Linear mixed model to identify the relationship between grain yield and other yield related traits and genotype selection for sorghum

Sorghum is the most popular crop in arid and semi-arid areas, especially in Sub-Saharan African countries. Genotype effects, environmental and the interaction of genotype by environmental factors have an influence on phenotypic traits. The aim of the study is to identify the relationship between grain yield and other yield-related traits and select the genotypes which perform better in grain yield as well as to examine the association between the uncorrelated phenotypic traits and grain yield via mixed model. The data was generated using a lattice square design. Principal component analysis was used to generate uncorrelated variables for the mixed model. The study revealed that there was a difference in grain yield due to the treatment and there was a pairwise relationship among the phenotypic variables. 77.12% of the total variance of the original phenotypic variables was explained by the first three principal components and decided to use PCAs as input variables for the mixed model. All PCs had significant effects on grain yield as well as grain yield variability due to random effects associated with genotypes, genotype interaction by treatment, and replication within the treatment. The variability of grain yield due to genotype effect was explained about 45.73%, the variation of grain yield due to the interaction of genotype by the treatment was also explained about 39.06% and 1.55% of the variation of grain was explained by replication within treatment. The best performer genotypes recommended for mass production were G40 (Genotype 40), G186 (Genotype 186) and G196 (Genotype 196) without any constraint of environment. The genotypes recommended for mass production under irrigation conditions were G40 (Genotype 40), G62 (Genotype 62) and G192 (Genotype 192). G26 (Genotype 26), G55 (Genotype 55) and G49 (Genotype 49) were the genotypes recommended for mass production under stress conditions. Overall, the study recommends using a mixed model to fit the grain yield, and future work will focus on to evaluate the performance of genotypes under different environments and years of production.

ERStruct: a fast Python package for inferring the number of top principal components from whole genome sequencing data

BACKGROUND

Large-scale multi-ethnic DNA sequencing data is increasingly available owing to decreasing cost of modern sequencing technologies. Inference of the population structure with such sequencing data is fundamentally important. However, the ultra-dimensionality and complicated linkage disequilibrium patterns across the whole genome make it challenging to infer population structure using traditional principal component analysis based methods and software.

RESULTS

We present the ERStruct Python Package, which enables the inference of population structure using whole-genome sequencing data. By leveraging parallel computing and GPU acceleration, our package achieves significant improvements in the speed of matrix operations for large-scale data. Additionally, our package features adaptive data splitting capabilities to facilitate computation on GPUs with limited memory.

CONCLUSION

Our Python package ERStruct is an efficient and user-friendly tool for estimating the number of top informative principal components that capture population structure from whole genome sequencing data.

Enzymatic Properties of Red Beet (Beta vulgaris L.) Leaf, root Pulp, and peel

BACKGROUND

Beetroot (Beta vulgaris L.) is botanically classified as a herbaceous biennial belonging to the Chenopodiaceae family and has several varieties with bulb colors ranging from yellow to red. Peroxidases are widely occurring in organisms including microorganisms, plants, and animals, and have been involved in various physiological and biochemical functions.

OBJECTIVE

The study was conducted to investigate the characteristics of enzyme extracts from red beet leaves, root pulp, and peel.

METHODOLOGY

The enzyme extraction involved the homogenization of the sample and filtrate in cold acetone and then the filtrate was homogenized in 0.1M sodium acetate buffer, pH 7. The protein content was determined using the Lowry assay using bovine serum albumin (BSA) as a standard protein. Then, enzymatic activity was determined by peroxidase, polyphenol oxidase, and catalase assays. The antioxidant activities of the enzyme extract were conducted by using DPPH and hydrogen peroxide free radical scavenging activities.

RESULTS

The result indicated that the Enzymatic activity of crude enzyme extract of red beet leaf, root pulp and peel indicated that significantly the highest total soluble protein (16.68mg/ml), peroxidase activity (PODA, 111.50U/ml), polyphenol oxidase activity (PPOA, 170.90U/ml), polyphenol oxidase specific activity (PPOspa, 10.25U/mg), catalase activity (CATA, 180.50U/ml) and catalase specific activity (CATspa, 10.82U/mg), were recorded for red beet leaf enzyme extract. The antioxidant activity of the enzyme extracts demonstrated that significantly higher DPPH radical scavenging activity of leaf extract (59.16) and peel extract (61.92) were recorded. The Pearson correlation coefficient of enzyme activity parameters and free radical scavenging activities presented that protein content was significantly and positively correlated with CATA, PPOA, and PPOspa. Catalase-specific activity (CATspa) was significant and positively correlated only with HPSA. Peroxidase-specific activity (PODspa) was significant and positively correlated with PODA and DPPH. Based on the plot for principal component PC2 vs PC1 for D statistics DPPH, PODA, and PODspa have close PC1 and PC2 scores (with vector angle [90]^0) showing similar/correlated effects.

CONCLUSION

In this study, B. vulgaris has shown promising peroxidase enzyme activity. Beetroot peel contained higher antioxidant compounds thus promising a more intense utilization of the peels in food and nutraceuticals.

Evaluation and characterization of indigenous rice (Oryza sativa L.) landraces resistant to brown planthopper Nilaparvata lugens (Stål.) biotype 4

Evaluation and identification of resistant donors for brown planthopper (BPH) Nilaparvata lugens (Stål.), an economically important insect pest of rice, is a continuous process to develop new resistant rice varieties. However, several rice landraces of north-eastern India are not yet characterized for BPH resistance. In the present study, a set of 218 rice landraces were screened in both greenhouse and open-field conditions for three consecutive years, and thereafter forty selected promising entries were explored to evaluate their phenotypic and genotypic reactions against BPH biotype 4. Based on phenotypic evaluations, five landraces were identified as resistant, while 31 were moderately resistant, and grouped under the major cluster I and II, respectively, in a circular dendrogram. Antixenosis and antibiosis studies of these landraces divulged that, compared to the susceptible check variety, resistant landraces exhibited the lowest feeding rate, survival, and nymphal and adult settling, but higher frequency of unhatched eggs of BPH. Un-infested resistant landraces registered higher levels of ascorbic acid, oxalic acid and crude silica, however, elevated levels of total free amino acid, potassium and crude silica were observed under BPH herbivory. The present study focuses on identifying new donors having BPH resistance resources which could be useful in genomic studies for the development of BPH biotype 4 resistant rice varieties.

Multivariate statistical and hydrochemical approaches for evaluation of groundwater quality in north Bahri city-Sudan

Groundwater has recently been considered one of the primary sources of water supply in Sudan. However, groundwater quality is continuously degraded due to overexploitation and long-term agricultural operations. The fossilized Cretaceous Nubian sandstone is the principal aquifer in the study area. This research aims to determine the major factors influencing groundwater quality and detect the suitability of groundwater for drinking and irrigation purposes by integrating hydrochemical and multivariate statistical methods. Hydrochemical plots such as Piper, Chadha, and Durov diagrams were applied to detect the groundwater facies and hydrochemical processes controlling the groundwater quality. They indicated Ca–Mg–HCO₃ water type as a dominant groundwater facies followed by Na–HCO₃ and Na–Cl types. Gibbs plots suggested that the dissolution of the minerals is the main factor influencing the water quality. The results of the Gibbs plot were further interpreted using saturation indices (SI). The SI values indicated that aragonite, calcite, and dolomite precipitated respectively in 58.33%, 75%, and 75% of groundwater samples. Multivariate statistical analyses, including Pearson's correlation analysis, hierarchical cluster analysis (HCA), and principal component analyses (PCA), were jointly employed to identify the structure of water quality data and deduce the main factors controlling groundwater quality. The statistical analysis revealed the effect of the physical and human-induced activities as the main factors influencing groundwater chemistry. These factors are rock-water interaction, agricultural practice, and organic contamination from septic tanks. Further, the suitability of groundwater for irrigation is determined using sodium adsorption ratio (SAR) and sodium percent (Na⁺%) indices. They carefully indicated that 75% of the groundwater samples in the study area are excellent for irrigation except for some sample location where the salinity hazard is stimulated by ion exchange. This integrated approach was effective in calibrating water quality assessment methodologies. The current research concluded that the implication of a groundwater quality monitoring scheme is crucial to ensure water supply sustainability in north Bahri city.

Study on categorization of factors affecting smallholder dairy production in Siltie Zone, Southern Ethiopia, applying multivariate analysis approaches

This study aims to categorize smallholder dairy farming systems through multivariate analysis. Nonlinear categorical principal component analysis (NLPCA) was used to lessen 35 variables into 4 sets of uncorrelated components. These four categories are environment-genetic interaction, management, hygiene, and genetic-related factors. Besides, within the two-step cluster analysis, a variable cluster membership was created that identified which family belonged to which cluster. For comparison purpose, hierarchical cluster analysis was used. A two-step cluster analysis results showed that most farms (41.50% and 31.90% in peri-urban and urban areas, respectively, had been in cluster 1 at the same time as most farms (66.70%) of urban areas had been in cluster 2. Overall, most (31.00% and 34.00%) of farms have been in clusters 1 and 2, respectively. Most farms in peri-urban areas had been challenged with scarcity of feed, mastitis, and animal sickness than farms within the urban and rural farming systems. Forming farmer groups as a cooperative to supply offerings together with feed processing devices, artificial insemination, and health services is usually recommended to triumph over such hassle. In addition, on account that it is primarily based on a couple of criteria, the category system evolved in this study depicts a lot better dairy farming systems with admiration to the variety of the components and the relative contribution of each component to dairy farming than do the single-criterion classifications. Hence, the results of such classifications should be seen as a start line from which to efficiently compare the modern extension system and eventually design the high-quality-fit extension models for a heterogeneous populace of smallholder dairy farmers.

Genome-wide signatures in flax pinpoint to adaptive evolution along its ecological gradient

BACKGROUND

Flax is one of the eight founder crops of agriculture. It is believed to have been domesticated as a long-day plant that has since spread to survive in a wide range of eco-geographic regions extending from the warm Indian subcontinent to the low latitude east African highlands and to the cool and high-latitude Eurasia. Understanding the genetic basis underlying its adaptation and selection events throughout its dispersion is essential to develop cultivars adapted to local environmental conditions.

METHODS

Here we detected genetic signatures of local adaptation and selection events of flax based on 385 accessions from all major flax growing regions of the world using genome scan methods and three genomic datasets: (1) a genome-wide dataset of more than 275K single nucleotide polymorphisms (SNPs), (2) a filtered dataset of 23K SNPs with minor allele frequency >10% and, (3) a 34K exon-derived SNP dataset.

RESULTS

Principal component (PC) and fixation index (FS⁢T)-based genome scans yielded consistent outlier SNP loci on chromosomes 1, 8, 9 and 12. Additional loci on chromosomes 3, 7, 8, 10, 11, 13 and 14 were detected using both the PC and FS⁢T methods in two of the three datasets. A genome-environment association (GEA) analysis using the 23K dataset and the first PC of cropping season temperature, day-length and latitude identified significant SNPs on chromosomes 3, 7, 9 and 13.

CONCLUSIONS

Most of the loci detected by the three methods harbored relevant genes for local adaptation, including some that play roles in day-length, light and other biotic and abiotic stresses responses. Such genetic signatures may help to select pre-breeding materials potentially adapted to specific growing niches prior to field performance trials. Given the current low genotyping cost and freely available environmental data, the genome scans along with GEA can readily provide opportunity to sort out materials suitable to various environmental conditions from large set of germplasm in gene banks and/or in situ, thereby assisting the breeding and genetic conservation efforts.

Groundwater fluoride concentrations in the watershed sedimentary basin of Quetta Valley, Pakistan

Litho-geochemical characteristics of low and high fluoride (F^-) groundwater along with hydrological processes were investigated to delineate its genesis and enrichment mechanism in a watershed sedimentary basin. In this study, groundwater F^- concentration ranged from 0 to 20 mg/L with a mean and standard deviation of 2.8 and ± 3.7 mg/L, respectively. Out of N = 87, 63% of samples exceeded the World Health Organization (WHO) limit of 1.5 mg/L. The order of cationic and anionic dominance in groundwater samples with mean was found in decreasing order as Na⁺  > Mg²⁺  > Ca²⁺  > K⁺ and HCO₃^-  > SO₄^2-  > Cl^-  > PO₄^3-  > NO₃^- measured in milligrams per liter. Groundwater chemistry changed from Ca-HCO₃ to Na-HCO₃ type and low to high fluoride as we moved from mountain foot towards the synclinal basin. Low fluoride groundwater reflected weathering, recharge, and reverse ion exchange processes with Ca-HCO₃- and Ca-Mg-Cl-type water while high fluoride groundwater revealed base ion exchange, mixing, and desorption as dominant hydrological processes with Na-HCO₃ and Na-Cl types of water. Gibb's diagram showed rock weathering and mineral dissolution as the major geochemical processes controlling water chemistry with an insignificant role of evaporation in the semi-arid area. Fluoride was undersaturated with mineral fluorite, indicating fluoride in groundwater is released by secondary minerals. However, due to complex geological features, groundwater fluoride enrichment was affected by a broad-scale process across a wide area such as depth, residence time, and most important geomorphological units hosting the aquifer.

AMMI and GGE biplot analysis of yield of different elite wheat line under terminal heat stress and irrigated environments

Wheat crop contributes to a major portion of the agriculture economy of Nepal. It is ranked as the third major cereal crop of the country even though, it faces terminal heat stress which speeds up the grain filling rate and shortens the filling period, causing reduction in grain weight, size, number and quality losses. We can minimize this loss through a genotypic selection of high-yielding lines by understanding the genotype-environment interaction. The objective of this research is to obtain a high yielding line with a stable performance across the environments. In order to do so, we conducted an experiment using eighteen elite wheat lines and two check varieties in alpha-lattice design with two replications in different environments viz. irrigated and terminal heat stress environment from November 2019 to April 2020. The analysis of variance revealed that genotype, environment and their interaction had a highly significant effect on the yield. Furthermore, the which-won–where model indicated specific adaptation of elite lines NL 1179, NL 1420, BL 4407, NL 1368 to the irrigated environment and Bhirkuti to the terminal heat-stressed environment. Similarly, the mean-versus-stability study indicated that elite lines BL 4407, NL 1368, BL 4919, NL 1350, and NL 1420 had above-average yield and higher stability whereas elite lines Gautam, NL 1412, NL 1376, NL 1387, NL 1404, and NL 1381 had below-average yield and lower stability. The ranking of elite lines biplot, PC1 explaining 73.6% and PC2 explaining 26.4% of the interaction effect, showed the rank of elite line, NL 1420 > NL 1368> NL 1350 > other lines, close to the ideal line. On the basis of the obtained results, we recommend NL 1420 with both the high yield and stability is suited across both the environments, while NL 1179 and Bhirkuti is adapted specifically for irrigated and terminal heat stress environment, respectively.

Mapping open knowledge institutions: an exploratory analysis of Australian universities

While the movement for open research has gained momentum in recent years, there remain concerns about the broader commitment to openness in knowledge production and dissemination. Increasingly, universities are under pressure to transform themselves to engage with the wider community and to be more inclusive. Open knowledge institutions (OKIs) provide a framework that encourages universities to act with the principles of openness at their centre; not only should universities embrace digital open access (OA), but also lead actions in cultivating diversity, equity, transparency and positive changes in society. This leads to questions of whether we can evaluate the progress of OKIs and what are potential indicators for OKIs. As an exploratory study, this article reports on the collection and analysis of a list of potential OKI indicators. Data for these indicators are gathered for 43 Australian universities. The indicators provide high-dimensional and complex signals about university performances. They show evidence of large disparities in characteristics such as Indigenous employment and gender equity, and a preference for repository-mediated OA across Australian universities. We demonstrate use of the OKI evaluation framework to categorise these indicators into three platforms of diversity, communication and coordination. The analysis provides new insights into the Australian open knowledge landscape and ways of mapping different paths of OKIs.

Principal Component Analysis of the Well-Being at Work and Respect for Human Rights Questionnaire (WWRRR) in the Mediterranean Region

Background:

The Well-Being at Work and Respect for human Rights Questionnaire (WWRR) was conceived based on the hypothesis that the perception of respect for users' rights is an essential element of well-being in the workplace in healthcare. The objective of the study is to examine the principal components of the WWRR.

Methods:

A random sample representative of a set of professionals working in three different healthcare networks in Tunisia, North-Macedonia, and Italy was enrolled (n=426). Each professional completed a questionnaire on sociodemographic data and the WWRR. The WWRR consists of six items on beliefs about: satisfaction at work, users’ satisfaction, organization at work, respect of users’ and staff human rights, adequacy of resources. A seventh item assesses the perceived needs of personnel. Correlation between the items was evaluated by analysing the principal components with Varimax rotation and Kaiser normalization (which included all components with an Eigen value> 1).

Results:

A single factor covered over 50% of the variance, all the items of the questionnaire were closely related and compose a single factor. Tunisia presented some differences regarding the item about the human rights of staff.

Conclusion:

Satisfaction with the respect for the rights of users is strongly correlated with the other factors that are part of the concept of the organizational well-being of health care providers. The WWRR provides a means of measuring this important and often neglected dimension.

Modeling Viral Suppression, Viral Rebound and State-Specific Duration of HIV Patients with CD4 Count Adjustment: Parametric Multistate Frailty Model Approach

INTRODUCTION

Combination antiretroviral therapy has become the standard care of human immunodeficiency virus (HIV)-infected patients and has further led to a dramatically decreased progression probability to acquired immune deficiency syndrome (AIDS) for patients under such a therapy. However, responses of the patients to this therapy have recorded heterogeneous complexity and high dynamism. In this paper, we simultaneously model long-term viral suppression, viral rebound, and state-specific duration of HIV-infected patients.

METHODS

Full-parametric and semi-parametric Markov multistate models were applied to assess the effects of covariates namely TB co-infection, educational status, marital status, age, quality of life (QoL) scores, white and red blood cell parameters, and liver enzyme abnormality on long-term viral suppression, viral rebound and state-specific duration for HIV-infected individuals before and after treatment. Furthermore, two models, one including and another excluding the effect of the frailty, were presented and compared in this study.

RESULTS

Results from the diagnostic plots, Akaike information criterion (AIC) and likelihood ratio test showed that the Weibull multistate frailty model fitted significantly better than the exponential and semi-parametric multistate models. Viral rebound was found to be significantly associated with many sex partners, higher eosinophils count, younger age, lower educational level, higher monocyte counts, having abnormal neutrophils count, and higher liver enzyme abnormality. Furthermore, viral suppression was also found to be significantly associated with higher QoL scores, and having a stable sex partner. The analysis result also showed that patients with a stable sex partner, higher educational levels, higher QoL scores, lower eosinophils count, lower monocyte counts, and higher RBC indices were more likely to spend more time in undetectable viral load state.

CONCLUSIONS

To achieve and maintain the UNAIDS 90% suppression targets, additional interventions are required to optimize antiretroviral therapy outcomes, specifically targeting those with poor clinical characteristics, lower education, younger age, and those with many sex partners. From a methodological perspective, the parametric multistate approach with frailty is a flexible approach for modeling time-varying variables, allowing for dealing with heterogeneity between the sequence of transitions, as well as allowing for a reasonable degree of flexibility with a few additional parameters, which then aids in gaining a better insight into how factors change over time.

Multivariate principal component analysis to evaluate growth performances in Malabari goats of India

Evaluation of growth performances in Malabari goats was done with body weight and major morphometric traits, viz. body height, body length and chest girth at 6, 9 and 12 months, respectively. Data pertaining on 1082 Malabari goats spread over a period of 5 years (from 2014 to 2018) were used in the study. Least squares analysis of traits was done to adjust the effect of major significant non-genetic factors. Traits were analysed by using Varimax rotated principal component analysis (PCA) with Kaiser normalization to explain growth performances. Out of twelve principal components, PCA revealed four components explained about 67.78% of total variation. The first component (PC1) explained 28.02% of total variation. It was represented by significantly positive high loading of BH9, BH12 and BH6. The second component explained 15.090% of total variance with high loading of distance between BL9, BL6 and BL12. The third component explained 12.643% of variance and showed high component loadings for CG9, CG6 and CG12. The fourth factor accounted for 12.020% of total variability with comparatively higher loading WT12, WT9 and WT6. The communality ranged from 0.562 for BL12 to 0.848 for BH9. The body weight of adult Malabari goats was predicted using stepwise multiple regression of different interdependent morphometric traits and principal components. The multiple regression model with PC1 and PC2 was most precise with coefficient of determination (R²) value 74%. Therefore, the study revealed that extracted components revealed maximum variability of growth performances in Malabari goats which could be effectively used for selection and breeding programmes.

Multivariate multilevel modeling of quality of life dynamics of HIV infected patients

BACKGROUND

Longitudinal quality of life (QoL) is an important outcome in many chronic illness studies aiming to evaluate the efficiency of care both at the patient and health system level. Although many QoL studies involve multiple correlated hierarchical outcome measures, very few of them use multivariate modeling. In this work, we modeled the long-term dynamics of QoL scores accounting for the correlation between the QoL scores in a multilevel multivariate framework and to compare the effects of covariates across the outcomes.

METHODS

The data is from an ongoing prospective cohort study conducted amongst adult women who were HIV-infected and on the treatment in Kwazulu-Natal, South Africa. Independent and related QoL outcome multivariate multilevel models were presented and compared.

RESULTS

The analysis showed that related outcome multivariate multilevel models fit better for our data used. Our analyses also revealed that higher educational levels, middle age, stable sex partners and higher weights had a significant effect on better improvements in the rate of change of QoL scores of HIV infected patients. Similarly, patients without TB co-infection, without thrombocytopenia, with lower viral load, with higher CD4 cell count levels, with higher electrolytes component score, with higher red blood cell (RBC) component score and with lower liver abnormality component score, were associated with significantly improved the rate of change of QoL, amongst HIV infected patients.

CONCLUSION

It is hoped that the article will help applied researchers to familiarize themselves with the models and including interpretation of results. Furthermore, three issues are highlighted: model building of multivariate multilevel outcomes, how this model can be used to assess multivariate assumptions, involving fixed effects (for example, to examine the size of the covariate effect varying across QoL domain scores) and random effects (for example, to examine the rate of change in one response variable associated to changes in the other).

Cellular and physiological responses to drought stress in Aegilops tauschii genotypes

Drought stress is one of the most important limiting factors in crop yield through impact on the cellular and physiological functions of the plant. Therefore, the study of physiological responses of plants can help to better understanding the drought tolerance mechanisms. In this experiment, 125 wild diploid wheat genotypes of Aegilops tauschii were evaluated for the physiological responses under rainfed and supplemental irrigation conditions. The physiological characteristics such as leaf relative water content (RWC), excised leaf water retention (ELWR), relative water loss (RWL), chlorophyll a, chlorophyll b, total chlorophyll, carotenoids, ion leakage, membrane stability index (MSI) and proline content were measured. The results showed that the higher proline content, lower chlorophyll degradation rate and low amount of the membrane stability index (MSI) may inhibit the grain yield reduction under rainfed conditions. It was also found that the lower ion leakage due to the low cell membrane damage may led to the higher yield under rain-fed conditions. The results of regression analysis in both rainfed and supplemental irrigation conditions showed that proline content and total chlorophyll were introduced into the model, and explained the most variation in the grain yield. So, considering the above traits, the genotypes 16, 22, 43, 66 and 106 seems to be more drought tolerant and could be exploited in wheat breeding programs after further assessments.

Nanominerals and potentially hazardous elements from coal cleaning rejects of abandoned mines: Environmental impact and risk assessment

Soils around coal mining are important reservoir of hazardous elements (HEs), nanominerals, and ultrafine compounds. This research reports and discusses the soil concentrations of HEs (As, Cd, Cr, Cu, Ni, Pb, and Zn) in coal residues of abandoned mines. To assess differences regarding environmental impact and risk assessment between coal abandoned mines from the Santa Catarina state, eighteen coal cleaning rejects with different mineralogical and chemical composition, from eight abandoned mines were collected. Nanominerals and ultra-fine minerals from mining-contaminated areas were analyzed by X-ray diffraction (XRD), field emission scanning electron microscopy (FE-SEM), and high-resolution transmission electron microscope (HR-TEM), providing new information on the mineralogy and nano-mineralogy of these coal residues. The total contents of 57 elements (HEs, alkali metals, and rare earth elements) were analyzed by Inductively Coupled Plasma Mass Spectrometry (ICP-MS). The calculation of NWACs (Normalized Average Weighted Concentration), together with the chemometric analysis by Principal component analysis (PCA) confirmed the variability of the samples regarding their city and their mine of origin. Moreover, the results confirmed the existence of hotspots in mines near urban areas.

Principal Component Analysis of Conformation and Blood Marker Traits at Pre- and Post-Weaning Stages of Growth in F2 Crossbred Nigerian Indigenous X Landrace Pigs

Linear body measurements and blood hematology and biochemistry parameters were taken on 92 and 15 grower pigs, respectively, chosen randomly from a crossbred F2 population of Nigerian Indigenous (NI) x Landrace pigs to evaluate body conformation and blood marker traits using a multivariate approach. For linear body measurements at pre-weaning stage (4 weeks old), two principal components accounted for 91.63% of the total variation in body conformation traits, while at post weaning stage (12 weeks old), one principal component accounted for 73.63% of the total variations in body conformation traits. The first factor (principal component) accounted for 72.34% of the total variance (body weight) while the second factor accounted for 19.29% (ear length) at pre-weaning stage. While at post-weaning stage only one factor contributed to the total variability of the original six traits tested, with 73.63% (body length). For blood parameters, three principal components accounted for 81.85% of the total variation at pre-weaning stage while 89.44% of the total variation at post-weaning stage was accounted for by four principal components. The first factor (principal component) accounted for 38.048% (Packed cell volume), the second accounted for 24.72% (hemoglobin) and the third accounted for 19.08% (aspartate transaminase). While at the post-weaning stage, the first factor was influenced by red blood corpuscles (37.17%), the second factor had its loading for hemoglobin (25.80%), the third factor had loading for white blood corpuscles (13.67%), while the fourth factor had loading for alkaline phosphatase only (12.80%). Using communality extraction factors, ear length best described body conformation at the pre-weaning stage; whereas, at post-weaning stage, body length was the best descriptor. In blood parameters, packed cell volume best described blood marker traits at the pre-weaning stage; whereas, at the post-weaning stage, red blood corpuscles best described it. Hence, the two extracted factors in linear body measurements and in blood parameters could be considered in breeding programs to improve and predict body conformation in crossbred pigs, as well as predict blood marker traits.