Event Phone: 1-610-715-0115

Cancellation Policy: If you cancel your registration at least two weeks before the course is scheduled to begin, you are entitled to a full refund (minus a processing fee of $50).

In the unlikely event that Statistical Horizons LLC must cancel a seminar, we will do our best to inform you as soon as possible of the cancellation. You would then have the option of receiving a full refund of the seminar fee or a credit towards another seminar. In no event shall Statistical Horizons LLC be liable for any incidental or consequential damages that you may incur because of the cancellation.

A 3-Day Livestream Seminar Taught by Daniel E. Adkins, Ph.D

Genomic data has transformed biomedical research and is now becoming a ubiquitous feature of large social and behavioral datasets. Integrating genomic data into the analysis of health, behavioral, and social outcomes makes it possible to model a range of processes, including polygenic risk and gene-environment interplay, as well as control for genetic factors to more accurately estimate environmental effects. However, the start-up costs of learning the specialized tools and techniques of statistical genetics can be a deterrent.

The goal of this seminar is to impart a thorough conceptual understanding of statistical genetics, and to equip you with the technical skills to conduct a range of popular statistical genetic analyses. Topics covered include: managing high dimensional genomic data; linear and logistic genome-wide association studies (GWAS); population stratification; multiple testing; mixed model GWAS methods for nonindependent samples; basic and advanced polygenic scoring; genome-environment interaction models; and an overview of the key software tools.

Statistical genetics provides the opportunity to map the genetic architecture of health outcomes, and to develop more accurate, comprehensive models of biosocial and behavioral processes. However, due to the unique aspects of genomic data, including its enormous size and complex correlational structure, getting started can be a challenge. To address this, we teach a full suite of methods for managing, cleaning, association testing, and modeling big genomic data. After completing the course, you will be able to manage and analyze your own GWAS data, including running a range of GWAS models and conducting diagnostics to ensure unbiased results. Additionally, you will learn to calculate and apply polygenic scores and genome-environment interaction models.

This seminar will introduce the computing environment, R and RStudio, before discussing multiple regression, and handling and cleaning big genomic data matrices. It will offer a comprehensive survey of GWAS methods, beginning with basic linear and logistic GWAS, modeling population stratification, and common covariate specifications. We then discuss post-GWAS processing, including addressing multiple testing and plotting results for diagnostic and visualization purposes. Subsequently, we will discuss GWAS models for nonindependent observations, such as repeated assessments and family data. We then explore polygenic scoring, which integrates prior information from large sample GWAS summary statistics to allow researchers to generate genetic scores that capture genome-wide genetic propensity to specific traits, as well as modeling genome-environment interactions. Throughout the course, you will gain experience with these methods through hands-on exercises.