General Linear Model Procedure Of SAS Research Articles

Validation of "sasLM," an R package for linear models with type III sum of squares.

The general linear model (GLM) describes the dependent variable as a linear combination of independent variables and an error term. The GLM procedure of SAS® and the “car” package in R calculate the type I, II, or III ANOVA (analysis of variance) tables. In this study, we validated the newly-developed R package, “sasLM,” which is compatible with the GLM procedure of SAS®. The “sasLM” package was validated by comparing the output with SAS®, which is the current gold standard for statistical programming. Data from ten books and articles were used for validation. The results of the “sasLM” and “car” packages were compared with those in SAS® using 194 models. All of the results in “sasLM” were identical to those of SAS®, whereas more than 20 models in “car” showed different results from those of SAS®. As the results of the “sasLM” package were similar to those in SAS® PROC GLM, the “sasLM” package could be a viable alternative method for calculating the type II and III sum of squares. The newly-developed “sasLM” package is free and open-source, therefore it can be used to develop other useful packages as well. We hope that the “sasLM” package will enable researchers to conveniently analyze linear models.

Pemberian Anti-Myostatin pada Induk Serta Keragaman Gen Myostatin dan Asosiasinya terhadap Potongan Karkas Anak Ayam Sentul (F1)

Myostatin (MSTN), a member of the transforming growth factor β (TGF-β) superfamily, is a negative regulator of skeletal muscle growth. Improving muscle growth is very important to increase the economic value of Indonesian indigenous chicken especially in sentul chicken. This study was aimed to identify the polymorphism of the Myostatin gene in sentul indonesian indigenous chicken with maternal immunization against Myostatin on post hatch and to evaluate its effect on carcass traits. In this study, 48 Sentul hens for up to 6 months have been divided into three groups with 16 birds in each group: CON, KLH, and KLH-MSTN. The CON group has not received immunization, the KLH group has been immunized with 1 mg of KLH, and KLH-MSTN has been immunized with 1 mg of KLH-MSTN conjugate intramuscularly. The effects of each group have been observed on F1 carcass production.The gene polymorphism was identified using PCR-RFLP and the effect of genotype on carcass chacteristics were analyzed using SAS General Linear Model Procedure. The product of amplification was 247 bp. This locus was polymorphic in sentul chicken with two alleles (G and T) and three genotypes (GG, GT, TT). Association analysis were not significant in carcass characteristics. The result of this study showed that interaction of each group with genotype not significant on carcass weight of F1 chicken sentul each age group. F1 sentul chicken 12 week-old significant association of genotype was observed only in thighs weight while 20 week-old was significant in live weight, carcass weight, breast weight, wings weight, thighs weight, drum sticks weight, breast muscle weight, thighs muscle weight and drum sticks muscle weight. These results suggest that no effect genotype on maternal immunization against myostatin to stimulate carcass characteristic in sentul chickens.

Genome-wide Association Study to Identify Quantitative Trait Loci for Meat and Carcass Quality Traits in Berkshire.

Meat and carcass quality attributes are of crucial importance influencing consumer preference and profitability in the pork industry. A set of 400 Berkshire pigs were collected from Dasan breeding farm, Namwon, Chonbuk province, Korea that were born between 2012 and 2013. To perform genome wide association studies (GWAS), eleven meat and carcass quality traits were considered, including carcass weight, backfat thickness, pH value after 24 hours (pH24), Commission Internationale de l’Eclairage lightness in meat color (CIE L), redness in meat color (CIE a), yellowness in meat color (CIE b), filtering, drip loss, heat loss, shear force and marbling score. All of the 400 animals were genotyped with the Porcine 62K SNP BeadChips (Illumina Inc., USA). A SAS general linear model procedure (SAS version 9.2) was used to pre-adjust the animal phenotypes before GWAS with sire and sex effects as fixed effects and slaughter age as a covariate. After fitting the fixed and covariate factors in the model, the residuals of the phenotype regressed on additive effects of each single nucleotide polymorphism (SNP) under a linear regression model (PLINK version 1.07). The significant SNPs after permutation testing at a chromosome-wise level were subjected to stepwise regression analysis to determine the best set of SNP markers. A total of 55 significant (p<0.05) SNPs or quantitative trait loci (QTL) were detected on various chromosomes. The QTLs explained from 5.06% to 8.28% of the total phenotypic variation of the traits. Some QTLs with pleiotropic effect were also identified. A pair of significant QTL for pH24 was also found to affect both CIE L and drip loss percentage. The significant QTL after characterization of the functional candidate genes on the QTL or around the QTL region may be effectively and efficiently used in marker assisted selection to achieve enhanced genetic improvement of the trait considered.

Comparison of gene expression and fatty acid profiles in concentrate and forage finished beef1

Fatty acid profiles and intramuscular expression of genes involved in fatty acid metabolism were characterized in concentrate- (CO) and forage- (FO) based finishing systems. Intramuscular samples from the adductor were taken at slaughter from 99 heifers finished on a CO diet and 58 heifers finished on a FO diet. Strip loins were obtained at fabrication to evaluate fatty acid profiles of LM muscle for all 157 heifers by using gas chromatography fatty acid methyl ester analysis. Composition was analyzed for differences by using the General Linear Model (GLM) procedure in SAS. Differences in fatty acid profile included a greater atherogenic index, greater percentage total MUFA, decreased omega-3 to omega-6 ratio, decreased percentage total PUFA, and decreased percentage omega-3 fatty acids in CO- compared with FO-finished heifers (P<0.05). Fatty acid profiles from intramuscular samples were ranked by the atherogenic index, and 20 heifers with either a high (HAI; n=10) or low (LAI; n=10) atherogenic index were selected for gene expression analysis using real-time PCR (RT-PCR). Gene expression data for the 20 individuals were analyzed as a 2 by 2 factorial arrangement of treatments using the GLM procedure in SAS. There was no significant diet × atherogenic index interaction identified for any gene (P>0.05). Upregulation was observed for PPARγ, fatty acid synthase (FASN), and fatty acid binding protein 4 (FABP4) in FO-finished compared with CO-finished heifers in both atherogenic index categories (P<0.05). Upregulation of diglyceride acyl transferase 2 (DGAT2) was observed in FO-finished heifers with a HAI (P<0.05). Expression of steroyl Co-A desaturase (SCD) was upregulated in CO-finished heifers with a LAI, and downregulated in FO-finished heifers with a HAI (P<0.05). Expression of adiponectin (ADIPOQ) was significantly downregulated in CO-finished heifers with a HAI compared with all other categories (P<0.05). The genes identified in this study which exhibit differential regulation in response to diet or in animals with extreme fatty acid profiles may provide genetic markers for selecting desirable fatty acid profiles in future selection programs.

Comparative Performance of Bovans Nera (Rhode Black) and other Commercial Pullet Strains

The present study examined the comparative performance between Bovans Nera and other commercial pullet strains in Nigeria. This study was carried out in the pullet house at the Teaching and Research Farm of the University of Ibadan, Ibadan, Nigeria, from January to April, 2002. The experiment was carried out to compare the growth performance and profitability of Bovans Nera with three other commercial pullet strains, Bovans Godline, Lohmann White and Lohmann Tradition. All data collected were subjected to the analysis of variance using the SAS General Linear Model procedure. Differences between treatment means were separated using Duncans New Multiple Range Test. Profitability was assessed using net returns by subtracting costs (rearing pullet, feed and packaging excluding overheads and labour) from income (from egg sales and spent birds). The results showed that Bovans Nera was better in feed conversion ratio, feed efficiency (a greater weight gain per unit feed intake) and attained a greater weight and a better market value than the other pullet strains. There was a higher production cost for Bovans Nera when compared with other strains. However, Bovans Nera will give more returns on investment even when their eggs are translated to monetary terms. These differences can be attributed to variation in the genetic constitution of individual strains.

(81) Effect of Ethephon on Establishment of Herbaceous Ornamentals in a Landscape—Response of Plants During Production Phase

Use of growth regulators in ornamental plant production is a common nursery practice. Research conducted in determining landscape establishment of herbaceous plants treated with various concentrations of growth regulators is limited. The first phase of this study was conducted to evaluate response of three herbaceous ornamental species to application of ethephon. Containerized plants of irish moss, scotch moss, and salvia were treated once with FLOREL® (ethephon) at 0, 250, 500, or 750 ppm 2 weeks after transplanting into #1 size containers. During the course of the 6-week production period, standard nursery practices of fertigation, pest control, and weed management were followed. Data were collected on growth indices and marketable quality ratings (scale of 1–5) every 2 weeks. Mean initial and final shoot and root dry weights were calculated at the start and end of this phase of the experiment. The experimental design was completely randomized and data analyses were made using the analysis of variance with SAS general linear model procedure. Growth indices (cm) were significantly affected by increasing concentrations of FLOREL®. At 750 ppm concentration the mean growth indices were low (2516 cm) whereas, control (0 ppm) produced the highest mean growth indices (4317 cm). Significant differences in marketable quality ratings were also observed among the treatments where control (0 ppm) plants had the best mean rating (4.3) as compared with 500 ppm (3.6) or 750 ppm (3.5) concentrations. Salvia produced mean growth indices and mean width of 9106 and 513 cm, respectively. However, irish moss had asignificantly higher mean quality rating (4.4) when compared with either salvia (3.7) or scotch moss (3.3).

Caractérisation de la performance de croissance des Namchi et des Kapsiki, races bovines camerounaises menacées de disparition

De 1985 à 1990, les performances de croissance de 80 veaux Kapsiki et de 83 veaux Namchi ont été suivies tous les trois mois de la naissance à un an à la station de Recherche agricole pour le développement de Yagoua. Les performances de croissance ont été analysées par la procédure du modèle linéaire général de SAS pour les poids à la naissance (PN) et aux âges de trois mois (PTM), six mois (PSM), neuf mois (PNM) et 12 mois (PDM). Les moyennes ont été de 13,2 et 15,5 kg, 42,9 et 58 kg, 68,3 et 75,2 kg, 85,9 et 97,6 kg, et 104,7 et 115,5 kg respectivement pour les PN, PTM, PSM, PNM et PDM chez les veaux Namchi et Kapsiki. Les gains de poids quotidiens cumulés ont été de 0,32 et 0,48 kg, 0,25 et 0,39 kg, 0,21 et 0,35 kg, et 0,21 et 0,32 kg respectivement entre les PN et PTM, PTM et PSM, PSM et PNM, et PNM et PDM, chez les veaux Namchi et Kapsiki. La performance de croissance a été significativement supérieure (P &lt; 0,001) chez les veaux Kapsiki par rapport aux veaux Namchi pour tous les caractères considérés. La saison n’a pas eu d’effet significatif (P &gt; 0,05) sur la croissance, excepté sur les PSM et PDM (P &lt; 0,05) chez les deux races. Le sexe n’a pas influé (P &gt; 0,05) sur les traits étudiés. Cependant, les veaux mâles des deux races ont eu un poids plus élevé et leur croissance a été plus rapide que ceux des femelles. La saison pluvieuse et la saison sèche froide ont été des périodes favorables pour la production de veaux Namchi plus performants et à croissance plus rapide tandis que la saison sèche chaude a eu les mêmes effets chez les veaux Kapsiki.

595 Computer Root Image Analysis of Citrus Rootstock Seedlings

Florida citrus has had an average annual on-tree-value of ≈1 billion dollars during the past decade in Florida. Nearly all of the 845,260 acres of citrus in Florida is produced on grafted trees consisting of a commercial scion cultivar and a rootstock selected specifically for local soil, environment, and pest pressures. With vastly different root-zone environments, ranging from deep sands to drained and cleared pine Flatwoods, a large number of different rootstocks are utilized. These rootstocks are started from seed at more than 100 commercial nurseries statewide, which currently produce an estimated 6 million trees a year. Although the optimum germination conditions, basic physiological performance, and adaptability of many rootstocks are known, there has been minimal investigation on early root development in seedling trays at the nursery. Four hundred seedlings of `Swingle' citrumelo (Citrus paradisi Macf. `Dunacn' × Poncirus trifoliata), `Smooth Flat Seville', `Volkamer' lemon (Citrus volkameriana), and `Sun Chu Sha' mandarin were seeded in a randomized block experimental design and grown at a commercial nursery. Seedling root systems (100/rootstock) were analyzed for a number of variables using the Rhizo (Regent Instruments, Inc.) software package and a dual light source scanner. Using the SAS general linear model procedure, hypothesis testing revealed rootstock selection had a significant effect on total root length, total root surface area, total root volume, number of root tips, number of root forks, root dry weight, and stem diameter. For most characteristics, rootstock genotype accounted for a greater portion of variability than samples (plant to plant variability).

Statistical analysis of stability data by means of general linear models (GLM)

AbstractApplication of a General Linear Model (GLM, “Analysis of Covariance”) to the statistical interpretation of stability data combines the methods of regression and analysis of variance in one common model. Expanding the well accepted method of linear regression upon time, the GLM model permits one to include supportive factors which may be either continuous regressors (temperature, humidity, etc.) or class effects (batch number, formulation type, manufacturer, etc.). Using the GLM procedure of SAS as a convenient software tool, the technique is illustrated by several examples. It is concluded that GLM provides a most suitable approach for the interpretation of stability data.

Interpretation of the four types of analysis of variance tables in sas

Various computational methods exist for generating sums of squares in an analysis of variance table. When the ANOVA design is balanced, most of these computational methods will produce equivalent sums of squares for testing the significance of the ANOVA model parameters. However, when the design is unbalanced, as is frequently the case in practice, these sums of squares depend on the computational method used.- The basic reason for the difference in these sums of squares is that different hypotheses are being tested. The purpose of this paper is to describe these hypotheses in terms of population or cell means. A numerical example is given for the two factor model with interaction. The hypotheses that are tested by the four computational methods of the SAS general linear model procedure are specified. Although the ultimate choice of hypotheses should be made by the researcher before conducting the experiment, this paper PENDLETON,VON TRESS,AND BREMER presents the following guidelines in selecting these hypotheses: When the design is balanced, all of the SAS procedures will agree. In unbalanced ANOVA designs when there are no missing cells. SAS Type III should be used. SAS Type III tests an unweighted hypothesis about cell means. SAS Types I and II test hypotheses that are functions of the ceil frequencies. These frequencies are often merely arti¬facts of the experimental process and not reflective of any underlying frequencies in the population. When there are missing cells, i.e. no observations for some factor level combinations. Type IV should be used with caution. SAS Type IV tests hypotheses which depend

Interpretation of the four types of analysis of variance tables in sas

Various computational methods exist for generating sums of squares in an analysis of variance table. When the ANOVA design is balanced, most of these computational methods will produce equivalent sums of squares for testing the significance of the ANOVA model parameters. However, when the design is unbalanced, as is frequently the case in practice, these sums of squares depend on the computational method used.- The basic reason for the difference in these sums of squares is that different hypotheses are being tested. The purpose of this paper is to describe these hypotheses in terms of population or cell means. A numerical example is given for the two factor model with interaction. The hypotheses that are tested by the four computational methods of the SAS general linear model procedure are specified. Although the ultimate choice of hypotheses should be made by the researcher before conducting the experiment, this paper PENDLETON,VON TRESS,AND BREMER presents the following guidelines in selecting these hypotheses: When the design is balanced, all of the SAS procedures will agree. In unbalanced ANOVA designs when there are no missing cells. SAS Type III should be used. SAS Type III tests an unweighted hypothesis about cell means. SAS Types I and II test hypotheses that are functions of the ceil frequencies. These frequencies are often merely arti¬facts of the experimental process and not reflective of any underlying frequencies in the population. When there are missing cells, i.e. no observations for some factor level combinations. Type IV should be used with caution. SAS Type IV tests hypotheses which depend