defining copy number variants related to

Defining Copy Number

Variants Related to

Neurodevelopmental

Disorders

Christa Lese Martin, PhD, FACMG

Director and Senior Investigator

Autism & Developmental Medicine Institute

Geisinger Health System

September 20, 2013

• Class of mutation resulting from the loss (deletion) or gain (duplication) of genomic material

• > 1 kb in size

• Recurrent – common breakpoints mediated by underlying mechanism, such as segmental duplications (e.g., 16p11.2)

• Non-recurrent – variable breakpoints throughout the genome

Copy Number Variation (CNV)

CNVs can be observed in normal populations

or cause disease

Normal Individuals Human Disease

• One of most common causes of human disease

• Diagnostic yield of 10-20% in DD, ID, ASD, birth defects

• In general:

– Larger in size

– Contain more genes

– Located in unique regions of the genome

– Often de novo

• Common cause of normal variation

• Identified in ~35% of human genome (Iafrate et al., 2004)

• In general:

– Smaller in size

– Contain fewer genes

– Highly variable regions (e.g., pericentromeric DNA, segmental duplications)

– Often inherited

Cataloging CNVs in Public Databases

Normal Variation Human Disease

• DECIPHER Database of Chromosomal Imbalance and Phenotype in Humans Using Ensembl Resources

• OMIM Online Mendelian Inheritance in Man

• ICCG (previously ISCA) International Collaboration for Clinical Genomics

• DGV Database of Genomic Variants

• dbVar Database of Genomic Structural Variation

The Clinical Genome Resource

(ClinGen) Launched September 2013

Collaboration between:

• NHGRI U41 Grant – PIs: Ledbetter (Geisinger), Martin (Geisinger), Nussbaum (UCSF),

Mitchell (Utah), Rehm (Partners/Harvard)

• NHGRI U01 “Clinically Relevant Variant Resource” Grants – Grant 1 PIs: Bustamante (Stanford), Plon (Baylor) – Grant 2 PIs: Berg (UNC), Ledbetter (Geisinger), Watson (ACMG)

• NCBI – ClinVar

• Purpose: Create a centralized repository and interconnected

resources of clinically annotated genes and variants to improve our

understanding of genomic variation and optimize its use in genomic

medicine.

• Main activities:

– Facilitate deposition of variants into ClinVar & develop methods for

defining pathogenicity

– Developing registry for genotype/phenotype correlations

(Geisinger/Harvard/UCSF/Utah)

– Organize clinical curation groups, lead consensus efforts for clinical

actionability, & ensure interoperability with EHRs (UNC/Geisinger/ACMG)

– Build informatics support for the consortium, facilitate data access by the

community, and develop novel machine learning algorithms

(Stanford/Baylor)

The Clinical Genome Resource

Determining the Pathogenicity of Individual CNVs

• Clinical Laboratory Approach:

- Evaluate each CNV based on published literature and databases, known disease regions/genes, inherited/de novo

• Statistical Approach:

- Identify rare variants enriched in cases vs. controls

Two large CNV studies from clinical data

of ID, DD, ASD, MCA

Datasets

• Kaminsky et al. (ISCA/ICCG Consortium)

– 15,749 cases vs. 10,118 published controls

– Analyzed 28 recurrent CNVs (14 loci)

– 21 pCNV

• Cooper et al. (Signature Genomics)

– 15,767 cases vs. 8,329 GWAS adult controls

– Analyzed 64 recurrent and 26 non-recurrent CNVs

(45 loci total)

– 59 pCNV

Leverage these two clinical datasets to infer pathogenicity of very rare genetic events in ASD cohorts

Two-Tiered Approach:

TIER 1: Combined Clinical Cohort

31,516 ID/ASD/MCA cases

13,696 published controls

Analyzed 48 Recurrent CNVs in case-control analysis

(24 del and 24 dup)

TIER 2: ASD Research Cohorts

SSC: 1,124 (Sanders et al.)

AGP: 996 (Pinto et al.)

AGRE: 1,835 (current study)

Very Rare, Pathogenic CNVs

Tier 1 Results: Clinical Datasets

D. Moreno De Luca et al. (2012) Mol Psych

19/24 deletion CNVs are pathogenic


Tier 1 Results: Clinical Datasets

11/24 duplication CNVs are pathogenic

*can’t differentiate between mechanisms because of array design or lack of

additional information

*

Leverage these two clinical datasets to infer pathogenicity of very rare genetic events in ASD cohorts

Two-Tiered Approach:

TIER 1: Combined Clinical Cohort

31,516 ID/ASD/MCA cases

13,696 published controls

Analyzed 48 Recurrent CNVs in case-control analysis

(24 del and 24 dup)

TIER 2: ASD Research Cohorts

SSC: 1,124 (Sanders et al.)

AGP: 996 (Pinto et al.)

AGRE: 1,835 (current study)

Very Rare, Pathogenic CNVs

Tier 2 Results: ASD Cohorts


Lancet Neurol 2013

CNVs Span Neurodevelopmental Disorders

Developmental Brain Dysfunction Model 1 CNV = multiple manifestations

A. Moreno De Luca et al. (2013) Lancet Neurol

A. Moreno De Luca et al. (2013) Lancet Neurol

Probands

Genetic Background

Diagnostic Threshold

(2 SD below mean)

The deleterious impact of a

CNV on a person’s

neurodevelopmental profile

is influenced by

genetic background…

same CNV can have

different phenotypic

consequences.

20

16p11.2 Deletion – Simons VIP

Zufferey et al. JMG 2012

15q Duplications – DUP15q Registry

Figure created with images from

Stephan Sanders & Laina Lusk

x 3-4 supernumerary

x 3

x 3

x 3

x 3

x 3

BP 1-3

BP 2-3

BP 1-2

BP 3-5

BP 4-5

2

3

5

51 cases

Other: 1 triplication; 1 mosaic dup/trip

Unverified – 30 interstitial dups; 115 idic 15

22

Summary & Relevance to DUP15q

• Chromosome 15q dups are “common” among rare variants

• Important to categorize the various CNVs for targeted research studies to reduce heterogeneity

• Large datasets are available for research recruitment

• Successful models for use of online communities for recruitment and phenotyping

23

Acknowledgements

Geisinger

David H. Ledbetter, PhD

Tom Challman, MD

David Evans, PhD

Andy Faucett, MS

Brenda Finucane, MS

Laina Lusk, BS

Andres Moreno De Luca, MD

Scott Myers, MD

Erin Riggs, MS

Emory

Erin Kaminsky, PhD

Washington Univ.

John Constantino, MD

Grant Support

NICHD

NIMH

NHGRI

Simons Foundation

SVIP Project

SVIP Investigators

Raphe Bernier, PhD

Wendy Chung, MD, PhD

Robin Goin-Kochel, PhD

LeeAnn Green-Snyder, PhD

Ellen Hanson, PhD

John Spiro, PhD

Yale

Daniel Moreno De Luca, MD

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