MERLIN Tutorial  QTL Regression Analysis
Quantitative trait linkage analyses examine whether a chromosomal region
is responsible for some of the variation in a trait of interest.
Here, we will describe how fast quantitative trait regression analyses
can be carried out using MERLIN.
Data for this exercise
For this example, we will use a simulated data set that you will find
in the examples subdirectory of the MERLIN distribution or in the
download page.
The dataset consists of a simulated 5cM scan of chromosome 24 in
200 sibpair families and is organized into
3 files, a data file
(asp.dat), a pedigree file (asp.ped) and a map file
(asp.map). A quantitative trait has been scored for each offspring.
The recommended first step in any analysis is to verify that input
files are being interpreted correctly. So let's start by running
pedstats... Pedstats requires an input data file (d parameter) and
pedigree file (p parameter):
prompt> pedstats d asp.dat p asp.ped
By examining the abbreviated pedstats output below, you should be
able to confirm that there are 200 pedigrees, each with 4 individuals
(two siblings and their parents). The pedigree includes a quantitative
trait that has been measured on all 400 offspring but none of the
founders.
Pedigree Statistics
(c) 19992001 Goncalo Abecasis
The following parameters are in effect:
QTDT Pedigree File : asp.ped (pname)
QTDT Data File : asp.dat (dname)
Missing Value Code : 99.999 (xname)
PEDIGREE STRUCTURE
==================
Individuals: 800 (400 founders, 400 nonfounders)
Families: 200
Average Family Sizes: 4.00
Average Generations: 2.00
QUANTITATIVE TRAIT STATISTICS
=============================
[Phenotypes] [Founders] Mean Var
trait 400 50.0% 0 0.0% 0.021 1.496
AFFECTION STATISTICS
====================
[Diagnostics] [Founders] Prevalence
affection 400 50.0% 0 0.0% 100.0%
Total 400 50.0% 0 0.0%
MARKER GENOTYPE STATISTICS
==========================
[Genotypes] [Founders] Hetero
MRK1 400 50.0% 0 0.0% 72.8%
MRK2 400 50.0% 0 0.0% 73.2%
(...statistics for other markers would appear here...)
Total 8000 50.0% 0 0.0% 74.1%
The most popular method of quantitative trait linkage is the HasemanElston
(1972) procedure where squared trait differences for sibpairs are regressed
on IBD allelesharing. If a gene in the region being
investigate influences trait levels, sibpairs who share more alleles are
expected to show similar phenotypes and, therefore, smaller squared trait
differences.
PedigreeWide Regression Analysis
The flexibility of the method of Haseman and Elston has lead many authors
to propose enhancements and extensions. Sham et al. (2002) have recently
described a regressionbased procedure for linkage analysis that uses
traitsquared sums and differences to predict IBD sharing between any
noninbred relative pairs. This method is implemented in the MERLINREGRESS
program, included in the merlin distribution. The method of Sham et al. can be
applied to selected samples but requires specification of the trait
distribution parameters in the general population.
Analysing a single trait
To run MERLINREGRESS, we will need to specify the input data(d
parameter), pedigree (p parameter) and map (m parameter)
file names. In addition, we will need to specify the trait distribution
parameters (mean, variance and heritability options). In this case,
we will assume that the trait of interest has mean=0.0, variance=1.5 and
heritability=80% in the general population:
prompt> merlinregress d asp.dat p asp.ped m asp.map mean 0.0 var 1.5 her 0.8
After running the command, you should first see the familiar MERLIN banner and a
summary of currently selected options:
MERLIN 0.9.1  (c) 20002002 Goncalo Abecasis
The following parameters are in effect:
Data File : asp.dat (dname)
Pedigree File : asp.ped (pname)
Missing Value Code : 99.999 (xname)
Map File : asp.map (mname)
Allele Frequencies : ALL INDIVIDUALS (f[aeffile])
Random Seed : 123456 (r9999)
Regression Analysis Options
Trait Model : mean [0.00], variance [1.50], heritability [0.80]
Recombination : zero, one, two, three, singlepoint
Positions : steps, maxStep, minStep, grid, start, stop
Limits : bits [24], megabytes, minutes
Output : quiet, markerNames
Others : simulate, swap, rankFamilies
Estimating allele frequencies... [using all genotypes]
MRK1 MRK2 MRK3 MRK4 MRK5 MRK6 MRK7 MRK8 MRK9 MRK10 MRK11 MRK12 MRK13 MRK14
MRK15 MRK16 MRK17 MRK18 MRK19 MRK20
After a few moments, you should see analysis results at each
location:
PedigreeWide Regression Analysis (Trait: trait)
======================================================
Position H2 Stdev Info LOD pvalue
0.000 0.406 0.192 64.8% 0.970 0.02
5.268 0.526 0.183 71.1% 1.792 0.002
10.536 0.598 0.182 72.1% 2.343 0.0005
15.804 0.733 0.182 72.1% 3.520 0.00003
21.072 0.586 0.182 72.2% 2.255 0.0006
26.340 0.596 0.190 66.0% 2.135 0.0009
31.608 0.535 0.189 67.0% 1.744 0.002
36.876 0.522 0.184 70.6% 1.752 0.002
42.144 0.414 0.181 73.0% 1.137 0.011
47.412 0.295 0.175 77.5% 0.614 0.05
(... results continue at other locations ...)
Successive columns indicate position along the chromosome (in CM),
estimated locus specific heritability, standard deviation for the estimate
of locus specific heritability, proportion of linkage information extracted
at this location (100% information corresponds to the smallest possible
confidence interval for estimated effect size), LOD score and corresponding
pvalue. In this case, linkage peaks at position 15.8 with an estimated
locus specific heritability of 73.3% and a LOD score of 3.52
(probability 0.00003).
Estimating family informativeness
Another useful option in MERLINREGRESS is the ability to quantify the
expected amount of linkage information in each family. This can be useful
when focusing genotyping efforts (for example, by genotyping the most
informative families first) or identifying problematic outliers (extreme
outliers will lead to some families with very large weights which can
reduce effective sample size in linkage analyses).
To estimate family informativeness, specify the trait
distribution in the population (by specifying it's mean, variance and
heritability) and use the rankFamilies option. Using the example input
files the command line would read:
prompt> merlinregress d asp.dat p asp.ped mean 0 var 1.5 her 0.8 rank
Running this command would produce the familiar MERLIN output screen
followed by a table looking like the one below:
Family Informativeness
======================
Family Trait People Phenos Pairs Info ELOD20
1 trait 4 2 1 0.099 0.001
2 trait 4 2 1 0.025 0.000
3 trait 4 2 1 1.989 0.017
4 trait 4 2 1 0.269 0.002
5 trait 4 2 1 0.327 0.003
(... additional rows follow for other families)
Each row indicates the family and trait of interest, followed by
number of individuals and phenotypes in each family, the number of
phenotyped relative pairs and the relative informativeness of the family. The
final column indicates the expected LOD score for a region with a locus
specific heritability of 20% when a fully informative marker is typed. In
this case family 3 seems particularly informative (you can try and find
out why by examining the phenotypes for each individual in the asp.ped
pedigree file).
Expected LOD scores are proportional to the squared locus specific
heritability. To calculate expected LOD scores for a different effect size, simply
multiply the expected LOD score by (heritability/20)^2, where H2 denotes your
desired effect size and ^2 denotes the square operator. For example, for an effect
size of 40%, you should multiply each expected LOD score by 4.
Comparing trait models and analysing multiple traits
Often multiple quantitative traits may be available in a particular dataset.
Each of these traits is likely to have a distinct mean, variance and heritability
in the population. The t models_file specifies the name of a text
file listing analysis models, one for each trait. Using a models table allows distinct
models to be specified for each phenotype in the pedigree file.
A models table includes four columns. The first column indicates the trait name and
is followed by columns indicating the trait mean, variance and heritability. Optionally,
a fifth column can be included with a label for each model. Here is an example:
<sample regression models file>
TRAIT MEAN VARIANCE HERITABILITY LABEL
Weight_Kilograms 75 10 0.63 metric_analysis
Weight_Pounds 160 40 0.63 imperial_analysis
<end of sample regression models file>
Where to go next?
Now that you know how to carry out a pedigreewide regression analysis
using MERLIN you might want to find out estimate empirical pvalues using
simulation, or perhaps explore the sections on
error detection, linkage
analysis, haplotyping or
ibd estimation.
