scholarly journals Estimating SNP heritability in presence of population substructure in biobank-scale datasets

2020 ◽  
Author(s):  
Zhaotong Lin ◽  
Souvik Seal ◽  
Saonli Basu
Keyword(s):  
Complex Traits ◽  
Population Stratification ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Population Substructure ◽  
Relationship Matrix ◽  
Phenotypic Variance ◽  
Genetic Contribution ◽  
Heritability Estimation ◽  
The Impact

AbstractSNP heritability of a trait is measured by the proportion of total variance explained by the additive effects of genome-wide single nucleotide polymorphisms (SNPs). Linear mixed models are routinely used to estimate SNP heritability for many complex traits. The basic concept behind this approach is to model genetic contribution as a random effect, where the variance of this genetic contribution attributes to the heritability of the trait. This linear mixed model approach requires estimation of ‘relatedness’ among individuals in the sample, which is usually captured by estimating a genetic relationship matrix (GRM). Heritability is estimated by the restricted maximum likelihood (REML) or method of moments (MOM) approaches, and this estimation relies heavily on the GRM computed from the genetic data on individuals. Presence of population substructure in the data could significantly impact the GRM estimation and may introduce bias in heritability estimation. The common practice of accounting for such population substructure is to adjust for the top few principal components of the GRM as covariates in the linear mixed model. Here we propose an alternative way of estimating heritability in multi-ethnic studies. Our proposed approach is a MOM estimator derived from the Haseman-Elston regression and gives an asymptotically unbiased estimate of heritability in presence of population stratification. It introduces adjustments for the population stratification in a second-order estimating equation and allows for the total phenotypic variance vary by ethnicity. We study the performance of different MOM and REML approaches in presence of population stratification through extensive simulation studies. We estimate the heritability of height, weight and other anthropometric traits in the UK Biobank cohort to investigate the impact of subtle population substructure on SNP heritability estimation.

scholarly journals Examining the Impact of Imputation Errors on Fine-Mapping Using DNA Methylation QTL as a Model Trait

Genetics ◽  
2019 ◽  
Vol 212 (3) ◽  
pp. 577-586 ◽  
Author(s):  
V. Kartik Chundru ◽  
Riccardo E. Marioni ◽  
James G. D. Prendergast ◽  
Costanza L. Vallerga ◽  
Tian Lin ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Fine Mapping ◽  
Complex Traits ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Imputation Accuracy ◽  
Reference Panel ◽  
Whole Genome Sequence ◽  
Strong Linkage Disequilibrium ◽  
The Impact

Genetic variants disrupting DNA methylation at CpG dinucleotides (CpG-SNP) provide a set of known causal variants to serve as models to test fine-mapping methodology. We use 1716 CpG-SNPs to test three fine-mapping approaches (Bayesian imputation-based association mapping, Bayesian sparse linear mixed model, and the J-test), assessing the impact of imputation errors and the choice of reference panel by using both whole-genome sequence (WGS), and genotype array data on the same individuals (n = 1166). The choice of imputation reference panel had a strong effect on imputation accuracy, with the 1000 Genomes Project Phase 3 (1000G) reference panel (n = 2504 from 26 populations) giving a mean nonreference discordance rate between imputed and sequenced genotypes of 3.2% compared to 1.6% when using the Haplotype Reference Consortium (HRC) reference panel (n = 32,470 Europeans). These imputation errors had an impact on whether the CpG-SNP was included in the 95% credible set, with a difference of ∼23% and ∼7% between the WGS and the 1000G and HRC imputed datasets, respectively. All of the fine-mapping methods failed to reach the expected 95% coverage of the CpG-SNP. This is attributed to secondary cis genetic effects that are unable to be statistically separated from the CpG-SNP, and through a masking mechanism where the effect of the methylation disrupting allele at the CpG-SNP is hidden by the effect of a nearby SNP that has strong linkage disequilibrium with the CpG-SNP. The reduced accuracy in fine-mapping a known causal variant in a low-level biological trait with imputed genetic data has implications for the study of higher-order complex traits and disease.

scholarly journals An integrative analysis of genomic and exposomic data for complex traits and phenotypic prediction

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Xuan Zhou ◽  
S. Hong Lee
Keyword(s):  
Complex Traits ◽  
Prediction Accuracy ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Complex Trait ◽  
Great Promise ◽  
Phenotypic Variance ◽  
Additive Effects ◽  
Mixed Model Approach ◽  
The Uk

AbstractComplementary to the genome, the concept of exposome has been proposed to capture the totality of human environmental exposures. While there has been some recent progress on the construction of the exposome, few tools exist that can integrate the genome and exposome for complex trait analyses. Here we propose a linear mixed model approach to bridge this gap, which jointly models the random effects of the two omics layers on phenotypes of complex traits. We illustrate our approach using traits from the UK Biobank (e.g., BMI and height for N ~ 35,000) with a small fraction of the exposome that comprises 28 lifestyle factors. The joint model of the genome and exposome explains substantially more phenotypic variance and significantly improves phenotypic prediction accuracy, compared to the model based on the genome alone. The additional phenotypic variance captured by the exposome includes its additive effects as well as non-additive effects such as genome–exposome (gxe) and exposome–exposome (exe) interactions. For example, 19% of variation in BMI is explained by additive effects of the genome, while additional 7.2% by additive effects of the exposome, 1.9% by exe interactions and 4.5% by gxe interactions. Correspondingly, the prediction accuracy for BMI, computed using Pearson’s correlation between the observed and predicted phenotypes, improves from 0.15 (based on the genome alone) to 0.35 (based on the genome and exposome). We also show, using established theories, that integrating genomic and exposomic data can be an effective way of attaining a clinically meaningful level of prediction accuracy for disease traits. In conclusion, the genomic and exposomic effects can contribute to phenotypic variation via their latent relationships, i.e. genome-exposome correlation, and gxe and exe interactions, and modelling these effects has a potential to improve phenotypic prediction accuracy and thus holds a great promise for future clinical practice.

scholarly journals An integrative analysis of genomic and exposomic data for complex traits and phenotypic prediction

2020 ◽  
Author(s):  
Xuan Zhou ◽  
S. Hong Lee
Keyword(s):  
Complex Traits ◽  
Prediction Accuracy ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Complex Trait ◽  
Great Promise ◽  
Phenotypic Variance ◽  
Additive Effects ◽  
Mixed Model Approach ◽  
The Uk

AbstractComplementary to the genome, the concept of exposome has been proposed to capture the totality of human environmental exposures. While there has been some recent progress on the construction of the exposome, few tools exist that can integrate the genome and exposome for complex trait analyses. Here we propose a linear mixed model approach to bridge this gap, which jointly models the random effects of the two omics layers on phenotypes of complex traits. We illustrate our approach using traits from the UK Biobank (e.g., BMI & height for N ~ 40,000) with a small fraction of the exposome that comprises 28 lifestyle factors. The joint model of the genome and exposome explains substantially more phenotypic variance and significantly improves phenotypic prediction accuracy, compared to the model based on the genome alone. The additional phenotypic variance captured by the exposome includes its additive effects as well as non-additive effects such as genome-exposome (gxe) and exposome-exposome (exe) interactions. For example, 19% of variation in BMI is explained by additive effects of the genome, while additional 7.2% by additive effects of the exposome, 1.9% by exe interactions and 4.5% by gxe interactions. Correspondingly, the prediction accuracy for BMI, computed using Pearson’s correlation between the observed and predicted phenotypes, improves from 0.15 (based on the genome alone) to 0.35 (based on the genome & exposome). We also show, using established theories, integrating genomic and exposomic data is essential to attaining a clinically meaningful level of prediction accuracy for disease traits. In conclusion, the genomic and exposomic effects can contribute to phenotypic variation via their latent relationships, i.e. genome-exposome correlation, and gxe and exe interactions, and modelling these effects has a great potential to improve phenotypic prediction accuracy and thus holds a great promise for future clinical practice.

scholarly journals Clinical implementation of a 3D4K-exoscope (Orbeye) in microneurosurgery

2021 ◽  
Author(s):  
Judith Rösler ◽  
Stefan Georgiev ◽  
Anna L. Roethe ◽  
Denny Chakkalakal ◽  
Güliz Acker ◽  
...  
Keyword(s):  
User Satisfaction ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Intraoperative Complications ◽  
Rank Test ◽  
User Group ◽  
Clinical Implementation ◽  
Physical Strain ◽  
Conventional Microscope ◽  
The Impact

AbstractExoscopic surgery promises alleviation of physical strain, improved intraoperative visualization and facilitation of the clinical workflow. In this prospective observational study, we investigate the clinical usability of a novel 3D4K-exoscope in routine neurosurgical interventions. Questionnaires on the use of the exoscope were carried out. Exemplary cases were additionally video-documented. All participating neurosurgeons (n = 10) received initial device training. Changing to a conventional microscope was possible at all times. A linear mixed model was used to analyse the impact of time on the switchover rate. For further analysis, we dichotomized the surgeons in a frequent (n = 1) and an infrequent (n = 9) user group. A one-sample Wilcoxon signed rank test was used to evaluate, if the number of surgeries differed between the two groups. Thirty-nine operations were included. No intraoperative complications occurred. In 69.2% of the procedures, the surgeon switched to the conventional microscope. While during the first half of the study the conversion rate was 90%, it decreased to 52.6% in the second half (p = 0.003). The number of interventions between the frequent and the infrequent user group differed significantly (p = 0.007). Main reasons for switching to ocular-based surgery were impaired hand–eye coordination and poor depth perception. The exoscope investigated in this study can be easily integrated in established neurosurgical workflows. Surgical ergonomics improved compared to standard microsurgical setups. Excellent image quality and precise control of the camera added to overall user satisfaction. For experienced surgeons, the incentive to switch from ocular-based to exoscopic surgery greatly varies.

scholarly journals The influence of xylanase on the fermentability, digestibility, and physiochemical properties of insoluble corn-based fiber along the gastrointestinal tract of growing pigs

2021 ◽  
Author(s):  
Amy L Petry ◽  
Nichole F Huntley ◽  
Michael R Bedford ◽  
John F Patience
Keyword(s):  
Mixed Model ◽  
Linear Mixed Model ◽  
Nutrient Digestibility ◽  
Growing Pigs ◽  
Neutral Detergent Fiber ◽  
Adaptation Period ◽  
High Fiber ◽  
Gross Energy ◽  
Digesta Viscosity ◽  
The Impact

Abstract In theory, supplementing xylanase in corn-based swine diets should improve nutrient and energy digestibility and fiber fermentability, but its efficacy is inconsistent. The experimental objective was to investigate the impact of xylanase on energy and nutrient digestibility, digesta viscosity, and fermentation when pigs are fed a diet high in insoluble fiber (>20% neutral detergent fiber; NDF) and given a 46-d dietary adaptation period. Three replicates of 20 growing gilts were blocked by initial body weight, individually housed, and assigned to 1 of 4 dietary treatments: a low-fiber control (LF) with 7.5% NDF, a 30% corn bran high-fiber control (HF; 21.9% NDF), HF+100 mg xylanase/kg [HF+XY, (Econase XT 25P; AB Vista, Marlborough, UK)] providing 16,000 birch xylan units/kg; and HF+50 mg arabinoxylan-oligosaccharide (AXOS) product/kg [HF+AX, (XOS 35A; Shandong Longlive Biotechnology, Shandong, China)] providing AXOS with 3-7 degrees of polymerization. Gilts were allowed ad libitum access to fed for 36-d. On d 36, pigs were housed in metabolism crates for a 10-d period, limit fed, and feces were collected. On d 46, pigs were euthanized and ileal, cecal, and colonic digesta were collected. Data were analyzed as a linear mixed model with block and replication as random effects, and treatment as a fixed effect. Compared with LF, HF reduced the apparent ileal digestibility (AID), apparent cecal digestibility (ACED), apparent colonic digestibility (ACOD), and apparent total tract digestibility (ATTD) of dry matter (DM), gross energy (GE), crude protein (CP), acid detergent fiber (ADF), NDF, and hemicellulose (P<0.01). Relative to HF, HF+XY improved the AID of GE, CP, and NDF (P<0.05), and improved the ACED, ACOD, and ATTD of DM, GE, CP, NDF, ADF, and hemicellulose (P<0.05). Among treatments, pigs fed HF had increased hindgut DM disappearance (P=0.031). Relative to HF, HF+XY improved cecal disappearance of DM (162 vs. 98g; P=0.008) and NDF (44 vs. 13g; P<0.01). Pigs fed xylanase had a greater proportion of acetate in cecal digesta and butyrate in colonic digesta among treatments (P<0.05). Compared with LF, HF increased ileal, cecal, and colonic viscosity, but HF+XY decreased ileal viscosity compared with HF (P<0.001). In conclusion, increased insoluble corn-based fiber decreases digestibility, reduces cecal fermentation, and increases digesta viscosity, but supplementing xylanase partially mitigated that effect.

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 The Use of Telehealth Technology in Assessing the Accuracy of Self-Reported Weight and the Impact of a Daily Immediate-Feedback Intervention among Obese Employees

2011 ◽  
Vol 2011 ◽  
pp. 1-6 ◽  
Author(s):  
Nicolaas P. Pronk ◽  
A. Lauren Crain ◽  
Jeffrey J. VanWormer ◽  
Brian C. Martinson ◽  
Jackie L. Boucher ◽  
...  
Keyword(s):  
Body Weight ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Controlled Trial ◽  
Intervention Group ◽  
Study Groups ◽  
Outcome Variable ◽  
Control Group ◽  
Immediate Feedback ◽  
The Impact

Objective.To determine the accuracy of self-reported body weight prior to and following a weight loss intervention including daily self-weighing among obese employees.Methods.As part of a 6-month randomized controlled trial including a no-treatment control group, an intervention group received a series of coaching calls, daily self-weighing, and interactive telemonitoring. The primary outcome variable was the absolute discrepancy between self-reported and measured body weight at baseline and at 6 months. We used general linear mixed model regression to estimate changes and differences between study groups over time.Results.At baseline, study participants underreported their weight by an average of 2.06 (se=0.33) lbs. The intervention group self-reported a smaller absolute body weight discrepancy at followup than the control group.Conclusions.The discrepancy between self-reported and measured body weight appears to be relatively small, may be improved through daily self-monitoring using immediate-feedback telehealth technology, and negligibly impacts change in body weight.

scholarly journals O003 The impact of wind farm noise in a laboratory setting on objective and subjective sleep efficiency

2021 ◽  
Vol 2 (Supplement_1) ◽  
pp. A1-A2
Author(s):  
T Liebich ◽  
L Lack ◽  
G Micic ◽  
K Hansen ◽  
B Zajamsek ◽  
...  
Keyword(s):  
Sleep Disturbance ◽  
Wind Farm ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Sleep Efficiency ◽  
Self Report ◽  
Sleep Diary ◽  
Noise Condition ◽  
Subjective Sleep ◽  
The Impact

Abstract Introduction Well-controlled studies of wind farm noise (WFN) on sleep are lacking despite complaints and known effects of other noise types on sleep. This laboratory-based study investigated the impact of continuous full-night WFN exposure replicated from field recordings on polysomnography-measured (objective) and sleep diary-determined (subjective) sleep efficiency compared to a quiet control night. Methods Based on residential location and self-report data, 50 participants were categorised into three groups (14 living <10km from a wind farm and self-reporting sleep disturbance; 19 living <10km from a wind farm and self-reporting no sleep disturbance and 18 controls living in a quiet rural area). Participants underwent full in-laboratory polysomnography during exposure to continuous WFN (25 dB(A)) throughout the night and a quiet control night (background noise 19 dB(A)) in random order. Group and noise condition effects were examined via linear mixed model analysis. Results Participants (30 females) were aged (mean±SD) 54.9±17.6 range: 18–80 years. Sleep efficiency in the control condition was (median [interquartile range]) objective: 85.5 [77.4 to 91.2]%; subjective: 85.7 [69.2 to 92.7]%) versus the WFN condition (objective: 86.1 [78.6 to 91.7]% subjective: 85.8 [66.2 to 93.8]%) with no significant main or interaction effects of group or noise condition (all p’s >0.05). Conclusion These results do not support that WFN at 25 dB(A) significantly impacts objective or subjective sleep efficiency in participants with or without prior WFN exposure or self-reported WFN-related sleep disturbance. Further analyses to investigate potential sleep micro-structural changes remain warranted.

scholarly journals THE IMPACT OF HOSPITAL BASED INTEGRATED MANAGEMENT OF CHILDHOOD ILLNESS TRAINING ON PEDIATRIC NURSE COMPETENCY

2018 ◽  
Vol 4 (1) ◽  
pp. 16-23
Author(s):  
Fitri Haryanti ◽  
Mohammad Hakimi ◽  
Yati Sunarto ◽  
Yayi S Prabandari
Keyword(s):  
Mixed Model ◽  
Linear Mixed Model ◽  
Integrated Management ◽  
Well Being ◽  
Childhood Illness ◽  
Internal Conflict ◽  
Child Health Services ◽  
Management Support ◽  
The Impact

Background: Although the WHO strategy integrated management of childhood illness (IMCI) for primary care has been implemented in over 100 countries, there is less global experience with hospital-based IMCI training. Until recently, no training had been done in Indonesia, and globally there has been limited experience of the role of IMCI in rebuilding health systems after complex emergencies.Objective: We aimed to examine the effect of hospital-based IMCI training on pedicatric nurse competency and explore the perception of Indonesian doctors, nurse managers and paediatricians about IMCI training and its development in West Aceh, a region that was severely affected by the South-Asian tsunami in December 2004.Methods: This study used stepped wedge design. Training was conducted for 39 nurses staff, 13 midwifes, 6 Head nurses, 5 manager of nurses, 5 doctors, 1 paediatricians, and 3 support facilities  (nutritionist, pharmacist, laboratory) in Cut Nyak Dien (CND) Hospital in Meulaboh, West Aceh, Indonesia. The IMCI training was developed based on the WHO Pocketbook of Hospital Care for Children. A nurses competency questionnaire was used based on the guideline of assessment of the quality of child health services at the first level reference hospitals in districts / municipalities issued by the Ministry of Health in 2007. A linear mixed model was used for data analysis.Results: The hospital based IMCI training improved the competences of nurses paediatric in assessing emergency signs of the sick children, management of cough and difficulty breathing, diarrhoea, fever, nutritional problems, supportive care, monitoring, discharge planning and follow up.  The assessment highlighted several problems in adaptation process of material training, training process and implementation in an environment soon after a major disaster.Conclusion: Hospital based IMCI training can be implemented in a setting after major disasters or internal conflict as part of a rebuilding process.  The program requires strong management support and the emergency phase to be subsided.  Other pre-requisites include the existence of standard operating procedures, adequate physical facilities and support for staff morale and well-being.  Improving the quality of paediatric care requires more than just training and clinical guidelines; internal motivation and health worker support are essential.

scholarly journals GWAS-Flow: A GPU accelerated framework for efficient permutation based genome-wide association studies

2019 ◽  
Author(s):  
Jan A. Freudenthal ◽  
Markus J. Ankenbrand ◽  
Dominik G. Grimm ◽  
Arthur Korte
Keyword(s):  
Complex Traits ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Association Studies ◽  
Large Datasets ◽  
Genome Wide Association ◽  
Small Data ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
Non Gaussian

AbstractMotivationGenome-wide association studies (GWAS) are one of the most commonly used methods to detect associations between complex traits and genomic polymorphisms. As both genotyping and phenotyping of large populations has become easier, typical modern GWAS have to cope with massive amounts of data. Thus, the computational demand for these analyses grew remarkably during the last decades. This is especially true, if one wants to implement permutation-based significance thresholds, instead of using the naïve Bonferroni threshold. Permutation-based methods have the advantage to provide an adjusted multiple hypothesis correction threshold that takes the underlying phenotypic distribution into account and will thus remove the need to find the correct transformation for non Gaussian phenotypes. To enable efficient analyses of large datasets and the possibility to compute permutation-based significance thresholds, we used the machine learning framework TensorFlow to develop a linear mixed model (GWAS-Flow) that can make use of the available CPU or GPU infrastructure to decrease the time of the analyses especially for large datasets.ResultsWe were able to show that our application GWAS-Flow outperforms custom GWAS scripts in terms of speed without loosing accuracy. Apart from p-values, GWAS-Flow also computes summary statistics, such as the effect size and its standard error for each individual marker. The CPU-based version is the default choice for small data, while the GPU-based version of GWAS-Flow is especially suited for the analyses of big data.AvailabilityGWAS-Flow is freely available on GitHub (https://github.com/Joyvalley/GWAS_Flow) and is released under the terms of the MIT-License.

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