scholarly journals An empirical evaluation of genotype imputation of ancient DNA

2021 ◽  
Author(s):  
Kristiina Ausmees ◽  
Federico Sanchez-Quinto ◽  
Mattias Jakobsson ◽  
Carl Nettelblad
Keyword(s):  
Ancient Dna ◽  
Empirical Evaluation ◽  
Genotype Imputation ◽  
Systematic Evaluation ◽  
High Coverage ◽  
Depth Analysis ◽  
Missing Genotypes ◽  
And Performance ◽  
Downstream Analysis ◽  
Reference Bias

With capabilities of sequencing ancient DNA to high coverage often limited by sample quality or cost, imputation of missing genotypes presents a possibility to increase power of inference as well as cost-effectiveness for the analysis of ancient data. However, the high degree of uncertainty often associated with ancient DNA poses several methodological challenges, and performance of imputation methods in this context has not been fully explored. To gain further insights, we performed a systematic evaluation of imputation of ancient data using Beagle 4.0 and reference data from phase 3 of the 1000 Genomes project, investigating the effects of coverage, phased reference and study sample size. Making use of five ancient samples with high-coverage data available, we evaluated imputed data with respect to accuracy, reference bias and genetic affinities as captured by PCA. We obtained genotype concordance levels of over 99% for data with 1x coverage, and similar levels of accuracy and reference bias at levels as low as 0.75x. Our findings suggest that using imputed data can be a realistic option for various population genetic analyses even for data in coverage ranges below 1x. We also show that a large and varied phased reference set as well as the inclusion of low- to moderate-coverage ancient samples can increase imputation performance, particularly for rare alleles. In-depth analysis of imputed data with respect to genetic variants and allele frequencies gave further insight into the nature of errors arising during imputation, and can provide practical guidelines for post-processing and validation prior to downstream analysis.

scholarly journals Evaluating genotype imputation pipeline for ultra-low coverage ancient genomes

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Ruoyun Hui ◽  
Eugenia D’Atanasio ◽  
Lara M. Cassidy ◽  
Christiana L. Scheib ◽  
Toomas Kivisild
Keyword(s):  
Ancient Dna ◽  
Imputation Accuracy ◽  
Single Step ◽  
Genotype Imputation ◽  
Common Variants ◽  
High Coverage ◽  
Input Mode ◽  
Missing Genotypes ◽  
Low Coverage ◽  
Reference Bias

Abstract Although ancient DNA data have become increasingly more important in studies about past populations, it is often not feasible or practical to obtain high coverage genomes from poorly preserved samples. While methods of accurate genotype imputation from > 1 × coverage data have recently become a routine, a large proportion of ancient samples remain unusable for downstream analyses due to their low coverage. Here, we evaluate a two-step pipeline for the imputation of common variants in ancient genomes at 0.05–1 × coverage. We use the genotype likelihood input mode in Beagle and filter for confident genotypes as the input to impute missing genotypes. This procedure, when tested on ancient genomes, outperforms a single-step imputation from genotype likelihoods, suggesting that current genotype callers do not fully account for errors in ancient sequences and additional quality controls can be beneficial. We compared the effect of various genotype likelihood calling methods, post-calling, pre-imputation and post-imputation filters, different reference panels, as well as different imputation tools. In a Neolithic Hungarian genome, we obtain ~ 90% imputation accuracy for heterozygous common variants at coverage 0.05 × and > 97% accuracy at coverage 0.5 ×. We show that imputation can mitigate, though not eliminate reference bias in ultra-low coverage ancient genomes.

Systematic benchmark of ancient DNA read mapping

2021 ◽  
Author(s):  
Adrien Oliva ◽  
Raymond Tobler ◽  
Alan Cooper ◽  
Bastien Llamas ◽  
Yassine Souilmi
Keyword(s):  
Ancient Dna ◽  
Dna Sequences ◽  
Population Genetic ◽  
Reference Genome ◽  
Population Data ◽  
Human Populations ◽  
Current Standard ◽  
Read Mapping ◽  
Reference Bias ◽  
The Impact

Abstract The current standard practice for assembling individual genomes involves mapping millions of short DNA sequences (also known as DNA ‘reads’) against a pre-constructed reference genome. Mapping vast amounts of short reads in a timely manner is a computationally challenging task that inevitably produces artefacts, including biases against alleles not found in the reference genome. This reference bias and other mapping artefacts are expected to be exacerbated in ancient DNA (aDNA) studies, which rely on the analysis of low quantities of damaged and very short DNA fragments (~30–80 bp). Nevertheless, the current gold-standard mapping strategies for aDNA studies have effectively remained unchanged for nearly a decade, during which time new software has emerged. In this study, we used simulated aDNA reads from three different human populations to benchmark the performance of 30 distinct mapping strategies implemented across four different read mapping software—BWA-aln, BWA-mem, NovoAlign and Bowtie2—and quantified the impact of reference bias in downstream population genetic analyses. We show that specific NovoAlign, BWA-aln and BWA-mem parameterizations achieve high mapping precision with low levels of reference bias, particularly after filtering out reads with low mapping qualities. However, unbiased NovoAlign results required the use of an IUPAC reference genome. While relevant only to aDNA projects where reference population data are available, the benefit of using an IUPAC reference demonstrates the value of incorporating population genetic information into the aDNA mapping process, echoing recent results based on graph genome representations.

scholarly journals Imputation of missing genotypes: an empirical evaluation of IMPUTE

BMC Genetics ◽  
2008 ◽  
Vol 9 (1) ◽  
pp. 85 ◽  
Author(s):  
Zhenming Zhao ◽  
Nadia Timofeev ◽  
Stephen W Hartley ◽  
David HK Chui ◽  
Supan Fucharoen ◽  
...  
Keyword(s):  
Empirical Evaluation ◽  
Missing Genotypes

scholarly journals CORPORATE PROFILE, PERFORMANCE AND GREEN SUPPLY CHAIN MANAGEMENT: A RESEARCH AGENDA

2017 ◽  
Vol 18 (3) ◽  
pp. 117-146
Author(s):  
FÁBIO YTOSHI SHIBAO ◽  
GERALDO CARDOSO DE OLIVEIRA NETO ◽  
FLAVIA CRISTINA DA SILVA ◽  
EDUARDO CABRINI POMPONE
Keyword(s):  
Supply Chain ◽  
Supply Chain Management ◽  
Environmental Economic ◽  
Green Supply Chain Management ◽  
Operational Performance ◽  
Green Supply Chain ◽  
Chain Management ◽  
Depth Analysis ◽  
And Performance ◽  
The Relationship

ABSTRACT Purpose: To evaluate the universe of published articles that propose frameworks about the relationship between green supply chain management (GSCM) and performance in the period from 1995 to 2014, in order to propose a conceptual model that can be applied to future studies, considering the green profile besides the practices of GSCM and performance. Originality/gap/relevance/implications: The investigation revealed a lack of relationship among the organizations' profile, its environmental, economic and operational performance and GSCM practices. Key methodological aspects: The relationship among constructs was established through bibliometric analysis obtained in the models/frameworks of GSCM practices and performance extracted from the databases "ProQuest", "EBSCO", "JSTOR", "Web of Science" and "Scopus". Further, the content analysis and network analysis were then performed. Summary of key results: GSCM internal and external practices, environmental performance, economic performance and operational performance were revealed as main topics addressed in GSCM. Moreover, it was noted that studies on internal practices prevailed over those addressed to other practices. Key considerations/conclusions: The models studied did not consider whether the corporate green profile could improve the performance of the organization. Therefore, they did not simultaneously measure environmental, economic and operational performance. It was concluded that the addition of the green profile in conjunction with GSCM practices and performance allows for a more in-depth analysis of the degree of a company's involvement with GSCM, as well as its intended objectives and results achieved in the future.

scholarly journals Exploring the causes of stress and coping with it amongst doctoral level students: Highlighting the importance of information collection and management

2016 ◽  
pp. 19-39
Author(s):  
Sheikh Usman Yousaf ◽  
Bushra Usman ◽  
Muhammad Akram
Keyword(s):  
Coping Strategies ◽  
Doctoral Students ◽  
Time Management ◽  
Information Collection ◽  
Stress Coping ◽  
Doctoral Level ◽  
Work Related ◽  
Depth Analysis ◽  
And Performance ◽  
Information Ambiguity

Stress may hinder the efficiency and performance of individuals. However, little attention has been given to academic stress especially stress experienced by doctoral level university students. Understanding and comprehending the causes of their stress and relevant coping strategies is indeed essential for their better performance. Hence, to address this gap, the purpose of the study was to explore the stressors produced by academic environment and the stress coping strategies adopted by doctoral scholars. Unit of analysis were the individuals enrolled in doctoral studies at the Business School of University Kebangsaan, Malaysia. In-depth analysis of eight doctoral level students revealed that they, in general, share the same experiences and adopt similar coping strategies as were reported to have been experienced and adopted by students of other disciplines (i.e., nursing or psychology students). However, a lack of ability to manage information, information ambiguity and ambiguity regarding quality of one's own work emerged as the major stressors in this study, which have not previously been commonly highlighted by past researches. This study, therefore, reveals that information collection, scarcity of information resources, information ambiguity and work related ambiguity are major stressors for doctoral students. Further, it is also identified that social support, problem diversion, effective information management and time management are significant stress coping techniques. The implications and future recommendation are also discussed in the paper.

Evaluating the learning and performance characteristics of self-organizing systems with different task features

2021 ◽  
pp. 1-19
Author(s):  
Hao Ji ◽  
Yan Jin
Keyword(s):  
Systematic Evaluation ◽  
Full Potential ◽  
Learning Capability ◽  
Reward Function ◽  
Reward Functions ◽  
Good Learning ◽  
And Performance ◽  
Generation Problem ◽  
Self Organizing

Abstract Self-organizing systems (SOS) are developed to perform complex tasks in unforeseen situations with adaptability. Predefining rules for self-organizing agents can be challenging, especially in tasks with high complexity and changing environments. Our previous work has introduced a multiagent reinforcement learning (RL) model as a design approach to solving the rule generation problem of SOS. A deep multiagent RL algorithm was devised to train agents to acquire the task and self-organizing knowledge. However, the simulation was based on one specific task environment. Sensitivity of SOS to reward functions and systematic evaluation of SOS designed with multiagent RL remain an issue. In this paper, we introduced a rotation reward function to regulate agent behaviors during training and tested different weights of such reward on SOS performance in two case studies: box-pushing and T-shape assembly. Additionally, we proposed three metrics to evaluate the SOS: learning stability, quality of learned knowledge, and scalability. Results show that depending on the type of tasks; designers may choose appropriate weights of rotation reward to obtain the full potential of agents’ learning capability. Good learning stability and quality of knowledge can be achieved with an optimal range of team sizes. Scaling up to larger team sizes has better performance than scaling downwards.

Process improvement approaches in emergency departments: a review of the current knowledge

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Ana Vitória Lachowski Volochtchuk ◽  
Higor Leite
Keyword(s):  
Process Improvement ◽  
Thematic Analysis ◽  
Emergency Departments ◽  
Current Knowledge ◽  
Descriptive Analysis ◽  
Content Type ◽  
Quality Healthcare ◽  
Depth Analysis ◽  
Methodological Procedures ◽  
And Performance

PurposeThe healthcare system has been under pressure to provide timely and quality healthcare. The influx of patients in the emergency departments (EDs) is testing the capacity of the system to its limit. In order to increase EDs' capacity and performance, healthcare managers and practitioners are adopting process improvement (PI) approaches in their operations. Thus, this study aims to identify the main PI approaches implemented in EDs, as well as the benefits and barriers to implement these approaches.Design/methodology/approachThe study is based on a rigorous systematic literature review of 115 papers. Furthermore, under the lens of thematic analysis, the authors present the descriptive and prescriptive findings.FindingsThe descriptive analysis found copious information related to PI approaches implemented in EDs, such as main PIs used in EDs, type of methodological procedures applied, as well as a set of barriers and benefits. Aiming to provide an in-depth analysis and prescriptive results, the authors carried out a thematic analysis that found underlying barriers (e.g. organisational, technical and behavioural) and benefits (e.g. for patients, the organisation and processes) of PI implementation in EDs.Originality/valueThe authors contribute to knowledge by providing a comprehensive review of the main PI methodologies applied in EDs, underscoring the most prominent ones. This study goes beyond descriptive studies that identify lists of barriers and benefits, and instead the authors categorize prescriptive elements that influence these barriers and benefits. Finally, this study raises discussions about the behavioural influence of patients and medical staff on the implementation of PI approaches.

An Empirical Evaluation of Information Sharing Between Australia-Singapore Beef Organisations in Light of Trust and ICT Diffusion

2017 ◽  
pp. 23-53
Author(s):  
Latif Al-Hakim ◽  
Melissa Johnson Morgan ◽  
Roberta Chau
Keyword(s):  
Information Exchange ◽  
Gap Analysis ◽  
Empirical Evaluation ◽  
Exchange Relationships ◽  
Validity Analysis ◽  
Business Partners ◽  
Cross Border ◽  
Perceived Performance ◽  
Use Of Technology ◽  
And Performance

This study investigates cross-border collaboration between beef organisations in Australia and Singapore. It aims to identify factors impacting trust and technology diffusion by gauging gaps between expected importance and perceived performance rating of the factors. The research presents results of a survey comprising 69 beef organisations from Australia and Singapore. The research identifies critical gaps using two methods of analysis; validity analysis and performance gap analysis. Each method comprises two types of tests. The WarpPLS software is used to perform the validity analysis. Results indicate gaps in level of responsiveness. The research concludes that the success of cross-border collaboration between organisations in both Australia and Singapore can be better achieved through the establishment of information exchange relationships, rather than through the use of technology alone, and by ensuring compatibility between business partners.

scholarly journals Applying Multiple Methods of Systematic Evaluation in Narrative Analysis for Greater Validity and Deeper Meaning

2019 ◽  
Vol 18 ◽  
pp. 160940691989247
Author(s):  
Rebecca Lynn Meraz ◽  
Kathryn Osteen ◽  
Jocelyn McGee
Keyword(s):  
Data Analysis ◽  
Narrative Analysis ◽  
Narrative Research ◽  
Study Data ◽  
Systematic Evaluation ◽  
Human Beings ◽  
Multiple Methods ◽  
Single Well ◽  
And Performance ◽  
Insight Into

Personal narrative is at the heart of how human beings share information, represent identity, and convey ideas. Narrative research is a form of qualitative analysis that assists researchers in gaining insight into the lived experiences of the persons they are studying within their unique life circumstances and contexts. Although many narrative investigations report themes from study data, there is no single, well-defined approach to data analysis in narrative research. In this article, we provide a method for analyzing the data beyond the spoken words by applying Riessman’s thematic, structural, and performance analysis. We describe how applying multiple methods of systematic evaluation to narrative data leads to a deeper and more valid insight into the told stories. The data analysis process outlined in this article contributes to the academic discourse and knowledge supporting the use of multiple methods of systematic evaluation to uncover deeper meaning and thus leading to greater validity of the findings from narrative data.

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.

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