Pedigrees and Relatedness


Prerequisites: STAT 311; some programming experience preferred

Description: We will explore statistical theory and methodology as it applies to the study of (human) heredity. The overarching theme of the readings are (1) to compute measures of relatedness (kinship and inbreeding) and conditional trait (disease) risk based on known family trees and (2) to estimate relatedness given dense SNP or entire genome sequence data. Readings will follow UW emeritus professor Elizabeth Thompson’s monograph “Statistical Inference from Genetic on Pedigrees” (SIGDP). We will cover conditional probabilities, likelihood models, Hardy-Weinberg equilibrium, the expectation-maximization algorithm to infer allele frequencies for the ABO blood group, Wright’s path counting formula, and identity by descent. During meetings we will work through practice exercises; for 1 or 2 meetings we will go through brief hands-on labs using current research software.

This site contains assigned readings and exercises. Accompanying videos, scripts, and data files will be shared with mentees through Google Drive. Readings and exercises are to be done the week of and discussed in the following week. Some assignments will be formally submitted to Canvas for course credit.

Students/mentees:

  • Rachel Ferina, Autumn 2020, project, slides, writeup
  • Selma Chihab, Winter 2021, project, slides, writeup
  • Meng (Michael) Yung, Autumn 2021, project, slides, writeup
  • Saleh Wehelie, Winter 2022, project , slides, writeup

  • Before Term: Genetics

  • Readings
  • Exercises

  • Week 1: Kinship

  • Readings
  • Exercises

  • Week 2: Inbreeding

  • Readings
  • Exercises
  • Errata: Pedigree Formulas

  • Week 3: Genotype Probabilities

  • Readings