eQTLHap: a tool for comprehensive eQTL analysis considering haplotypic and genotypic effects

Abstract Motivation The high accuracy of recent haplotype phasing tools is enabling the integration of haplotype (or phase) information more widely in genetic investigations. One such possibility is phase-aware expression quantitative trait loci (eQTL) analysis, where haplotype-based analysis has the potential to detect associations that may otherwise be missed by standard SNP-based approaches. Results We present eQTLHap, a novel method to investigate associations between gene expression and genetic variants, considering their haplotypic and genotypic effect. Using multiple simulations based on real data, we demonstrate that phase-aware eQTL analysis significantly outperforms typical SNP-based methods when the causal genetic architecture involves multiple SNPs. We show that phase-aware eQTL analysis is robust to phasing errors, showing only a minor impact ($<4\%$) on sensitivity. Applying eQTLHap to real GEUVADIS and GTEx datasets detects numerous novel eQTLs undetected by a single-SNP approach, with 22 eQTLs replicating across studies or tissue types, highlighting the utility of phase-aware eQTL analysis. Availability and implementation https://github.com/ziadbkh/eQTLHap. Contact [email protected] Supplementary information Supplementary data are available at Briefings in Bioinformatics online.

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A novel haplotype-based eQTL approach identifies genetic associations not detected through conventional SNP-based methods

10.1101/2020.07.23.206391 ◽

2020 ◽

Author(s):

Ziad Al Bkhetan ◽

Gursharan Chana ◽

Cheng Soon Ong ◽

Benjamin Goudey ◽

Kotagiri Ramamohanarao

Keyword(s):

Gene Expression ◽

Genetic Architecture ◽

High Accuracy ◽

Eqtl Analysis ◽

Genetic Associations ◽

Phase Information ◽

Multiple Snps ◽

Haplotype Blocks ◽

Haplotype Phasing ◽

Link Type

AbstractMotivationThe high accuracy of current haplotype phasing tools has enabled the interrogation of haplotype (or phase) information more widely in genetic investigations. Including such information in eQTL analysis complements SNP-based approaches as it has the potential to detect associations that may otherwise be missed.ResultsWe have developed a haplotype-based eQTL approach called eQTLHap to investigate associations between gene expression and haplotype blocks. Using simulations, we demonstrate that eQTLHap significantly outperforms typical SNP-based eQTL methods when the causal genetic architecture involves multiple SNPs. We show that phasing errors slightly impact the sensitivity of the proposed method (< 4%). Finally, the application of eQTLHap to real GEUVADIS and GTEx datasets finds 22 associations that replicated in larger studies or other tissues and could not be detected using a single-SNP approach.Availabilityhttps://github.com/ziadbkh/eQTLHap.

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HIVID2: an accurate tool to detect virus integrations in the host genome

Bioinformatics ◽

10.1093/bioinformatics/btab031 ◽

2021 ◽

Author(s):

Xi Zeng ◽

Linghao Zhao ◽

Chenhang Shen ◽

Yi Zhou ◽

Guoliang Li ◽

...

Keyword(s):

Simulated Data ◽

Real Data ◽

Host Genome ◽

Supplementary Information ◽

Future Research ◽

Specificity And Sensitivity ◽

Novel Method ◽

Advanced Analysis ◽

Virus Integration ◽

Better Than

Abstract Motivation Virus integration in the host genome is frequently reported to be closely associated with many human diseases, and the detection of virus integration is a critically challenging task. However, most existing tools show limited specificity and sensitivity. Therefore, the objective of this study is to develop a method for accurate detection of virus integration into host genomes. Results Herein, we report a novel method termed HIVID2 that is a significant upgrade of HIVID. HIVID2 performs a paired-end combination (PE-combination) for potentially integrated reads. The resulting sequences are then remapped onto the reference genomes, and both split and discordant chimeric reads are used to identify accurate integration breakpoints with high confidence. HIVID2 represents a great improvement in specificity and sensitivity, and predicts breakpoints closer to the real integrations, compared with existing methods. The advantage of our method was demonstrated using both simulated and real datasets. HIVID2 uncovered novel integration breakpoints in well-known cervical cancer-related genes, including FHIT and LRP1B, which was verified using protein expression data. In addition, HIVID2 allows the user to decide whether to automatically perform advanced analysis using the identified virus integrations. By analyzing the simulated data and real data tests, we demonstrated that HIVID2 is not only more accurate than HIVID but also better than other existing programs with respect to both sensitivity and specificity. We believe that HIVID2 will help in enhancing future research associated with virus integration. Availabilityand implementation HIVID2 can be accessed at https://github.com/zengxi-hada/HIVID2/. Supplementary information Supplementary data are available at Bioinformatics online.

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UNIQ

ACM Transactions on Computer Systems ◽

10.1145/3444943 ◽

2021 ◽

Vol 37 (1--4) ◽

pp. 1-15

Author(s):

Chaim Baskin ◽

Natan Liss ◽

Eli Schwartz ◽

Evgenii Zheltonozhskii ◽

Raja Giryes ◽

...

Keyword(s):

Neural Network ◽

State Of The Art ◽

Low Complexity ◽

High Accuracy ◽

Trade Off ◽

Training Time ◽

Uniform Quantization ◽

Novel Method ◽

A Minor ◽

Prior State

We present a novel method for neural network quantization. Our method, named UNIQ , emulates a non-uniform k -quantile quantizer and adapts the model to perform well with quantized weights by injecting noise to the weights at training time. As a by-product of injecting noise to weights, we find that activations can also be quantized to as low as 8-bit with only a minor accuracy degradation. Our non-uniform quantization approach provides a novel alternative to the existing uniform quantization techniques for neural networks. We further propose a novel complexity metric of number of bit operations performed (BOPs), and we show that this metric has a linear relation with logic utilization and power. We suggest evaluating the trade-off of accuracy vs. complexity (BOPs). The proposed method, when evaluated on ResNet18/34/50 and MobileNet on ImageNet, outperforms the prior state of the art both in the low-complexity regime and the high accuracy regime. We demonstrate the practical applicability of this approach, by implementing our non-uniformly quantized CNN on FPGA.

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Dynamic Indoor Localization Using Maximum Likelihood Particle Filtering

Sensors ◽

10.3390/s21041090 ◽

2021 ◽

Vol 21 (4) ◽

pp. 1090

Author(s):

Wenxu Wang ◽

Damián Marelli ◽

Minyue Fu

Keyword(s):

Maximum Likelihood ◽

Particle Filtering ◽

Indoor Localization ◽

Real Data ◽

High Accuracy ◽

Classical Particle ◽

Popular Approach ◽

Filtering Method ◽

Essential Idea ◽

Number Of Particles

A popular approach for solving the indoor dynamic localization problem based on WiFi measurements consists of using particle filtering. However, a drawback of this approach is that a very large number of particles are needed to achieve accurate results in real environments. The reason for this drawback is that, in this particular application, classical particle filtering wastes many unnecessary particles. To remedy this, we propose a novel particle filtering method which we call maximum likelihood particle filter (MLPF). The essential idea consists of combining the particle prediction and update steps into a single one in which all particles are efficiently used. This drastically reduces the number of particles, leading to numerically feasible algorithms with high accuracy. We provide experimental results, using real data, confirming our claim.

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Calibration of Camera and Flash LiDAR System with a Triangular Pyramid Target

Applied Sciences ◽

10.3390/app11020582 ◽

2021 ◽

Vol 11 (2) ◽

pp. 582

Author(s):

Zean Bu ◽

Changku Sun ◽

Peng Wang ◽

Hang Dong

Keyword(s):

Simulated Data ◽

Real Data ◽

Calibration Method ◽

Multiple Sensors ◽

Triangular Pyramid ◽

World Coordinate System ◽

Flash Lidar ◽

Novel Method ◽

3D Information ◽

Incremental Validation

Calibration between multiple sensors is a fundamental procedure for data fusion. To address the problems of large errors and tedious operation, we present a novel method to conduct the calibration between light detection and ranging (LiDAR) and camera. We invent a calibration target, which is an arbitrary triangular pyramid with three chessboard patterns on its three planes. The target contains both 3D information and 2D information, which can be utilized to obtain intrinsic parameters of the camera and extrinsic parameters of the system. In the proposed method, the world coordinate system is established through the triangular pyramid. We extract the equations of triangular pyramid planes to find the relative transformation between two sensors. One capture of camera and LiDAR is sufficient for calibration, and errors are reduced by minimizing the distance between points and planes. Furthermore, the accuracy can be increased by more captures. We carried out experiments on simulated data with varying degrees of noise and numbers of frames. Finally, the calibration results were verified by real data through incremental validation and analyzing the root mean square error (RMSE), demonstrating that our calibration method is robust and provides state-of-the-art performance.

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sepal: identifying transcript profiles with spatial patterns by diffusion-based modeling

Bioinformatics ◽

10.1093/bioinformatics/btab164 ◽

2021 ◽

Author(s):

Alma Andersson ◽

Joakim Lundeberg

Keyword(s):

Spatial Patterns ◽

Expression Profiles ◽

Synthetic Data ◽

Real Data ◽

Cell Types ◽

Statistical Hypothesis ◽

Supplementary Information ◽

Statistical Hypothesis Testing ◽

Transcriptomics Data ◽

Transcript Profiles

Abstract Motivation Collection of spatial signals in large numbers has become a routine task in multiple omics-fields, but parsing of these rich datasets still pose certain challenges. In whole or near-full transcriptome spatial techniques, spurious expression profiles are intermixed with those exhibiting an organized structure. To distinguish profiles with spatial patterns from the background noise, a metric that enables quantification of spatial structure is desirable. Current methods designed for similar purposes tend to be built around a framework of statistical hypothesis testing, hence we were compelled to explore a fundamentally different strategy. Results We propose an unexplored approach to analyze spatial transcriptomics data, simulating diffusion of individual transcripts to extract genes with spatial patterns. The method performed as expected when presented with synthetic data. When applied to real data, it identified genes with distinct spatial profiles, involved in key biological processes or characteristic for certain cell types. Compared to existing methods, ours seemed to be less informed by the genes’ expression levels and showed better time performance when run with multiple cores. Availabilityand implementation Open-source Python package with a command line interface (CLI), freely available at https://github.com/almaan/sepal under an MIT licence. A mirror of the GitHub repository can be found at Zenodo, doi: 10.5281/zenodo.4573237. Supplementary information Supplementary data are available at Bioinformatics online.

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Variation in the SERPINA6/SERPINA1 locus alters morning plasma cortisol, hepatic corticosteroid binding globulin expression, gene expression in peripheral tissues, and risk of cardiovascular disease

Journal of Human Genetics ◽

10.1038/s10038-020-00895-6 ◽

2021 ◽

Author(s):

Andrew A. Crawford ◽

◽

Sean Bankier ◽

Elisabeth Altmaier ◽

Catriona L. K. Barnes ◽

...

Keyword(s):

Gene Expression ◽

Cardiovascular Disease ◽

Genetic Variants ◽

Plasma Cortisol ◽

Statistical Power ◽

Mendelian Randomisation ◽

Eqtl Analysis ◽

Causative Role ◽

Peripheral Tissues ◽

Corticosteroid Binding Globulin

AbstractThe stress hormone cortisol modulates fuel metabolism, cardiovascular homoeostasis, mood, inflammation and cognition. The CORtisol NETwork (CORNET) consortium previously identified a single locus associated with morning plasma cortisol. Identifying additional genetic variants that explain more of the variance in cortisol could provide new insights into cortisol biology and provide statistical power to test the causative role of cortisol in common diseases. The CORNET consortium extended its genome-wide association meta-analysis for morning plasma cortisol from 12,597 to 25,314 subjects and from ~2.2 M to ~7 M SNPs, in 17 population-based cohorts of European ancestries. We confirmed the genetic association with SERPINA6/SERPINA1. This locus contains genes encoding corticosteroid binding globulin (CBG) and α1-antitrypsin. Expression quantitative trait loci (eQTL) analyses undertaken in the STARNET cohort of 600 individuals showed that specific genetic variants within the SERPINA6/SERPINA1 locus influence expression of SERPINA6 rather than SERPINA1 in the liver. Moreover, trans-eQTL analysis demonstrated effects on adipose tissue gene expression, suggesting that variations in CBG levels have an effect on delivery of cortisol to peripheral tissues. Two-sample Mendelian randomisation analyses provided evidence that each genetically-determined standard deviation (SD) increase in morning plasma cortisol was associated with increased odds of chronic ischaemic heart disease (0.32, 95% CI 0.06–0.59) and myocardial infarction (0.21, 95% CI 0.00–0.43) in UK Biobank and similarly in CARDIoGRAMplusC4D. These findings reveal a causative pathway for CBG in determining cortisol action in peripheral tissues and thereby contributing to the aetiology of cardiovascular disease.

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GAMIBHEAR: whole-genome haplotype reconstruction from Genome Architecture Mapping data

Bioinformatics ◽

10.1093/bioinformatics/btab238 ◽

2021 ◽

Author(s):

Julia Markowski ◽

Rieke Kempfer ◽

Alexander Kukalev ◽

Ibai Irastorza-Azcarate ◽

Gesa Loof ◽

...

Keyword(s):

Genetic Variants ◽

Embryonic Stem Cell Line ◽

Embryonic Stem ◽

Mouse Embryonic Stem Cell ◽

Supplementary Information ◽

Model Organisms ◽

Genome Architecture ◽

Chromatin Conformation ◽

Haplotype Reconstruction ◽

Allele Specific

Abstract Motivation Genome Architecture Mapping (GAM) was recently introduced as a digestion- and ligation-free method to detect chromatin conformation. Orthogonal to existing approaches based on chromatin conformation capture (3C), GAM’s ability to capture both inter- and intra-chromosomal contacts from low amounts of input data makes it particularly well suited for allele-specific analyses in a clinical setting. Allele-specific analyses are powerful tools to investigate the effects of genetic variants on many cellular phenotypes including chromatin conformation, but require the haplotypes of the individuals under study to be known a-priori. So far however, no algorithm exists for haplotype reconstruction and phasing of genetic variants from GAM data, hindering the allele-specific analysis of chromatin contact points in non-model organisms or individuals with unknown haplotypes. Results We present GAMIBHEAR, a tool for accurate haplotype reconstruction from GAM data. GAMIBHEAR aggregates allelic co-observation frequencies from GAM data and employs a GAM-specific probabilistic model of haplotype capture to optimise phasing accuracy. Using a hybrid mouse embryonic stem cell line with known haplotype structure as a benchmark dataset, we assess correctness and completeness of the reconstructed haplotypes, and demonstrate the power of GAMIBHEAR to infer accurate genome-wide haplotypes from GAM data. Availability GAMIBHEAR is available as an R package under the open source GPL-2 license at https://bitbucket.org/schwarzlab/gamibhear Maintainer [email protected] Supplementary information Supplementary information is available at Bioinformatics online.

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Bias in two-sample Mendelian randomization when using heritable covariable-adjusted summary associations

International Journal of Epidemiology ◽

10.1093/ije/dyaa266 ◽

2021 ◽

Author(s):

Fernando Pires Hartwig ◽

Kate Tilling ◽

George Davey Smith ◽

Deborah A Lawlor ◽

Maria Carolina Borges

Keyword(s):

Waist Circumference ◽

Genetic Variants ◽

Mendelian Randomization ◽

Causal Effect ◽

Association Studies ◽

Real Data ◽

Sensitivity Analyses ◽

Effect Estimate ◽

Genome Wide Association Studies ◽

Residual Confounding

Abstract Background Two-sample Mendelian randomization (MR) allows the use of freely accessible summary association results from genome-wide association studies (GWAS) to estimate causal effects of modifiable exposures on outcomes. Some GWAS adjust for heritable covariables in an attempt to estimate direct effects of genetic variants on the trait of interest. One, both or neither of the exposure GWAS and outcome GWAS may have been adjusted for covariables. Methods We performed a simulation study comprising different scenarios that could motivate covariable adjustment in a GWAS and analysed real data to assess the influence of using covariable-adjusted summary association results in two-sample MR. Results In the absence of residual confounding between exposure and covariable, between exposure and outcome, and between covariable and outcome, using covariable-adjusted summary associations for two-sample MR eliminated bias due to horizontal pleiotropy. However, covariable adjustment led to bias in the presence of residual confounding (especially between the covariable and the outcome), even in the absence of horizontal pleiotropy (when the genetic variants would be valid instruments without covariable adjustment). In an analysis using real data from the Genetic Investigation of ANthropometric Traits (GIANT) consortium and UK Biobank, the causal effect estimate of waist circumference on blood pressure changed direction upon adjustment of waist circumference for body mass index. Conclusions Our findings indicate that using covariable-adjusted summary associations in MR should generally be avoided. When that is not possible, careful consideration of the causal relationships underlying the data (including potentially unmeasured confounders) is required to direct sensitivity analyses and interpret results with appropriate caution.

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TreeMerge: a new method for improving the scalability of species tree estimation methods

Bioinformatics ◽

10.1093/bioinformatics/btz344 ◽

2019 ◽

Vol 35 (14) ◽

pp. i417-i426 ◽

Cited By ~ 7

Author(s):

Erin K Molloy ◽

Tandy Warnow

Keyword(s):

Large Scale ◽

Species Tree ◽

New Method ◽

Divide And Conquer ◽

Supplementary Information ◽

Estimation Methods ◽

Running Time ◽

Tree Estimation ◽

Computationally Intensive ◽

A Minor

Abstract Motivation At RECOMB-CG 2018, we presented NJMerge and showed that it could be used within a divide-and-conquer framework to scale computationally intensive methods for species tree estimation to larger datasets. However, NJMerge has two significant limitations: it can fail to return a tree and, when used within the proposed divide-and-conquer framework, has O(n5) running time for datasets with n species. Results Here we present a new method called ‘TreeMerge’ that improves on NJMerge in two ways: it is guaranteed to return a tree and it has dramatically faster running time within the same divide-and-conquer framework—only O(n2) time. We use a simulation study to evaluate TreeMerge in the context of multi-locus species tree estimation with two leading methods, ASTRAL-III and RAxML. We find that the divide-and-conquer framework using TreeMerge has a minor impact on species tree accuracy, dramatically reduces running time, and enables both ASTRAL-III and RAxML to complete on datasets (that they would otherwise fail on), when given 64 GB of memory and 48 h maximum running time. Thus, TreeMerge is a step toward a larger vision of enabling researchers with limited computational resources to perform large-scale species tree estimation, which we call Phylogenomics for All. Availability and implementation TreeMerge is publicly available on Github (http://github.com/ekmolloy/treemerge). Supplementary information Supplementary data are available at Bioinformatics online.

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