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machine learning and snp-snp

Sangkyu Lee, Sarah Kerns, Harry Ostrer, Barry Rosenstein, Joseph O Deasy, Jung Hun Oh
PURPOSE: Late genitourinary (GU) toxicity after radiation therapy limits the quality of life of prostate cancer survivors; however, efforts to explain GU toxicity using patient and dose information have remained unsuccessful. We identified patients with a greater congenital GU toxicity risk by identifying and integrating patterns in genome-wide single nucleotide polymorphisms (SNPs). METHODS AND MATERIALS: We applied a preconditioned random forest regression method for predicting risk from the genome-wide data to combine the effects of multiple SNPs and overcome the statistical power limitations of single-SNP analysis...
January 31, 2018: International Journal of Radiation Oncology, Biology, Physics
K Heer, D Behringer, A Piermattei, C Bässler, R Brandl, B Fady, H Jehl, S Liepelt, S Lorch, A Piotti, G G Vendramin, M Weller, B Ziegenhagen, U Büntgen, L Opgenoorth
Genetic association studies in forest trees would greatly benefit from information on the response of trees to environmental stressors over time, which can be provided by dendroecological analysis. Here, we jointly analyzed dendroecological and genetic data of surviving silver fir trees to explore the genetic basis of their response to the iconic stress episode of the 1970s and 80s that led to large-scale forest dieback in Central Europe and has been attributed to air pollution. Specifically, we derived dendrophenotypic measures from 190 trees in the Bavarian Forest that characterize the resistance, resilience and recovery during this growth depression, and in the drought year in 1976...
February 14, 2018: Molecular Ecology
Emma V A Sylvester, Paul Bentzen, Ian R Bradbury, Marie Clément, Jon Pearce, John Horne, Robert G Beiko
Genetic population assignment used to inform wildlife management and conservation efforts requires panels of highly informative genetic markers and sensitive assignment tests. We explored the utility of machine-learning algorithms (random forest, regularized random forest and guided regularized random forest) compared with FST ranking for selection of single nucleotide polymorphisms (SNP) for fine-scale population assignment. We applied these methods to an unpublished SNP data set for Atlantic salmon (Salmo salar) and a published SNP data set for Alaskan Chinook salmon (Oncorhynchus tshawytscha)...
February 2018: Evolutionary Applications
A Jacobs, M De Noia, K Praebel, Ø Kanstad-Hanssen, M Paterno, D Jackson, P McGinnity, A Sturm, K R Elmer, M S Llewellyn
Caligid sea lice represent a significant threat to salmonid aquaculture worldwide. Population genetic analyses have consistently shown minimal population genetic structure in North Atlantic Lepeophtheirus salmonis, frustrating efforts to track louse populations and improve targeted control measures. The aim of this study was to test the power of reduced representation library sequencing (IIb-RAD sequencing) coupled with random forest machine learning algorithms to define markers for fine-scale discrimination of louse populations...
January 19, 2018: Scientific Reports
Michelle L Krishnan, Zi Wang, Paul Aljabar, Gareth Ball, Ghazala Mirza, Alka Saxena, Serena J Counsell, Joseph V Hajnal, Giovanni Montana, A David Edwards
Preterm infants show abnormal structural and functional brain development, and have a high risk of long-term neurocognitive problems. The molecular and cellular mechanisms involved are poorly understood, but novel methods now make it possible to address them by examining the relationship between common genetic variability and brain endophenotype. We addressed the hypothesis that variability in the Peroxisome Proliferator Activated Receptor (PPAR) pathway would be related to brain development. We employed machine learning in an unsupervised, unbiased, combined analysis of whole-brain diffusion tractography together with genomewide, single-nucleotide polymorphism (SNP)-based genotypes from a cohort of 272 preterm infants, using Sparse Reduced Rank Regression (sRRR) and correcting for ethnicity and age at birth and imaging...
December 26, 2017: Proceedings of the National Academy of Sciences of the United States of America
Raquel E Reinbolt, Stephen Sonis, Cynthia D Timmers, Juan Luis Fernández-Martínez, Ana Cernea, Enrique J de Andrés-Galiana, Sepehr Hashemi, Karin Miller, Robert Pilarski, Maryam B Lustberg
Many breast cancer (BC) patients treated with aromatase inhibitors (AIs) develop aromatase inhibitor-related arthralgia (AIA). Candidate gene studies to identify AIA risk are limited in scope. We evaluated the potential of a novel analytic algorithm (NAA) to predict AIA using germline single nucleotide polymorphisms (SNP) data obtained before treatment initiation. Systematic chart review of 700 AI-treated patients with stage I-III BC identified asymptomatic patients (n = 39) and those with clinically significant AIA resulting in AI termination or therapy switch (n = 123)...
November 23, 2017: Cancer Medicine
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
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
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
Moritz Hess, Stefan Lenz, Tamara J Blätte, 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
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
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
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
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
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
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
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
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
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
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
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