1. An quick overview of human genetic linkage analysis Terry Speed Genetics & Bioinformatics, WEHI Statistics, UCB NWO/IOP Genomics Winterschool Mathematics and Biology December 17, 2001 Lecture 1

2. Purpose of human linkage analysis To obtain a crude chromosomal location of the gene or genes associated with a phenotype of interest, e.g. a genetic disease or an important quantitative trait. Examples: cystic fibrosis (found), diabetes, multiple sclerosis, and blood pressure

3. Why at a Genomics Winter School? Because the identification of genes contributing to genetic disease or other phenotypes is a (perhaps the) major application of the tools and techniques of genomics. Examples include: the physical mapping of clones, sequencing candidate regions, identification of genes in DNA sequence, sequence analysis of candidate genes, and mapping expression differences between genes in broad regions..

4. Linkage Strategies Traditional (from the 1980s or earlier) – Linkage analysis on pedigrees – Allele-sharing methods: candidate genes, genome screen – Association studies: candidate genes – Animal models: identifying candidate genes Newer (from the 1990s) – Focus on special populations (Finland, Hutterites) – Haplotype-sharing (many variants) – Congenic/consomic lines in mice (new for complex traits)

5. Linkage analysis

6. Allele-sharing methods

7. Association Studies

8. Animal Models

9. Linkage Strategies II On the horizon (here) – Single-nucleotide polymorphism (SNPs) – Functional analyses: finding candidate genes Needed (starting to happen) – New multilocus analysis techniques, especially – Ways of dealing with large pedigrees – Better phenotypes: ones closer to gene products – Large collaborations

10. Horses for courses Each of these strategies has its domain of applicability Each of them has a different theoretical basis and method of analysis Which is appropriate for mapping genes for a disease of interest depends on a number of matters, most importantly the disease, and the population from which the sample comes.

11. The disease matters Definition (phenotype), prevalence, features such as age of onset Genetics: nature of genes (resistance, susceptibility), number of genes, nature of their contributions (additive, interacting), size of effect Environment, other relevant variables (e.g. sex) Genotype-by-environment interactions

12. The population matters History: pattern of growth, immigration Composition: homogeneous or melting pot, or in between Mating patterns: family sizes, mate choice (level of consanguinity) Frequencies of disease-related alleles, and of marker alleles Ages of disease-related alleles

13. Complex traits Definition vague, but usually thought of as having multiple, possibly interacting loci, with unknown penetrances; and phenocopies. The terms polygenic and oligogenic are also used, but these do have more specific meanings. There is some evidence that using a range of made-up models can help map genes for complex traits, but no-one really knows. Affected only methods are widely used, with variance component methods becoming popular. The jury is still out on which, if any will succeed. Few success stories so far. Important: heart disease, cancer susceptibility, diabetes, … are all “ complex ” traits.

14. Design of gene mapping studies How good are your data implying a genetic component to your trait? Can you estimate the size of the genetic component? Have you got, or will you eventually have enough of the right sort of data to have a good chance of getting a definitive result? Power studies Simulations.

15. Genotyping Choice of markers: highly polymorphic preferred. Heterozygosity and PIC value are the measures commonly used. Reliability of markers important too Good quality data critical: errors can play a surprisingly large role.

16. Preparing genotype data for analysis Data cleaning is the big issue here. Need much ancillary data…how good is it?

17. Analysis A very large range of methods/programs are available. Effort to understand their theory will pay off in leading to the right choice of analysis tools. Trying everything is not recommended, but not uncommon. Many opportunities for innovation.

18. Interpretation of results of analysis An important issue here is whether you have established linkage. The standards seem to be getting increasingly stringent. What p-value or LOD should you use? Dealing with multiple testing, especially in the context of genome scans and the use of multiple models and multiple phenotypes, is one of the big issues.

19. Replication of results This has recently become a big issue with complex diseases, especially in psychiatry. Nature Genetics suggested in May 1998 that they will require replication before publishing results mapping complex traits. Simulations by Suarez et al (1994) show that sample sizes necessary for replication may be substantially greater than that needed for first detection.

20. Topics not mentioned Sex-linked traits, sex-specific recombination fractions, liability classes, mutations, genetic heterogeneity, exclusion mapping, homozygosity mapping, interference, variance component methods, twin studies, and much more. Some of these topics plus the ones are covered in two books: Handbook of Human Genetic Linkage by J.D. Terwilliger & J. Ott (1994) Johns Hopkins University Press Analysis of Human Genetic Linkage by J. Ott, 3rd Edition (1999), Johns Hopkins University Press