scholarly journals Evaluation of consensus strategies for haplotype phasing

2020 ◽  
Author(s):  
Ziad Al Bkhetan ◽  
Gursharan Chana ◽  
Kotagiri Ramamohanarao ◽  
Karin Verspoor ◽  
Benjamin Goudey
Keyword(s):  
Sample Size ◽  
Comprehensive Evaluation ◽  
Genotype Imputation ◽  
Consensus Approach ◽  
Critical Step ◽  
Haplotype Phasing ◽  
Multiple Outputs ◽  
Individual Estimate ◽  
European Populations ◽  
Small Improvement

AbstractMotivationHaplotype phasing is a critical step for many genetic applications but incorrect estimates of phase can negatively impact downstream analyses. One proposed strategy to improve phasing accuracy is to combine multiple independent phasing estimates to overcome the limitations of any individual estimate. As such a strategy is yet to be thoroughly explored, this study provides a comprehensive evaluation of consensus strategies for haplotype phasing, exploring their performance, along with their constituent tools, across a range of real and simulated datasets with different data characteristics and on the downstream task of genotype imputation.ResultsBased on the outputs of existing phasing tools, we explore two different strategies to construct haplotype consensus estimators: voting across outputs from multiple phasing tools and multiple outputs of a single non-deterministic tool. We find the consensus approach from multiple tools reduces switch error by an average of 10% compared to any constituent tool when applied to European populations and has the highest accuracy regardless of population ethnicity, sample size, SNP-density or SNP frequency. Furthermore, a consensus provides a small improvement indirectly the downstream task of genotype imputation regardless of which genotype imputation tools were used. Our results provide guidance on how to produce the most accurate phasing estimates and the tradeoffs that a consensus approach may have.AvailabilityOur implementation of consensus haplotype phasing, consHap, is available freely at https://github.com/ziadbkh/consHap.

scholarly journals Evaluation of consensus strategies for haplotype phasing

2020 ◽  
Author(s):  
Ziad Al Bkhetan ◽  
Gursharan Chana ◽  
Kotagiri Ramamohanarao ◽  
Karin Verspoor ◽  
Benjamin Goudey
Keyword(s):  
Comprehensive Evaluation ◽  
Genotype Imputation ◽  
Supplementary Information ◽  
Consensus Approach ◽  
Haplotype Phasing ◽  
Individual Estimate ◽  
Trade Offs ◽  
Size Variant ◽  
Variant Frequency ◽  
Different Characteristics

Abstract Haplotype phasing is a critical step for many genetic applications but incorrect estimates of phase can negatively impact downstream analyses. One proposed strategy to improve phasing accuracy is to combine multiple independent phasing estimates to overcome the limitations of any individual estimate. However, such a strategy is yet to be thoroughly explored. This study provides a comprehensive evaluation of consensus strategies for haplotype phasing. We explore the performance of different consensus paradigms, and the effect of specific constituent tools, across several datasets with different characteristics and their impact on the downstream task of genotype imputation. Based on the outputs of existing phasing tools, we explore two different strategies to construct haplotype consensus estimators: voting across outputs from multiple phasing tools and multiple outputs of a single non-deterministic tool. We find that the consensus approach from multiple tools reduces SE by an average of 10% compared to any constituent tool when applied to European populations and has the highest accuracy regardless of population ethnicity, sample size, variant density or variant frequency. Furthermore, the consensus estimator improves the accuracy of the downstream task of genotype imputation carried out by the widely used Minimac3, pbwt and BEAGLE5 tools. Our results provide guidance on how to produce the most accurate phasing estimates and the trade-offs that a consensus approach may have. Our implementation of consensus haplotype phasing, consHap, is available freely at https://github.com/ziadbkh/consHap. Supplementary information: Supplementary data are available at Briefings in Bioinformatics online.

A comprehensive evaluation of SNP genotype imputation

2008 ◽  
Vol 125 (2) ◽  
pp. 163-171 ◽  
Author(s):  
Michael Nothnagel ◽  
David Ellinghaus ◽  
Stefan Schreiber ◽  
Michael Krawczak ◽  
Andre Franke
Keyword(s):  
Comprehensive Evaluation ◽  
Genotype Imputation

scholarly journals Practical consideration of genotype imputation: Sample size, window size, reference choice, and untyped rate

2011 ◽  
Vol 4 (3) ◽  
pp. 339-351 ◽  
Author(s):  
Guimin Gao ◽  
Nita A. Limdi ◽  
Nianjun Liu ◽  
Boshao Zhang ◽  
Kui Zhang ◽  
...  
Keyword(s):  
Sample Size ◽  
Window Size ◽  
Genotype Imputation ◽  
Practical Consideration

scholarly journals High-resolution population-specific recombination rates and their effect on phasing and genotype imputation

2020 ◽  
Author(s):  
Shabbeer Hassan ◽  
Ida Surakka ◽  
Marja-Riitta Taskinen ◽  
Veikko Salomaa ◽  
Aarno Palotie ◽  
...  
Keyword(s):  
High Resolution ◽  
Error Rates ◽  
Rapid Expansion ◽  
Genotype Imputation ◽  
Specific Reference ◽  
Founder Population ◽  
Recombination Rates ◽  
Isolated Populations ◽  
Haplotype Phasing ◽  
Population Genomic

AbstractFounder population size, demographic changes (eg. population bottlenecks or rapid expansion) can lead to variation in recombination rates across different populations. Previous research has shown that using population-specific reference panels has a significant effect on downstream population genomic analysis like haplotype phasing, genotype imputation and association, especially in the context of population isolates. Here, we developed a high-resolution recombination rate mapping at 10kb and 50kb scale using high-coverage (20-30x) whole-genome sequenced 55 family trios from Finland and compared it to recombination rates of non-Finnish Europeans (NFE). We tested the downstream effects of the population-specific recombination rates in statistical phasing and genotype imputation in Finns as compared to the same analyses performed by using the NFE-based recombination rates. We found that Finnish recombination rates have a moderately high correlation (Spearman’s ρ =0.67-0.79) with non-Finnish Europeans, although on average (across all autosomal chromosomes), Finnish rates (2.268±0.4209 cM/Mb) are 12-14% lower than NFE (2.641±0.5032 cM/Mb). Finnish recombination map was found to have no significant effect in haplotype phasing accuracy (switch error rates ~ 2%) and average imputation concordance rates (97-98% for common, 92-96% for low frequency and 78-90% for rare variants). Our results suggest that downstream population genomic analyses like haplotype phasing and genotype imputation mostly depend on population-specific contexts like appropriate reference panels and their sample size, but not on population-specific recombination maps or effective population sizes. Currently, available HapMap recombination maps seem robust for population-specific phasing and imputation pipelines, even in the context of relatively isolated populations like Finland.

scholarly journals Comparison of Machine Learning Regression Algorithms for Cotton Leaf Area Index Retrieval Using Sentinel-2 Spectral Bands

2019 ◽  
Vol 9 (7) ◽  
pp. 1459 ◽  
Author(s):  
Huihui Mao ◽  
Jihua Meng ◽  
Fujiang Ji ◽  
Qiankun Zhang ◽  
Huiting Fang
Keyword(s):  
Machine Learning ◽  
Sample Size ◽  
Computational Efficiency ◽  
Comprehensive Evaluation ◽  
Training Sample ◽  
Model Accuracy ◽  
Area Index ◽  
Training Sample Size ◽  
Spectral Bands ◽  
Sentinel 2

Leaf area index (LAI) is a crucial crop biophysical parameter that has been widely used in a variety of fields. Five state-of-the-art machine learning regression algorithms (MLRAs), namely, artificial neural network (ANN), support vector regression (SVR), Gaussian process regression (GPR), random forest (RF) and gradient boosting regression tree (GBRT), have been used in the retrieval of cotton LAI with Sentinel-2 spectral bands. The performances of the five machine learning models are compared for better applications of MLRAs in remote sensing, since challenging problems remain in the selection of MLRAs for crop LAI retrieval, as well as the decision as to the optimal number for the training sample size and spectral bands to different MLRAs. A comprehensive evaluation was employed with respect to model accuracy, computational efficiency, sensitivity to training sample size and sensitivity to spectral bands. We conducted the comparison of five MLRAs in an agricultural area of Northwest China over three cotton seasons with the corresponding field campaigns for modeling and validation. Results show that the GBRT model outperforms the other models with respect to model accuracy in average ( R 2 ¯ = 0.854, R M S E ¯ = 0.674 and M A E ¯ = 0.456). SVR achieves the best performance in computational efficiency, which means it is fast to train, and to validate that it has great potentials to deliver near-real-time operational products for crop management. As for sensitivity to training sample size, GBRT behaves as the most robust model, and provides the best model accuracy on the average among the variations of training sample size, compared with other models ( R 2 ¯ = 0.884, R M S E ¯ = 0.615 and M A E ¯ = 0.452). Spectral bands sensitivity analysis with dCor (distance correlation), combined with the backward elimination approach, indicates that SVR, GPR and RF provide relatively robust performance to the spectral bands, while ANN outperforms the other models in terms of model accuracy on the average among the reduction of spectral bands ( R 2 ¯ = 0.881, R M S E ¯ = 0.625 and M A E ¯ = 0.480). A comprehensive evaluation indicates that GBRT is an appealing alternative for cotton LAI retrieval, except for its computational efficiency. Despite the different performance of the ML models, all models exhibited considerable potential for cotton LAI retrieval, which could offer accurate crop parameters information timely and accurately for crop fields management and agricultural production decisions.

scholarly journals High-resolution population-specific recombination rates and their effect on phasing and genotype imputation

2020 ◽  
Author(s):  
Shabbeer Hassan ◽  
Ida Surakka ◽  
Marja-Riitta Taskinen ◽  
Veikko Salomaa ◽  
Aarno Palotie ◽  
...  
Keyword(s):  
High Resolution ◽  
Recombination Rate ◽  
Rare Variants ◽  
Error Rates ◽  
Genotype Imputation ◽  
Specific Reference ◽  
Recombination Rates ◽  
Isolated Populations ◽  
High Coverage ◽  
Haplotype Phasing

AbstractPrevious research has shown that using population-specific reference panels has a significant effect on downstream population genomic analyses like haplotype phasing, genotype imputation, and association, especially in the context of population isolates. Here, we developed a high-resolution recombination rate mapping at 10 and 50 kb scale using high-coverage (20–30×) whole-genome sequenced data of 55 family trios from Finland and compared it to recombination rates of non-Finnish Europeans (NFE). We tested the downstream effects of the population-specific recombination rates in statistical phasing and genotype imputation in Finns as compared to the same analyses performed by using the NFE-based recombination rates. We found that Finnish recombination rates have a moderately high correlation (Spearman’s ρ = 0.67–0.79) with NFE, although on average (across all autosomal chromosomes), Finnish rates (2.268 ± 0.4209 cM/Mb) are 12–14% lower than NFE (2.641 ± 0.5032 cM/Mb). Finnish recombination map was found to have no significant effect in haplotype phasing accuracy (switch error rates ~2%) and average imputation concordance rates (97–98% for common, 92–96% for low frequency and 78–90% for rare variants). Our results suggest that haplotype phasing and genotype imputation mostly depend on population-specific contexts like appropriate reference panels and their sample size, but not on population-specific recombination maps. Even though recombination rate estimates had some differences between the Finnish and NFE populations, haplotyping and imputation had not been noticeably affected by the recombination map used. Therefore, the currently available HapMap recombination maps seem robust for population-specific phasing and imputation pipelines, even in the context of relatively isolated populations like Finland.

scholarly journals Leveraging TOPMed Imputation Server and Constructing a Cohort-Specific Imputation Reference Panel to Enhance Genotype Imputation among Cystic Fibrosis Patients

2021 ◽  
Author(s):  
Quan Sun ◽  
Weifang Liu ◽  
Jonathan D Rosen ◽  
Le Huang ◽  
Rhonda G Pace ◽  
...  
Keyword(s):  
Cystic Fibrosis ◽  
Sample Size ◽  
Association Studies ◽  
Genetic Disorder ◽  
Genome Project ◽  
Genotype Imputation ◽  
Reference Panel ◽  
Effective Sample Size ◽  
Polygenic Risk Score ◽  
Genome Wide Association Studies

Cystic fibrosis (CF) is a severe genetic disorder that can cause multiple comorbidities affecting the lungs, the pancreas, the luminal digestive system and beyond. In our previous genome-wide association studies (GWAS), we genotyped ~8,000 CF samples using a mixture of different genotyping platforms. More recently, the Cystic Fibrosis Genome Project (CFGP) performed deep (~30x) whole genome sequencing (WGS) of 5,095 samples to better understand the genetic mechanisms underlying clinical heterogeneity among CF patients. For mixtures of GWAS array and WGS data, genotype imputation has proven effective in increasing effective sample size. Therefore, we first performed imputation for the ~8,000 CF samples with GWAS array genotype using the TOPMed freeze 8 reference panel. Our results demonstrate that TOPMed can provide high-quality imputation for CF patients, boosting genomic coverage from ~0.3 - 4.2 million genotyped markers to ~11 - 43 million well-imputed markers, and significantly improving Polygenic Risk Score (PRS) prediction accuracy. Furthermore, we built a CF-specific CFGP reference panel based on WGS data of CF patients. We demonstrate that despite having ~3% the sample size of TOPMed, our CFGP reference panel can still outperform TOPMed when imputing some CF disease-causing variants, likely due to allele and haplotype differences between CF patients and general populations. We anticipate our imputed data for 4,656 samples without WGS data will benefit our subsequent genetic association studies, and the CFGP reference panel built from CF WGS samples will benefit other investigators studying CF.

scholarly journals Basic concepts for sample size calculation: Critical step for any clinical trials!

2016 ◽  
Vol 10 (3) ◽  
pp. 328 ◽  
Author(s):  
KK Gupta ◽  
JP Attri ◽  
A Singh ◽  
H Kaur ◽  
G Kaur
Keyword(s):  
Clinical Trials ◽  
Sample Size ◽  
Sample Size Calculation ◽  
Critical Step ◽  
Basic Concepts

scholarly journals A Comprehensive Evaluation of Single-end Sequencing Data Analyses for Environmental Microbiome Research

2021 ◽  
Author(s):  
Meganathan Ramakodi
Keyword(s):  
Sample Size ◽  
Comprehensive Evaluation ◽  
Illumina Miseq ◽  
Sequencing Data ◽  
Core Microbiome ◽  
Data Analyses ◽  
Illumina Miseq Platform ◽  
Microbiome Research ◽  
Miseq Platform ◽  
Sequencing Platforms

Abstract Illumina sequencing platforms have been widely used for amplicon-based environmental microbiome research. Analyses of amplicon data of environmental samples, generated from Illumina MiSeq platform illustrate the reverse (R2) reads in the PE datasets to have low quality towards the 3’ end of the reads which affect the sequencing depth of samples and ultimately impact the sample size which may possibly lead to an altered outcome. This study evaluates the usefulness of single-end (SE) sequencing data in microbiome research when the Illumina MiSeq PE dataset shows significantly high number of low quality reverse reads. In this study, the amplicon data (V1V3, V3V4, V4V5 and V6V8) from 128 environmental (soil) samples, downloaded from SRA, demonstrate the efficiency of single-end (SE) sequencing data analyses in microbiome research. The SE datasets were found to infer the core microbiome structure as comparable to the PE dataset. Conspicuously, the forward (R1) datasets inferred a higher number of taxa as compared to PE datasets for most of the amplicon regions, except V3V4. Thus, analyses of SE sequencing data, especially R1 reads, in environmental microbiome studies could ameliorate the problems arising on sample size of the study due to low quality reverse reads in the dataset. However, care must be taken while interpreting the microbiome structure as few taxa observed in the PE datasets were absent in the SE datasets. In conclusion, this study demonstrates the availability of choices in analyzing the amplicon data without having the need to remove samples with low quality reverse reads.

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