398 research outputs found
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The functional impact of copy number variation in the human genome
No full textCopy number variation (CNV) is a class of genetic variation where large segments of the genome vary in copy number among different individuals. It has become clear in the past decade that CNV affects a significant proportion of the human genome and can play an important role in human disease. With array-based copy number detection and the current generation of sequencing technologies, our ability to discover genetic variants is running far ahead of our ability to interpret their functional impact. One approach to close this gap is to explore statistical association between genetic variants and phenotypes. In contrast to the successes of genome-wide association studies for common disease using common single nucleotide polymorphism (SNP) as markers, the majority of disease CNVs discovered so far have low population frequencies and are mainly involved in rare developmental disorders. Another strategy to improve interpretation of genomic variants is to establish a predictive understanding of their functional impact. Large heterozygous deletions are of particular interest, since (i) loss-of-function (LOF) of coding sequences encompassed by large deletions can be relatively unambiguously ascribed and (ii) haploinsufficiency (HI), wherein only one functional copy of a gene is not sufficient to maintain normal phenotype, is a major cause of dominant diseases.
This thesis explored both approaches. Initially, I developed an informatics pipeline for robust discovery of CNVs from large numbers of samples genotyped using the Affymetrix whole-genome SNP array 6.0, to support both the association-based and prediction-based study. For the disease association strategy, I studied the role of both common and rare CNVs in severe early-onset obesity using a case-control design, from which a rare 220kb heterozygous deletion at 16p11.2 that encompasses SH2B1 was found causal for the phenotype and an 8kb common deletion upstream of NEGR1 was found to be significantly associated with the disease, particularly in females. Using the prediction-based approach, I characterized the properties of HI genes by comparing with genes observed to be deleted in apparently healthy individuals and I developed a prediction model to distinguish HI and haplosufficient (HS) genes using the most informative properties identified from these comparisons. An HI-based pathogenicity score was devised to distinguish pathogenic genic CNVs from benign genic CNVs. Finally, I proposed a probabilistic diagnostic framework to incorporate population variation, and integrate other sources of evidence, to enable an improved, and quantitative, identification of causal variants
Characterising and Predicting Haploinsufficiency in the Human Genome
No full textNi Huang is with the Wellcome Trust Sanger Institute, Insuk Lee is with UT Austin and Yonsei University, Edward M. Marcotte is with UT Austin, Matthew E. Hurles is with the Wellcome Trust Sanger Institute.Haploinsufficiency, wherein a single functional copy of a gene is insufficient to maintain normal function, is a major cause of dominant disease. Human disease studies have identified several hundred haploinsufficient (HI) genes. We have compiled a map of 1,079 haplosufficient (HS) genes by systematic identification of genes unambiguously and repeatedly compromised by copy number variation among 8,458 apparently healthy individuals and contrasted the genomic, evolutionary, functional, and network properties between these HS genes and known HI genes. We found that HI genes are typically longer and have more conserved coding sequences and promoters than HS genes. HI genes exhibit higher levels of expression during early development and greater tissue specificity. Moreover, within a probabilistic human functional interaction network HI genes have more interaction partners and greater network proximity to other known HI genes. We built a predictive model on the basis of these differences and annotated 12,443 genes with their predicted probability of being haploinsufficient. We validated these predictions of haploinsufficiency by demonstrating that genes with a high predicted probability of exhibiting haploinsufficiency are enriched among genes implicated in human dominant diseases and among genes causing abnormal phenotypes in heterozygous knockout mice. We have transformed these gene-based haploinsufficiency predictions into haploinsufficiency scores for genic deletions, which we demonstrate to better discriminate between pathogenic and benign deletions than consideration of the deletion size or numbers of genes deleted. These robust predictions of haploinsufficiency support clinical interpretation of novel loss-of-function variants and prioritization of variants and genes for follow-up studies.NH and MEH are funded by the Wellcome Trust [grant number 077014/Z/05/Z]. IL is funded by grants from the National Research Foundation of Korea (NRF) funded by the Korea government (MEST) (No. 2010-0017649, 2010-0015754, 2009-0087951) and EMM by the NIH, Welch (F-1515), and Packard Foundations, by the Texas Institute for Drug and Diagnostic Development, and by the Texas Advanced Research Program. This study makes use of data provided by the Genetic Association Information Network (GAIN) and the Wellcome Trust Case Control Consortium 2 (WTCCC2), through work funded by NIH and the Wellcome Trust. This study also makes use of data generated by the DECIPHER consortium, which is funded by the Wellcome Trust. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Cellular and Molecular Biolog
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Integrated approaches to elucidate the genetic architecture of congenital heart defects
No full textCongenital heart defects (CHD) are structural anomalies affecting the heart, are found in 1% of the population and arise during early stages of embryo development. Without surgical and medical interventions, most of the severe CHD cases would not survive after the first year of life. The improved health care for CHD patients has increased CHD prevalence significantly, and it has been estimated that the population of adults with CHD is growing ~5% per year. Understanding the causes of CHD would greatly help improve our knowledge of the pathophysiology, family counseling and planning and possibly prevention and treatment in the future.
Several lines of evidence from humans and animal models have supported a substantial genetic component for CHD. However, gene discovery in CHD has been difficult due to the extreme locus heterogeneity and the lack of a distinct genotype–phenotype correlation. Currently, genetic causes are identified in fewer than 20-‐30% of the cases, most of which are syndromic while the isolated CHD cases remain largely without explanation.
The aim of my thesis was to identify novel or known CHD genes enriched for rare coding genetic variants in isolated CHD cases and learn about the relative performance of different study designs. High-throughput next generation sequencing (NGS) was used to sequence all coding genes (whole exome) coupled with various analytical pipelines and tools to identify candidate genes in different family-based study designs.
Since there is no general consensus on the underlying genetic model of isolated CHD, I developed a suite of software tools to enable different family-based exome analyses of de novo and inherited variants (chapter 2) and then piloted these tools in several gene discovery projects where the mode of inheritance was already known to identify previously described and novel pathogenic genes, before applying them to an analysis of families with two or more siblings with CHD.
Based on the tools developed in chapter 2, I designed a two-stage study to investigate isolated parent-offspring trios with Tetralogy of Fallot (chapter 3). In the first stage, I used whole exome sequence data from 30 trios to identify genes with de novo coding variants. This analysis identified six de novo loss-of-function and 13 de novo missense variants. Only one gene showed recurrent de novo mutations in NOTCH1, a well known CHD gene that has mostly been associated with left ventricle outflow tract malformations (LVOT). Besides NOTCH1, the de novo analysis identified several possibly pathogenic novel genes such as ZMYM2 and ARHGAP35, that harbor de novo loss-of-function variants (frameshift and stop gain, respectively).
In the second stage of the study, I designed custom baits to capture 122 candidate genes for additional sequencing using NGS in a larger sample size of 250 parent-offspring trios with isolated Tetralogy of Fallot and identified six de novo variants in four genes, half of them are loss-of-function variants. Both of NOTCH1 and its ligand JAG1 harbor two additional de novo mutations (two stop gains in NOTCH1 and one missense and a splice donor in JAG1). The analysis showed a strongly significant over-representation of de novo loss-of-function variants in NOTCH1 (P=3.8 ×10-9).
Additionally, when compared with 1,080 control trios, NOTCH1 exhibit significant burden of inherited rare missense variant (minor allele frequency < 1% in 1000 genomes) (Fisher exact test, P= 8.8 × 10^‐05) in about 10% of the isolated Tetralogy of Fallot patients. I also modified the transmission disequilibrium test (TDT) to detect any distortion of rare coding allele transmission from healthy parent to their affected children. This modified TDT test identified ARHGAP35 gene, which exhibits an over-‐transmission of rare missense variants in children (P=0.025). Although, the p value does not reach a genome-‐wide significant level after correcting for multiple tests, ARHGAP35 gene has also a de novo stop gain variant in one trio from the primary cohort and recently shown to play a role in cardiomyocyte fate which make it an interesting novel ToF candidate gene for future studies.
To assess alternative family-based study design in CHD, I combined the analysis from 13 isolated parent-offspring trios with 112 unrelated index cases of isolated atrioventricular septal defects (AVSD) in chapter 4. Initially, I started with a case/control analysis to test the burden of rare missense variants in cases compared with 5,194 ethnically matching controls and identified the gene NR2F2 (Fisher exact test P=7.7×10-07, odds ratio=54). The de novo analysis in the AVSD trios identified two de novo missense variants in the same gene. NR2F2 encodes a pleiotropic developmental transcription factor, and decreased dosage of NR2F2 in mice has been shown to result in abnormal development of atrioventricular septa. The results from luciferase assays show that all coding sequence variants observed in patients significantly alter the activity of NR2F2 target promoters.
My work has identified both known and novel CHD genes enriched for rare coding variants using next-generation sequencing data. I was able to show how using single or combined family-based study designs is an effective approach to study the genetic causes of isolated CHD subtypes. Despite the extreme heterogeneity of CHD, combining NGS data with the proper study design has proved to be an effective approach to identify novel and known CHD genes. Future studies with considerably larger sample sizes are required to yield deeper insights into the genetic causes of isolated CHD
How homologous recombination generates a mutable genome
No full textAbstract Recombination and mutation have traditionally been regarded as independent evolutionary processes: the latter generates variation, which the former reshuffles. Recent studies, however, have suggested that allelic recombination influences the underlying mutation rate, as high mutation rates are inferred in regions of high recombination. Furthermore, recombination between duplicated sequences introduces structural variation into the human genome and facilitates the formation of clustered gene families. Comparisons of wholegenome sequences reveal the expansion of gene family clusters to be an important mode of genome evolution. The negative aspect of this genomic dynamism is the contribution of these rearrangements to genetic diseases.</p
Data analysis methods for copy number discovery and interpretation
No full textCopy
number
variation
(CNV)
is
an
important
type
of
genetic
variation
that
can
give
rise
to
a
wide
variety
of
phenotypic
traits.
Differences
in
copy
number
are
thought
to
play
major
roles
in
processes
that
involve
dosage
sensitive
genes,
providing
beneficial,
deleterious
or
neutral
modifications
to
individual
phenotypes.
Copy
number
analysis
has
long
been
a
standard
in
clinical
cytogenetic
laboratories.
Gene
deletions
and
duplications
can
often
be
linked
with
genetic
Syndromes
such
as:
the
7q11.23
deletion
of
Williams-‐Bueren
Syndrome,
the
22q11
deletion
of
DiGeorge
syndrome
and
the
17q11.2
duplication
of
Potocki-‐Lupski
syndrome.
Interestingly,
copy
number
based
genomic
disorders
often
display
reciprocal
deletion
/
duplication
syndromes,
with
the
latter
frequently
exhibiting
milder
symptoms.
Moreover,
the
study
of
chromosomal
imbalances
plays
a
key
role
in
cancer
research.
The
datasets
used
for
the
development
of
analysis
methods
during
this
project
are
generated
as
part
of
the
cutting-‐edge
translational
project,
Deciphering
Developmental
Disorders
(DDD).
This
project,
the
DDD,
is
the
first
of
its
kind
and
will
directly
apply
state
of
the
art
technologies,
in
the
form
of
ultra-‐high
resolution
microarray
and
next
generation
sequencing
(NGS),
to
real-‐time
genetic
clinical
practice.
It
is
collaboration
between
the
Wellcome
Trust
Sanger
Institute
(WTSI)
and
the
National
Health
Service
(NHS)
involving
the
24
regional
genetic
services
across
the
UK
and
Ireland.
Although
the
application
of
DNA
microarrays
for
the
detection
of
CNVs
is
well
established,
individual
change
point
detection
algorithms
often
display
variable
performances.
The
definition
of
an
optimal
set
of
parameters
for
achieving
a
certain
level
of
performance
is
rarely
straightforward,
especially
where
data
qualities
vary ... [cont.]
Gene conversion homogenizes the CMT1A paralogous repeats
No full textAbstract Background Non-allelic homologous recombination between paralogous repeats is increasingly being recognized as a major mechanism causing both pathogenic microdeletions and duplications, and structural polymorphism in the human genome. It has recently been shown empirically that gene conversion can homogenize such repeats, resulting in longer stretches of absolute identity that may increase the rate of non-allelic homologous recombination. Results Here, a statistical test to detect gene conversion between pairs of non-coding sequences is presented. It is shown that the 24 kb Charcot-Marie-Tooth type 1A paralogous repeats (CMT1A-REPs) exhibit the imprint of gene conversion processes whilst control orthologous sequences do not. In addition, Monte Carlo simulations of the evolutionary divergence of the CMT1A-REPs, incorporating two alternative models for gene conversion, generate repeats that are statistically indistinguishable from the observed repeats. Bounds are placed on the rate of these conversion processes, with central values of 1.3 × 10-4 and 5.1 × 10-5 per generation for the alternative models. Conclusions This evidence presented here suggests that gene conversion may have played an important role in the evolution of the CMT1A-REP paralogous repeats. The rates of these processes are such that it is probable that homogenized CMT1A-REPs are polymorphic within modern populations. Gene conversion processes are similarly likely to play an important role in the evolution of other segmental duplications and may influence the rate of non-allelic homologous recombination between them.</p
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