Commit 022489d7 authored by Laros's avatar Laros
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Added lecture.

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\documentclass[slidestop]{beamer}
\title{Prioritisation and variant\\ effect prediction}
\providecommand{\myConference}{3Gb-TEST Final meeting}
\providecommand{\myDate}{August 24, 2015}
\author{Jeroen F.J. Laros}
\providecommand{\myGroup}{Leiden Genome Technology Center}
\providecommand{\myDepartment}{Department of Human Genetics}
\providecommand{\myCenter}{Center for Human and Clinical Genetics}
\providecommand{\lastCenterLogo}{
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\includegraphics[height=1cm]{logos/lgtc_logo}
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\usetheme{lumc}
\begin{document}
% Abstract
%
% When we do a full genome sequencing experiment, we find that about one in
% every 1,000 nucleotides differ from the reference sequence. Obviously most of
% the variants we encounter is not causative of any disease, so when we want to
% look for any functionally relevant variant, we have to do prioritisation.
%
% Currently we prioritise variants based on their annotation, which is
% available from large databases such as those of the EBI. This annotation
% is mainly focused on coding variants. Prioritisation of non-coding variants
% remains problematic.
%
% In this talk we give an overview of the currently available methods as well
% as some novel methods that will be potentially useful in the future.
% This disables the \pause command, handy in the editing phase.
%\renewcommand{\pause}{}
% Make the title page.
\bodytemplate
% First page of the presentation.
\section{Introduction}
\subsection{Variants}
\begin{pframe}
When we do a resequencing experiment, we find that about one in
every $1,\!000$ nucleotides differs from the reference sequence.
\bigskip
We expect roughly:
\begin{itemize}
\item $30,\!000$ variants in an exome.
\item $3,\!000,\!000$ variants in a full genome.
\end{itemize}
\bigskip
We need some way to go through all of these variants.
\end{pframe}
\subsection{Exome sequencing}
\begin{pframe}
In \emph{exome sequencing}, we select genomic regions of interest using a
\emph{target-enrichment strategy}.
\begin{itemize}
\item PCR.
\item On array capture.
\item \hl{In-solution capture}.
\end{itemize}
\medskip
\pause
Overview of an in-solution capture.
\begin{itemize}
\item Fragmentation.
\item Size selection.
\item Linker ligation.
\item Capture.
\end{itemize}
\medskip
These regions are then \emph{sequenced}.
\end{pframe}
\subsection{Illumina}
\begin{pframe}
\begin{minipage}[t]{0.47\textwidth}
\begin{figure}
\includegraphics[width=\textwidth]{hiseq_2000}
\caption{HiSeq 2000.}
\end{figure}
\end{minipage}
\hfill
\begin{minipage}[t]{0.47\textwidth}
Characteristics:
\begin{itemize}
\item High throughput.
\item Paired end.
\item High accuracy.
\item Read length $2 \times 150$bp.
\item Relatively long run time.
\item Relatively expensive.
\end{itemize}
\end{minipage}
\end{pframe}
\subsection{Life Technologies}
\begin{pframe}
\begin{minipage}[t]{0.47\textwidth}
\begin{figure}
\includegraphics[width=\textwidth]{Ion_Proton_s}
\caption{Ion proton.}
\end{figure}
\end{minipage}
\hfill
\begin{minipage}[t]{0.47\textwidth}
Characteristics:
\begin{itemize}
\item Moderate throughput.
\item Single end (for now).
\item High accuracy.
\item Read length $\pm 200$bp.
\item Short run time.
\item Cheap runs.
\end{itemize}
\end{minipage}
\end{pframe}
\subsection{Pipelines}
\begin{pframe}
\begin{figure}[]
\begin{center}
\includegraphics[height=0.85\textheight]{assemblyline}
\end{center}
\caption{Scene from ``Modern times''.}
\end{figure}
\end{pframe}
\subsection{Data analysis}
\begin{pframe}
Resequencing pipelines can roughly be divided in five steps.
\begin{enumerate}
\item Pre-alignment.
\begin{itemize}
\item Quality control.
\item Data cleaning.
\end{itemize}
\item Alignment.
\begin{itemize}
\item Post-alignment quality control.
\end{itemize}
\item Variant calling.
\item Filtering.
\begin{itemize}
\item Post-variant calling quality control.
\end{itemize}
\item Annotation.
\end{enumerate}
\end{pframe}
\subsection{Prioritisation}
\begin{pframe}
Prioritisation is mainly done by filtering variants that we expect to be
irrelevant.
\bigskip
This can be because the variant does not follow the \emph{inheritance
pattern} of the disease.
\begin{itemize}
\item The disease is recessive, but the variant is \emph{homozygous} in an
unaffected individual.
\end{itemize}
\bigskip
It can be because the \emph{predicted effect} of a variant does not fit in
the phenotype.
\begin{itemize}
\item A variant found in an unrelated gene.
\item A variant that does not alter the protein.
\end{itemize}
\end{pframe}
\section{Inheritance based filtering}
\subsection{Trio analyses}
\begin{pframe}
Sequence the index patient and its parents.
\bigskip
Commonly used when:
\begin{itemize}
\item We expect a \textit{de novo} variant.
\item The disease is autosomal recessive.
\item The disease is X-linked recessive.
\end{itemize}
\medskip
These variants are relatively easy to find.
\end{pframe}
\subsection{\protect\textit{De novo}}
\begin{pframe}
\begin{minipage}[t]{0.47\textwidth}\begin{figure}[]
\begin{center}
\includegraphics[height=0.8\textheight]{de_novo}
\end{center}
\caption{\textit{De novo} variant.}
\end{figure}
\end{minipage}
\hfill
\begin{minipage}[t]{0.47\textwidth}
Hypotheses:
\begin{itemize}
\item Both parents are unaffected.
\item Both parents are not likely to be a \emph{carrier}.
\item The child is affected.
\end{itemize}
\bigskip
Filter all variants found in the parents from those of the patient.
\end{minipage}
\end{pframe}
\subsection{Autosomal recessive}
\begin{pframe}
\begin{minipage}[t]{0.47\textwidth}\begin{figure}[]
\begin{center}
\includegraphics[height=0.8\textheight]{recessive}
\end{center}
\caption{Recessive disease.}
\end{figure}
\end{minipage}
\hfill
\begin{minipage}[t]{0.47\textwidth}
Hypotheses:
\begin{itemize}
\item Both parents are \emph{carrier}.
\item The child is affected.
\end{itemize}
\bigskip
Select all \emph{homozygous} variants that are \emph{heterozygous} in both
parents.
\end{minipage}
\end{pframe}
\subsection{X-linked recessive}
\begin{pframe}
\begin{minipage}[t]{0.47\textwidth}\begin{figure}[]
\begin{center}
\includegraphics[height=0.8\textheight]{x-linked}
\end{center}
\caption{X-linked disease.}
\end{figure}
\end{minipage}
\hfill
\begin{minipage}[t]{0.47\textwidth}
Hypotheses:
\begin{itemize}
\item The mother is a \emph{carrier}.
\item The child is male and affected.
\end{itemize}
\bigskip
Select the variants that are present on chromosome X which are
\emph{heterozygous} in the mother.
\end{minipage}
\end{pframe}
\section{Annotation}
\subsection{Effect prediction}
\begin{pframe}
In most cases we are still left with a lot of variants.
\bigskip
Variant annotation.
\begin{itemize}
\item Frequency within a population.
\item Location of the variant.
\begin{itemize}
\item Gene panels.
\item Location within a gene.
\end{itemize}
\item Conservation.
\end{itemize}
\end{pframe}
\subsection{Databases}
\begin{pframe}
In most cases we are not interested in common variants.
\begin{itemize}
\item dbSNP.
\item 1000 Genomes.
\item Exome Variant Server (EVS).
\end{itemize}
\medskip
A cut-off of $1\%$ is usually fine.
\bigskip
Databases containing detailed information about variants:
\begin{itemize}
\item \emph{Locus specific} databases.
\begin{itemize}
\item LOVD.
\end{itemize}
\item Human Gene Mutation Database (HGMD).
\end{itemize}
\end{pframe}
\subsection{Gene panels}
\begin{pframe}
Sometimes we know which genes are associated with the disease.
\begin{itemize}
\item Online Mendelian Inheritance in Man (OMIM).
\end{itemize}
\bigskip
To take it one step further, we can select the \emph{transcripts} that are
expressed in the tissue of interest.
\medskip
Example: The DMD transcript expressed in brain cells is different from the
one expressed in muscle cells.
\vfill
\permfoot{http://www.omim.org/}
\end{pframe}
\subsection{Location within a gene}
\begin{pframe}
A selection of VEP annotation:
\begin{itemize}
\item Genes and transcripts affected by the variants.
\item Location of the variant.
\begin{itemize}
\item Upstream of a transcript, in coding sequence, in non-coding RNA,
in regulatory region.
\end{itemize}
\item Consequence of your variants on the protein sequence.
\begin{itemize}
\item Stop gained, missense, stop lost, frameshift.
\end{itemize}
\item Known variants that match yours, and associated minor allele
frequencies from the 1000 Genomes Project.
\item SIFT and PolyPhen scores for changes to protein sequence.
\end{itemize}
\end{pframe}
\subsection{Conservation}
\begin{pframe}
\begin{figure}[]
\begin{center}
\includegraphics[width=\textwidth]{phylop}
\end{center}
\caption{PhyloP scores based on $100$ vertebrate species.}
\end{figure}
\end{pframe}
\section{Phasing}
\subsection{Unphased variants}
\begin{pframe}
\bt{NM\_003002.2(SDHD\_v001):c.[272del;301\_302del]}
\begin{figure}
\includegraphics[width=\textwidth]{fs1}
\caption{Predicted frameshift.}
\end{figure}
\vfill
\bt{NM\_003002.2(SDHD\_v001):c.272del}
\end{pframe}
\begin{pframe}
\bt{NM\_003002.2(SDHD\_v001):c.[272del;301\_302del]}
\begin{figure}
\includegraphics[width=\textwidth]{fs2}
\caption{Predicted frameshift.}
\end{figure}
\vfill
\bt{NM\_003002.2(SDHD\_v001):c.301\_302del}
\end{pframe}
\subsection{Phased variants}
\begin{pframe}
\bt{NM\_003002.2(SDHD\_v001):c.[272del;301\_302del]}
\begin{figure}
\includegraphics[width=\textwidth]{fsc}
\caption{Predicted indel.}
\end{figure}
\vfill
\bt{NM\_003002.2(SDHD\_v001):c.[272del;301\_302del]}
\end{pframe}
\section{Questions?}
\lastpagetemplate
\begin{pframe}
\begin{center}
\end{center}
\end{pframe}
\end{document}
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