r-bioc-snpstats 1.28.0+dfsg-1 / usr / lib / R / site-library / snpStats / doc / ld-vignette.Rnw

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\usepackage[pdftex,
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pdfauthor={David Clayton},
pdftitle={TDT and snpStats Vignette}]
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\title{LD vignette\\Measures of linkage disequilibrium}
\author{David Clayton}
\date{\today}

\usepackage{Sweave}

\SweaveOpts{echo=TRUE, pdf=TRUE, eps=FALSE}

\begin{document}
\setkeys{Gin}{width=1.0\textwidth}

%\VignetteIndexEntry{LD statistics}
%\VignettePackage{snpStats}

<<lib,echo=FALSE>>=
require(snpStats)
require(hexbin)
@ 

\maketitle

\section*{Calculating linkage disequilibrium statistics}
We shall first load some illustrative data. 
<<data>>=
data(ld.example)
@ 
The data are drawn from the International HapMap Project and concern 603 SNPs 
over a 1mb region of
chromosome 22 in sample of Europeans ({\tt ceph.1mb}) and a sample of
Africans ({\tt yri.1mb}):
<<showgt>>=
ceph.1mb
yri.1mb
@ 
The details of these SNP are stored  in the dataframe {\tt support.ld}:
<<showsp>>=
head(support.ld)
@ 
The function for calculating measures of linkage disequilibrium (LD) in
{\tt snpStats} is {\tt ld}. The following two commands call this
function to calculate the D-prime and
R-squared measures of LD between pairs of SNPs for the European and
African samples:
<<ldstats>>=
ld.ceph <- ld(ceph.1mb, stats=c("D.prime", "R.squared"), depth=100)
ld.yri <- ld(yri.1mb, stats=c("D.prime", "R.squared"), depth=100)
@ 
The argument {\tt depth} specifies the maximum separation between
pairs of SNPs to be considered, so that {\tt depth=1} would have
specified calculation of LD only between immediately adjacent SNPs.

Both {\tt ld.ceph} and {\tt ld.yri} are lists with two elements each,
named {\tt D.prime} and {\tt R.squared}. These elements are (upper
triangular) band
matrices, stored in a packed form defined in the {\tt Matrix}
package. They are too large to be listed, but the {\tt Matrix} package
provides an {\tt image} method, a convenient way to examine patterns
in the matrices. You should look at these carefully and
note any differences. 
<<image1,eval=FALSE>>=
image(ld.ceph$D.prime, lwd=0)
@ 
<<image1a,echo=FALSE,fig=TRUE>>=
print(image(ld.ceph$D.prime, lwd=0))
@ 
<<image2,eval=FALSE>>=
image(ld.yri$D.prime, lwd=0)
@ 
<<image2a,echo=FALSE,fig=TRUE>>=
print(image(ld.yri$D.prime, lwd=0))
@ 
The important things to note are 
\begin{enumerate}
\item there are fairly well-defined ``blocks'' of LD, and
\item LD is more pronounced in the Europeans than in the Africans.
\end{enumerate}
The second point is demonstrated by extracting the D-prime values from
the matrices (they are to be found in a slot named {\tt x}) and
calculating quartiles of their distribution:
<<quartiles>>=
quantile(ld.ceph$D.prime@x, na.rm=TRUE)
quantile(ld.yri$D.prime@x, na.rm=TRUE)
@

If preferred, {\tt image} can produce colour plots. We first create a
set of 10 colours ranging from yellow (for low values) to red (for high values) 
<<colors>>=
spectrum <- rainbow(10, start=0, end=1/6)[10:1]
@ 
and plot the image, with a colour key down its right hand side
<<imagecol,eval=FALSE>>=
image(ld.ceph$D.prime, lwd=0, cuts=9, col.regions=spectrum, colorkey=TRUE)
@ 
<<imagecola,echo=FALSE,fig=TRUE>>=
print(image(ld.ceph$D.prime, lwd=0, cuts=9, col.regions=spectrum, colorkey=TRUE))
@ 

The R-squared matrices provide similar pictures, although they are rather less
regular. To show this clearly, we focus on the 200 SNPs starting from
the 75-th, using the European data
<<use>>=
use <- 75:274
@ 
<<image3,eval=FALSE>>=
image(ld.ceph$D.prime[use,use], lwd=0)
@ 
<<image3a,echo=FALSE,fig=TRUE>>=
print(image(ld.ceph$D.prime[use,use], lwd=0))
@ 
<<image4,eval=FALSE>>=
image(ld.ceph$R.squared[use,use], lwd=0)
@ 
<<image4a,echo=FALSE,fig=TRUE>>=
print(image(ld.ceph$R.squared[use,use], lwd=0))
@ 
The R-squared values are smaller and there are ``holes'' in the LD blocks; 
SNPs within an LD block do not necessarily have large R-squared
between them. This is further demonstrated in the next section.
\section*{D-prime, R-squared, and distance}
To examine the relationship between LD and physical distance, we first
need to construct a similar matrix holding the physical
distances. This is carried out, by first calculating each
off-diagonal, and then combining them into a band matrix
<<distance>>=
pos <- support.ld$Position
diags <- vector("list", 100)
for (i in 1:100) diags[[i]] <- pos[(i+1):603] - pos[1:(603-i)]
dist <- bandSparse(603, k=1:100, diagonals=diags)
@ 
The values in the body of the band matrix are contained in a slot
named {\tt x}, so the following commands extract the physical
distances and the corresponding LD statistics for the Europeans:
<<values>>=
distance <- dist@x
D.prime <- ld.ceph$D.prime@x
R.squared <- ld.ceph$R.squared@x
@ 
These are very long vectors so we use the {\tt hexbin} package to
produce abreviated plots. We first demonstrate the relationship
between D-prime and R-squared
<<drplot,fig=TRUE>>=
plot(hexbin(D.prime^2, R.squared))
@ 
We see that the square of D-prime provides an upper bound for
R-squared; a high D-prime indicates the {\em potential} for two SNPs
to be highly correlated, but they need not be. The following commands
examine the relationship between the two LD measures and physical distance
<<dpplot1,fig=TRUE>>=
plot(hexbin(distance, D.prime, xbin=10))
@ 
<<dpplot2,fig=TRUE>>=
plot(hexbin(distance, R.squared, xbin=10))
@ 
Although the data are very noisy, the first plot is consistent with an
approximately exponential decline in mean D-prime with distance, as
predicted by the Malecot model. 
\section*{A view of the calculations}
To understand the calculations let us consider the first and fifth
SNPs in the Europeans. We shall first converting these to character
data for legibility, and then tabulate the two-SNP genotypes, saving
the $3\times 3$ table of genotype frequencies as {\tt tab33}:
<<two>>=
snp1 <- as(ceph.1mb[,1], "character")
snp5 <- as(ceph.1mb[,5], "character")
tab33 <- table(snp1, snp5)
tab33
@ 
These two SNPs have a moderately high D-prime, but a very low R-squared:
<<twoDR>>=
ld.ceph$D.prime[1,5]
ld.ceph$R.squared[1,5]
@ 
The LD measures cannot be directly calculated from the $3\times 3$
table above, but from a $2\times 2$ table of {\em haplotype
frequencies}. In only eight cells around the periphery of the table we can 
unambiguously count haplotypes and these give us the following table
of haplotype frequencies:
\begin{center}
  \begin{tabular}{crr}
    & \multicolumn{2}{c}{\Sexpr{rownames(support.ld)[5]}}\\
      \cline{2-3}
      \Sexpr{rownames(support.ld)[1]} & A & B\\
      \hline
      A& 
      \Sexpr{2*tab33[1,1] + tab33[1,2] + tab33[2,1]}&
      \Sexpr{2*tab33[1,3] + tab33[1,2] + tab33[2,3]}\\
      B&
      \Sexpr{2*tab33[3,1] + tab33[2,1] + tab33[3,2]}&
      \Sexpr{2*tab33[3,3] + tab33[3,2] + tab33[2,3]}
      \\
      \hline
      
  \end{tabular}
\end{center}
However, in the central cell of the $3\times 3$ table ({\it i.e.\,} 
  {\tt tab33[2,2]}) we have
\Sexpr{tab33[2,2]} doubly heterozygous subjects, whose genotype could
correspond either to the pair of haplotypes {\tt A-A/B-B} or to the
pair of haplotypes {\tt A-B/B-A}. These are said to have {\em unknown
  phase}. The expected split between these
possible phases is determined by a further measure of LD --- the
odds ratio. If the odds ratio is $\theta$, we expect a proportion
$\theta/(1+\theta)$ of the doubly heterozygous subjects to be {\tt
  A-A/B-B}, and a proportion  $1/(1+\theta)$ to be  {\tt A-B/B-A}. 

We next use {\tt ld} to obtain an estimate of this odds
ratio\footnote{Here {\tt ld} is called with two arguments of class
  {\tt SnpStats} and, since only the odds ratio is to be calculated, it
  returns the odds ratio rather than a list.}
and, using this, we partition the doubly
heterozygous individuals between the two possible phases: 
<<digits,echo=FALSE>>=
options(digits=4)
@ 
<<OR>>=
OR <- ld(ceph.1mb[,1], ceph.1mb[,5], stats="OR")
OR
AABB <- tab33[2,2]*OR/(1+OR)
ABBA <- tab33[2,2]*1/(1+OR)
AABB
ABBA
@ 
<<twoxtwo,echo=FALSE>>=
a <- format(2*tab33[1,1] + tab33[1,2] + tab33[2,1] + AABB, digits=4)
b <- format(2*tab33[1,3] + tab33[1,2] + tab33[2,3] + ABBA, digits=4)
c <- format(2*tab33[3,1] + tab33[2,1] + tab33[3,2] + ABBA, digits=4)
d <- format(2*tab33[3,3] + tab33[3,2] + tab33[2,3] + AABB, digits=4)
@ 
We are now able to construct the table of haplotype frequencies:
\begin{center}
  \begin{tabular}{crr}
    & \multicolumn{2}{c}{\Sexpr{rownames(support.ld)[5]}}\\
      \cline{2-3}
      \Sexpr{rownames(support.ld)[1]} & A & B\\
      \hline
      A& \Sexpr{a}& \Sexpr{b}\\
      B& \Sexpr{c}& \Sexpr{d}
      \\
      \hline
  \end{tabular}
\end{center}
It is easy to confirm that the odds ratio in this table, 
$(\Sexpr{a}\times\Sexpr{d})/(\Sexpr{b}\times\Sexpr{c})$, 
corresponds closely with that given by the {\tt ld} function. 
Having obtained the $2\times 2$ table of haplotype frequencies, 
any LD statistic may be calculated. 

Of course, there is a circularity here; we needed to know the odds
ratio in order to be able to construct the $2\times 2$ table from
which it is calculated! That is why these calculations are not
simple. The usual method involves iterative solution using an EM
algorithm:  an initial guess at the odds ratio is used, as in the
calculations above, to compute a new estimate, and these calculations 
are repeated
until the estimate stabilizes. However, in {\tt snpStats} the estimate
is calculated in one step, by solving a cubic equation.
\section*{The extent of LD around a point}
Often we wish to guage how far LD extends from a given point (for
example, from a SNP which is associated with disease incidence). For
illustrative purposes we shall consider the region surroung the 168-th
SNP, rs2385786. We first calculate D-prime values for the 100 SNPs on
either side of rs2385786, and their positions:
<<extent>>=
lr100 <- c(68:167, 169:268)
D.prime <- ld(ceph.1mb[,168], ceph.1mb[,lr100], stats="D.prime")
where <- pos[lr100]
@ 
We now plot D.prime against position, adding a simple smoother:
<<eplot,fig=TRUE>>=
plot(where, D.prime)
lines(where, smooth(D.prime))
@ 
Although the data are somewhat noisy (the sample size is small), the
region of LD is fairly clearly delineated.
\section*{Selecting tag SNPs}
Several ways have been suggested to select a set of ``tag'' SNPs which
can be used to test for associations in a given region. That described
below is based upon a heirarchical cluster analysis. We shall apply it
to the region of high LD identified in the previous section, which
lies between positions  $1.579\times 10^7$ and $1.587\times 10^7$. 

The following commands identify which SNPs lie in this region, and
extracts the relevant part of the $R^2$ matrix, as a symmetric matrix
rather than as an upper triangular matrix.
<<ldregion>>=
use <- pos>=1.579e7 & pos<=1.587e7
r2 <- forceSymmetric(ld.ceph$R.squared[use, use])
@ 
The next step is to convert $(1-R^2)$ into a distance matrix, stored as
required for the hierachical clustering function {\tt hclust}, and to
carry out a complete linkage cluster analysis 
<<dist>>=
D <- as.dist(1-r2)
hc <- hclust(D, method="complete")
@ 
To plot the dendrogram, we must first adjust the character size for legibility:
<<clplot,fig=TRUE>>=
par(cex=0.5)
plot(hc)
@ 
The interpretation of this dendrogram is that, if we were to draw a
horizontal line at a ``height'' of 0.5, then this would divide the
SNPs into clusters in such a way that the value of $(1-R^2)$ between
any pair of SNPs in a cluster would be no more than 0.5 (so that $R^2$
would be at least 0.5). This can be carried out using the {\tt cutree}
function, which returns the cluster membership of each SNP:
<<clusters>>=
clusters <- cutree(hc, h=0.5)
head(clusters)
table(clusters)
@ 
It can be seen that there are \Sexpr{max(clusters)} clusters. 
To have a reasonable chance of picking up an association with the SNPs
in this 80kb region, we would need to type a SNP from each one of these
clusters. Of these, \Sexpr{sum(table(clusters)==1)} SNPs
would only tag themselves!

A threshold $R^2$ of 0.5 might seem rather low. However, this is a
``worst case'' figure and most values of $R^2$ would be substantially
better than this, particularly if an effort is made to choose tag SNPs
which are in the center of clusters rather than on their edges. Also,
this process has only considered tagging by single SNPs; it can be
that two or more tag SNPs, taken together, can provide substantially
better prediction than any one of them alone.
\end{document}