Computational Genetic Genealogy

Course Textbook & Resources

Course Modules

Getting Started

Lab 0.1: Environment Setup

Setting up your computational environment for genetic analysis

Lab 0.2: Getting Data

Overview of data resources for genetic analysis

Lab 0.3: OpenSNP Data

Obtaining and processing OpenSNP genetic data

Lab 0.4: 1000 Genomes Data

Accessing and working with 1000 Genomes Project data

Data Exploration & Preparation

Lab 1: Exploring Data

Understanding population genomic data structure

Lab 2: Processing Raw DNA

Converting and preparing DNA profile data

Lab 3: Quality Control

Ensuring data quality for genetic analysis

IBD Detection

Lab 4: IBIS

Identity-by-descent detection using IBIS

Lab 5: Hap-IBD

IBD detection with haplotype-based methods

Lab 6: Refined-IBD

Advanced techniques for IBD segment detection

Simulation & Evaluation

Lab 7: Ped-Sim

Simulating genetic data from known pedigrees

Lab 8: IBD Evaluation

Evaluating IBD detection methods

Lab 9: MSPrime

Coalescent-based genetic simulation

Pedigree Reconstruction

Lab 10: Distributions

Statistical analysis of genetic relatedness

Lab 11: Bonsai Introduction

Reconstructing pedigrees from genetic data

Advanced Bonsai Specialization

Lab 12: Bonsai Fundamentals

Fundamentals of genetic genealogy and IBD segments

Lab 13: Mathematical Foundations

Mathematical principles behind Bonsai algorithm

Lab 14: Data Structures

Data structures and algorithmic design in Bonsai

Lab 15: Model Calibration

Calibrating Bonsai models for optimal performance

Lab 16: Architecture

Architecture and implementation of Bonsai

Lab 17: Likelihood Calculations

Advanced likelihood calculations in Bonsai

Lab 18: Data Quality

Handling data quality issues in pedigree reconstruction

Lab 19: Advanced Construction

Advanced pedigree construction techniques

Lab 20: Visualization

Visualizing and interpreting pedigree structures

Lab 21: Applications

Real-world applications of Bonsai

Additional Resources

Learning Pathway

This course is structured as a progressive learning journey:

Environment Setup

Lab 0.1

Data Acquisition

Labs 0.2-0.4

Data Exploration

Lab 1

Data Processing

Lab 2

Quality Control

Lab 3

IBD Detection

Labs 4-6

Simulation

Labs 7-9

Statistics

Lab 10

Bonsai Algorithm

Labs 11-21

Course Structure: This course builds progressively from foundational skills to advanced topics. Begin with environment setup and data acquisition, then move through data processing and quality control. The middle section covers IBD detection and simulation techniques. The final section explores the Bonsai algorithm for pedigree reconstruction in depth, with specialized labs covering its mathematical foundations, architecture, and applications.