Estimation of Parent-Sib Correlations for Quantitative Traits Using the Linear Mixed Regression Model: Applications to Arterial Blood Pressures Data Collected From Nuclear Families  - Pages 59-68

Maha Al-Eid, Sarah AL-Gahtani and Mohamed M. Shoukri

https://doi.org/10.6000/1929-6029.2020.09.07

Published: 03 November 2020

Abstract: A fundamental question in quantitative genetics is whether observed variation in the phenotypic values of a particular trait is due to environmental or to biological factors. Proportion of variations attributed to genetic factors is known as heritability of the trait. Heritability is a concept that summarizes how much of the variation in a trait is due to variation in genetic factors. Often, this term is used in reference to the resemblance between parents and their offspring. In this context, high heritability implies a strong resemblance between parents and offspring with regard to a specific trait, while low heritability implies a low level of resemblance. While many applications measure the offspring resemblance to their parents using the mid-parental value of a quantitative trait of interest as an input parameter, others focus on estimating maternal and paternal heritability. In this paper we address the problem of estimating parental heritability using the nuclear family as a unit of analysis. We derive moment and maximum likelihood estimators of parental heritability, and test their equality using the likelihood ratio test, the delta method. We also use Fieller’s interval on the ratio of parental heritability to address the question of bioequivalence. The methods are illustrated on published arterial blood pressures data collected from nuclear families.

Keywords: Genetic epidemiology, Familial correlations, Heritability, Linear Mixed normal models, Maximum Likelihood estimation, Estimating Ratio of parameters, Bootstrap Confidence interval.

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Relationship between Pretreatment Serum Albumin Levels with the Risk of Malignant Pleural Mesothelioma  - Pages 69-82

Sabyasachi Mukherjee

https://doi.org/10.6000/1929-6029.2020.09.08

Published: 31 December 2020

Abstract: Background: Malignant Pleural Mesothelioma (MPM) is a very rare and aggressive form of cancer. Recently it was found that pretreatment Serum Albumin (SA), the main circulating protein in blood is a significant prognostic factor for MPM patients. The objective of this present article is to show the relationship between pretreatment Serum albumin (SA) levels with the risk of MPM.

Methods: Generalized additive model (GAM), an advanced regression analysis method has been introduced here to find this mathematical relationship between the response variable (SA) and the cofactors.

Results: The main determinates of SA are identified - asbestos exposure, hemoglobin, disease diagnosis status (patients having MPM) are the factors having significant association with SA, whereas duration of asbestos exposure, duration of disease symptoms, total protein (TP), Pleural lactic dehydrogenise (PLD), pleural protein (PP), pleural glucose (PG) and C-reactive protein (CRP) are the significant continuous variables for SA. The non-parametric estimation part of this model shows Lactate dehydrogenase (LDH) and Glucose level are the significant smoothing terms. Additionally it is also found that, second and third order interactions between cofactors are highly significant for SA.

Conclusions: The findings of this present work can conclude that - serum albumin may play the role of a very significant prognostic factor for MPM disease and it has been established here through mathematical modeling. Few of the findings are already been exist in MPM research literature whereas some of the findings are completely new in the literature.

Keywords: Malignant Pleural Mesothelioma, Serum albumin, Gamma distribution, Generalized additive model, Probabilistic Modeling.

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