Statistical methods in genetics, fall 2008

Last modified by mjsillan@helsinki_fi on 2024/03/27 10:07

Statistical methods in genetics, fall 2008

The third examn will be arranged at the general examination day on
 Thursday, April 2th, 2009, from 16-20 in room A111 (Exactum).

Please inform about your participation by sending an e-mail to the lecturer.

The second examn will be arranged at the general examination day on
 Thursday, January 22th, 2009, from 16-20 in room A111 (Exactum).

The examination will be at the last lecture Thursday, December 11th, from 10.15-11.45 in room B120 (Exactum).

The project assignments can be done after the examn. There is no strick deadline for that.

Course folder, containing the course material other than the book, is available in room C326 of Exactum building.

Lecturer

Mikko Sillanpää

Scope

6-8 cu

Type

Advanced studies.

Prerequisites

Basic knowledge of probability calculus and of likelihood based methods in statistical inference. There are no molecular biological prerequisites for the course.

Lectures

Weeks 44-50, Tuesday 10-12 and Thursday 10-12 in room B120 (Exactum).

Schedule

Tuesday 28.10.
 Chapter 1, Familial Aggregation; pp. 11-13.
 Chapter 1, Linkage Analysis; pp.15-16.
 Chapter 2, Chromosomes; pp. 25-27.
 Chapter 2, Genetic Recombination; pp. 31-32.
 Chapter 2, DNA Polymorphism; pp. 42-43.

Thursday 30.10.
 Chapter 3, Principles of Mendelian Inheritance; pp. 45-57.

Tuesday 04.11.
 Chapter 3, Major gene / Polygene; p. 47.
 Chapter 3; Multiallelic loci / Two loci; pp. 57-59.
 Chapter 5; Familial Aggregation; pp. 95-99.
 Chapter 7; Sib Pair Methods for Quantitative Traits; pp. 188-189.

Thursday 06.11.
 Chapter 6; Likelihood Methods in Pedigree Analysis; pp. 146-150.

Tuesday 11.11.
 Chapter 6; The Elston-Stewart peeling algorithm; pp. 153-157.
 Chapter 4; Maximum likelihood; pp. 77-84.
 Chapter 7; Linkage Analysis; pp. 167-170.

Thursday 13.11; Tuesday 18.11; Thursday 20.11
 Chapter 7, Linkage analysis (map functions, direct counting method); pp. 167-176.
 Chapter 7, Relative pair method; Lander-Green algorithm for sib-pairs; pp.176-184.
 Chapter 7, Prior probability of linkage; page 209.
 Chapter 7, Lod-score methods; two-point linkage; pp. 191-196.
 Chapter 7, Multipoint linkage; ordering of loci (briefly); pp. 200-205.
 Chapter 7, Lander-Green algorithm; pp. 205-206.

Tuesday 25.11.
 Chapter 7, Genetic heterogeneity; pp. 211-212.
Quantitative trait analysis using variance component models (major locus + polygene) based on pp. 1190-1192 and Figures 1 and 2 in Xu and Atchley (1995) [see exact reference below] page 2082 in George et al. (2000) [see exact reference below]
Haseman-Elston method; Chapter 7, pp. 188.

Thursday 27.11.
 LD, HWE and association mapping.
 Chapter 8; pp. 227-230.

Tuesday 2.12; Thursday 4.12
 Measures of LD;
 Correcting methods for population stratification;
 (Structured association, Genomic control, TDT, matching, smoothing)
 Chapter 8; pp. 235-246.
 Chapter 9; pp. 253-280.
 Suggested reading: Yu et al. (2006) A unified mixed model method for
 association mapping that accounts for multiple levels of relatedness.
 Nature Genetics 38: 203-208.

Thursday 4.12
 Association and linkage information. Role of LD to mapping accuracy;
 Association model; Modeling association in mixed inheritance model

Tuesday 9.12.
 Logistic regression.
 Overall view and revision of covered material.

Thursday 11.12.
 Examn.

Contents

The course provides an introduction to statistical methods in gene mapping and genetic epidemiology. Basic concepts of linkage and association analysis as well as some concepts of population genetics will be covered.

Bibliography

Duncan C. Thomas: Statistical Methods in Genetic Epidemiology, Oxford University Press (2004).

Registration at the first lecture.

To complete the course

To score 6 credit points (6op), a student have to pass the examn and complete the practical work (essee) on some statistical topic(s) involved in the course. The length of the essee may be around 10-15 pages.

To score 8 credit points (8op), student have to pass the examn and complete the practical work (essee) on some statistical topic(s) involved in the course + perform (and document) an example genetic analysis with some real data set (students' own or public data). The example analysis can be done with some public genetic analysis software (e.g., SOLAR, GENEHUNTER, PLINK or TASSEL) or general statistical software like SAS or R.

Evaluation (degree) of the course will be based on both the examn and the practical work.

The topics of the practical work (essee) may be consideration of 1-2 research papers, or some other statistical topic(s) involved in the course. It is OK if two/three students study the same topic together but eventually make their own practical works. Possible topics of practical works based on research papers are for example:

1. "Principles of nonparametric simulation-based statistics for detecting linkage in general pedigrees."

The paper describing the method:
 Davis S, Schroeder M, Goldin LR, Weeks DE (1996)
Nonparametric simulation-based statistics for detecting linkage in general pedigrees.
 American Journal of Human Genetics 58: 867-880.

The complementary paper helping to understand principle of the permutation (i.e. simulation-based) test:
 Churchill GA, Doerge RW (1994)
Empirical threshold values for quantitative trait mapping.
Genetics 138: 963-971.

2. "Principles of Lander-Green algorithm in nuclear families"

The paper describing the method:
 Kruglyak L, Daly MJ, Lander ES (1995)
Rapid multipoint linkage analysis of recessive traits in nuclear families including homozygosity mapping.
 American Journal of Human Genetics 56: 519-527.

The complementary paper helping to understand principle of the hidden Markov models:
 Rabiner LR (1989)
A tutorial on Hidden Markov Models and selected applications in speech recognition.
Proceedings of the IEEE 77: 257-286.

The complementary paper describing the GENEHUNTER package:
 Kruglyak L, Daly MJ, Reeve-Daly MP, Lander ES (1996)
Parametric and nonparametric linkage analysis: a unified multipoint approach.
 American Journal of Human Genetics 58: 1347-1363.

3. "Principles of Lander-Green algorithm in sib-pair data"

The paper describing the method:
 Kruglyak L, Lander ES (1995)
Complete multipoint sib-pair analysis of quantitative and qualitative traits.
 American Journal of Human Genetics 57: 439-454.

The complementary paper helping to understand principle of the hidden Markov models:
 Rabiner LR (1989)
A tutorial on Hidden Markov Models and selected applications in speech recognition.
 Proceedings of the IEEE 77: 257-286.

The complementary paper describing the GENEHUNTER package:
 Kruglyak L, Daly MJ, Reeve-Daly MP, Lander ES (1996)
Parametric and nonparametric linkage analysis: a unified multipoint approach.
 American Journal of Human Genetics 58: 1347-1363.

4. "Principles of mixed inheritance (major gene + polygene) model"

The paper describing the method for sib-pairs:
 Xu S, Achley WR (1995)
A random model approach to interval mapping of quantitative trait loci.
 Genetics 141: 1189-1197.

The paper describing the method for general pedigrees:
 George AW, Visscher PM, Haley CS (2000)
 _Mapping quantitative trait loci in complex pedigrees: a two-step variance
 component approach._
 Genetics 156: 2081-2092.

The complementary paper describing the SOLAR package:
 Almasy L, Blangero J (1998)
Multipoint quantitative trait linkage analysis in general pedigrees.
 American Journal of Human Genetics 62: 1198-1211.