental factors. The goal of this study was to embrace this complexity using a bioinformatics analysis pipeline designed to use machine learning to measure synergistic interactions between single nucleotide polymorphisms (SNPs) in two genome-wide association studies (GWAS) and then to assess their enrichment within functional groups defined by Gene Ontology. The significance of the results was evaluated using permutation testing and those results that replicated between the two GWAS data sets were reported...
2016: BioData Mining
https://www.readbyqxmd.com/read/27925593/a-review-of-machine-learning-and-statistical-approaches-for-detecting-snp-interactions-in-high-dimensional-genomic-data
#13
Suneetha Uppu, Aneesh Krishna, Raj Gopalan
In this era of genome-wide association studies (GWAS), the quest for understanding the genetic architecture of complex diseases is rapidly increasing more than ever before. The development of high throughput genotyping and next generation sequencing technologies enables genetic epidemiological analysis of large scale data. These advances have led to the identification of a number of single nucleotide polymorphisms (SNPs) responsible for disease susceptibility. The interactions between SNPs associated with complex diseases are increasingly being explored in the current literature...
December 2, 2016: IEEE/ACM Transactions on Computational Biology and Bioinformatics
https://www.readbyqxmd.com/read/27798253/oxytocin-receptor-gene-variations-predict-neural-and-behavioral-response-to-oxytocin-in-autism
#14
Takamitsu Watanabe, Takeshi Otowa, Osamu Abe, Hitoshi Kuwabara, Yuta Aoki, Tatsunobu Natsubori, Hidemasa Takao, Chihiro Kakiuchi, Kenji Kondo, Masashi Ikeda, Nakao Iwata, Kiyoto Kasai, Tsukasa Sasaki, Hidenori Yamasue
Oxytocin appears beneficial for autism spectrum disorder (ASD), and more than 20 single-nucleotide polymorphisms (SNPs) in oxytocin receptor (OXTR) are relevant to ASD. However, neither biological functions of OXTR SNPs in ASD nor critical OXTR SNPs that determine oxytocin's effects on ASD remains known. Here, using a machine-learning algorithm that was designed to evaluate collective effects of multiple SNPs and automatically identify most informative SNPs, we examined relationships between 27 representative OXTR SNPs and six types of behavioral/neural response to oxytocin in ASD individuals...
March 1, 2017: Social Cognitive and Affective Neuroscience
https://www.readbyqxmd.com/read/27539266/use-of-a-novel-nonparametric-version-of-depth-to-identify-genomic-regions-associated-with-prostate-cancer-risk
#15
Robert J MacInnis, Daniel F Schmidt, Enes Makalic, Gianluca Severi, Liesel M FitzGerald, Matthias Reumann, Miroslaw K Kapuscinski, Adam Kowalczyk, Zeyu Zhou, Benjamin Goudey, Guoqi Qian, Quang M Bui, Daniel J Park, Adam Freeman, Melissa C Southey, Ali Amin Al Olama, Zsofia Kote-Jarai, Rosalind A Eeles, John L Hopper, Graham G Giles
BACKGROUND: We have developed a genome-wide association study analysis method called DEPTH (DEPendency of association on the number of Top Hits) to identify genomic regions potentially associated with disease by considering overlapping groups of contiguous markers (e.g., SNPs) across the genome. DEPTH is a machine learning algorithm for feature ranking of ultra-high dimensional datasets, built from well-established statistical tools such as bootstrapping, penalized regression, and decision trees...
December 2016: Cancer Epidemiology, Biomarkers & Prevention
https://www.readbyqxmd.com/read/27488097/prioritizing-individual-genetic-variants-after-kernel-machine-testing-using-variable-selection
#16
Qianchuan He, Tianxi Cai, Yang Liu, Ni Zhao, Quaker E Harmon, Lynn M Almli, Elisabeth B Binder, Stephanie M Engel, Kerry J Ressler, Karen N Conneely, Xihong Lin, Michael C Wu
Kernel machine learning methods, such as the SNP-set kernel association test (SKAT), have been widely used to test associations between traits and genetic polymorphisms. In contrast to traditional single-SNP analysis methods, these methods are designed to examine the joint effect of a set of related SNPs (such as a group of SNPs within a gene or a pathway) and are able to identify sets of SNPs that are associated with the trait of interest. However, as with many multi-SNP testing approaches, kernel machine testing can draw conclusion only at the SNP-set level, and does not directly inform on which one(s) of the identified SNP set is actually driving the associations...
December 2016: Genetic Epidemiology
https://www.readbyqxmd.com/read/27322251/genome-wide-discriminatory-information-patterns-of-cytosine-dna-methylation
#17
Robersy Sanchez, Sally A Mackenzie
Cytosine DNA methylation (CDM) is a highly abundant, heritable but reversible chemical modification to the genome. Herein, a machine learning approach was applied to analyze the accumulation of epigenetic marks in methylomes of 152 ecotypes and 85 silencing mutants of Arabidopsis thaliana. In an information-thermodynamics framework, two measurements were used: (1) the amount of information gained/lost with the CDM changes I R and (2) the uncertainty of not observing a SNP L C R . We hypothesize that epigenetic marks are chromosomal footprints accounting for different ontogenetic and phylogenetic histories of individual populations...
June 17, 2016: International Journal of Molecular Sciences
https://www.readbyqxmd.com/read/27113568/use-of-systems-biology-to-decipher-host-pathogen-interaction-networks-and-predict-biomarkers
#18
REVIEW
A Dix, S Vlaic, R Guthke, J Linde
In systems biology, researchers aim to understand complex biological systems as a whole, which is often achieved by mathematical modelling and the analyses of high-throughput data. In this review, we give an overview of medical applications of systems biology approaches with special focus on host-pathogen interactions. After introducing general ideas of systems biology, we focus on (1) the detection of putative biomarkers for improved diagnosis and support of therapeutic decisions, (2) network modelling for the identification of regulatory interactions between cellular molecules to reveal putative drug targets and (3) module discovery for the detection of phenotype-specific modules in molecular interaction networks...
July 2016: Clinical Microbiology and Infection
https://www.readbyqxmd.com/read/27076664/do-staphylococcus-epidermidis-genetic-clusters-predict-isolation-sources
#19
Isaiah Tolo, Jonathan C Thomas, Rebecca S B Fischer, Eric L Brown, Barry M Gray, D Ashley Robinson
Staphylococcus epidermidis is a ubiquitous colonizer of human skin and a common cause of medical device-associated infections. The extent to which the population genetic structure of S. epidermidis distinguishes commensal from pathogenic isolates is unclear. Previously, Bayesian clustering of 437 multilocus sequence types (STs) in the international database revealed a population structure of six genetic clusters (GCs) that may reflect the species' ecology. Here, we first verified the presence of six GCs, including two (GC3 and GC5) with significant admixture, in an updated database of 578 STs...
July 2016: Journal of Clinical Microbiology
https://www.readbyqxmd.com/read/27065257/evaluation-of-developed-low-density-genotype-panels-for-imputation-to-higher-density-in-independent-dairy-and-beef-cattle-populations
#20
M M Judge, J F Kearney, M C McClure, R D Sleator, D P Berry
The objective of this study was to develop, using alternative algorithms, low-density SNP genotyping panels (384 to 12,000 SNP), which can be accurately imputed to higher-density panels across independent cattle populations. Single nucleotide polymorphisms were selected based on genomic characteristics (i.e., linkage disequilibrium [LD], minor allele frequency [MAF], and genomic distance) in a population of 1,267 Holstein-Friesian animals genotyped on the Illumina Bovine50 Beadchip (54,001 SNP). Single nucleotide polymorphism selection methods included 1) random; 2) equidistant location; 3) combination of SNP MAF and LD structure while maintaining relatively equal genomic distance between adjacent SNP; 4) a combination of high MAF, genomic distance between selected and candidate SNP, and correlation between genotypes of selected and candidate SNP; and 5) a machine learning algorithm...
March 2016: Journal of Animal Science
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