Graphics for relatedness research

Studies of relatedness have been crucial in molecular ecology over the last decades. Good evidence of this is the fact that studies of population structure, evolution of social behaviours, genetic diversity and quantitative genetics all involve relatedness research. The main aim of this article is to review the most common graphical methods used in allele sharing studies for detecting and identifying family relationships. Both IBS and IBD based allele sharing studies are considered. Furthermore, we propose two additional graphical methods from the field of compositional data analysis: the ternary diagram and scatterplots of isometric log-ratios of IBS and IBD probabilities. We illustrate all graphical tools with genetic data from the HGDP-CEPH diversity panel, using mainly 377 microsatellites genotyped for 25 individuals from the Maya population of this panel. We enhance all graphics with convex hulls obtained by simulation and use these to confirm the documented relationships. The proposed compositional graphics are shown to be useful in relatedness research, as they also single out the most prominent related pairs. The ternary diagram is advocated for its ability to display all three allele sharing probabilities simultaneously. The log-ratio plots are advocated as an attempt to overcome the problems with the Euclidean distance interpretation in the classical graphics.Peer ReviewedPostprint (published version

Non-parametric regression on compositional covariates using Bayesian P-splines

Methods to perform regression on compositional covariates have recently been proposed using isometric log-ratios (ilr) representation of compositional parts. This approach consists of first applying standard regression on ilr coordinates and second, transforming the estimated ilr coefficients into their contrast log-ratio counterparts. This gives easy-to-interpret parameters indicating the relative effect of each compositional part. In this work we present an extension of this framework, where compositional covariate effects are allowed to be smooth in the ilr domain. This is achieved by fitting a smooth function over the multidimensional ilr space, using Bayesian Psplines. Smoothness is achieved by assuming random walk priors on spline coefficients in a hierarchical Bayesian framework. The proposed methodology is applied to spatial data from an ecological survey on a gypsum outcrop located in the Emilia Romagna Region, Italy

Exploration of geochemical data with compositional canonical biplots

The study of the relationships between two compositions is of paramount importance in geochemical data analysis. This paper develops a compositional version of canonical correlation analysis, called CoDA-CCO, for this purpose. We consider two approaches, using the centred log-ratio transformation and the calculation of all possible pairwise log-ratios within sets. The relationships between both approaches are pointed out, and their merits are discussed. The related covariance matrices are structurally singular, and this is efficiently dealt with by using generalized inverses. We develop compositional canonical biplots and detail their properties. The canonical biplots are shown to be powerful tools for discovering the most salient relationships between two compositions. Some guidelines for compositional canonical biplots construction are discussed. A geochemical data set with X-ray fluorescence spectrometry measurements on major oxides and trace elements of European floodplains is used to illustrate the proposed method. The relationships between an analysis based on centred log-ratios and on isometric log-ratios are also shown.Peer ReviewedPostprint (author's final draft

Cox regression survival analysis with compositional covariates: application to modelling mortality risk from 24-h physical activity patterns

Survival analysis is commonly conducted in medical and public health research to assess the association of an exposure or intervention with a hard end outcome such as mortality. The Cox (proportional hazards) regression model is probably the most popular statistical tool used in this context. However, when the exposure includes compositional covariables (that is, variables representing a relative makeup such as a nutritional or physical activity behaviour composition), some basic assumptions of the Cox regression model and associated significance tests are violated. Compositional variables involve an intrinsic interplay between one another which precludes results and conclusions based on considering them in isolation as is ordinarily done. In this work, we introduce a formulation of the Cox regression model in terms of log-ratio coordinates which suitably deals with the constraints of compositional covariates, facilitates the use of common statistical inference methods, and allows for scientifically meaningful interpretations. We illustrate its practical application to a public health problem: the estimation of the mortality hazard associated with the composition of daily activity behaviour (physical activity, sitting time and sleep) using data from the U.S. National Health and Nutrition Examination Survey (NHANES)

Nitrogen And Potassium Fertilization In A Guava Orchard Evaluated For Five Cycles: Effects On The Plant And On Production

Guava response to fertilization can be monitored through plant tissue analysis. Correct interpretation of these results, based on standard levels, is of great importance for correct nutrient management of the crop. However, standard levels are constantly criticized for not considering interactions among elements. To improve the nutritional diagnosis of ‘Paluma’ guava (Psidium guajava L., Myrtaceae), an experiment was conducted using nitrogen fertilization (0, 0.5, 1.0, and 2.0 kg per plant per cycle of N, with urea as a source with 45 % N), and potassium fertilization (0, 0.55, 1.1, and 2.2 kg per plant per cycle of K2O, with potassium chloride as a source with 60 % K2O) in an irrigated commercial area for five consecutive cycles, 2009 through 2012, observing the influence of fertilizers and climate and assessing yield and leaf element content, using the concept of isometric log ratios (ilr) to interpret leaf analysis results (N, P, Ca, Mg, K, and S). This paper showed that nutrient balances and nutrient concentration values can be interpreted coherently using compositional data analysis. Ranges of nutrient balances also were established for “Paluma” guava and validated through ranges grounded in nutrient contents currently used in Brazil. Nitrogen fertilization increased “Paluma” guava yield. The 0.5 kg N application rate per plant and the other studied treatments practically showed the same results, and their values were affected by pruning time as well as the nutrient balances. © 2016, Revista Brasileira de Ciencia do Solo. All rights reserved.4

Measuring Unemployment: A Composition Model

In looking at statistics of the labour market, the labour force status categories are often considered as independent, but when expressed as proportions they are observations over a simplex of non-negative components summing to 1. This leads to serious deficiencies in the conventional tools of analysis, and a framework commonly referred to as compositional modelling has been developed to address them. This paper explores the application of these tools to labour force data and demonstrates simple consistent patterns between job search (unemployment) and current participation levels across both aggregate and age and gender sub populations. Unlike previous uses of composition models in labour market studies we use a simple transform with a direct interpretation for our analysis

Contrasting compositions of sitting, standing, stepping, and sleeping time: associations with glycaemic outcome by diabetes risk

BACKGROUND: Recent evidence suggests that prolonged sitting and its adverse impact on glycaemic indicators appear to be proportional to the degree of insulin resistance. To investigate this finding in a free-living context, we aimed to examine associations of device-measured 24-h time-use compositions of sitting, standing, stepping, and sleeping with fasting glucose (FPG) and 2 h post-load glucose (2hPLG) levels, and to examine separately the associations with time-use compositions among those at lower and at higher risk of developing type 2 diabetes. METHODS: Cross-sectional analyses examined thigh-worn inclinometer data (activPAL, 7 day, 24 h/day protocol) from 648 participants (aged 36-80 years) at either lower (< 39 mmol/mol; < 5.7% HbA1c) or higher (≥39 mmol/mol; ≥5.7% HbA1c) diabetes risk from the 2011-2012 Australian Diabetes, Obesity and Lifestyle study. Multiple linear regression models were used to examine associations of differing compositions with FPG and 2hPLG, with time spent in each behaviour allowed to vary up to 60 min. RESULTS: In general, the associations with the FPG within the time-use compositions were small, with statistically significant associations observed for sitting and sleeping (in the lower diabetes risk group) and standing (in higher diabetes risk group) only. For 2hPLG, statistically significant associations were observed for stepping only, with findings similar between lower (β = - 0.12 95%CI:-0.22, - 0.02) and higher (β = - 0.13 95%CI:-0.26, - 0.01) risk groups. Varying the composition had minimal impact on FPG; however 1 h less sitting time and equivalent increase in standing time was associated with attenuated FPG levels in higher risk only (Δ FPG% = - 1.5 95%CI: - 2.4, - 0.5). Large differences in 2hPLG were observed for both groups when varying the composition. One hour less sitting with equivalent increase in stepping was associated with attenuated 2hPLG, with estimations similar in lower (Δ 2hPLG% = - 3.8 95%CI: - 7.3, - 0.2) and higher (Δ 2hPLG% = - 5.0 95%CI: - 9.7, - 0.0) risk for diabetes. CONCLUSIONS: In middle-aged and older adults, glycaemic control could be improved by reducing daily sitting time and replacing it with stepping. Standing could also be beneficial for those at higher risk of developing type 2 diabetes

Compositional meta-analysis of the nutrient profile of potato cultivars

While several potato (Solanum tuberosum L.) cultivars of different maturity groups (e.g. early, mid-season, late) are being selected each year as a result of successful breeding for disease resistance and market requirements, their nutrient management is based on past experience and few experiments. Nutrient profiles from leaf analysis can guide fertilization and liming programs of potato cultivars. Since leaf analytical data are strictly positive and compositional, nutrient profiling using raw data is spoiled by non normal distribution, resonance and spurious correlations. Compositional data analysis provides log ratio transformations that avoid such problems. Our objective was to derive nutrient profiles from tissue analysis using isometric log ratio (ilr) coordinates and meta-analysis for classification of cultivars into uniform nutrient management groups. The dataset comprised 678 potato fields producing more than 28.5 Mg marketable tuber ha-1, i.e. above Quebec average, of the early-, mid-, and late-season cultivars. The first mature leaf from top was sampled at the beginning of flowering for N, P, K, Ca, and Mg analysis. Anionic (N, P) and cationic (K, Ca, Mg) nutrients were arranged into binary partitions representing positive and negative nutrient interactions. Groups of cultivars were compared to ‘Superior’ using ilr mean and standard deviation in the mixed model of meta-analysis. We minimized the within-group heterogeneity (I2 value) by allocating cultivars iteratively between ilr groups. We derived group-specific ilr norms to compute the Aitchison distance. The critical value for nutrient imbalance was 0.38. To guide correcting nutrient deficiencies with appropriate nutrient management techniques, nutrient composition can be altered numerically by a perturbation vector on nutrients that lead to the largest and most negative ilr differences from ilr norms until the Aitchison distance falls below critical value

How Europeans move: a moderate-to-vigorous physical activity and sitting time paradox in the European Union

Objectives: This study aimed to assess the interactions between physical activity (PA) and sedentary behaviour in a large population taking account of major sociodemographic characteristics. Study design: Cross-sectional population-based study. Methods: Data from 28,031 individuals living in the European Union who were aged ≥15 years were retrieved from a cross-sectional survey, the Eurobarometer 2017. Interactions among the four mobility components (vigorous, moderate, walking activity and sitting time) were assessed at the individual level across age, gender and place of residence, and at the country level by compositional data analysis, hierarchical linear regressions and principal component analysis. Results: The most frequently reported PA was walking; however, sitting time represented >95% of the reported weekly times, whereas moderate-to-vigorous PA (MVPA) represented <1%. Women reported less PA and sitting time, age decreased total PA and increased sitting time, and individuals living in large urban areas reported lower PA and higher sitting times. MVPA decreased with age (β = -0.047, P < 0.001) and was lower in women (β = -0.760, P < 0.001) and those living in large urban areas (β = -0.581, P < 0.001), while walking and sitting times increased with age, being higher in women and lower in those living in rural areas. At the country level, sitting time was positively associated with moderate activity (β = 0.389, P = 0.041) and marginally non-significant with MVPA (β = 0.330, P = 0.087). Conclusions: Walking was the highest contributor to weekly PA, whereas sitting time was paradoxically associated with higher MVPA. Specific measures to reduce sitting time are required to achieve an active lifestyle

The physical activity paradox revisited: a prospective study on compositional accelerometer data and long-term sickness absence

Background The 'physical activity paradox' advocates that leisure physical activity (PA) promotes health while high occupational PA impairs health. However, this paradox can be explained by methodological limitations of the previous studies-self-reported PA measures, insufficient adjustment for socioeconomic confounding or not addressing the compositional nature of PA. Therefore, this study investigated if we still observe the PA paradox in relation to long-term sick absence (LTSA) after adjusting for the abovementioned limitations. Methods Time spent on moderate-to-vigorous physical activity (MVPA) and remaining physical behaviors (sedentary behavior, standing, light PA and time in bed) at work and in leisure was measured for 929 workers using thigh accelerometry and expressed as isometric log-ratios (ilrs). LTSA was register-based first event of >= 6 consecutive weeks of sickness absence during 4-year follow-up. The association betweenilrsand LTSA was analyzed using a Cox proportional hazards model adjusted for remaining physical behaviors and potential confounders, then separately adjusting for and stratifying by education and type of work. Results During the follow-up, 21% of the workers experienced LTSA. In leisure, more relative MVPA time was negatively associated with LTSA (20% lower risk with 20 min more MVPA,p = 0.02). At work, more relative MVPA time was positively associated with LTSA (15% higher risk with 20 min more MVPA,p = 0.02). Results remained unchanged when further adjusted for or stratified by education and type of work. Conclusion These findings provide further support to the 'PA paradox'