MMLS-C

By : Laurence Bisht

References :The Power to Detect Linkage in Complex

Diseases Means of Simple LOD-score Analyses. By David A.,Paula Abreu and Susan E. hodge

Overview

Introducing the problem. Goals.. Intuition. What is MMLS? What is MMLS-C? Generating Models. Results.

Discussion

Introducing The Problem …

What is it? Analyzing Complex diseases, i.e. analyzing

human linkage data.

Our Goal Is …

Finding the disease gene’s locations.

Limitations : Complex Disease. MOI (Mode Of Inheritance) is unknown. Using all data available, somehow… Get a Powerful Method, stable and reliable one.

What was in Lecture 8… Affected sib pair (ASP) Affected Pedigree Member (APM) Nonparametric linkage (NPL)

Fact 1:We need to Exploit all data we have.

But… These method’s use ONLY affected family

members.

Intuition To MMLS-C

Intuition Cont.Fact 2: Maximum Likelihood analysis via LOD-

score, assuming we have the inheritance model is most powerful method for finding linkage.

Solution 1.Use Maximum likelihood analyzes trying all

modes of inheritance .. Why not? Is it logical?

Suppose given a super machine that can do it… how will this work?problem :

1. How will we compare?

Solution 2 – MMLS Choose several models. Run the Maximum likelihood analyzes for

every chosen model- (LOD-score). Take max(Z) as the test statistic for

linkage.

This is MMLS – Maximizing the Maximum LOD-Score

MMLS – analyzes Negative sides.1. Using Several parameters (models),

Multiple tests … Result: Increase of type I error.

2. Unknown effect on the statistical power.

3. Most important: Is there a reason to believe that the models we used can lead us somewhere close to the true model?

Solving 1 Using Several parameters (models), Multiple

tests … Result: Increase of type I error.

We will show that: If we perform linkage analyzes twice, once

assuming recessive and once assuming dominant, with an arbitrary penetrance of 50% Then :

The Z threshold must be increased by at most ~0.3 for Zmax <3.

Solution facts Too stringent.. in most cases examined..

Suggestion: Perform the test twice with the two models proposed. take arbitrary penetrance (0.5 is good) take the larger between the two resultant Zmax subtract 0.3 to “correct” the result

It has been shown that: when there is linkage, Zmax relatively modest as the penetrance is varied. (relatively little information is lost assuming a single penetrance).

Points 2 and 3. Simulation study will answer them …

Simulation will : Quantify the effect of correction for multiple

testing. Examine the power to detect linkage in two

cases discussed later…

Generating Models D20,D80- Dominant with 20% and 80%

penetrance. R20,R80- Recessive with 20% and 80%

penetrance. Int10,Int30,Int50,Int80 – Intermediate (i.e.

heterozygote penetrance is 10%, 30%, 50%, 80% while the homozygote will always be 90% and 0%).

note that when f2=0 (homozygote penetrance) its simple recessive

Generating Models Cont. The MMLS power is expressed when f2=5-15% .. A hard case…

Additive-3 , additive-2: Two loci models. when it is required at least 3, 2 (accordingly) disease alleles at the two loci.

Generating Models Cont. Always one disease locus linked to the

marker with recombination fraction (theta = 0.01).

for the additive model the other one is linked and for the other we will examine 3 recombination fractions:

0.1, 0.05, 0.01

Simulation Parameters They examined 14 Generated Models one

of each.

On modified version of the Two-locus simulation program for Greenberg (their program)

1000 datasets of 20 families.

Simple MMLS-C Running MMLS for R50 and D50, as

previously described. Correction factor was varied*

~0.24 when Zmax< 0.59

~0.3 when Zmax< 0.59

*(according to hodge (1997))

Results Table Notice that:

Max[D50,R50] <

E[raw MMLS]=~

E[MMLS-C]+0.30<

E[True]

Power Vs. LOD-Score

Power Vs. LOD-Score

Power Vs. LOD-Score

Power Table

Discussion … Our main goal was : Examine the power to detect linkage using

MMLS-C After we passed over the results we can

see the following: MMLS-C doesn’t substantially decrease the

power compared with the True MOI. The range of the MMLS-C – TRUE was [0.3,0.7]

(except for three case )

ASP Vs. MMLS-C

Conclusions Pro :

MMLS-C is a simple method for analyzing complex diseases.

Exploits all data available. Reliable. The assumption that the linkage at the locus

being tested is critical.

Against : Was tested on small data set. not always the best method.

The End!