scholarly journals A NOTE ON PHASING LONG GENOMIC REGIONS USING LOCAL HAPLOTYPE PREDICTIONS

2006 ◽  
Vol 04 (03) ◽  
pp. 639-647 ◽  
Author(s):  
ELEAZAR ESKIN ◽  
RODED SHARAN ◽  
ERAN HALPERIN
Keyword(s):  
Large Scale ◽  
Computational Cost ◽  
Nucleotide Polymorphisms ◽  
Single Nucleotide ◽  
Novel Approach ◽  
Maximum Likelihood Criterion ◽  
The Common ◽  
Genomic Regions ◽  
High Computational Cost ◽  
Combining Information

The common approaches for haplotype inference from genotype data are targeted toward phasing short genomic regions. Longer regions are often tackled in a heuristic manner, due to the high computational cost. Here, we describe a novel approach for phasing genotypes over long regions, which is based on combining information from local predictions on short, overlapping regions. The phasing is done in a way, which maximizes a natural maximum likelihood criterion. Among other things, this criterion takes into account the physical length between neighboring single nucleotide polymorphisms. The approach is very efficient and is applied to several large scale datasets and is shown to be successful in two recent benchmarking studies (Zaitlen et al., in press; Marchini et al., in preparation). Our method is publicly available via a webserver at .

scholarly journals Orthogonal and Nonnegative Graph Reconstruction for Large Scale Clustering

2017 ◽  
Author(s):  
Junwei Han ◽  
Kai Xiong ◽  
Feiping Nie
Keyword(s):  
Spectral Clustering ◽  
Large Scale ◽  
Computational Cost ◽  
Post Processing ◽  
Normalized Cut ◽  
Clustering Problem ◽  
Novel Approach ◽  
Graph Reconstruction ◽  
Final Cluster ◽  
High Computational Cost

Spectral clustering has been widely used due to its simplicity for solving graph clustering problem in recent years. However, it suffers from the high computational cost as data grow in scale, and is limited by the performance of post-processing. To address these two problems simultaneously, in this paper, we propose a novel approach denoted by orthogonal and nonnegative graph reconstruction (ONGR) that scales linearly with the data size. For the relaxation of Normalized Cut, we add nonnegative constraint to the objective. Due to the nonnegativity, ONGR offers interpretability that the final cluster labels can be directly obtained without post-processing. Extensive experiments on clustering tasks demonstrate the effectiveness of the proposed method.

scholarly journals Genetic Variants in Smoking-Related Genes in Two Smoking Cessation Programs: A Cross-Sectional Study

2021 ◽  
Vol 18 (12) ◽  
pp. 6597
Author(s):  
Gloria Pérez-Rubio ◽  
Luis Alberto López-Flores ◽  
Ana Paula Cupertino ◽  
Francisco Cartujano-Barrera ◽  
Luz Myriam Reynales-Shigematsu ◽  
...  
Keyword(s):  
Smoking Cessation ◽  
Cross Sectional Study ◽  
Nucleotide Polymorphisms ◽  
Sectional Study ◽  
Cross Sectional ◽  
Single Nucleotide ◽  
Genotype Frequencies ◽  
Quitting Smoking ◽  
Genes Encoding ◽  
Genomic Regions

Previous studies have identified variants in genes encoding proteins associated with the degree of addiction, smoking onset, and cessation. We aimed to describe thirty-one single nucleotide polymorphisms (SNPs) in seven candidate genomic regions spanning six genes associated with tobacco-smoking in a cross-sectional study from two different interventions for quitting smoking: (1) thirty-eight smokers were recruited via multimedia to participate in e-Decídete! program (e-Dec) and (2) ninety-four attended an institutional smoking cessation program on-site. SNPs genotyping was done by real-time PCR using TaqMan probes. The analysis of alleles and genotypes was carried out using the EpiInfo v7. on-site subjects had more years smoking and tobacco index than e-Dec smokers (p < 0.05, both); in CYP2A6 we found differences in the rs28399433 (p < 0.01), the e-Dec group had a higher frequency of TT genotype (0.78 vs. 0.35), and TG genotype frequency was higher in the on-site group (0.63 vs. 0.18), same as GG genotype (0.03 vs. 0.02). Moreover, three SNPs in NRXN1, two in CHRNA3, and two in CHRNA5 had differences in genotype frequencies (p < 0.01). Cigarettes per day were different (p < 0.05) in the metabolizer classification by CYP2A6 alleles. In conclusion, subjects attending a mobile smoking cessation intervention smoked fewer cigarettes per day, by fewer years, and by fewer cumulative pack-years. There were differences in the genotype frequencies of SNPs in genes related to nicotine metabolism and nicotine dependence. Slow metabolizers smoked more cigarettes per day than intermediate and normal metabolizers.

PARAMETER IDENTIFICATION FOR ELECTROCHEMICAL MODELS OF LITHIUM ION BATTERIES USING SENSITIVITY ANALYSIS

2021 ◽  
pp. 1-33
Author(s):  
Chitra Dangwal ◽  
Marcello Canova
Keyword(s):  
Parameter Identification ◽  
Large Scale ◽  
Initial Conditions ◽  
Lithium Ion ◽  
Optimization Methods ◽  
Particle Model ◽  
Aerospace Applications ◽  
Novel Approach ◽  
Consistent Solution ◽  
The Common

Abstract Predicting the chemical and physical processes occurring in Lithium-ion cells with high-fidelity electrochemical models is today a critical requirement to accelerate the design and optimization of battery packs for automotive and aerospace applications. One of the common issues associated with electrochemical models is the complexity of parameter identification, particularly when relying only on experimental data obtained via non-invasive techniques. This paper presents a novel approach to improve the common methods of parameter calibration that consists of matching the predicted terminal voltage to test data via optimization methods. The study is conducted for an NMC-graphite cell, modeled using a reduced order Extended Single Particle Model (ESPM). The proposed approach relies on using a large-scale Particle Swarm Optimization (PSO), modified by including a term that accounts for the parameter sensitivity information, such that the rate of convergence and robustness of the algorithm to obtain a consistent solution in the presence of uncertainties in the initial conditions are significantly improved.

scholarly journals DNA Melting Analysis for Detection of Single Nucleotide Polymorphisms

2001 ◽  
Vol 47 (4) ◽  
pp. 635-644 ◽  
Author(s):  
Robert H Lipsky ◽  
Chiara M Mazzanti ◽  
Joseph G Rudolph ◽  
Ke Xu ◽  
Gopal Vyas ◽  
...  
Keyword(s):  
Single Nucleotide Polymorphisms ◽  
Large Scale ◽  
Denaturing Gradient Gel Electrophoresis ◽  
Dna Melting ◽  
Nucleotide Polymorphisms ◽  
Single Nucleotide ◽  
Efficient System ◽  
Gradient Gel Electrophoresis ◽  
Melting Analysis ◽  
Scale Detection

Abstract Background: Several methods for detection of single nucleotide polymorphisms (SNPs; e.g., denaturing gradient gel electrophoresis and denaturing HPLC) are indirectly based on the principle of differential melting of heteroduplex DNA. We present a method for detecting SNPs that is directly based on this principle. Methods: We used a double-stranded DNA-specific fluorescent dye, SYBR Green I (SYBR) in an efficient system (PE 7700 Sequence Detector) in which DNA melting was controlled and monitored in a 96-well plate format. We measured the decrease in fluorescence intensity that accompanied DNA duplex denaturation, evaluating the effects of fragment length, dye concentration, DNA concentration, and sequence context using four naturally occurring polymorphisms (three SNPs and a single-base deletion/insertion). Results: DNA melting analysis (DM) was used successfully for variant detection, and we also discovered two previously unknown SNPs by this approach. Concentrations of DNA amplicons were readily monitored by SYBR fluorescence, and DNA amplicon concentrations were highly reproducible, with a CV of 2.6%. We readily detected differences in the melting temperature between homoduplex and heteroduplex fragments 15–167 bp in length and differing by only a single nucleotide substitution. Conclusions: The efficiency and sensitivity of DMA make it highly suitable for the large-scale detection of sequence variants.

scholarly journals TNF-αPolymorphisms in Juvenile Idiopathic Arthritis: Which Potential Clinical Implications?

2012 ◽  
Vol 2012 ◽  
pp. 1-16 ◽  
Author(s):  
A. Scardapane ◽  
L. Breda ◽  
M. Lucantoni ◽  
F. Chiarelli
Keyword(s):  
Juvenile Idiopathic Arthritis ◽  
Large Scale ◽  
Nucleotide Polymorphisms ◽  
Necrosis Factor Alpha ◽  
Single Nucleotide ◽  
Adult Rheumatoid Arthritis ◽  
Functional Consequences ◽  
Factor Alpha ◽  
Necrosis Factor ◽  
Important Cytokine

Whether tumor necrosis factor alpha (TNF-α) gene polymorphisms (SNPs) influence disease susceptibility and treatment of patients with juvenile idiopathic arthritis (JIA) is presently uncertain. TNF-αis one of the most important cytokine involved in JIA pathogenesis. Several single nucleotide polymorphisms (SNPs) have been identified within the region of the TNF-αgene but only a very small minority have proven functional consequences and have been associated with susceptibility to JIA. An association between some TNF-αSNPs and adult rheumatoid arthritis (RA) susceptibility, severity and clinical response to anti-TNF-αtreatment has been reported. The most frenquetly studied TNF-αSNP is located at −308 position, where a substitution of the G allele with the rare A allele has been found. The presence of the allele −308A is associated to JIA and to a poor prognosis. Besides, the −308G genotype has been associated with a better response to anti-TNF-αtherapy in JIA patients, confirming adult data. Psoriatic and oligoarticular arthritis are significantly associated to the −238 SNP only in some works. Studies considering other SNPs are conflicting and inconclusive. Large scale studies are required to define the contribution of TNF-αgene products to disease pathogenesis and anti-TNF-αtherapeutic efficacy in JIA.

scholarly journals A regression framework to uncover pleiotropy in large-scale electronic health record data

2019 ◽  
Vol 26 (10) ◽  
pp. 1083-1090 ◽  
Author(s):  
Ruowang Li ◽  
Rui Duan ◽  
Rachel L Kember ◽  
Daniel J Rader ◽  
Scott M Damrauer ◽  
...  
Keyword(s):  
Large Scale ◽  
Pleiotropic Effects ◽  
Nucleotide Polymorphisms ◽  
Reduced Rank Regression ◽  
Electronic Health Record Data ◽  
Single Nucleotide ◽  
Rank Regression ◽  
Reduced Rank ◽  
Multiple Phenotypes ◽  
Regression Framework

Abstract Objective Pleiotropy, where 1 genetic locus affects multiple phenotypes, can offer significant insights in understanding the complex genotype–phenotype relationship. Although individual genotype–phenotype associations have been thoroughly explored, seemingly unrelated phenotypes can be connected genetically through common pleiotropic loci or genes. However, current analyses of pleiotropy have been challenged by both methodologic limitations and a lack of available suitable data sources. Materials and Methods In this study, we propose to utilize a new regression framework, reduced rank regression, to simultaneously analyze multiple phenotypes and genotypes to detect pleiotropic effects. We used a large-scale biobank linked electronic health record data from the Penn Medicine BioBank to select 5 cardiovascular diseases (hypertension, cardiac dysrhythmias, ischemic heart disease, congestive heart failure, and heart valve disorders) and 5 mental disorders (mood disorders; anxiety, phobic and dissociative disorders; alcohol-related disorders; neurological disorders; and delirium dementia) to validate our framework. Results Compared with existing methods, reduced rank regression showed a higher power to distinguish known associated single-nucleotide polymorphisms from random single-nucleotide polymorphisms. In addition, genome-wide gene-based investigation of pleiotropy showed that reduced rank regression was able to identify candidate genetic variants with novel pleiotropic effects compared to existing methods. Conclusion The proposed regression framework offers a new approach to account for the phenotype and genotype correlations when identifying pleiotropic effects. By jointly modeling multiple phenotypes and genotypes together, the method has the potential to distinguish confounding from causal genotype and phenotype associations.

scholarly journals Genome-Wide Association Study of Body Weight Traits in Chinese Fine-Wool Sheep

Animals ◽  
2020 ◽  
Vol 10 (1) ◽  
pp. 170 ◽  
Author(s):  
Zengkui Lu ◽  
Yaojing Yue ◽  
Chao Yuan ◽  
Jianbin Liu ◽  
Zhiqiang Chen ◽  
...  
Keyword(s):  
Body Weight ◽  
Muscle Development ◽  
Genome Wide Association Study ◽  
Nucleotide Polymorphisms ◽  
Single Nucleotide ◽  
Genome Wide ◽  
Economic Trait ◽  
Significance Levels ◽  
Genomic Regions ◽  
Selection Of

Body weight is an important economic trait for sheep and it is vital for their successful production and breeding. Therefore, identifying the genomic regions and biological pathways that contribute to understanding variability in body weight traits is significant for selection purposes. In this study, the genome-wide associations of birth, weaning, yearling, and adult weights of 460 fine-wool sheep were determined using resequencing technology. The results showed that 113 single nucleotide polymorphisms (SNPs) reached the genome-wide significance levels for the four body weight traits and 30 genes were annotated effectively, including AADACL3, VGF, NPC1, and SERPINA12. The genes annotated by these SNPs significantly enriched 78 gene ontology terms and 25 signaling pathways, and were found to mainly participate in skeletal muscle development and lipid metabolism. These genes can be used as candidate genes for body weight in sheep, and provide useful information for the production and genomic selection of Chinese fine-wool sheep.

scholarly journals Animal-ImputeDB: a comprehensive database with multiple animal reference panels for genotype imputation

2019 ◽  
Vol 48 (D1) ◽  
pp. D659-D667 ◽  
Author(s):  
Wenqian Yang ◽  
Yanbo Yang ◽  
Cecheng Zhao ◽  
Kun Yang ◽  
Dongyang Wang ◽  
...  
Keyword(s):  
Large Scale ◽  
Association Studies ◽  
Genotype Imputation ◽  
Genome Wide Association Studies ◽  
Nucleotide Polymorphisms ◽  
High Quality ◽  
Single Nucleotide ◽  
Genome Wide ◽  
Whole Genome Resequencing ◽  
Missing Genotypes

Abstract Animal-ImputeDB (http://gong_lab.hzau.edu.cn/Animal_ImputeDB/) is a public database with genomic reference panels of 13 animal species for online genotype imputation, genetic variant search, and free download. Genotype imputation is a process of estimating missing genotypes in terms of the haplotypes and genotypes in a reference panel. It can effectively increase the density of single nucleotide polymorphisms (SNPs) and thus can be widely used in large-scale genome-wide association studies (GWASs) using relatively inexpensive and low-density SNP arrays. However, most animals except humans lack high-quality reference panels, which greatly limits the application of genotype imputation in animals. To overcome this limitation, we developed Animal-ImputeDB, which is dedicated to collecting genotype data and whole-genome resequencing data of nonhuman animals from various studies and databases. A computational pipeline was developed to process different types of raw data to construct reference panels. Finally, 13 high-quality reference panels including ∼400 million SNPs from 2265 samples were constructed. In Animal-ImputeDB, an easy-to-use online tool consisting of two popular imputation tools was designed for the purpose of genotype imputation. Collectively, Animal-ImputeDB serves as an important resource for animal genotype imputation and will greatly facilitate research on animal genomic selection and genetic improvement.

scholarly journals Association between a Variant in MicroRNA-646 and the Susceptibility to Hepatocellular Carcinoma in a Large-Scale Population

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Rui Wang ◽  
Jun Zhang ◽  
Weiru Jiang ◽  
Yanyun Ma ◽  
Wenshuai Li ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Single Nucleotide Polymorphisms ◽  
Protective Effect ◽  
Large Scale ◽  
Cancer Development ◽  
Nucleotide Polymorphisms ◽  
Single Nucleotide ◽  
Scale Population

Background. Single-nucleotide polymorphisms in microRNAs play important roles in oncogenesis and cancer development.Objective. We aim to explore whether miR-646 rs6513497 is associated with the risk of hepatocellular carcinoma.Methods. Total 997 HCC patients and 993 cancer-free controls were enrolled in this study. Genotyping was performed using MassARRAY method.Results. Compared with the T allele of rs6513497, the G allele was associated with a significantly decreased risk of HCC (OR = 0.788, 95% CI = 0.631–0.985,P= 0.037); moreover, a more protective effect of the G allele was shown in males (OR = 0.695, 95% CI = 0.539–0.897,P= 0.005 in HCC and OR = 0.739, 95% CI = 0.562–0.972,P= 0.030 in HBV-related HCC), basically in a dominant manner (HCC: OR = 0.681, 95% CI = 0.162–0.896,P= 0.006; HBV-related HCC: OR = 0.715, 95% CI = 0.532–0.962,P= 0.027).Conclusions. Our findings support the view that the miR-646 SNP rs6513497 may contribute to the susceptibility of HCC.

Sign in / Sign up

Export Citation Format

Share Document