Assignment - Review of Vissers et al. (2010), "A de novo paradigm for mental retardation."

A de novo paradigm for mental retardation
Presented by Kelly Clemenza. Wednesday, April 10th 2013.
Vissers L. E. (2010). Nature Genetics. 42: 1109-1113

Stangor C. (2011). Introduction to Psychology, v. 1.0.
“Mental retardation (MR) ”- A basic deﬁnition
But it’s really not that simple…
Diagnoses for Mental Retardation as deﬁned in both the DSM-IV-TR and ICD-10
DSM = Diagnostics and Statistics Manual for Mental Illness (United States)
ICD = International Classifcation of Disease (International)
Class IQ ‘
Borderline intellectual functioning: 70–84
Mild mental retardation: 50–69
Moderate mental retardation: 35–49
Severe mental retardation: 20–34
Profound mental retardation: Below 20

A varied global understanding
World Health Organization. (2007). Atlas: global resources for persons with intellectual disabilities.
Verney S. P., et al. (2005) . Culture-fair cognitive ability assessment: information processing and psychophysiological approaches. Assessment. 12: 303-319
•  Understanding mental retardation on a global scale is incredibly difficult considering there is not one universally accepted term or definition for what it means to be ‘cognitively impaired.’
•  When dealing with psychiatric disorders it may be important to understand that a person may find the demands of one culture perfectly manageable but may find the demands of another culture
completely overwhelming.
•  To this day many scientists and psychologists argue that the metrics used for measuring IQ are largely biased toward western cultural constructs, sensibilities, and values. Can IQ really provide a global
understanding of mental retardation?

Toward a new definition: Intellectual Developmental Disorder
  IDD generally involves difficulties with verbal comprehension, perceptual
reasoning, working memory and processing speed.
  This cognitive impairment will lead to difficulties in different domains of learning,
including academic and practical knowledge.
  IDD also involves difficulties in adaptive behavior; that is, meeting the demands of
daily life expected for one’s age peers, cultural, and community environment.
  Persons with IDD often have difficulties in managing their behavior, emotions, and
interpersonal relationships, and maintaining motivation in the learning process.
  IDD is a life span condition requiring consideration of developmental stages
and life transitions.!
Salavador-Carulla L., et al. (2011). Toward a new name, definition and framework for“mental retardation/intellectual disability”in ICD-11. World Psychiatry. 10: 174-180
According the the ICD-11 Beta Draft:
“Intellectual developmental disorder (IDD) is characterized by a marked impairment of core
cognitive functions necessary for the development of knowledge, reasoning, and symbolic
representation of the level expected of one’s age peers, cultural and community environment.”
•  The ICD, being an international guide, is better suited to provide a globally-culturally-conscious definition of what it means to be ‘mentally retarded.’
ICD-11 is in the beta stages, so it is refered to with the caveat that the definitions are not final and are changing every day. However, updated definitions are assumed to be the best informed,
drawing from the most up-to-date research.
•  When using any guide to diagnose mental retardation/intellectual developmental disorder, it is extremely important to keep in mind that all degrees and varieties of mental retardation will present
with their own unique cognitive impairments - and that one case of mental retardation is not representative of all cases.

Mental retardation is a symptom
Mental retardation is not in itself a disorder, but is a symptom of any number of
pathologies. Some genetic causes of mental retardation are well characterized, while
others remain a total mystery:
Down’s Syndrome (chromosomal duplication - trisomy 21)
Fragile X Syndrome (chromosomal structural abnormality)
…And maybe hundreds of other x-linked disorders
DiGeorge syndrome (chromosomal deletion – 22q11.2)
De novo copy number variation (CNVs)
De novo point mutations (this will lead us to our study!)

Calles J. L. (2011). Cognitive-adaptive disabilities. Pediatr Clin North Am. 58: 189-203
•  Mental retardation is a symptom of an overwhelming number of pathologies.
•  Non-genetic causes of mental retardation include malnutrition, lead and other toxin poisonings, pre-natal and post-natal infections, fetal alcohol syndrome, head injury, among other factors;
all of which are essentially preventable.
**It is interesting to note that these causes of mental retardation are much more prevalent in low-income countries, where nutrition, education and medical care are less available.
However, GENETIC causes of MR are roughly equally represented across all nations, regardless of income.

•  Per-generation mutation rate for humans is between 7.6 × 10−9 and 2.2 × 10−8
•  This results in 50 to 100 new mutations in each offspring’s genome.
•  Those nucleotide mutations will yield an average of 0.86 new amino-acid–
altering mutations.
Resolving the paradox of common,
harmful, heritable mental disorders:
Which evolutionary genetic models
work best?
Matthew C. Keller
Virginia Institute for Psychiatric and Behavioral Genetics, Virginia
Commonwealth University, Richmond, VA 23219.
matthew.c.keller@gmail.com www.matthewckeller.com
Geoffrey Miller
Department of Psychology, University of New Mexico, Albuquerque,
NM 87131-1161.
gfmiller@unm.edu www.unm.edu/psych/faculty/gmiller.html
Abstract: Given that natural selection is so powerful at optimizing complex adaptations, why does it seem unable to eliminate genes
(susceptibility alleles) that predispose to common, harmful, heritable mental disorders, such as schizophrenia or bipolar disorder? We
assess three leading explanations for this apparent paradox from evolutionary genetic theory: (1) ancestral neutrality (susceptibility
alleles were not harmful among ancestors), (2) balancing selection (susceptibility alleles sometimes increased fitness), and
(3) polygenic mutation-selection balance (mental disorders reflect the inevitable mutational load on the thousands of genes
underlying human behavior). The first two explanations are commonly assumed in psychiatric genetics and Darwinian psychiatry,
while mutation-selection has often been discounted. All three models can explain persistent genetic variance in some traits under
some conditions, but the first two have serious problems in explaining human mental disorders. Ancestral neutrality fails to explain
low mental disorder frequencies and requires implausibly small selection coefficients against mental disorders given the data on the
reproductive costs and impairment of mental disorders. Balancing selection (including spatio-temporal variation in selection,
heterozygote advantage, antagonistic pleiotropy, and frequency-dependent selection) tends to favor environmentally contingent
adaptations (which would show no heritability) or high-frequency alleles (which psychiatric genetics would have already found).
Only polygenic mutation-selection balance seems consistent with the data on mental disorder prevalence rates, fitness costs, the
likely rarity of susceptibility alleles, and the increased risks of mental disorders with brain trauma, inbreeding, and paternal age.
This evolutionary genetic framework for mental disorders has wide-ranging implications for psychology, psychiatry, behavior
genetics, molecular genetics, and evolutionary approaches to studying human behavior.
Keywords: adaptation; behavior genetics; Darwinian psychiatry; evolution; evolutionary genetics; evolutionary psychology; mental
disorders; mutation-selection balance; psychiatric genetics; quantitative trait loci (QTL)
1. Introduction
Mental disorders such as schizophrenia, depression,
phobias, obsessive-compulsive disorder, and mental
retardation are surprisingly prevalent and disabling. In
industrialized countries such as the United States, an esti-
mated 4% of people have a severe mental disorder
(National Institute of Mental Health 1998), and almost
half of people will meet the criteria for some type of less
severe mental disorder at some point in their lives
(Kessler et al. 2005). The annual economic costs in treat-
ment and lost productivity are in the hundreds of billions
of dollars (Rice et al. 1992). The less quantifiable personal
costs of mental disorders to sufferers, families, and friends
are even more distressing. For example, schizophrenia
affects about 1% of people worldwide (Jablensky et al.
1992), typically beginning in early adulthood and often
following a chronic lifelong course. People with
schizophrenia often imagine hostile, confusing voices;
they have trouble thinking clearly, feeling normal
emotions, or communicating effectively; and they tend to
lose jobs, friendships, and sexual partners. In response,
many people with schizophrenia kill themselves, and a
much larger proportion dies childless.
This is an evolutionary puzzle, because differences in
the risk of developing schizophrenia and other common,
debilitating mental disorders are due, in large part, to
differences in people’s genes. Given that natural selection
has built the most exquisitely complex machinery known to
humankind – millions of species of organic life-forms –
why do so many people suffer from such debilitating and
heritable mental disorders? If these mental disorders are
as disabling as they appear, natural selection should have
eliminated the genetic variants (susceptibility alleles)
that predispose to them long ago. Does the prevalence
of heritable mental disorders therefore imply that mental
BEHAVIORAL AND BRAIN SCIENCES (2006) 29, 385–452
Printed in the United States of America
# 2006 Cambridge University Press 0140-525X/06 $12.50 385
MR is present in ~3% of the population, why?
The paradox of common, harmful, heritable mental disorders:
Why does mental illness persist in the population at an almost steady rate, despite it’s obviously deleterious affects to health and reproduction?
Each mental disorder may have it’s own evolutionary genetic model; however, it seems mental retardation may share some evolutionary mechanisms with schizophrenia, bipolar disorder and autism.

Adapted from Huang K. (2011). De novo paradigm: the ultimate answer to the paradox in mental retardation? Clin Genet. 79: 427-428
Stages where de novo mutations may occur
Spermatocyte
Oocyte
Sperms
Eggs
Fetal
Development
Postnatal
Growth
Embryogenesis
Risk with paternal age
Risk with maternal age
SujayGhoshandSubrataKumarDey(2013).

Hypothesis
“Together with de novo copy number variation, de novo point mutations of large effect
could explain the majority of all mental retardation cases in the population.”
This study set out not only to prove this hypothesis but also to develop a rigorous
pipeline for de novo point mutation discovery as a way to test this hypothesis.

Subject Sample
A cce s s io n in fo rm a tio n : (0 0 )0 0 1 9 9 -X a.p d f (s h o rt c o d e: fig 0 0 5 trn ); 1 4 Sep tem b er 2 0 0
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Figure 5. Schematic of cytogenetic analysis using spectral karyotyping (SKY) (see next page for legend)
(fig005trn).
1B 1C 2E 3A 3C 3D
Labelling of the individual chromosome-painting probes
using the various combinations of fluorochromes
chromosome-painting probes
Hybridisation at 37 C
for 24—72 hours
Detection steps to
visualise probes and
to remove unbound
nucleotides
b
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Expert Reviews in Molecular Medicine 2000 Cambridge University Press
Analysis using a
Spectracube connected
to an epifluorescence
microscope
(1) Display colours (2) Classification colours
c
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Schematic of cytogenetic analysis using spectral karyotyping (SKY)
Normalisedintensity
Wavelength (nm)
1.0
0.8
0.6
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0.2
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500 550 600 650 700 750
Selected normalised
chromosome spectra
Metaphase chromosome
preparation
•  10 case-parent trios, eight male patients and two female patients
•  Patients all had moderate to severe mental retardation (IQ
•  Negative family history
•  Clinical evaluation eliminated possibility of syndromic to etiological diagnoses
•  Repeat expansion analysis eliminated possibility of Fragile X Syndrome
•  Cytogenic analysis showed no chromosomal abnormalities
Clinical evaluations were given to eliminate any non-de novo genetic causes of mental retardation.
Requirements included:
•  Subjects result from uncomplicated pregnancies
•  Parents be non-consanguinous (not related through any recent ancestors)
•  Absence of known post-natal causes of mental retardation, including malnutrition, infections, head injury, etc.
•  Any abnormal health history must be either unrelated to MR or caused by MR, but cannot be suspected to be the cause of MR.

Subject Sample
•  10 case-parent trios, eight male patients and two female patients
•  Patients all had moderate to severe mental retardation
•  Negative family history
•  Clinical evaluation eliminated possibility of syndromic to etiological diagnoses
•  Repeat expansion analysis eliminated possibility of Fragile X Syndrome
•  Cytogenic analysis showed no chromosomal abnormalities
•  Array-based genomic proﬁling discounted any known CNV’s associated with MR
Targeted Array CGH for Diagnostics 529
JMD November 2006, Vol. 8, No. 5
edented resolution. The value of their use for routine
diagnostic applications is less obvious and is fraught with
difficulties that will be discussed below.
A more defined and targeted array is one designed for
a specific region(s) of the genome for the purpose of
evaluating that targeted segment. It may be designed to
study a specific chromosome 10,11
or chromosomal seg-
ment12–16
or to identify and evaluate specific DNA dos-
age abnormalities in individuals with suspected microde-
letion syndromes3
or subtelomeric rearrangements.17
The crucial goal of a targeted microarray in medical
practice is to provide clinically useful results for diagno-
sis, genetic counseling, prognosis, and clinical manage-
ment of unbalanced cytogenetic abnormalities. Thus, a
Figure 1. Schematic representation of CGH microarray technology. Whole genomic DNA from a control or reference (left) and genomic DNA from a test or patient
(right) are differentially labeled with two different fluorophores. The two genomic DNA samples are competitively cohybridized with large-insert clone DNA
targets that have been robotically printed onto the microarray (middle). Computer imaging programs assess the relative fluorescence levels of each DNA for each
target on the array (lower left). The ratio between control and test DNA for each clone can be linearly plotted using data analysis software to visualize dosage
variations (lower right), indicated by a deviation from the normal log2 ratio of zero.
Targeted Array CGH for Diagnostics 529
JMD November 2006, Vol. 8, No. 5
Array-based genomic proﬁling is only useful when you already know the genes/variants you are looking for.
Probes are synthesized to match to genomic areas of interest. (In this study we don’t know our areas of interest!)
Bejjani B. A. and Schaffer L. G. (2006). Application of Array-Based Comparative Genomic Hybridization to Clinical Diagnostics. J Mol Diagn. 8: 528-533

d gene in relation
m to play a role in
BPIL3 is involved
involved in pro-
F599 is currently
mutations, func-
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hen disrupted, are
or the remaining
mental retardation
interaction stud-
cts as a motor for
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nuclear position-
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with PAFAH1B1,
ch involves gross
al cortex19. YY1
actor yin-yang 1
ansferases, impli-
. Complete abla-
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urulation defects
of phyloP scores and Grantham scores differed markedly between
dbSNP and the HGMD (Online Methods and Supplementary Fig. 5).
The four mutations in genes functionally linked to mental retardation
all showed higher probability values for being observed in HGMD
Exome data of 10 mental retardation cases
sequenced on SOLiD 3 Plus System
Read mapping and
variant calling
Default mapping settings
High-stringency variant calling
Exclude low quality
Exclude nongenic, intronic and synonymous
Exclude known SNPs and in-house database
Exclude inherited
Exclude non-validated
Exclude inherited
Test occurrence in control cohort
Mutation impactGene function
Variant analysis
Validation
Interpretation
Figure 1 Experimental work flow for detecting and prioritizing sequence
variants. For all ten mental retardation trios, prioritization of variants
Analysis pipeline for detecting prioritizing variants

Why only the exome?
•  Time, money and computational contraints.
•  Only about 2% of the human genome is comprised of protein-coding DNA.
•  For this reason, the exome is better understood, while the non-coding genome is
still largly a mystery!
•  The exome can be wildly informative in detecting mendelian disorders.
•  Exome sequencing has already discovered CNV’s relating to MR.
A disorder of mendelian inherentence is a single-mutation disorder.
Short of total gene activation/inactivation, most non-coding changes won’t have the potential for such profound phenotypic affects as seen with mental retardation.
Bamshad M. J., et al. (2011). Exome sequencing as a tool for Mendelian disease gene discovery. Nat Rev Genet. 11: 745-755

Exome Sequencing
1.  DNA was isolated from peripheral blood of probands and parents using QIAamp DNA Mini Kit
2.  Enriched exome libraries were created using the AB SOLiD Optimized SureSelect Human Exome Kit
Contains the primers for the exonic sequences of ~18,000 genes and covering a total of ~37 Mb of
genomic sequence, speciﬁc for the human genome.
3.  Exome libraries were used for emulsion PCRs – SOLiD Sequencing
VOLUME 42 | NUMBER 12 | DECEMBER 2010 NATURE GENETICS
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dbSNP and the HGMD (Online Methods and Supplementary Fig. 5).
The four mutations in genes functionally linked to mental retardation
all showed higher probability values for being observed in HGMD
Read mapping and
variant calling
Exclude low quality
Exclude inherited
Exclude inherited
Variant analysis
Validation
Interpretation
observed in the probands was based on selection for non-synonymous
changes of high quality only and exclusion of all variants previously
observed in healthy individuals, together with those variants that were
inherited from an unaffected parent. Interpretation of de novo variants
was based on gene function and the impact of the mutation.

Exome Sequencing – SOLiD Sequencing
Metzker M. L. (2010) Sequencing technologies — the next generation. Nature. 11: 31-46
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Roche/454 — PyrosequencingLife/APG — Sequencing by ligation
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Figure3| Next-generation sequencing technologies that use emulsion
PCR. a | A four-colour sequencing by ligation method using Life/APG’s
support oligonucleotide ligation detection (SOLiD) platform is shown.
Upon the annealing of a universal primer, a library of 1,2-probes is added.
Unlike polymerization, the ligation of a probe to the primer can be
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SOLiD Sequencing
Source: Wikipedia, because look there’s too many citations in the table to include them all!

Read Mapping and Variant Calling
1.  Obtained 3.1 Gb of mappable sequence data
per individual
2.  After mapping to reference genome:
• An average of 79.6% of bases originated from the
targeted exome
• 90% of the targeted exons were covered at least 10x
• Median exon coverage was 42-fold!
3.  Identiﬁed an average of 21,755 genetic
variants per individual of high conﬁdence
Read mapping and
variant calling
Exclude low quality
Exclude inherited
Exclude inherited
Variant analysis
Validation
Interpretation
igure 1 Experimental work flow for detecting and prioritizing sequence
ariants. For all ten mental retardation trios, prioritization of variants
bserved in the probands was based on selection for non-synonymous
hanges of high quality only and exclusion of all variants previously
bserved in healthy individuals, together with those variants that were
nherited from an unaffected parent. Interpretation of de novo variants
as based on gene function and the impact of the mutation.
Supplementary Figures
Supplementary Figure 1: Coverage plots of all 30 individuals
Figure legend
Coverage for all exons targeted by enrichment was evaluated. The median coverage for all
30 individuals was 42-fold, with on average 90% of all targets covered at least 10-fold. The
numbers in the figure legends refer to the corresponding MR trios; M: Mother; F: Father; C:
Child
The exon coverage was excellent and indicated that the majority of variants present in each exome could be robustly detected using the researcher’s bioinformatics pipeline.
A coverage plot shows the variation between fold coverage and percent of targets per each individual’s exome, as representation by each colored line (each line is labeled #_A where # is the trio
number and A is either M, F, or C for mother, father and child. This chart shows that almost every single target was covered at least once, with an average coverage of 42-fold for all 30 individuals.

Variant Analysis
1.  Exlcluding all nongenic, intronic, and synonymous variants other than those occuring at
canonical splice sites reduce the # of candidates to an average of 5,640.
2.  This number was further reduced to 143 by excluding known, likely benign, variants by
referencing data from dbSNP database v130 and an in-house variant database.
3.  Comparing patient’s data to parent’s exome reduce the results to an average of 5
candidate de novo mutations, with 51 total candidate mutations.
variant calling
Exclude low quality
Exclude inherited
Exclude inherited
Variant analysis
Validation
Interpretation
as based on gene function and the impact of the mutation.L E T T E R S
(covered by a median of 17 variant reads). Parental analysis validated
the de novo occurrence for 9 of these 13 mutations, detected in seven
different individuals (Table 2 and Supplementary Figs. 3 and 4).
We did not identify these mutations in a total of 1,664 control chro-
mosomes, nor did we see other likely pathogenic mutations identi-
for the gene in early development22. Additional evidence is provided
by Deaf1-deficient mice, which show neural tube defects including
exencephaly23. Finally, CIC is a member of the HMG-box transcrip-
tion factor superfamily, which is associated with neuronal and glial
development of the nervous system. CIC is predominantly and tran-
Table 1 Overview of all variants detected per proband and impact of the prioritization steps for selecting candidate non-synonymous
de novo mutations
Trio 1 2 3 4 5 6 7 8 9 10 Average
High-conﬁdence variant calls 20,810 21,658 21,338 22,647 17,694 22,333 21,369 22,658 24,085 22,962 21,755
After exclusion of nongenic, intronic
and synonymous variants
5,556 5,665 5,691 5,991 4,607 5,567 5,716 5,628 5,985 5,994 5,640
After exclusion of known variants 165 159 157 155 120 136 120 149 96 171 143
After exclusion of inherited variants 4 7 3 7 7 2 2 6 6 7 5
The observed ratio of non-synonymous-to-synonymous de novo mutations is far greater than would be expected for protein-coding genes under purifying selection.
This indicates that many of these mutations will result in a reproductive disadvantage.
**Purifying selection is the evolutionary process by which the selective pressure to maintain a certain variant is so strong that all other variants are rapidly removed as soon
as they are introduced into a population.

Sanger validation of de novo mutations
Patient
Father
Mother
Patient
Father
Mother
Patient
Father
Supplementary Figure 4: Sanger validation of de novo mutations
a
b
Patient
Father
Mother
Patient
Father
Supplementary Figure 4: Sanger validation of de novo mutations
a
b
Sequence Chromatograms
Exclude inherited
Exclude inherited
Validation
Interpretation
Only 13 variants could be validated, 9 of these variants conﬁrmed as de novo mutations
•  DNA sequences are generated using algorithms that can read thisgraphical display and convert the visual output into text – A,C,G,T.
•  Veriﬁcation by eye is important here because the algorithms aren’t perfect and may call the wrong base if there is overlap of peaks (but overlap is the only visual indicator of heterozygosity!)
•  M is the IUPAC symbol for heterozygous A and C. R is the IUPAC symbol for heterozygous G and A.
•  Notice how the mother and father are homozygous A, a de novo mutation from A to C has occurred on one of the chromosomes.
•  The goal of this step is to validate the mutations observed in the probands and (validate the absence of the mutations in the parental DNA. Thirty-eight candidates could not be validated.

Study subjects vs. control cohort
•  None of the 9 variants could be found in 1,664 control chromosomes.
•  No likely pathogenic mutations were identiﬁed in the genes containing the 9 variants.
This indicates the population frequency of de novo mutations in these genes will be lower than 22%.
•  All 9 variants occurred In different genes, including two that have been previously implicated in MR.
•  8 mutations were found in a heterozygous state, and 1 was present in a hemizygous state on the X
chromosome.
•  The hemizygous variant was actually inhereted, it had occurred occurred de novo in the patient’s mother.
This was a non-synonymous variant in JARID1C, which is an X-linked mental retardation gene.
Exclude inherited
Exclude inherited
Validation
Interpretation

Interpretation
-
1,
s
1
1
-
-
1
s
-
1.
A
1
e
Exclude inherited
Validation
Interpretation
observed in the probands was based on selection for non-synonymous
changes of high quality only and exclusion of all variants previously
observed in healthy individuals, together with those variants that were
inherited from an unaffected parent. Interpretation of de novo variants
was based on gene function and the impact of the mutation.
L E T T E R S
(mean, 0.83) than for being observed in dbSNP (mean, 0.17). The
three mutations in genes without a functional link to mental retar-
dation showed an average probability of 0.94 for being observed
in dbSNP and an average probability of 0.06 for being observed in
HGMD (Table 2). Additionally, the inherited JARID1C mutation
−6
estimated background mutation rate of 0.86 amino-acid–altering
mutations per newborn in controls2, but it will be important to com-
pare this result to similar data from healthy control trios when avail-
able. Notably, after applying the same systematic filtering approach
and Sanger sequencing, we could only validate a single de novo syn-
Table 2 Overview of all de novo variants identified by exome sequencing in ten individuals with unexplained mental retardation
Gene Trio Sexa NM number
cDNA level
change
Protein level
change
PhyloP
score
Grantham
score
Probability of
being observed in
dbSNPb
Probability of
being observed
in HGMDb Gene function
De novo mutations
DYNC1H1 1 M NM_001376 c.11465AC p.His3822Pro 5.5 77 0.20 0.80 Retrograde axonal transporter;
interacts with PAFAH1B1
(mutation of which causes
lissencephaly, a neurodevel-
opmental disorder)
ZNF599 1 M NM_001007248 c.532CT p.Leu187Phe –1.5 22 1.00 2.65 × 10−4 Unknown
RAB39B 2 M NM_171998 c.557GA p.Trp186X 4.8 – – – Known X-linked mental
retardation gene
YY1 3 M NM_003403 c.1138GT p.Asp380Tyr 6.9 160 2.27 × 10−6 1.00 Ubiquitously expressed
transcription factor; mouse
knockdown results in growth
retardation, neurulation defects
and brain abnormalities;
interacts with MECP2, a known
mental retardation gene
BPIL3 3 M NM_174897 c.887GA p.Arg269His 0.5 29 0.97 0.03 Innate immune response
PGA5 4 F NM_014224 c.1058TC p.Val353Ala 0.7 64 0.84 0.16 Precursor of pepsin
DEAF1 5 M NM_021008 c.683TG p.Ile228Ser 4.9 142 0.01 0.99 Transcription factor; regulator
of 5-HT1A receptor in the
brain; mouse knockout causes
neural tube defects
CIC 6 M NM_015125 c.1474CT p.Arg492Trp 2.6 101 0.46 0.54 Granule cell development in
central nervous system
SYNGAP1 8 F NM_006772 c.998_999del p.Val333AlafsX 3.3 – – – Known autosomal dominant
X-linked inherited mutations
JARID1C 10 M NM_001146702 c.1919GA p.Cys640Tyr 5.1 194 2.09 × 10−6 1.00 Known X-linked mental
retardation gene
aSex of proband, with M for male and F for female. bVisual representation of probabilities are provided in Supplementary Figure 5. Grantham scores for nonsense (in RAB39B) and frameshift
mutations (in SYNGAP1) could not be calculated.
•  ZNF599, BPIL3, and PGA5 are not known to have a functional link mental retardation.
Genes of interest include:
•  DYNC1H1, which is implicated in lissencephaly, which is a condition where the brain gyri and sulci are much less pronounced, leading to a ‘smoothi’ appearance of the brain and drastically
reducing the surface area for neuronal connections.
•  DEAF1, which is a regulator of the 5-HT1A receptor, a receptor for the neurotransmitter serotonin. Serotonin is ubiquitously expresed throughout the brain and is very important for pre-natal
neuronal migrations and maturation.

Impact of variants on function L E T T E R S
(mean, 0.83) than for being observed in dbSNP (mean, 0.17). The
three mutations in genes without a functional link to mental retar-
dation showed an average probability of 0.94 for being observed
in dbSNP and an average probability of 0.06 for being observed in
HGMD (Table 2). Additionally, the inherited JARID1C mutation
showed a probability of 1.00 for being in HGMD versus 2.09 × 10−6
for being in dbSNP.
This analysis of the mutated nucleotides and their impact on gene
function strongly supports pathogenicity for six of the nine de novo
mutations. Importantly, these six mutations occurred in genes with a
estimated background mutation rate of 0.86 amino-acid–altering
mutations per newborn in controls2, but it will be important to com-
pare this result to similar data from healthy control trios when avail-
able. Notably, after applying the same systematic filtering approach
and Sanger sequencing, we could only validate a single de novo syn-
onymous mutation, which occurred in GRIN1 (c.351CT, seen in
trio 10). This base pair is not conserved through evolution (phyloP
score = −3.2) and does not seem to alter splicing, suggesting that
this mutation is an unlikely candidate for causing mental retarda-
tion. Of note, the individual carrying this mutation also carries the
Table 2 Overview of all de novo variants identified by exome sequencing in ten individuals with unexplained mental retardation
Gene Trio Sexa NM number
cDNA level
change
Protein level
change
PhyloP
score
Grantham
score
Probability of
being observed in
dbSNPb
Probability of
being observed
De novo mutations
DYNC1H1 1 M NM_001376 c.11465AC p.His3822Pro 5.5 77 0.20 0.80 Retrograde axonal transporter;
opmental disorder)
ZNF599 1 M NM_001007248 c.532CT p.Leu187Phe –1.5 22 1.00 2.65 × 10−4 Unknown
RAB39B 2 M NM_171998 c.557GA p.Trp186X 4.8 – – – Known X-linked mental
retardation gene
YY1 3 M NM_003403 c.1138GT p.Asp380Tyr 6.9 160 2.27 × 10−6 1.00 Ubiquitously expressed
BPIL3 3 M NM_174897 c.887GA p.Arg269His 0.5 29 0.97 0.03 Innate immune response
PGA5 4 F NM_014224 c.1058TC p.Val353Ala 0.7 64 0.84 0.16 Precursor of pepsin
DEAF1 5 M NM_021008 c.683TG p.Ile228Ser 4.9 142 0.01 0.99 Transcription factor; regulator
neural tube defects
CIC 6 M NM_015125 c.1474CT p.Arg492Trp 2.6 101 0.46 0.54 Granule cell development in
SYNGAP1 8 F NM_006772 c.998_999del p.Val333AlafsX 3.3 – – – Known autosomal dominant
X-linked inherited mutations
JARID1C 10 M NM_001146702 c.1919GA p.Cys640Tyr 5.1 194 2.09 × 10−6 1.00 Known X-linked mental
retardation gene
aSex of proband, with M for male and F for female. bVisual representation of probabilities are provided in Supplementary Figure 5. Grantham scores for nonsense (in RAB39B) and frameshift
mutations (in SYNGAP1) could not be calculated.
PhyloP score described the evolutionary conservation of affected nucleotides.
Grantham score describes the potential of the de novo mutations to affect the structure or
function of the resulting proteins
Most genes tend to vary linearly when plotting their PhyloP scores against their Grantham scores.
A higher PhylopP score means the gene has been subject to greater purifying selection, any changes to that gene will usually not last in the population. This is frequently because
any genetic changes will causes too great a change to the protein structure - the protein will be unable to carry out the very function that has been under such selective pressure. This kind of gene would
have also a higher Grantham score.

L E T T E R S
mated background mutation rate of 0.86 amino-acid–altering
tations per newborn in controls2, but it will be important to com-
e this result to similar data from healthy control trios when avail-
e. Notably, after applying the same systematic filtering approach
d Sanger sequencing, we could only validate a single de novo syn-
ymous mutation, which occurred in GRIN1 (c.351CT, seen in
o 10). This base pair is not conserved through evolution (phyloP
re = −3.2) and does not seem to alter splicing, suggesting that
s mutation is an unlikely candidate for causing mental retarda-
n. Of note, the individual carrying this mutation also carries the
RID1C mutation. The observed ratio of non-synonymous to syn-
ymous de novo mutations is far greater than would be expected
n ten individuals with unexplained mental retardation
Grantham
score
Probability of
being observed in
dbSNPb
Probability of
being observed
77 0.20 0.80 Retrograde axonal transporter;
opmental disorder)
22 1.00 2.65 × 10−4 Unknown
– – – Known X-linked mental
retardation gene
160 2.27 × 10−6 1.00 Ubiquitously expressed
29 0.97 0.03 Innate immune response
64 0.84 0.16 Precursor of pepsin
142 0.01 0.99 Transcription factor; regulator
neural tube defects
101 0.46 0.54 Granule cell development in
– – – Known autosomal dominant
194 2.09 × 10−6 1.00 Known X-linked mental
retardation gene
Supplementary Figure 5. Grantham scores for nonsense (in RAB39B) and frameshift
Supplementary Figure 5: Distribution of PhyloP and Grantham scores for dbSNP, HGMD
and the de novo mutations identified in this study
0
50
100
150
200
Granthamscore
PhyloP score
'benign'
'pathogenic'
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
Relativefrequency
PhyloP score
dbSNP
HGMD
c
min max
b
a
a
d
Supplementary Figure 5: Distribution of PhyloP and Grantham scores for dbSNP, HGMD
and the de novo mutations identified in this study
0
50
100
150
200
Granthamscore
PhyloP score
'benign'
'pathogenic'
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
Relativefrequency
PhyloP score
dbSNP
HGMD
c
min max
b
a
a
d
Comparing with other human variants
dbSNP = Single Nucleotide Polymorphism Database
HGMD = Human Gene Mutation Database
dbSNP is a database of variation across all domains of life. It of course includes the human species and all known human genetic variation.
The HGMD is a database cataloguing specifically deleterious, or disease causing variations found in the human population.
•  The four de novo mutations in genes functionally linked to mental retardation all showed a higher probability for being observed in HGMD (mean probability value of 0.83), than for being observed in
dbSNP (mean probability value of 0.17.) The inherited JARID1C mutation showed a probability of 1.00 for being in HGMD versus 2.09 × 10−6 for being in dbSNP.
•  The three mutations in genes without a functional link to MR showed an average probability of 0.94 for being observed in dbSNP and an average probability of 0.06 for being observed in HGMD.
•  So this is good news! It looks like the newly discovered de novo mutations with a functional link to mental retardation may be the ACTUAL causes of each patient’s MR.
.

(2013)
Future Directions
This study has set a precedent for de novo point mutation discoveriy in psychiatric disorders.
Recent studies have begun to extened the ‘de novo paradigm’ to autism and schizophrenia, disorders which have previously shown to involve a high number of de novo CNV’s just as in MR.
Further research has implicated a mostly paternal influence on de novo CNV mutations in mental illness as well, counteracting the mostly maternal influence of chromosomal arrangement disorders like
Down’s Syndrome. {Hehir-Kwa J. Y. Et al (2011)}
In addition, understanding the genetic causes of any pathology will always hold the promise of developing new screenings and therapies to treat that pathology, and this certainly applies to MR.
Maybe most important, a better scientific understanding will foster more respect and outreach toward those individuals and families dealing with cognitive deficiences.

Conclusions
•  I really like the paper! I thought it was exceedingly well organized…
•  …which was made more important considering this paper set out to design a speciﬁc set
of methods for variant discovery that would hopefully be used in the future by others.
•  The experimental design was simple yet fairly effective.
•  Those variants found in genes functionally implicated in MR were most likely the ones that caused MR.
•  Comparing PhyloP with Granthma scores, and referencing their relationship across dbSNP and HGMD
was a simple, yet intuitive method for verifying de novo mutations.
•  Were the variants in the genes ‘not functionally implicated’ in MR somehow, maybe indirectly, still the
cause of MR?
•  If not, this study failed to detect the cause of MR in those three probands.
•  There are surely many variations in non-coding DNA, as well as epi-genetic factors, that
can cause MR - and this method won’t ﬁnd them.
But this was a great start!

References
1.  Stangor C. (2011). Introduction to Psychology, v. 1.0.
2.  World Health Organization. (2007). Atlas: global resources
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3.  Verney S. P., et al. (2005) . Culture-fair cognitive ability
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4.  Salavador-Carulla L., et al. (2011). Intellectual
developmental disorders: towards a new name, definition
and framework for 'mental retardation/intellectual
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5.  Calles J. L. (2011). Cognitive-adaptive disabilities. Pediatr
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6.  Keller M. C. and Miller G. (2006) Resolving the paradox of
common, harmful, heritable mental disorders: Which
evolutionary genetic models work best? Behav Brain Sci.
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7.  Bejjani B. A. and Schaffer L. G. (2006). Application of
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8.  Bamshad M. J., et al. (2011). Exome sequencing as a tool
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9.  Robinson P. N. (2010). Whole-exome sequencing for
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Genome Biol. 11:144
10.  Metzker M. L. (2010) Sequencing technologies — the next
generation. Nature. 11: 31-46
11.  Hehir-Kwa J. Y. (2011). De novo copy number variants
associated with intellectual disability have a paternal origin
and age bias.
J Med Genet. 11: 776-778.
12.  Ellison J. W., Rosenfeld J. A. and Shaffer L. G. (2013)
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13.  O'Roak B. J., et al. (2011). Exome sequencing in sporadic
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