Download Summary

Summary Lecture
This class has been edited from several sources. Primarily from Terry Speed’s homepage at
Stanford and the Technion course “Introduction to Genetics” and several other courses as
specified on some slides. Changes made by Dan Geiger.
.
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, Alzheimer, and
Blood pressure.
2
Linkage Strategies I
Traditional (from the 1980s or earlier)





Linkage analysis on pedigrees
Association studies: candidate genes
Allele-sharing methods: Affected siblings
Animal models: identifying candidate genes
Cell – hybrids
Newer (from the 1990s)


Focus on special populations (Finland, Hutterites)
Haplotype-sharing (many variants)
3
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
4
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.
5
The disease matters
Definition (phenotype), prevalence, features
such as age at onset
Genetics: nature of genes (Penetrance),
number of genes, nature of their contributions
(additive, interacting), size of effect
Other relevant variables: Sex, obesity, etc.
Genotype-by-environment interactions:
Exposure to sun.
6
Example: Age at onset
7
The population matters
History: pattern of growth, immigration
Composition: homogeneous or melting pot, or in
between
Mating patterns: family sizes, mate choice
Frequencies of disease-related alleles, and of
marker alleles
Ages of disease-related alleles
8
Complex traits
Definition vague, but usually thought of as having multiple,
possibly interacting loci, with unknown penetrances; and
phenocopies.
Affected only methods are widely used. 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.
We focused more on simple traits where success has been
demonstrated very often. About 6-8 percent of human
diseases are thought o be simple Mendelian diseases.
9
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.
10
Genotyping
A person is said to be typed if its markers have been genotyped.
Choice of markers: highly polymorphic preferred.
Heterozygosity and polymorphism information content
(PIC) value are measures commonly used.
Reliability of markers important too
Good quality data critical: errors can play a surprisingly
large role.
11
Preparing genotype data for analysis
Data cleaning is the big issue
here.
Need much ancillary
data…how good is it?
12
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.
13
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. E.g., Bonferroni correction.
14
Problem with standard P-values
If a single test was to be employed to test a null hypothesis, using 0.05 as
the significance level and if the null hypothesis was actually true; the
probability of reaching the right conclusion (i.e., not significant) is 0.95.
If two such hypotheses were tested, then the probably of reaching the right
conclusion (i.e., not significant) on both occasions would be 0.95X0.95 =
0.90.
If more hypotheses (n) were tested and if all of them were in fact true, the
probability of being right on all occasions would decrease substantially
(0.95n).
In other words, the probability of being wrong at least once (or getting a
significant result erroneously) would increase drastically (1-0.95n).
Put simply, by running more tests on a given data set, there is an
increasing likelihood of getting a significant result by chance alone
Source: http://www.edu.rcsed.ac.uk/statistics/the%20bonferroni%20correction.htm
15
The Bonferroni Correction for Non-statisticians
The Bonferroni correction for multiple significance testing is simply to
multiply the p value by the number of tests k carried out. The
corrected value kp is then compared against the level of 0.05 to decide if
it is significant. If the corrected value is still less than 0.05, only then is
the null hypothesis rejected.
Source: http://www.edu.rcsed.ac.uk/statistics/the%20bonferroni%20correction.htm
16
Some Problems with the Bonferroni Correction [1]
1.
This test is for independent tests not for depended ones.
2.
If one carries out multiple tests on a single set of data, the interpretation of
a single relationship between two variables (or the p value) would actually
depend on how many other tests were performed.
3.
Perhaps too cautious. This means that significant results are lost and the
power of the study is reduced.
4.
If Bonferroni correction were to be made universal, to make results
significant, authors would not include many other tests they would have
done with non-significant results and thus would not apply Bonferroni to
same extent they should.
Also for tests published in other papers on the same set of patients or tests
done subsequently would need to be corrected taking into account the
number of previous tests.
Source (modified from): http://www.edu.rcsed.ac.uk/statistics/the%20bonferroni%20correction.htm
17
When to use Bonferroni Correction ?
Because of the above problems due to the disagreements among statisticians
over its universal use, the use of the Bonferroni correction may best be
limited to instances like
•
a group of cases and controls subjected to a number of independent tests
of associations with different biological parameters
•
the same test being repeated in many subsamples, such as when stratified
by age, sex, income status, etc.
Even in these instances, if there is a biological explanation for the null
hypothesis to be rejected and only the non-corrected p value is significant,
but kp is not, one is allowed to conclude (with appropriate explanations,
of course!), the significant nature of the findings.
Source: http://www.edu.rcsed.ac.uk/statistics/the%20bonferroni%20correction.htm
18
References to Bonferonni and other multiple test
1. Perneger, T.V. What’s wrong with Bonferroni
adjustments. BMJ, 1998. 316(7139):p. 1236-1238.
2. Bender, R. and S. Lange, Multiple test procedures other
than Bonferroni’s deserve wide use. BMJ, 1999.
318(7138):p.600-601.
3. Sankoh, A.J., M.F. Huque, and S.D. Dubey, Some comments
on frequently used multiple endpoint adjustment methods
in clinical trials. Stat Med, 1997. 16(22):p.2529-2542.
Source: http://www.edu.rcsed.ac.uk/statistics/the%20bonferroni%20correction.htm
19