Authors: Lauren Silvatti.
Mentors: Zoe Thompson
Insitution: Utah Valley University
Proopiomelanocortin (Pomc) deficiency, stemming from a mutation in the Pomc gene, presents a myriad of health challenges, including extreme hyperphagia, early-onset obesity, and infertility. This study leverages a mouse model exhibiting Pomc-deficiency to delve into the root causes of infertility. While the correlation between obesity and fertility is well-established, our primary focus is to discern whether the POMC mutation independently contributes to reproductive challenges. Our investigation extends into the interplay of genetics, endocrinology, and obesity through data analysis using R, with an emphasis on deploying advanced statistical models.
Video recordings of sexual behavior interactions have been analyzed manually, with independent observers marking each behavior. The frequency and duration of each behavior will be compared among three groups of pairings: wildtype-wildtype, heterozygote-heterozygote, and homozygous POMC-deficient mice. Techniques will include data cleaning, timestamp manipulation, and behavior categorization in R, a programming language commonly used for data analysis. To discern patterns and variations, our analysis will also employ statistical models such as linear mixed-effects models. We can account for potential confounding variables and significant differences in durations of previously identified important reproductive behaviors.
Visualization tools, including box plots and violin plots, will provide an initial glimpse into the distribution of behavior durations. Subsequently, we plan to conduct inferential statistics, employing techniques such as Analysis of Variance (ANOVA) to assess the significance of differences across multiple groups. Our investigation extends beyond descriptive statistics, with a focus on predictive modeling. Regression analyses will explore potential relationships between behavior durations and reproductive success. Machine learning algorithms will be applied to uncover complex interactions within the dataset.
The anticipated results promise not only a nuanced understanding of the interplay between Pomc mutations and reproductive challenges but also the identification of potential biomarkers or predictors of successful reproduction. This comprehensive statistical approach contributes significantly to the fields of genetics, endocrinology, and obesity research, offering a robust framework for future investigations into the intricate relationship between genotype and behavior.