Variance Explained
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2021 ◽  
Author(s):  
Tarekegn Argaw Woldemeskel ◽  
Brehanu Amsalu Fenta ◽  
Girum Azmach Mekonnen ◽  
Habtemariam Zegeye Endalamaw ◽  
Assefa Funga Alemu

Abstract The analysis of multi-environment trials (MET) data has a long history in plant breeding and agricultural research, with the earliest approaches being based on ANOVA methods. ANOVA-based biplot analysis has been used for a long time in analyzing MET data, and advances have been made employing different modeling approaches. This paper presents MET data analysis using mixed model approaches, and compares three methods of biplot analysis, namely genotype main effects plus genotype by environment interaction (GGE) analysis, factor analytic multiplicative mixed (FAMM) model analysis, and combined model analysis. Ten grain yield datasets from the national variety trial series conducted by the Ethiopian institute of agricultural research were used for this study. Our results revealed that spatial and FA model provide a significant improvement in analyzing MET data. This was demonstrated with evidence of heritability measure. We demonstrated that biplot analysis based on the approached of combined model analysis provides a substantial increase in the total percentage of genotype by environment (G×E) variance explained by the first two multiplicative components for both types of balanced and unbalanced datasets. Thus, by estimating the G×E mean values with the best linear unbiased predictions using spatial+FA (FAMM model analysis), and thereby conducting biplot analysis based on the combined model analysis, plant breeding and trial evaluation programs can have a more robust platform for evaluation of crop cultivars with greater confidence in discriminating superior cultivars across a range of environments.


2021 ◽  
Vol 11 (23) ◽  
pp. 11227
Author(s):  
Arnold Kamis ◽  
Yudan Ding ◽  
Zhenzhen Qu ◽  
Chenchen Zhang

The purpose of this paper is to model the cases of COVID-19 in the United States from 13 March 2020 to 31 May 2020. Our novel contribution is that we have obtained highly accurate models focused on two different regimes, lockdown and reopen, modeling each regime separately. The predictor variables include aggregated individual movement as well as state population density, health rank, climate temperature, and political color. We apply a variety of machine learning methods to each regime: Multiple Regression, Ridge Regression, Elastic Net Regression, Generalized Additive Model, Gradient Boosted Machine, Regression Tree, Neural Network, and Random Forest. We discover that Gradient Boosted Machines are the most accurate in both regimes. The best models achieve a variance explained of 95.2% in the lockdown regime and 99.2% in the reopen regime. We describe the influence of the predictor variables as they change from regime to regime. Notably, we identify individual person movement, as tracked by GPS data, to be an important predictor variable. We conclude that government lockdowns are an extremely important de-densification strategy. Implications and questions for future research are discussed.


Genome ◽  
2021 ◽  
Author(s):  
Ranjan K. Shaw ◽  
Mobeen Shaik ◽  
M. Santha Lakshmi Prasad ◽  
R.D. Prasad ◽  
manmode darpan mohanrao ◽  
...  

Fusarium wilt caused by <i>Fusarium oxysporum</i> f. sp <i>ricini</i> is the most destructive disease in castor. Host plant resistance is the best strategy for management of wilt problem. Identification of molecular markers linked to wilt resistance will enhance the efficiency and effectiveness of breeding for wilt resistance. In the present study, mapping of genomic regions linked to wilt resistance was attempted using a bi-parental population of 185 F<sub>6</sub>- RILs and a genetically diverse panel of 300 germplasm accessions. Quantitative trait loci (QTL) analysis performed using a linkage map consisting of 1,090 SNP markers identified a major QTL on chromosome-7 with LOD score of 18.7, which explained 44 per cent of phenotypic variance. The association mapping performed using genotypic data from 3,465 SNP loci revealed 69 significant associations (p <1×10-4) for wilt resistance. The phenotypic variance explained by the individual SNPs ranged from 0.063 to 0.210. The QTL detected in the bi-parental mapping population was not identified in the association analysis. Thus, the results of this study indicate the possibility of vast gene diversity for Fusarium wilt resistance in castor.


SOIL ◽  
2021 ◽  
Vol 7 (2) ◽  
pp. 743-766
Author(s):  
Virginie Sellier ◽  
Oldrich Navratil ◽  
John Patrick Laceby ◽  
Cédric Legout ◽  
Anthony Foucher ◽  
...  

Abstract. Tracing the origin of sediment is needed to improve our knowledge of hydro-sedimentary dynamics at the catchment scale. Several fingerprinting approaches have been developed to provide this crucial information. In particular, spectroscopy provides a rapid, inexpensive and non-destructive alternative technique to the conventional analysis of the geochemical properties. Here, we investigated the performance of four multi-proxy approaches based on (1) colour parameters, (2) geochemical properties, (3) colour parameters coupled with geochemical properties and (4) the entire visible spectrum to discriminate sediment source contributions in a mining catchment of New Caledonia. This French archipelago located in the south-west Pacific Ocean is the world's sixth largest producer of nickel. Open-cast nickel mining increases soil degradation and the downstream transfer of sediments in river systems, leading to the river system siltation. The sediment sources considered in the current research were therefore sediment eroded from mining sub-catchments and non-mining sub-catchments. To this end, sediment deposited during two cyclonic events (i.e. 2015 and 2017) was collected following a tributary design approach in one of the first areas exploited for nickel mining on the archipelago, the Thio River catchment (397 km2). Source (n=24) and river sediment (n=19) samples were analysed by X-ray fluorescence and spectroscopy in the visible spectra (i.e. 365–735 nm). The results demonstrated that the individual sediment tracing methods based on spectroscopy measurements (i.e. (1) and (4)) were not able to discriminate sources. In contrast, the geochemical approach (2) did discriminate sources, with 83.1 % of variance in sources explained. However, it is the inclusion of colour properties in addition to geochemical parameters (3) which provides the strongest discrimination between sources, with 92.6 % of source variance explained. For each of these approaches ((2) and (3)), the associated fingerprinting properties were used in an optimized mixing model. The predictive performance of the models was validated through tests with artificial mixture samples, i.e. where the proportions of the sources were known beforehand. Although with a slightly lower discrimination potential, the “geochemistry” model (2) provided similar predictions of sediment contributions to those obtained with the coupled “colour + geochemistry” model (3). Indeed, the geochemistry model (2) showed that mining tributary contributions dominated the sediments inputs, with a mean contribution of 68 ± 25 % for the 2015 flood event, whereas the colour + geochemistry model (3) estimated that the mining tributaries contributed 65 ± 27 %. In a similar way, the contributions of mining tributaries were evaluated to 83 ± 8 % by the geochemistry model (2) versus 88 ± 8 % by the colour + geochemistry model (3) for the 2017 flood event. Therefore, the use of these approaches based on geochemical properties only (2) or of those coupled to colour parameters (3) was shown to improve source discrimination and to reduce uncertainties associated with sediment source apportionment. These techniques could be extended to other mining catchments of New Caledonia but also to other similar nickel mining areas around the world.


2021 ◽  
Vol 12 ◽  
Author(s):  
Mojca Simčič ◽  
Barbara Luštrek ◽  
Miran Štepec ◽  
Betka Logar ◽  
Klemen Potočnik

The aim of this study was to estimate genetic parameters of 26 individual and four composite type traits in first parity Cika cows. An analysis of variance was performed with the generalized linear model procedure of the SAS/STAT statistical package, where the fixed effects of year of recording, cow’s age at recording and days after calving as a linear regression were included in the model. The variance components for the direct additive genetic effect and the herd effect in all type traits were estimated using the REML method in the VCE-6 software package. The estimated heritabilities ranged from 0.42 to 0.67 for the measured body frame traits, from 0.36 to 0.80 for the scored autochthonous traits, from 0.11 to 0.61 for the scored body frame traits, and from 0.20 to 0.47 for the scored udder traits. The estimated heritabilities for the composite traits called “autochthonous characteristics”, “muscularity”, “body frame” and “udder” were 0.55, 0.19, 0.19, and 0.26, respectively. The estimated genetic correlations among the measured body frame traits were positive and high, while the majority of them among the scored body frame traits were low to moderate. The estimated proportions of variance explained by the herd effect for the composite traits “autochthonous characteristics,” “muscularity,” “body frame” and “udder” were 0.09, 0.28, 0.14, and 0.10, respectively. The estimated heritabilities for the type traits of first parity Cika cows were similar to those reported for other breeds where breeding values have been routinely predicted for a long time. All estimated genetic parameters are already used for breeding value prediction in the Cika cattle population.


2021 ◽  
Author(s):  
Pei-Wen Wu ◽  
Yi-Wen Lai ◽  
Yu-Ting Chin ◽  
Sharon Tsai ◽  
Tun-Min Yang ◽  
...  

Abstract Background Underlying pathophysiological mechanisms drive excessive clustering of cardiometabolic risk factors, causing metabolic syndrome (MetS). However, MetS status may transform as adolescents transition to young adulthood. This study evaluated the latent clustering structure and its stability for MetS during adolescence and investigated the determinants for MetS transformation over 2 years of follow-up. Methods A community-based representative adolescent cohort (n=1516) was evaluated for MetS using four diagnostic criteria and followed for 2.2 years to identify new-onset MetS. Factor analysis and polytomous logistic regression were separately applied to investigate the latent clustering structure for MetS and the relationship between changes in metabolic risk factors and transformations in MetS status. Results The clustering pattern of cardiometabolic parameters was comparable at baseline and follow-up surveys; both comprised a fat‒blood pressure‒glucose three-factor structure (total variance explained: 68.8% and 69.7%, respectively). Among adolescents who were MetS-negative at baseline, 3.2%‒4.4% had incident MetS after 2 years. Among adolescents who were MetS-positive at baseline, 52.0%‒61.9% experienced MetS remission, and 38.1%‒48.0% experienced MetS persistence. Increased systolic blood pressure (SBP) was associated with a higher risk of MetS incidence, and decreased SBP, triglycerides, and glucose levels were associated with MetS remission. Compared with adolescents with a normal metabolic status at baseline, those having an initial abnormal status in the five MetS components all had greater risks for persistent metabolic abnormality 2 years later, with abdominal obesity and increased triglycerides rendering a 15.0- and 5.7-fold risk, respectively. Conclusions The structure of cardiometabolic parameter clustering for MetS is stable during adolescence. Changes in metabolic risk factors affect typological transformation of adolescent MetS. Abnormal MetS components have a high probability of persisting. Early identification of each abnormal component and attendant intervention are vital in adolescents to minimize the future risk of cardiometabolic disorders.


2021 ◽  
Vol 193 (12) ◽  
Author(s):  
MRS Coffin ◽  
KM Knysh ◽  
SD Roloson ◽  
CC Pater ◽  
E Theriaul ◽  
...  

AbstractIn temperate estuaries of the southern Gulf of St. Lawrence, intermittent seasonal anoxia coupled with phytoplankton blooms is a regular occurrence in watersheds dominated by agricultural land use. To examine the spatial relationship between dissolved oxygen and phytoplankton throughout the estuary to assist in designing monitoring programs, oxygen depth profiles and chlorophyll measurements were taken bi-weekly from May to December in 18 estuaries. In five of those estuaries, dissolved oxygen data loggers were set to measure oxygen at hourly intervals and at multiple locations within the estuary the subsequent year. The primary hypothesis was that dissolved oxygen in the upper estuary (first 10% of estuary area) is predictive of dissolved oxygen mid-estuary (50% of estuary area). The second hypothesis was that hypoxia/superoxia in the estuary is influenced by temperature and tidal flushing. Oxygen depth profiles conducted in the first year of study provided preliminary support that dissolved oxygen in the upper estuary was related to dissolved oxygen throughout the estuary. However, dissolved oxygen from loggers deployed at 10% and 50% of estuary area did not show as strong a correlation as expected (less than half the variance explained). The strength of the correlation declined towards the end of summer. Spatial decoupling of oxygen within the estuary suggested influence of local conditions. Chlorophyll concentration seemed also to be dependent on local conditions as it appeared to be coupled with the presence of sustained anoxia in the upper estuary with blooms typically occurring within 7 to 14 days of anoxia. The practical implication for oxygen monitoring is that one location within the most severely impacted part of the estuary is not sufficient to fully evaluate the severity of eutrophication effects.


2021 ◽  
Author(s):  
Agustin Fuentes ◽  
Kevin A Bird

Heritability is not a measure of the relative contribution of nature vis-à-vis nurture, nor is it the phenotypic variance explained by or due to genetic variance. Heritability is a correlative value. The evolutionary and developmental processes associated with human culture challenge the use of ‘heritability’ for understanding human behavior.


2021 ◽  
Author(s):  
Mathew Wheto ◽  
Nkiruka Goodness Chima ◽  
Henry T Ojoawo ◽  
Matthew A Adeleke ◽  
Sunday O Peters ◽  
...  

Abstract This study aimed to assess the relationship among carcass traits of meat line FUNAAB Alpha chicken genotype, to identify the components that defined bled weight in them using multivariate principal component regression. A total of 14 different carcass traits from sixty-eight birds were recorded and subjected to one-way analysis of variance to vet for sex effect. Phenotypic relationships among the carcass traits were also established to pave way for the principal component analysis. The results reveal significant effects between the traits measured. The male significantly (P<0.05) had greater mean values for the traits measured. Correlations among the considered carcass traits were found to be positive and significant ranging from r = 0.406 (LrWt) - 0.981 (EdWt) for the female chicken; r = 0.330 (Head Wt) - 0.978 (BdWt) for the male chicken. The extracted components PC1 to PC7 contributed 95.66% with PC1 accounting for 68.68% of the variability in the original parameters. Communality estimates varied from 0.466 (thigh weight) to 0.983 (liver weight). In the principal component regression models, Eviscerated weight accounted for 95% of the variation observed in bled weight. The use of PC1 as a single predictor, explained 96.4% of the variability, whilst combining PC1 and PC4 showed improvements in the variance explained (R2 = 96.7%) with a lower Mallow's cp (5.31). Using the principal components scores from the chicken morphometric traits was more appropriate than using the original traits in bled weight prediction.


2021 ◽  
Author(s):  
Luhua Li ◽  
Xicui Yang ◽  
Zhongni Wang ◽  
Mingjian Ren ◽  
Chang An ◽  
...  

Abstract Wheat powdery mildew (Pm), caused by Blumeria graminis f. sp. tritici (Bgt), is a destructive disease of wheat (Triticum aestivum L.) worldwide that causes severe yield losses. Resistant wheat cultivars easily lose effective resistance against newly emerged Bgt strains; therefore, identifying new resistance genes is necessary for breeding resistant cultivars. ‘Guizi 1’ is a Chinese wheat cultivar with effective moderate and stable resistance against powdery mildew. A genetic analysis indicated that powdery mildew resistance in ‘Guizi 1’ was controlled by a single dominant gene, designated PmGZ1. In total, 110 F2 individual plants and the 2 parents were used for genotyping-by-sequencing, which produced 23,134 high-quality single-nucleotide polymorphisms (SNPs). The SNP distributions on the 21 chromosomes ranged from 134 on chromosome 6D to 6,288 on chromosome 3B. Chromosome 6A has 1,866 SNPs, among which 16 are located in a physical region between positions 307,802,221 and 309,885,836 in an approximate 2.3-cM region, which possessed the greatest SNP density. The average map distance between SNP markers was 0.1 cM. A quantitative trait locus with a significant epistatic effect on powdery mildew resistance was mapped to Chromosome 6A. The LOD value of PmGZ1 reached 34.8, and PmGZ1 was located within the confidence interval marked by chr6a-307802221 and chr6a-309885836. The phenotypic variance explained by PmGZ1 was 74.7%. Four candidate genes (two each encoding TaAP2-A and actin proteins) were annotated as resistance genes. The present results provide valuable information for wheat genetic improvement, quantitative trait loci fine mapping, and candidate gene validation.


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