Buscando Patrones Ecomorfológicos Comunes Entre Ungulados Actuales y Xenartros Extintos

Variability in climatic conditions of low-latitude tropical grass cultivation can affect forage production dynamics. Pasture ecosystems are complex and preferably studied from a multifactorial point of view through multivariate approaches. Therefore, in this study, we characterized different growing conditions for grasses of the Megathyrsus genus through studies conducted in tropical humid and semi-arid conditions. We applied principal component, canonical correlation, and discriminant function analyses to the measurements of agronomic and agrometeorological variables in six studies with Guinea and Massai grasses. The principal component analysis, through the climatic characterization by the first principal component, reflects the contrast between water availability and nitrogen variables and energy supply. Agronomic characterization occurred through the distinction between the density of tillers, forage accumulation, and increase in height, versus the accumulation of stems and dead material. The canonical correlation analysis generated a correlation coefficient of 0.84 between the agronomic and agrometeorological variables. There was a contrast between the dead material accumulation and the other agronomic variables, while the agrometeorological variables showed characteristics similar to the first principal component. Discriminant function 1, with 70.36% separation power, distinguished the cultivation conditions based on the study locations. Grass cultivars were differentiated by discriminant function 2, with a 19.20% separation power. From a multivariate variability analysis, despite the similarities of radiation and temperature in the regions studied, the availability of water and nutrients and measurements of agronomic variables can aid in future modeling studies on forage production.

It is of great significance to realize the rapid, nondestructive and accurate identification of tire rubber particles in forensic science. However, there are no relevant reports. This study collected and tested infrared spectroscopy data of 240 samples from 15 brands. Baseline correction, multivariate scatter correction, standard normal variables and Savitzky-Golay smoothing were adopted to preprocess the infrared spectra. The comparison of the fingerprint region and full infrared spectrum was explored. The identification models were established by principal component analysis and discriminant analysis. The results showed that there is no significant discrimination in the peak position and shape; instead the peak intensity and relative height were slightly different. The cumulative variance contribution rate of principal components 1 through 12 reached 98.3690% demonstrating 98.3690% of the details in the original measurements. The 39-dimensional principal components of infrared fingerprint region accurately differentiated sample brands and employed computational complexity. Styrene butadiene samples from six brands were accurately identified based on the three discriminant functions Za1, Zb1, and Zc1. Butadiene samples from five brands were differentiated using discriminant functions Za2, Zb2, and Zc2. Isoprene samples from brands were separated using the discriminant functions Za3, Zb3, and Zc3. All samples were precisely identified by this rapid and nondestructive protocol. The results of this study demonstrate the potential of attenuated total reflectance-Fourier transform infrared spectroscopy in combination with principal component analysis and discriminant analysis as a new method for the identification of tire rubber particles.

A methodology to predict the stability status of mine rock slopes is proposed. Two techniques of multivariate statistics are used: principal component analysis and discriminant analysis. Firstly, principal component analysis was applied in order to change the original qualitative variables into quantitative ones, as well as to reduce data dimensionality. Then, a boosting procedure was used to optimize the resulting function by the application of discriminant analysis in the principal components. In this research two analyses were performed. In the first analysis two conditions of slope stability were considered: stable and unstable. In the second analysis three conditions of slope stability were considered: stable, overall failure and failure in set of benches. A comprehensive geotechnical database consisting of 18 variables measured in 84 pit-walls all over the world was used to validate the methodology. The discriminant function was validated by two different procedures, internal and external validations. Internal validation presented an overall probability of success of 94.73% in the first analysis and 68.42% in the second analysis. In the second analysis the main source of errors was due to failure in set of benches. In external validation, the discriminant function was able to classify all slopes correctly, in analysis with two conditions of slope stability. In the external validation in the analysis with three conditions of slope stability, the discriminant function was able to classify six slopes correctly of a total of nine slopes. The proposed methodology provides a powerful tool for rock slope hazard assessment in open-pit mines.

Morphometric analysis of tule perch from six sites in three isolated drainages was conducted to assess the validity of previous systematic work. Nine variables were subjected to principal component and discriminant analysis. Principal component analysis indicated that groupings by drainage were real. Classification by discriminant functions among drainages was 96% accurate. Classification among collection sites was less accurate (88% overall), but most misclassified cases were assigned to another site in the same drainage. Ecophenotypic variation was apparent but the degree of overlap between drainages was minimal and indicated that the within-drainage differences were less than the among-drainage differences. Our analysis supports the view that populations in separate drainages are sufficiently distinct to warrant subspecific designation. T HE tule perch (Hysterocarpus traski) is the only exclusively freshwater member of the surfperch family (Embiotocidae) and is confined to three drainages in central California. It is unique among the freshwater fauna of California in that it is the only native viviparous fish. One population is largely confined to two lakes in the Clear Lake drainage basin, another is confined to the Russian River, a large coastal stream, and the third is found in a wide variety of habitats in the Sacramento-San Joaquin drainage including small tributary streams, main river channels, impoundments, and sloughs in the Sacramento-San Joaquin Estuary. Hopkirk (1962, 1973) recognized the populations in each drainage as morphological subspecies but Hubbs (1974) questioned Hopkirk's findings, mostly on the basis of his methodol

Genetic diversity is one of the fundamental success factors in plant breeding programs. The objective of this study was to assess the genetic diversity in lowland sorghum landraces using 25 agro-morphological and 30 microsatellite markers. Phenotypic diversity of 267 genotypes originated from Ethiopia was determined using diversity indices, principal component, cluster, and discriminant analyses. High phenotypic diversity indices were recorded, ranging from 0.67 to 1.00, with a mean of 0.88. Principal component analysis and discriminant analyses identified four PCAs and five discriminant functions which contributed 82% and 92% of the total phenotypic variation among the landraces and their respective geographic origin, respectively. The PIC ranged from 0.26 to 0.88, with a mean of 0.61. The mean gene diversity was 0.69, which largely explained variation among genotypes within geographic origin. The SSR markers and phenotypic traits showed similar clustering patters of landraces except some discrepancies. Information obtained in this study may be useful for future sorghum breeding improvement program.

Long-term effects of conservation tillage on organic fractions in two soils in southwest of Spain

Full text Figures and data Side by side Abstract Editor's evaluation Introduction Results Discussion Materials and methods Appendix 1 Data availability References Decision letter Author response Article and author information Metrics Abstract Multivariable Mendelian randomisation (MVMR) is an instrumental variable technique that generalises the MR framework for multiple exposures. Framed as a regression problem, it is subject to the pitfall of multicollinearity. The bias and efficiency of MVMR estimates thus depends heavily on the correlation of exposures. Dimensionality reduction techniques such as principal component analysis (PCA) provide transformations of all the included variables that are effectively uncorrelated. We propose the use of sparse PCA (sPCA) algorithms that create principal components of subsets of the exposures with the aim of providing more interpretable and reliable MR estimates. The approach consists of three steps. We first apply a sparse dimension reduction method and transform the variant-exposure summary statistics to principal components. We then choose a subset of the principal components based on data-driven cutoffs, and estimate their strength as instruments with an adjusted F-statistic. Finally, we perform MR with these transformed exposures. This pipeline is demonstrated in a simulation study of highly correlated exposures and an applied example using summary data from a genome-wide association study of 97 highly correlated lipid metabolites. As a positive control, we tested the causal associations of the transformed exposures on coronary heart disease (CHD). Compared to the conventional inverse-variance weighted MVMR method and a weak instrument robust MVMR method (MR GRAPPLE), sparse component analysis achieved a superior balance of sparsity and biologically insightful grouping of the lipid traits. Editor's evaluation This paper investigated the identification of causal risk factors on health outcomes. It applies sparse dimension reduction methods on highly correlated traits in the Mendelian randomization framework. The implementation of this method helps to identify risk factors when given high dimensional traits data. https://doi.org/10.7554/eLife.80063.sa0 Decision letter Reviews on Sciety eLife's review process Introduction Mendelian randomisation (MR) is an epidemiological study design that uses genetic variants as instrumental variables (IVs) to investigate the causal effect of a genetically predicted exposure on an outcome of interest (Smith and Ebrahim, 2003). In a randomised controlled trial (RCT) the act of randomly allocating patients to different treatment groups precludes the existence of systematic confounding between the treatment and outcome and therefore provides a strong basis for causal inference. Likewise, the alleles that determine a small proportion of variation of the exposure in MR are inherited randomly. We can therefore view the various genetically proxied levels of a lifelong modifiable exposure as a 'natural' RCT, avoiding the confounding that hinder traditional observational associations. Genetically predicted levels of an exposure are also less likely to be affected by reverse causation, as genetic variants are allocated before the onset of the outcomes of interest. When evidence suggests that multiple correlated phenotypes may contribute to a health outcome, multivariable MR (MVMR), an extension of the basic univariable approach can disentangle more complex causal mechanisms and shed light on mediating pathways. Following the analogy with RCTs, the MVMR design is equivalent to a factorial trial, in which patients are simultaneously randomised to different combinations of treatments (Burgess and Thompson, 2015). An example of this would be investigation into the effect of various lipid traits on coronary heart disease (CHD) risk (Burgess and Harshfield, 2016). While MVMR can model correlated exposures, it performs suboptimally when there are many highly correlated exposures due to multicollinearity in their genetically proxied values. This can be equivalently understood as a problem of conditionally weak instruments (Sanderson et al., 2019) that is only avoided if the genetic instruments are strongly associated with each exposure conditionally on all the other included exposures. An assessment of the extent to which this assumption is satisfied can be made using the conditional F-statistic, with a value of 10 for all exposures being considered sufficiently strong (Sanderson et al., 2019). In settings when multiple highly correlated exposures are analysed, a set of genetic instruments are much more likely to be conditionally weak instruments. In this event, causal estimates can be subject to extreme bias and are therefore unreliable. Estimation bias can be addressed to a degree by fitting weak instrument robust MVMR methods (Sanderson et al., 2020; Wang et al., 2021), but at the cost of a further reduction in precision. Furthermore, MVMR models investigate causal effects for each individual exposure, under the assumption that it is possible to intervene and change each one whilst holding the others fixed. In the high-dimensional, highly correlated exposure setting, this is potentially an unachievable intervention in practice. Our aim in this paper is instead to use dimensionality reduction approaches to concisely summarise a set of highly correlated genetically predicted exposures into a smaller set of independent principal components (PCs). We then perform MR directly on the PCs, thereby estimating their effect on health outcomes of interest. We additionally suggest employing sparsity methods to reduce the number of exposures that contribute to each PC, in order to improve their interpretability in the resulting factors. Using summary genetic data for multiple highly correlated lipid fractions and CHD (Kettunen et al., 2016; Nelson et al., 2017), we first illustrate the pitfalls encountered by the standard MVMR approach. We then apply a range of sparse principal component analysis (sPCA) methods within an MVMR framework to the data. Finally, we examine the comparative performance of the sPCA approaches in a detailed simulation study, in a bid to understand which ones perform best in this setting. Results Workflow overview Our proposed analysis strategy is presented in Figure 1. Using summary statistics for the single-nucleotide polymorphism (SNP)-exposure (γ^) and SNP-outcome (Γ^) association estimates, where γ^ (dimensionality 148 SNPs× 97 exposures) exhibits strong correlation, we initially perform a PCA on γ^. Additionally, we perform multiple sPCA modalities that aim to provide sparse loadings that are more interpretable (block 3, Figure 1). The choice of the number of PCs is guided by permutation testing or an eigenvalue threshold. Finally, the PCs are used in place of γ^ in an IVW MVMR meta-analysis to obtain an estimate of the causal effect of the PC on the outcome. Similar to PC regression and in line with unsupervised methods, the outcome (SNP-outcome associations (Γ^) and corresponding standard error (S⁢EΓ^)) is not transformed by PCA and is used in the second-step MVMR in the original scale. In the real data application and in the simulation study, the best balance of sparsity and statistical power was observed for the method of sparse component analysis (SCA) (Chen and Rohe, 2021). This favoured method and the related steps are coded in an R function and are available at GitHub (https://github.com/vaskarageorg/SCA_MR/, copy archived at Karageorgiou, 2023). Figure 1 Download asset Open asset Proposed workflow. Step 1: MVMR on a set of highly correlated exposures. Each genetic variant contributes to each exposure. The high correlation is visualised in the similarity of the single-nucleotide polymorphism (SNP)-exposure associations in the correlation heatmap (top right). Steps 2 and 3: PCA and sparse PCA on γ^. Step 4. MVMR analysis on a low dimensional set of principal components (PCs). X: exposures; Y: outcome; k: number of exposures; PCA: principal component analysis; MVMR: multivariable Mendelian randomisation. UVMR and MVMR A total of 66 traits were associated with CHD at or below the Bonferroni-corrected level (p=0.05/97, Table 1). Two genetically predicted lipid exposures (M.HDL.C, M.HDL.CE) were negatively associated with CHD and 64 were positively associated (Table 3). In an MVMR model including only the 66 Bonferroni-significant traits, fitted with the purpose of illustrating the instability of IVW-MVMR in conditions of severe collinearity, conditional F-statistic (CFS) (Materials and methods) was lower than 2.2 for all exposures (with a mean of 0.81), highlighting the severe weak instrument problem. In Appendix 1—figure 3, the MVMR estimates are plotted against the corresponding univariable MR (UVMR) estimates. We interpret the reduction in identified effects as a result of the drop in precision in the MVMR model (variance inflation). Only the independent causal estimate for ApoB reached our pre-defined significance threshold and was less precise (ORMVMR (95% CI): 1.031⁢(1.012,1.37), ORUVMR (95% CI): 1.013⁢(1.01,1.016) (Appendix 1—figure 4). We note that, for M.LDL.PL, the UVMR estimate (1.52⁢(1.35,1.71), p < 10-10)) had an opposite sign to the MVMR estimate (ORMVMR=0.905(0.818,1.001)). To see if the application of a weak instrument robust MVMR method could improve the analysis, we applied MR GRAPPLE (Wang et al., 2021). As the GRAPPLE pipeline suggests, the same three-sample MR design described above is employed. In the external selection GWAS study (GLGC), a total of 148 SNPs surpass the genome-wide significance level for the 97 exposures and were used as instruments. Although the method did not identify any of the exposures as significant at nominal or Bonferroni-adjusted significance level, the strongest association among all exposures is ApoB. Table 1 Univariable Mendelian randomisation (MR) results for the Kettunen dataset with coronary heart disease (CHD) as the outcome. Positive: positive causal effect on CHD risk; Negative: negative causal effect on CHD risk. PositiveNegativeVLDLAM.VLDL.C, M.VLDL.CE, M.VLDL.FC, M.VLDL.L,M.VLDL.P, M.VLDL.PL, M.VLDL.TG, XL.VLDL.L,XL.VLDL.PL, XL.VLDL.TG, XS.VLDL.L, XS.VLDL.P, XS.VLDL.PL,XS.VLDL.TG, XXL.VLDL.L, XXL.VLDL.PL,L.VLDL.C, L.VLDL.CE, L.VLDL.FC, L.VLDL.L, L.VLDL.P,L.VLDL.PL, L.VLDL.TG, SVLDL.C, S.VLDL.FC,S.VLDL.L, S.VLDL.P, S.VLDL.PL, S.VLDL.TGNoneLDLALDL.C, L.LDL.C, L.LDL.CE, L.LDL.FC, L.LDL.L, L.LDL.P, L.LDL.PL,M.LDL.C, M.LDL.CE, M.LDL.L, M.LDL.P,M.LDL.PL, S.LDL.C, S.LDL.L, S.LDL.PNoneHDLS.HDL.TG, XL.HDL.TGM.HDL.C, M.HDL.CE PCA Standard PCA with no sparsity constraints was used as a benchmark. PCA estimates a square loadings matrix of coefficients with dimension equal to the number of genetically proxied exposures K. The coefficients in the first column define the linear combination of exposures with the largest variability (PC1). Column 2 defines PC2, the linear combination of exposures with the largest variability that is also independent of PC1, and so on. This way, the resulting factors seek to reduce redundant information and project highly correlated SNP-exposure associations to the same PC. In PC1, very low-density lipoprotein (VLDL)- and low-density lipoprotein (LDL)-related traits were the major contributors (Figure 2a). ApoB received the 8th largest loading (0.1371, maximum was 0.1403 for cholesterol content in small VLDL) and LDL.C received the 48th largest (0.1147). In PC2, high-density lipoprotein (HDL)-related traits were predominant. The first 18 largest positive loadings are HDL-related and 12 describe either large or extra-large HDL traits. PC3 received its scores mainly from VLDL traits. Six components were deemed significant through the permutation-based approach (Figure 1, Materials and methods). Figure 2 Download asset Open asset Heatmaps for the loadings matrices in the Kettunen dataset for all methods (one with no sparsity constraints [a], four with sparsity constraints under different assumptions [b–e]). The number of the exposures plotted on the vertical axis is smaller than K=97 as the exposures that do not contribute to any of the sparse principal components (PCs) have been left out. Blue: positive loading; red: negative loading; yellow: zero. In the second-step IVW regression (step 4 in Figure 1), MVMR results are presented. A modest yet precise (OR = 1.002⁢(1.0015,1.0024), p<10−10) association of PC1 with CHD was observed. Conversely, PC3 was marginally significant for CHD at the 5% level (OR = 0.998 (0.998, 0.999), p=0.049). Since γ^ has been transformed with linear coefficients (visualised in loadings matrix, Figure 2), the underlying causal effects are also transformed and interpreting the magnitude of an effect estimate is not straightforward, since it reflects the effect of changing the PC by one unit on the outcome; however, significance and orientation of effects can be interpreted. When positive loadings are applied to exposures that are positively associated with the outcome, the MR estimate is positive; conversely, if negative loadings are applied, the MR estimate is negative. sPCA methods We next employed multiple sPCA methods (Table 2) that each shrink a proportion of loadings to zero. The way this is achieved differs in each method. Their underlying assumptions and details on differences in optimisation are presented in Table 2 and further described in Materials and methods. Table 2 Overview of sparse principal component analysis (sPCA) methods used. KSS: Karlis-Saporta-Spinaki criterion. Package: R package implementation; Features: short description of the method; Choice: method of selection of the number of informative components in real data; PCs: number of informative PCs. MethodPackageAuthorsFeaturesChoicePCsRSPCApcaPPCroux et al., 2013Robust sPCA (RSPCA), different measure of dispersion (Qn)Permutation KSS6SFPCACode in publication, Supplementary MaterialGuo et al., 2010Fused penalties for block correlationKSS6sPCAelasticnetZou et al., 2006Formulation of sPCA as a regression problemKSS6SCASCAChen and Rohe, 2021Rotation of eigen vectors for approximate sparsityPermutation KSS6 RSPCA (Croux et al., 2013) Optimisation and the KSS criterion pick six PCs to be informative (Karlis et al., 2003). The loadings in Figure 2 show a VLDL-, LDL-dominant PC1, with some small and medium HDL-related traits. LDL.C and ApoB received the 5th and 40th largest positive loadings. PCs 1 and 6 are positively associated with CHD and PCs 3 and 5 negatively so (Appendix 1—table 1). SFPCA (Guo et al., 2010) The KSS criterion retains six PCs. The loadings matrix (Figure 2) shows the 'fused' loadings with the identical colouring. In the two first PCs, all groups are represented. Both ApoB and LDL.C received the seventh and tenth largest loadings, together with other metabolites (Figure 2). PC1 (all groups represented) was positively associated with CHD and PC4 (negative loadings from large HDL traits) negatively so (Appendix 1—table 1). sPCA (Zou et al., 2006) The number of non-zero metabolites per PC was set at 14897∼16 (see Appendix 1—figure 6). Under this level of sparsity, the permutation-based approach suggested that six sPCs should be retained. Seventy exposures received a zero loading across all components. PC1 is constructed predominantly from LDL traits and is positively associated with CHD, but this does not retain statistical significance at the nominal level in MVMR analysis (Figure 3). Only PC4 that is comprised of small and medium HDL traits (Figure 2b) appears to exert a negative causal effect on CHD (OR (95% CI): 0.9975⁢(0.9955,0.9995)). The other PCs were not associated with CHD (all p values > 0.05, Appendix 1—table 1). Figure 3 Download asset Open asset Comparison of univariable Mendelian randomisation (UVMR) and multivariable MR (MVMR) estimates and presentation of the major group represented in each principal component (PC) per method. SCA (Chen and Rohe, 2021) Six components were retained after a permutation test. In the final model, five metabolites were regularised to zero in all PCs (CH2.DB.ratio, CH2.in.FA, FAw6, S.VLDL.C, S.VLDL.FC, Figure 2). Little overlap is noted among the metabolites. PC1 receives loadings from LDL and IDL, and PC2 from VLDL. The contribution of HDL to PCs is split in two, with large and extra-large HDL traits contributing to PC3 and small and medium ones to PC4. PC1 and PC2 were positively associated with CHD (Appendix 1—table 1, Figure 3). PC4 was negatively associated with CHD. Comparison with UVMR In principle, all PC methods derive independent components. This is strictly the case in standard PCA, where subsequent PCs are perfectly orthogonal, but is only approximately true in sparse implementations. We hypothesised that UVMR and MVMR could provide similar causal estimates of the associations of metabolite PCs with CHD. The results are presented in Figure 3 and concordance between UVMR and MVMR is quantified with the R2 from a linear regression. The largest agreement of the causal estimates is observed in PCA. In the sparse methods, SCA (Chen and Rohe, 2021) and sPCA (Zou et al., 2006) provide similarly consistent estimates, whereas some disagreement is observed in the estimate of PC6 for RSPCA (Croux et al., 2013) on CHD. A previous study implicated LDL.c and ApoB as causal for CHD (Zuber et al., 2020b). In Appendix 1—figure 7, we present the loadings for these two exposures across the PCs for the various methods. Ideally, we would like to see metabolites contributing to a small number of components for the sparse methods. Using a visualisation technique proposed by Kim and Kim, 2012, this is indeed observed (see Appendix 1—figure 7). In PCA, LDL.c and ApoB contribute to multiple PCs, whereas the sPCA methods limit this to one PC. Only in RSPCA do these exposures contribute to two PCs. In the second-step IVW meta-analysis, it appears that the PCs comprising of predominantly VLDL/LDL and HDL traits robustly associate with CHD, with differences among methods (Table 3). Table 3 Results for principal component analysis (PCA) approaches. Overlap: Percentage of metabolites receiving non-zero loadings in ≥1 component. Overlap in PC1, PC2: overlap as above but exclusively for the first two components which by definition explain the largest proportion of variance. Very low-density lipoprotein (VLDL), low-density lipoprotein (LDL), and high-density lipoprotein (HDL) significance: results of the IVW regression model with CHD as the outcome for the respective sPCs (the sPCs that mostly received loadings from these groups). The terms VLDL and LDL refer to the respective transformed blocks of correlated exposures; for instance, VLDL refers to the weighted sum of the correlated VLDL-related γ^ associations, such as VLDL phospholipid content and VLDL triglyceride content. †: RSPCA projected VLDL- and LDL-related traits to the same PC (sPC1). ‡: SCA discriminated HDL molecules in two sPCs, one for traits of small- and medium-sized molecules and one for large- and extra-large-sized. PCARSPCASFPCAsPCASCAOverlap10.93810.1870.196Overlap in PC1,PC210.43310.0100Sparse %00.4740.0820.8350.796VLDL significance in MR†YesNoYesNoYesLDL significance in MRNoYesNoNoYesHDL significance in MR‡YesYesYesNoNoSmall, medium HDL significance in MRYesNoYesYesYes Instrument strength Instrument strength for the chosen PCs was assessed via an F-statistic, calculated using a bespoke formula that accounts for the PC process (see Materials and methods and Appendix). The F-statistics for all transformed exposures cross the cutoff of 10. There was a trend for the first components being more strongly instrumented in all methods (see Appendix 1—figure 5), which is to be expected. In the MVMR analyses, the CFS for all exposures was less than three. Thus the move to PC-based analysis significantly improved instrument strength and mitigated against weak instrument bias. Simulation studies We consider the case of a data that reflects in we consider a set of exposures which can be into blocks based on groups of variants contribute exclusively to blocks of exposures, no effect on other This in to correlation among the exposure blocks and a much correlation of between exposure due only to This is visualised in Figure This data to reduce the strength in all exposures. The dataset consists of exposures, p SNPs (with and p of and a outcome, We split the simulation results into one example and one Figure 4 Download asset Open asset Simulation Data for the simulation study, with six exposures and two In the exposures that are correlated due to a genetic component are Simulation results for six exposures and three methods component analysis and Rohe, principal component analysis multivariable Mendelian randomisation The exposures that contribute to are presented in of and that do not in of In the each exposure is a In the first and the PCs that to these exposures are presented as in and are visualised as error proportion of where the is example We data under the presented in Figure with six individual exposures split into two blocks and A outcome is that is only affected by the exposures in block 1 A range of were used in the simulation in order to a range of CFS values from approximately We apply MVMR with the six individual exposures and PCA and The aim of approach is to the of the exposure into two PCs, so that the first PC has high loadings for block 1 and the PC has high loadings for block 2 Although two PCs were chosen by PCA methods using a KSS criterion in a large of to the simulation we a the number of PCs at two across all Our was to the of MVMR PCA than as the two approaches are not in this To do this we each method as a which true positive true negative positive and negative In a is an exposure that is causal in the underlying model and causal estimate is deemed In the PCA and sPCA methods, this is with to which determine each PC and if the causal estimate of this PC is are considered to be major contributors to a PC if only their individual PC loading is than the the causal effect estimate of a PC in the analysis deemed major contributors that are causal and are as and and are error therefore to the and power to the statistical were at the = = PCA, and MVMR error and power are in the three to in Figure results suggest an improved power in true causal associations with PCA and SCA with MVMR when the CFS is at the cost of an error As and CFS MVMR performs the PC of the exposures, PCA to have a error in Figure In this setting, the of PCA therefore appears to be example The aim of the simulation is to estimate the comparative performance of the methods in a that more real data We genetic data and individual level exposure and outcome data for between exposures, in The underlying data and the process of method performance is identical to the but the number of exposures, and the blocks is We results across all by and and then all methods by their under the using the approach of et al., the method performs a meta-analysis of multiple studies that and of a in order to provide a summary A model is and estimates are In our the the results of different simulation settings with of exposures and was also with high values being of Two sPCA methods and Rohe, sPCA et al., the (Figure This is mainly by an in for these methods with A at the individual simulation results the of these two methods, as high (Appendix 1—figure Both standard and Bonferroni-corrected MVMR in terms of and due to PCA with equal and results PCA and RSPCA did not identify negative results and RSPCA and This extreme result can be understood by at the individual simulation results in Appendix 1—figure PCA and RSPCA to the of the a low performance in exposures. the estimates with these methods were very precise across and this in many results and low We note a performance among the methods methods are on the results of SCA are more variable in and (Table 4). The for these methods are also the (Figure the instrument strength in γ^ from to and mean conditional F-statistic (Appendix 1—figure suggests a similar for sparse methods. Figure 5 Download asset Open asset for all methods. sparse component analysis (Chen and Rohe, 2021) sparse PCA (Zou et al., 2006) robust sparse PCA (Croux et al., PCA: principal component analysis; MVMR: multivariable Mendelian MVMR with with large MVMR can not between positive and negative exposures as robustly as the sPCA methods. A major of the of these methods appears to be the number of causal exposures, as in a simulation with only four of the exposures being there was a drop in and across all methods. sPCA methods other methods in this (Appendix 1—table 2). Table 4 and presented as and range across all as and range across all under the PCA In the example of Figure 4 and indeed any other if two PCs are PCA between causal and exposures. The only information used in this of the 2 and 3 in Figure is the association the of to PCs is genetic correlation and correlation due to than these blocks if only a of the exposures it is likely that, PCA identify the block as This the proportion of exposures within blocks of exposures that is a of To we the proportion of exposures by the sparsity of the causal effect

It is always better to use mixed effects models over principal components association regression for genetic association studies of continuous traits, since the former models family structure and close relatives are always found in real human studies.

It is always better to use mixed effects models over principal components association regression for genetic association studies of continuous traits, since the former models family structure and close relatives are always found in real human studies.

It is always better to use mixed effects models over principal components association regression for genetic association studies of continuous traits, since the former models family structure and close relatives are always found in real human studies.

&lt;strong&gt;Abstract&lt;/strong&gt; Morphometric variation in 62 characters of 84 skulls of &lt;em&gt;Martes foina&lt;/em&gt; from Italy was analyzed. Stepwise discriminant analysis was used to estimate three discriminant functions as craniometric keys for sex determination. The first two keys were based on absolute measurements, the third on absolute and relative measurements. This last key seems to be the best and provides 89.8% correct classification for males and 91.4% for females. Parametric or nonparametric univariate methods, cluster analysis of cases, and principal component analysis (PCA) were used to investigate morphological differences between skulls of males and females. In &lt;em&gt;Martes foina&lt;/em&gt; the sexual dimorphism is more due to the size than to the shape, unlike other Mustelids. The size dimorphism was shown to be related to the measurements of the masticatory apparatus, like in other Mustelids. &lt;strong&gt;Riassunto&lt;/strong&gt; Sono state analizzate statisticamente le variazioni morfometriche di 62 parametri su 84 crani di &lt;em&gt;Martes foina&lt;/em&gt; provenienti da collezioni museali italiane. L'analisi discriminante "stepwise" è stata utilizzata per calcolare tre funzioni discriminanti quali chiavi craniometriche per l'identificazione del sesso. Le prime due chiavi sono basate su misurazioni assolute, la terza su misurazioni sia assolute che relative. Quest'ultima chiave sembra essere la migliore e fornisce ne11'89,8% dei casi una corretta classificazione degli individui di sesso maschile e nel 91,4% dei casi degli individui di sesso femminile. L'analisi univariata di tipo parametrico o non parametrico e l'analisi dei "cluster" dei casi e della componente principale (PCA) sono state utilizzate per valutare le differenze morfo- logiche esistenti tra i crani delle femmine e dei maschi. In &lt;em&gt;Martes foina&lt;/em&gt; il dimorfismo sessuale è dovuto alle dimensioni più che alla forma, a differenza di altri Mustelidi. Si è osservato che tale dimorfismo nelle dimensioni può essere correlato con le misure relative all'apparato masticatorio, come in altri Mustelidi.

Selecting an appropriate variable subset in linear multivariate methods is an important methodological issue for ecologists. Interest often exists in obtaining general predictive capacity or in finding causal inferences from predictor variables. Because of a lack of solid knowledge on a studied phenomenon, scientists explore predictor variables in order to find the most meaningful (i.e. discriminating) ones. As an example, we modelled the response of the amphibious softwater plant Eleocharis multicaulis using canonical discriminant function analysis. We asked how variables can be selected through comparison of several methods: univariate Pearson chi-square screening, principal components analysis (PCA) and step-wise analysis, as well as combinations of some methods. We expected PCA to perform best. The selected methods were evaluated through fit and stability of the resulting discriminant functions and through correlations between these functions and the predictor variables. The chi-square subset, at P < 0.05, followed by a step-wise sub-selection, gave the best results. In contrast to expectations, PCA performed poorly, as so did step-wise analysis. The different chi-square subset methods all yielded ecologically meaningful variables, while probable noise variables were also selected by PCA and step-wise analysis. We advise against the simple use of PCA or step-wise discriminant analysis to obtain an ecologically meaningful variable subset; the former because it does not take into account the response variable, the latter because noise variables are likely to be selected. We suggest that univariate screening techniques are a worthwhile alternative for variable selection in ecology.

Studies on selection indices for activating seedling growth in mangosteen were conducted in the central orchard at the main campus of Kerala Agricultural University. The present investigation was undertaken with the main aim of identifying some of the basic reasons for slow growth in mangosteen, and to address this problem by developing and identifying criteria to select the age of the mother plant, fruit, seed and seedling characters or direct selection indices at all four stages with respect to seedling growth. Mother plants of four distinct age groups were used in the study. Variables were generated using all fruit, seed and seedling characters such as fruit index, seed index and seedling index by principal component analysis (PCA). Using PCA and multiple regression analysis, prediction model was fitted for the three indices. Major fruit, seed and seedling characters were identified by stepwise regression. Hierarchial analysis was performed based on Euclidean distance to find the similarities between the four age groups. Discriminant function analysis was performed and six discriminant functions were fitted with corresponding D2 values to discriminate the six pairs involving the four age groups of the mother plants. For practical purposes, selection indices and best age group of mother plants are described in the work.

The aims of the study were: 1. To analyse the discriminative power of neuromuscular components for classifying the pre and post muscle fatigued states. 2. To examine whether the modification of neural recruitment strategies become more/less heterogeneous due to fatigue. 3. To research the effect of Erector Spinae (ES) muscle activity collectively with Rectus Abdominis (RA) and External Oblique (EO) muscle activity to identify the reduced spine stability during fatiguing Plank. &#x0D; Material and methods. Twelve boys (age – 12-14 years, height 148.75 ± 10 cm, body mass 38.9 ± 7.9 kg) participated in the study. Multivariate Discriminant Analysis (DA) and Principal Component Analysis (PCA) were applied to identify the changes in the pattern of the electromyographic signals during muscle fatigue. In DA the Wilks’ lambda, p-value, canonical correlation, classification percentage and structure matrix were used. To evaluate the component validity the standard limit for Kaiser-Meyer-Olkin (KMO) was set at ≥0.529 and the p-value of Bartlett’s test was ≤0.001. The eigenvalues ≥1 were used to determine the number of Principal Components (PCs). The satisfactory percentage of non-redundant residuals were set at ≤50% with standard value &gt;0.05. The absolute value of average communality (x̄ h2) and component loadings were set at ≥0.6, ≥0.4 respectively. &#x0D; Results. Standardized canonical discriminant analysis showed that pre and post fatigued conditions were significantly different (p = 0.000, Wilks’ lambda = 0.297, χ2 = 24.914, df=3). The structure matrix showed that the parameter that correlated highly with the discriminant function was ES ARV (0.514). The results showed that the classification accuracy was 95.8% between fatigued conditions. In PCA the KMO values were reduced [0.547Pre fatigue vs. 0.264Post fatigue]; the value of Bartlett’s sphericity test was in pre χ2 = 90.72 (p = 0.000) and post fatigue χ2 = 85.32 (p = 0.000); The Promax criterion with Kaiser Normalization was applied because the component rotation was non-orthogonal [Component Correlation Matrix (rCCM) = 0.520 Pre fatigue &gt;0.3Absolute&lt;0.357Post fatigue]. In pre fatigue two PCs (cumulative s2 – 80.159%) and post fatigue three PCs (cumulative s2 – 83.845%) had eigenvalues ≥1. The x̄ h2 increased [0.802 Pre fatigue vs. 0.838 Post fatigue] and the percentage of nonredundant residuals reduced [50% Pre fatigue vs. 44% Post fatigue] from pre to post fatigue. &#x0D; Conclusions. The variability and heterogeneity increase in the myoelectric signals due to fatigue. The co-activity of antagonist ES muscle is significantly sensitive to identify the deteriorating spine stability during the fatiguing Plank. Highly correlated motor unit recruitment strategies between ES and RA, providing supportive evidence to the concept of shared agonist-antagonist motoneuron pool or “Common Drive” phenomenon during fatigue.

Preliminary study on using near-infrared spectroscopy at 1.6–2.4 µm for document examination