Download Introduction to BST775: Statistical Methods for Genetic Analysis I

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
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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
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Homework Assignments
Student-initiated Project (Written)
Student-initiated Project (Presentation)
Participation in Class Discussions
40%
25%
25%
10%
Teaching team
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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
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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 10-20 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.
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Manolio et al 2009 Nature