Introduction to BST775: Statistical Methods for Genetic Analysis I Course master: Degui Zhi, Ph.D. Assistant professor Section on Statistical Genetics

COURSE LOGISTICS

What is this course about? The first of a series in Statistical Genetics Rigorous mathematical emphasis. This course will provide a statistical basis for describing variation in qualitative (disease) and quantitative traits due to genetic and/or environmental factors.

Related courses BST675 Introduction to statistical genetics BST676 Statistical bioinformatics BST775 This course BST776 Statistical genetics II (next semester) EPI731 Genetic Epidemiology

Learning objectives Provide an understanding of statistical models used in gene mapping studies Survey commonly used algorithms and procedures in genetic analysis Provide hands-on opportunities for running popular computer programs to analyze genetic data.

Grading Homework Assignments 40% Student-initiated Project (Written)25% Student-initiated Project (Presentation)25% Participation in Class Discussions10%

Teaching team Kui Zhang Gustavo de los Campos Xiang-Yang Lou Nengjun Yi Nianjun Liu Degui Zhi Guodong Wu

COURSE CONTENT

Main topics Maximum Likelihood Modeling genes in families – Family association studies – Linkage analysis Genome wide association studies

Today’s topics How information is stored in DNA How DNA is inherited Types of DNA variation The process of gene mapping Types of studies

DNA is information store Encodes the information required for cells and organisms to function and produce new cells and organisms. DNA variation is responsible for many individual differences, some of which are medically important.

Human genome 22 autosomes – Present in 2 copies per individual – One from mother; one from father 1 pair of sex chromosomes – Females have XX – Males have XY Totaling 3 billion base pairs About 22,000 protein-coding genes

Inheritance of DNA Through recombination, a new “DNA string” is formed by combining two parental DNA strings. Thus, each chromosome we carry is a mosaic of the two chromosomes from our grandparents. Only a small number of crossovers between the two parental chromosomes – On average 1 per chromosome (1 Morgan) Copying of DNA sequences is imperfect (10 -8 mutation rate)

Human genetic variation About 1 per 1000 bases differs between pairs of human chromosomes. 1 in 100 bases are polymorphic found in 1000 Genomes project. – Most of variants are rare.

Important vocabulary Locus Polymorphism Allele Mutation Linkage Genetic marker Genotype Phenotype – Mendelian traits – Complex traits Chromosomal landmarks – Centromeres – Telomeres Gene

Data for a genetic study Pedigree – Set of individuals of known relationship Observed marker genotypes – SNPs, VNTRs, microsatellites Phenotype data for individuals

Genetic markers Genetic variants that can be measured conveniently Typically, we characterize them by – Number of alleles The most commonly used genetic markers are microsatellites and SNPs.

Phenotypes Measured characters of individuals Mendelian phenotypes – Completely determined by genes – E.g., color blindness, Retinoblastoma Complex phenotypes – Controlled by multiple genes and environmental factors – E.g., Diabetes, BMI

Ultimate aim of gene-mapping experiments Localize and identify genes and variants that control interesting traits – Susceptibility to human disease – Phenotypic variation in the population However, – Human genetic studies are often restricted – Human genome is a big place

The process of gene mapping 1.Descriptive epidemiological study 2.Familial aggregation 1.Does the disease run in families? 2.Is the disease heritable? 3.Linkage analysis 1.Are the markers are transmitted through families in a manner that parallels the transmission of the disease (co-segregation) 2.Can help find a broad region, typically Mb. 3.Need fine mapping.

Association studies Simplest case compares frequencies of allele among cases and controls Initially, most association studies focus on candidate genes With new technologies, it is possible to do genome scans -> Genome-wide association studies (GWAS). However, large sample size needed to find variants with small contribution to disease risk.

Manolio et al 2009 Nature 22