1. • De novo mutations were identified in some patients with pediatric-onset lupus and no family
history of lupus or autoimmune disease.
• Bioinformatic analysis suggests that some of the identified de novo mutations may be
deleterious and possibly disease causing.
• In our sample we did not find an increase in the frequency of de novo mutations detected,
compared to controls from the literature.
• De novo mutations, relatively small additive genetic variants, and environment could all
increase lupus risk through mechanisms that are not mutually exclusive.
Figure 2. Deep sequencing data showing the coverage (number of times a position is sequenced) at
11:94370858. Blue and green colors show sequencing reads from forward and reverse sequences
spanning the same position.
• We collected DNA from 46 trios - child, mother, father
• We performed Next Generation Sequencing on the exomes of each trio using the Illumina HiSeq
• Exome: the part of the DNA that encodes for a gene product (usually a protein).
• We identified the amino-acid changing variants in the child that could not possibly be inherited
from the parents.
• De novo mutations are identified by filtering on:
•High quality data (read depth)
•Non-inherited, non-synonymous variant
•Mutation is extremely rare
• Sanger Sequencing was used to confirm the presence of de novo mutations.
• Bioinformatic analyses were performed to determine if the identified de novo mutations were
expected to be deleterious.
Table 1. We assessed 46 trios and found 29
de novo, amino acid changing mutations.
Introduction
Assessing de novo mutations in pediatric-onset lupus
Gwendolyn Kuzmishin, Mojtaba Kohram, Erin Zoller, Rick Ballweg, Sara Lazaro, Zubin Patel, Lindsey Hays, Ken Kaufman, Leah Kottyan, John Harley
Center of Autoimmune Genomics and Etiology, Cincinnati Children’s Hospital Medical Center
Table 2. The expected number of de novo mutations. These control studies were assessed using an exon
capture kit produced by Nimblegen. Our studies used the Illumina, Nimblegen, and Agilent kits; therefore, the
results are only indirectly comparable.
Pediatric SLE can be caused by de novo mutations with
large effect sizes.
Rationale:
•Severe pediatric lupus (pSLE) would lead to death
without therapy.
•Consequently, newly arising de novo genetic variants that
by themselves cause pSLE do not persist in the human
population and are “selected” against.
Conclusions
Research Aim
Methods
Future Directions
Results
• Systemic Lupus Erythematous (SLE or lupus) is a chronic
autoimmune disease that is most common in women.
• Pediatric-onset lupus (pSLE): lupus is diagnosed in children
less than 18 years old.
• The prevalence of lupus in a general population ranges from 20
to 150 cases per 100,000 individuals, depending on the
population.
• We know from twin studies that there is an environmental and
genetic component to lupus. The concordance of lupus in
• Siblings: 2-5%.
• Dizygotic twins: 5%
• Monozygotic twins: 20-40%
• Lupus is known to occur as a complex genetic trait: many
genetic variants with small effects can add up to an increased
risk for SLE.
• SLE can sometimes be characterized as a monogenic trait.
• Single gene knock-outs in mouse models
• C1q and TREX mutations in patients
• De novo mutations are mutations that are not found in either of
the parents and arise from errors in replication.
• We focused on families with little/no history of autoimmune
disease to select for large magnitude effect size of the de novo
changes instead of an inherited propensity for lupus.
Figure 1. Pedigree of a trio
Number of
Trios Tested
Total de novo
SNPs in cohort
De novo
mutations per
lupus proband
46 29 0.636 +/- 0.8
Number of trios
tested
Total de novo SNPs
in cohort
De novo mutations
per control proband
Reference
50 50 1 Nature 285, 246-250 (2012)
200 126 0.63 Nature 485 237-241 (2012)
343 288 0.84 Neuron 74 285-299 (2012)
Hypothesis
Objective:
To determine if de novo mutations contribute to disease
onset in children with no family history of lupus or other
autoimmune diseases.
• Mechanistic biological experiments will be performed in order to determine if the de novo
mutations are actually disease causing.
• We may test the hypothesis that amino acid changing, de novo mutations in USP15 and
LSm14A (members of a cytosolic nucleic acid sensing pathway) that were found in subjects
with pSLE will affect the production of type I IFN from cells with these mutations.
• We will sequence the exomes of unaffected siblings to confirm that none of the de novo
mutations are found in the siblings.
• We will extend our analysis to compound heterozygous mutations.
• These data were focused on the exomic regions, but future studies could assess the
hypothesis that de novo mutations disrupt regulatory regions.
Figure 3. Distribution of confirmed amino-acid changing
de novo mutations.
What were the bioinformatic predictions
about these mutations?
We used several different algorithms to identify
possibly deleterious mutations. Because our
tools are based on the current understanding of
the human genome (which is incomplete), these
predictions were complementary (but
sometimes contradictory).
•SIFT (Sorting Tolerant from Intolerant): Predicts how
protein function is affected by amino acid changes.
•PolyPhen2: Assesses amino acid changes based on
changes to structure and conservation.
•Mutation Taster: Predicts whether amino acid
changes will be disease causing based on
evolutionary conservation, splice site changes, loss of
protein features, and changes to expression level.
•Radial SVM: Uses sequence and structural
information to predict protein stability changes for
mutations through support vector machines.
•LRT (Likelihood Ratio Test): Predicts the
deleteriousness of a mutation by determining if the
amino acid change occurs in a highly conserved
region.
•CADD (Combined Annotation Dependent Depletion):
Uses an integrated framework that takes into account
both natural selection and simulated mutations to
predict the deleteriousness of mutations.
Figure 4: Each de novo mutation was assessed using 6 different
bioinformatic prediction tools. These tools reported predictions of
benign (blue) or deleterious (red). Some mutations, especially
deletions, were not assessable (black) with some of the tools.
SIFT PolyPhen2
Mutation
Taster
Radial
SVM
LRT CADD
11:94370858-A
8:95485535-T
9:395030-G
19:56544026-G
19:18378153-A
14:105222961-G
2:102835442-T
2:26798960-T
18:46385815-C
1:8420991-A
6:168363201-A
12:6859401-T
17:27022460-G
9:114184461-C
13:114156110-C
2:105885908-A
6:13791096-A
12:62784951-A
9:96323444-T
5:114604664-A
12:109702165-T
17:10309447-T
19:47181892-G
17:38950231-G
10:78869935-T
12:2968479-del
1:150530506-G
9:129643022-del
19:34706130-del