scholarly journals Refining fine-mapping: effect sizes and regional heritability

2018 ◽  
Author(s):  
Christian Benner ◽  
Aki S. Havulinna ◽  
Veikko Salomaa ◽  
Samuli Ripatti ◽  
Matti Pirinen
Keyword(s):  
Complex Traits ◽  
Fixed Effect Model ◽  
Efficient Estimation ◽  
Effect Sizes ◽  
P Value ◽  
Variance Component Model ◽  
Phenotypic Variance ◽  
Computationally Efficient ◽  
Causal Variants ◽  
The Individual

AbstractRecent statistical approaches have shown that the set of all available genetic variants explains considerably more phenotypic variance of complex traits and diseases than the individual variants that are robustly associated with these phenotypes. However, rapidly increasing sample sizes constantly improve detection and prioritization of individual variants driving the associations between genomic regions and phenotypes. Therefore, it is useful to routinely estimate how much phenotypic variance the detected variants explain for each region by taking into account the correlation structure of variants and the uncertainty in their causal status. Here we extend the FINEMAP software to estimate the effect sizes and regional heritability under the probabilistic model that assumes a handful of causal variants per each region. Using the UK Biobank data to simulate GWAS regions with only a few causal variants, we demonstrate that FINEMAP provides higher precision and enables more detailed decomposition of regional heritability into individual variants than the variance component model implemented in BOLT or the fixed-effect model implemented in HESS. Using data from 51 serum biomarkers and four lipid traits from the FINRISK study, we estimate that FINEMAP captures on average 24% more regional heritability than the variant with the lowest P-value alone and 20% less than BOLT. Our simulations suggest how an upward bias of BOLT and a downward bias of FINEMAP could together explain the observed difference between the methods. We conclude that FINEMAP enables computationally efficient estimation of effect sizes and regional heritability in the era of biobank scale data.

scholarly journals Effect sizes of causal variants for gene expression and complex traits differ between populations

2021 ◽  
Author(s):  
Roshni A. Patel ◽  
Shaila A. Musharoff ◽  
Jeffrey P. Spence ◽  
Harold Pimentel ◽  
Catherine Tcheandjieu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Complex Traits ◽  
Association Studies ◽  
Causal Variant ◽  
Effect Sizes ◽  
European Ancestry ◽  
Genome Wide Association Studies ◽  
Polygenic Scores ◽  
Causal Variants ◽  
Variant Effect

Despite the growing number of genome-wide association studies (GWAS) for complex traits, it remains unclear whether effect sizes of causal genetic variants differ between populations. In principle, effect sizes of causal variants could differ between populations due to gene-by-gene or gene-by-environment interactions. However, comparing causal variant effect sizes is challenging: it is difficult to know which variants are causal, and comparisons of variant effect sizes are confounded by differences in linkage disequilibrium (LD) structure between ancestries. Here, we develop a method to assess causal variant effect size differences that overcomes these limitations. Specifically, we leverage the fact that segments of European ancestry shared between European-American and admixed African-American individuals have similar LD structure, allowing for unbiased comparisons of variant effect sizes in European ancestry segments. We apply our method to two types of traits: gene expression and low-density lipoprotein cholesterol (LDL-C). We find that causal variant effect sizes for gene expression are significantly different between European-Americans and African-Americans; for LDL-C, we observe a similar point estimate although this is not significant, likely due to lower statistical power. Cross-population differences in variant effect sizes highlight the role of genetic interactions in trait architecture and will contribute to the poor portability of polygenic scores across populations, reinforcing the importance of conducting GWAS on individuals of diverse ancestries and environments.

An empirical Bayesian ranking method, with applications to high throughput biology

2019 ◽  
Vol 36 (1) ◽  
pp. 177-185
Author(s):  
John Ferguson ◽  
Joseph Chang
Keyword(s):  
Empirical Bayes ◽  
Final Analysis ◽  
R Package ◽  
Effect Sizes ◽  
Supplementary Information ◽  
P Value ◽  
Ranking Algorithm ◽  
Computationally Efficient ◽  
P Values ◽  
Bayesian Ranking

Abstract Motivation In bioinformatics, genome-wide experiments look for important biological differences between two groups at a large number of locations in the genome. Often, the final analysis focuses on a P-value-based ranking of locations which might then be investigated further in follow-up experiments. However, this strategy may result in small effect sizes, with low P-values, being ranked more favorably than larger more scientifically important effects. Bayesian ranking techniques may offer a solution to this problem provided a good prior distribution for the collective distribution of effect sizes is available. Results We develop an Empirical Bayes ranking algorithm, using the marginal distribution of the data over all locations to estimate an appropriate prior. In simulations and analysis using real datasets, we demonstrate favorable performance compared to ordering P-values and a number of other competing ranking methods. The algorithm is computationally efficient and can be used to rank the entirety of genomic locations or to rank a subset of locations, pre-selected via traditional FWER/FDR methods in a 2-stage analysis. Availability and implementation An R-package, EBrank, implementing the ranking algorithm is available on CRAN. Supplementary information Supplementary data are available at Bioinformatics online.

The Orchard Plot: Cultivating a Forest Plot for Use in Ecology, Evolution and Beyond

2019 ◽  
Author(s):  
Shinichi Nakagawa ◽  
Malgorzata Lagisz ◽  
Rose E O'Dea ◽  
Joanna Rutkowska ◽  
Yefeng Yang ◽  
...  
Keyword(s):  
Meta Analysis ◽  
R Package ◽  
Effect Sizes ◽  
Forest Plot ◽  
Point Estimates ◽  
Aggregate Effect ◽  
The Individual ◽  
Meta Analyses ◽  
Heterogeneous Effect ◽  
Intuitive Interpretation

‘Classic’ forest plots show the effect sizes from individual studies and the aggregate effect from a meta-analysis. However, in ecology and evolution meta-analyses routinely contain over 100 effect sizes, making the classic forest plot of limited use. We surveyed 102 meta-analyses in ecology and evolution, finding that only 11% use the classic forest plot. Instead, most used a ‘forest-like plot’, showing point estimates (with 95% confidence intervals; CIs) from a series of subgroups or categories in a meta-regression. We propose a modification of the forest-like plot, which we name the ‘orchard plot’. Orchard plots, in addition to showing overall mean effects and CIs from meta-analyses/regressions, also includes 95% prediction intervals (PIs), and the individual effect sizes scaled by their precision. The PI allows the user and reader to see the range in which an effect size from a future study may be expected to fall. The PI, therefore, provides an intuitive interpretation of any heterogeneity in the data. Supplementing the PI, the inclusion of underlying effect sizes also allows the user to see any influential or outlying effect sizes. We showcase the orchard plot with example datasets from ecology and evolution, using the R package, orchard, including several functions for visualizing meta-analytic data using forest-plot derivatives. We consider the orchard plot as a variant on the classic forest plot, cultivated to the needs of meta-analysts in ecology and evolution. Hopefully, the orchard plot will prove fruitful for visualizing large collections of heterogeneous effect sizes regardless of the field of study.

scholarly journals Use of Thromboelastography Platelet Mapping for Assessment of Individual Platelet Response Secondary to Oral Antiplatelet Therapy after Percutaneous Coronary Intervention: An Attempt to Start Personalized Antiplatelet Therapy in India

2021 ◽  
Author(s):  
Suvro Sankha Datta ◽  
Dibyendu De ◽  
Nadeem Afroz Muslim
Keyword(s):  
Percutaneous Coronary Intervention ◽  
Antiplatelet Therapy ◽  
Platelet Reactivity ◽  
Coronary Intervention ◽  
Platelet Inhibition ◽  
P Value ◽  
Platelet Response ◽  
Percutaneous Coronary ◽  
The Individual ◽  
Platelet Functions

AbstractHigh on-treatment platelet reactivity (HPR) with P2Y12 receptor antagonists in patients treated with dual antiplatelet therapy (DAPT) is strongly associated with adverse ischemic events after percutaneous coronary intervention (PCI). This prospective study was conducted to assess individual platelet response and HPR to antiplatelet medications in post-PCI cases by thromboelastography platelet mapping (TEG-PM). Total 82 patients who were on aspirin and on either clopidogrel, prasugrel, or ticagrelor were evaluated. The percentage of platelet inhibition to arachidonic acid (AA) and adenosine disdiphosphate (ADP) was calculated by [100-{(MA ADP/AA–MA Fibrin) / (MA Thrombin–MA Fibrin) × 100}], taking 50% response as cut-off for HPR. HPR to clopidogrel and prasugrel was 14.29 and 12.5%, respectively. No HPR was detected to aspirin and ticagrelor. The mean percentage of platelet inhibition was significantly higher in patients with ticagrelor 82.99, 95% confidence interval (CI) of [77.3, 88.7] as compared with clopidogrel 72.21, 95% CI of [65.3, 79.1] and prasugrel 64.2, 95% CI of [52.5, 75.9] (p-value of 0.041 and 0.003, respectively). Aspirin along with ticagrelor is associated with a higher mean percentage of platelet inhibition, and lower HPR as compared with the usage of aspirin combined with clopidogrel or prasugrel. Additionally, it might also be concluded that TEG-PM could be used effectively to measure the individual platelet functions which would make oral antiplatelet therapy more personalized for cardiac patients.

Safety climate and related factors in rehabilitation nurses of hospitals in Iran

Work ◽  
2021 ◽  
pp. 1-8
Author(s):  
Javad vatani ◽  
Zahra Khanikosarkhizi ◽  
Mohammad Ali Shahabi Rabori ◽  
mohammad khandan ◽  
Mohsen aminizadeh ◽  
...  
Keyword(s):  
Safety Climate ◽  
Cross Sectional Study ◽  
P Value ◽  
Related Factors ◽  
Cross Sectional ◽  
Climate Assessment ◽  
Safety Work ◽  
Significant Difference ◽  
Training Courses ◽  
The Individual

BACKGROUND: Safety climate is a common insight of staff that indicates individuals’ attitudes toward safety and priority of safety at work. OBJECTIVES: Nursing is a risky job where paying attention to safety is crucial. The assessment of the safety climate is one of the methods to measure the safety conditions in this occupation. The aim of this study was to assess the safety climate of rehabilitation nurses working in hospitals in Tehran. METHODS: This is a cross-sectional study which was carried out on 140 rehabilitation nurses selected from all hospitals and clinics in Tehran in 2019. To collect the required data, a two-section questionnaire was used. The first section was related to demographic factors and the second part (22 statements) was to measure the safety climate using nurses’ safety climate assessment questionnaire. The collected data were analyzed by SPSS V16 using independent t-test, ANOVA, Kruskal-Wallis and Mann-Whitney U test at the 5% level. RESULTS: Findings showed that the total mean of safety climate was 3.06±0.56. According to the results, a significant difference was found between the positive and negative satisfaction of nurses with safety climates (P-value = 0.03), communication with nurses (P-value = 0.01) and supervisors’ attitude (P-value = 0.02). Furthermore, a significant difference in safety climate between the individual with the second job and the individual without second could be observed (P-value = 0.01). CONCLUSIONS: The results indicated that the safety climate was not at an acceptable level. Thus, it is essential to introduce safety training courses (e.g. safety, work-rest balance, and so on) and to improve the safety performance at work.

Factors associated with successful return to work in young heart failure and ischaemic heart disease patients following index hospital admission

2021 ◽  
Vol 28 (Supplement_1) ◽  
Author(s):  
HV Thakkar ◽  
L Hollingsworth ◽  
JA Enright ◽  
S Sanderson ◽  
RJ Macfadyen ◽  
...  
Keyword(s):  
Heart Failure ◽  
Return To Work ◽  
Decompensated Heart Failure ◽  
Individual Factors ◽  
P Value ◽  
Complex Interplay ◽  
Coronary Syndrome ◽  
Funding Sources ◽  
Financial Benefits ◽  
The Individual

Abstract Funding Acknowledgements Type of funding sources: None. Background Factors influencing return to remunerated work following an acute cardiac illness are poorly defined. We wished to compare the factors in our cohorts following first presentation of acute coronary syndrome(ACS) and decompensated heart failure(HF). Methods Prospectively identified subjects, aged 18-65years, from a rehabilitation population for ACS and HF during 2018-2019 underwent a survey. Results Of 133cases meeting inclusion criteria, 84 completed the survey(41 HF, 80% male, mean age 55years; 43 ACS, 86% male, mean age 57years). Socio-economic indexes for Areas(SIEFA) index were similar for HF(900) & ACS(909) groups, which represents 11th and 14th percentile for Australia respectively. Cardiovascular risk factors were similar except hypercholesterolemia(37% v 60%; p = 0.029) was more common in ACS. Many subjects did not continue beyond Yr12, (54% HF v 30% ACS; p = 0.029). A majority of ACS cases returned to work as compared with HF(70% v 44%; p = 0.017)(Figure). On multivariate analysis, male gender[p = 0.031;OR 13.71 (1.28-147.36)]; access to financial benefits[p < 0.001;OR 22.75 (4.31-119.99)] and a desire to return to work [p = 0.014;OR 12.1 (1.67-87.82)] were associated with successful return to work (Table). Limitations Our study has small numbers so will be difficult to generalise to a wider population. We do show a signal towards the complex interplay of the social and individual factors in determining return to work. Further larger studies are required to tease out the differences between the individual factors to help predict return to work in the Australian context. Conclusion Successful return to work for patients with first presentation of ACS or HF could not be reliably predicted. Patients with ACS returned to work more often than HF. In HF patients who do n to return to work, recurrent symptoms, individual motivation, social support and access to financial benefits have a complex interplay. Predictors of return to work Predictor P value OR (95% CI) Diagnosis (heart failure) 0.095 0.29 (0.07, 1.24) Gender (male) 0.031 13.71 (1.28, 147.36) Access to benefit (none) <0.001 22.75 (4.31, 119.99) Desire to RTW (yes) 0.014 12.1 (1.67, 87.82) Abstract Figure. Rates of return to work in the 2 groups

scholarly journals Responsiveness of the Scripps Neurologic Rating Scale During a Multiple Sclerosis Clinical Trial

1999 ◽  
Vol 26 (4) ◽  
pp. 283-289 ◽  
Author(s):  
James A. Koziol ◽  
Adriana Lucero ◽  
Jack C. Sipe ◽  
John S. Romine ◽  
Ernest Beutler
Keyword(s):  
Multiple Sclerosis ◽  
Clinical Trial ◽  
Rating Scale ◽  
Effect Sizes ◽  
Active Therapy ◽  
Double Blind ◽  
Treatment Groups ◽  
Negative Effect ◽  
The Individual ◽  
Positive Effect

Objective:The Scripps neurologic rating scale (SNRS) is a summary measure of individual components comprising a neurological examination, designed for use in multiple sclerosis (MS). Our objective is to evaluate the responsiveness of the SNRS, within the context of a 2-year, randomized, double-blind crossover study of the efficacy of cladribine for treatment of secondary progressive MS.Methods:Effect sizes were determined for the SNRS and its components, separately for each treatment group (initial placebo, and initial cladribine) over both years of the clinical trial, using a standard random effects model.Results:Individual components tended to show positive effect sizes (improvement) during periods of active therapy in both treatment groups, and negative effect sizes (deterioration) during periods of no active therapy. Summation indices derived from the individual components of the SNRS seemed somewhat more stable than the individual components. The two components mentation and mood, and bladder, bowel, or sexual dysfunction, were rather unresponsive in our clinical trial.Conclusion:Changes in the components of the SNRS over the course of our clinical trial were consistent between the two treatment groups. Most components were moderately responsive; and, the summary SNRS score appropriately summarized the moderate magnitudes of change evinced in the individual components.

scholarly journals The distribution of SNP marker effects for faecal worm egg count in sheep, and the feasibility of using these markers to predict genetic merit for resistance to worm infections

2011 ◽  
Vol 93 (3) ◽  
pp. 203-219 ◽  
Author(s):  
KATHRYN E. KEMPER ◽  
DAVID L. EMERY ◽  
STEPHEN C. BISHOP ◽  
HUTTON ODDY ◽  
BENJAMIN J. HAYES ◽  
...  
Keyword(s):  
Complex Traits ◽  
Sampling Error ◽  
Snp Markers ◽  
Snp Marker ◽  
Genomic Relationship Matrix ◽  
Relationship Matrix ◽  
Phenotypic Variance ◽  
Breeding Values ◽  
Genetic Merit ◽  
Independent Population

SummaryGenetic resistance to gastrointestinal worms is a complex trait of great importance in both livestock and humans. In order to gain insights into the genetic architecture of this trait, a mixed breed population of sheep was artificially infected with Trichostrongylus colubriformis (n=3326) and then Haemonchus contortus (n=2669) to measure faecal worm egg count (WEC). The population was genotyped with the Illumina OvineSNP50 BeadChip and 48 640 single nucleotide polymorphism (SNP) markers passed the quality controls. An independent population of 316 sires of mixed breeds with accurate estimated breeding values for WEC were genotyped for the same SNP to assess the results obtained from the first population. We used principal components from the genomic relationship matrix among genotyped individuals to account for population stratification, and a novel approach to directly account for the sampling error associated with each SNP marker regression. The largest marker effects were estimated to explain an average of 0·48% (T. colubriformis) or 0·08% (H. contortus) of the phenotypic variance in WEC. These effects are small but consistent with results from other complex traits. We also demonstrated that methods which use all markers simultaneously can successfully predict genetic merit for resistance to worms, despite the small effects of individual markers. Correlations of genomic predictions with breeding values of the industry sires reached a maximum of 0·32. We estimate that effective across-breed predictions of genetic merit with multi-breed populations will require an average marker spacing of approximately 10 kbp.

scholarly journals GxEsum: a novel approach to estimate the phenotypic variance explained by genome-wide GxE interaction based on GWAS summary statistics for biobank-scale data

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Jisu Shin ◽  
Sang Hong Lee
Keyword(s):  
Complex Traits ◽  
Error Rates ◽  
Type I ◽  
Phenotypic Variance ◽  
Environment Interaction ◽  
Summary Statistics ◽  
Gxe Interaction ◽  
Genome Wide ◽  
Scale Data ◽  
Variance Explained

AbstractGenetic variation in response to the environment, that is, genotype-by-environment interaction (GxE), is fundamental in the biology of complex traits and diseases. However, existing methods are computationally demanding and infeasible to handle biobank-scale data. Here, we introduce GxEsum, a method for estimating the phenotypic variance explained by genome-wide GxE based on GWAS summary statistics. Through comprehensive simulations and analysis of UK Biobank with 288,837 individuals, we show that GxEsum can handle a large-scale biobank dataset with controlled type I error rates and unbiased GxE estimates, and its computational efficiency can be hundreds of times higher than existing GxE methods.

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