UCSB Summer Institute in Cognitive Neuroscience, June 29 2015

Giovanni Coppola, MD Semel Institute for Neuroscience and Human Behavior

David Geffen School of Medicine UCLA

Methods and approaches in identifying genes critical for brain development and developmental disorders

2015 Summer Institute in Cognitive Neuroscience

Outline

1. Key Concepts 2. Mendelian Genes 3. Risk Genes: Common Variants 4. Risk Genes: Rare Variants 5. Genomic Approaches

Steps in Conducting a Genetic Study of a Trait

1. Define a phenotype 2. Quantify degree of genetic effect 3. Collect families/cohorts for study 4. Measure genetic variation 5. Assess its statistical contribution to

the trait

1. Phenotype

(1) The form taken by some trait (or group of traits) in a specific individual.

(2) The detectable outward manifestations of a specific genotype.

• Qualitative (disease) • Quantitative

Sullivan et al, Nat Rev Genet 2012

AD: Alzheimer's disease ADHD: attention-deficit hyperactivity disorder ALC: alcohol dependence AN: anorexia nervosa ASD: autism spectrum disorder BIP: bipolar disorder BRCA: breast cancer CD: Crohn's disease MDD: major depressive disorder NIC: nicotine usage (cigarettes per day) SCZ: schizophrenia T2DM: type 2 diabetes mellitus

2. Heritability in Complex Diseases

Gender Bias (M:F)

• Developmental delay: 1.4:1 • ASD 4:1 • Asperger 6:1

Genetic Risk and ASD

0

25

50

75

100

Estimated Heritability (80%)

3. Genetic Architecture of Complex Diseases

Manolio et al 2009

Genome Sequencing

High-throughput genotyping

Genome Sequencing

Outline


1. CACATAGATCGATCGATTGGCGATGAATGAT 2. CACATAGATCGATCGATTGGCGATGAATGAT 3. CACATAGATCGATCGATTGGCGATGAATGAT 4. CACATAGATCGATCGATTGGCGATGAATGAT 5. CACATAGATCGATCGATTGGCGATGAATGAT 6. CACATAGATCGATCGATTGGCGATGAATGAT 7. CACATAGATCGATCGATTGGCGATGAATGAT 8. CACATAGATCGATCGATTGGCGATGAATGAT 9. CACATAGATCGATCGATTGGCGATGAATGAT 10. CACATAGATCGATCGATTGGCGATGAATGAT 11. CACATAGATCGATCGATTGGCGATGAATGAT 12. CACATAGATCGATCGATTGGCGATGAATGAT 13. CACATAGATCGATCTATTGGCGATGAATGAT 14. CACATAGATCGATCGATTGGCGATGAATGAT 15. CACATAGATCGATCGATTGGCGATGAATGAT 16. CACATAGATCGATCGATTGGCGATGAATGAT 17. CACATAGATCGATCGATTGGCGATGAATGAT 18. CACATAGATCGATCGATTGGCGATGAATGAT 19. CACATAGATCGATCGATTGGCGATGAATGAT 20. CACATAGATCGATCGATTGGCGATGAATGAT 21. CACATAGATCGATCGATTGGCGATGAATGAT 22. CACATAGATCGATCGATTGGCGATGAATGAT 23. CACATAGATCGATCGATTGGCGATGAATGAT 24. CACATAGATCGATCGATTGGCGATGAATGAT 25. CACATAGATCGATCGATTGGCGATGAATGAT

... 100. CACATAGATCGATCGATTGGCGATGAATGAT

Patients Controls1. CACATAGATCGATCGATTGGCGATGAATGAT 2. CACATAGATCGATCGATTGGCGATGAATGAT 3. CACATAGATCGATCGATTGGCGATGAATGAT 4. CACATAGATCGATCGATTGGCGATGAATGAT 5. CACATAGATCGATCGATTGGCGATGAATGAT 6. CACATAGATCGATCGATTGGCGATGAATGAT 7. CACATAGATCGATCGATTGGCGATGAATGAT 8. CACATAGATCGATCGATTGGCGATGAATGAT 9. CACATAGATCGATCGATTGGCGATGAATGAT 10. CACATAGATCGATCGATTGGCGATGAATGAT 11. CACATAGATCGATCGATTGGCGATGAATGAT 12. CACATAGATCGATCGATTGGCGATGAATGAT 13. CACATAGATCGATCGATTGGCGATGAATGAT 14. CACATAGATCGATCGATTGGCGATGAATGAT 15. CACATAGATCGATCGATTGGCGATGAATGAT 16. CACATAGATCGATCGATTGGCGATGAATGAT 17. CACATAGATCGATCGATTGGCGATGAATGAT 18. CACATAGATCGATCGATTGGCGATGAATGAT 19. CACATAGATCGATCGATTGGCGATGAATGAT 20. CACATAGATCGATCGATTGGCGATGAATGAT 21. CACATAGATCGATCGATTGGCGATGAATGAT 22. CACATAGATCGATCGATTGGCGATGAATGAT 23. CACATAGATCGATCGATTGGCGATGAATGAT 24. CACATAGATCGATCGATTGGCGATGAATGAT 25. CACATAGATCGATCGATTGGCGATGAATGAT


Pathogenic Mutation

Genetic Architecture of Complex Diseases

Manolio et al 2009

Mendelian mutations

Hagerman et al, Pediatrics 2009

FMR1-related disorders

• fragile X syndrome • FMR1-related premature ovarian failure (POF) • fragile X-associated tremor/ataxia syndrome (FXTAS)

FXS • craniofacial abnormalities • delayed attainment of motor milestones and speech • abnormal temperament • abnormal behavior: shyness, gaze aversion • macro-orchidism • cardiac: mitral valve prolapse • dermatologic: usually soft and smooth skin


Hagerman et al, Pediatrics 2009



Manolio et al 2009

• FMR1 (Fragile X) • MECP2 (Rett) • TSC1/TSC2 (Tuberous Sclerosis) • CACNA1C (Timothy) • Dup15q • 22q11.2 DS


0

25

50

75

100

Mendelian forms (10%)


Outline


1. CACATAGATCGATCGATTGGCGATGAATGAT 2. CACATAGATCGATCTATTGGCGATGAATGAT 3. CACATAGATCGATCGATTGGCGATGAATGAT 4. CACATAGATCGATCGATTGGCGATGAATGAT 5. CACATAGATCGATCGATTGGCGATGAATGAT 6. CACATAGATCGATCGATTGGCGATGAATGAT 7. CACATAGATCGATCTATTGGCGATGAATGAT 8. CACATAGATCGATCGATTGGCGATGAATGAT 9. CACATAGATCGATCGATTGGCGATGAATGAT 10. CACATAGATCGATCTATTGGCGATGAATGAT 11. CACATAGATCGATCGATTGGCGATGAATGAT 12. CACATAGATCGATCGATTGGCGATGAATGAT 13. CACATAGATCGATCTATTGGCGATGAATGAT 14. CACATAGATCGATCGATTGGCGATGAATGAT 15. CACATAGATCGATCGATTGGCGATGAATGAT 16. CACATAGATCGATCTATTGGCGATGAATGAT 17. CACATAGATCGATCGATTGGCGATGAATGAT 18. CACATAGATCGATCGATTGGCGATGAATGAT 19. CACATAGATCGATCTATTGGCGATGAATGAT 20. CACATAGATCGATCGATTGGCGATGAATGAT 21. CACATAGATCGATCGATTGGCGATGAATGAT 22. CACATAGATCGATCTATTGGCGATGAATGAT 23. CACATAGATCGATCGATTGGCGATGAATGAT 24. CACATAGATCGATCTATTGGCGATGAATGAT 25. CACATAGATCGATCGATTGGCGATGAATGAT


Patients Controls1. CACATAGATCGATCGATTGGCGATGAATGAT 2. CACATAGATCGATCGATTGGCGATGAATGAT 3. CACATAGATCGATCTATTGGCGATGAATGAT 4. CACATAGATCGATCGATTGGCGATGAATGAT 5. CACATAGATCGATCGATTGGCGATGAATGAT 6. CACATAGATCGATCGATTGGCGATGAATGAT 7. CACATAGATCGATCGATTGGCGATGAATGAT 8. CACATAGATCGATCGATTGGCGATGAATGAT 9. CACATAGATCGATCGATTGGCGATGAATGAT 10. CACATAGATCGATCGATTGGCGATGAATGAT 11. CACATAGATCGATCTATTGGCGATGAATGAT 12. CACATAGATCGATCGATTGGCGATGAATGAT 13. CACATAGATCGATCGATTGGCGATGAATGAT 14. CACATAGATCGATCTATTGGCGATGAATGAT 15. CACATAGATCGATCGATTGGCGATGAATGAT 16. CACATAGATCGATCGATTGGCGATGAATGAT 17. CACATAGATCGATCGATTGGCGATGAATGAT 18. CACATAGATCGATCGATTGGCGATGAATGAT 19. CACATAGATCGATCTATTGGCGATGAATGAT 20. CACATAGATCGATCGATTGGCGATGAATGAT 21. CACATAGATCGATCGATTGGCGATGAATGAT 22. CACATAGATCGATCGATTGGCGATGAATGAT 23. CACATAGATCGATCGATTGGCGATGAATGAT 24. CACATAGATCGATCTATTGGCGATGAATGAT 25. CACATAGATCGATCGATTGGCGATGAATGAT


Disease-Associated Sequence Variant

• Assumption • Principle • Technology

Genome-Wide Association Studies (GWAS)

Genetic component Linkage disequilibrium Microarrays

From Lichten Nature 2008;454:421

GWAS - rationale meiotic recombination

Cardon & Bell Nat Rev Genet 2001;2:91

GWAS

Kruglyak Nat Rev Genet 2008;9:314

GWAS

Genotyping using Microarrays

www.affymetrix.com

http://www.affymetrix.com

Corvin et al 2010

GWAS analysis steps

Pearson & Manolio, JAMA 2008;299:1335

Manhattan Plot

https://www.genome.gov/26525384

NHGRI&GWA&Catalog&www.genome.gov/GWAStudies&www.ebi.ac.uk/fgpt/gwas/&

Published&GenomeBWide&Associations&through&12/2013&Published&GWA&at&p≤5X10B8&for&17&trait&categories

GWAS in ASD

Weiss et al, Nature 2009 Wang et al, Nature 2009

GWAS in ASD

Wang et al, Nature 2009

Cardon & Bell, Nat Rev Genet 2001;2:91

GWAS

GWAS confounders - population stratification

Novembre et al, Nature 2008;456:98

! Pop

Structure

GWAS confounders: population stratification


Manolio et al 2009

• FMR1 (Fragile X) • MECP2 (Rett) • TSC1/TSC2 (Tuberous Sclerosis) • CACNA1C (Timothy) • 15q duplication • 22q11 deletion

• CDH9 and CDH10 • 5p15 (SEMA5A?) • MACROD2 • CNTNAP2


0

25

50

75

100

Common variation (1%)



?

Nature 2008


Manolio et al 2009

hundreds of common

variants with small effect s

GWAS in Psychiatric Disease

Sullivan et al, Nat Rev Genet 2012

Why Do We Need So Many Samples?

Altshuler et al, 2008

Franke et al Nat Genet 2010

What to Expect: Insights from Other Complex Traits

Cumulative fraction of genetic variance explained by 71 Crohn's disease risk loci.

Allen et al Nature 2010

What to Expect: Insights from Other Complex Traits


0

25

50

75

100

Common variation (20%??)



estimated


Manolio et al 2009

hundreds of common

variants with small effect s

[hundredsof rare variants with moderate

effect size

Outline


Rare Copy Number Variants (CNVs)

rare CNVs

Cooper et al, Nat Genet 2011


Manolio et al 2009



rare CNVs


0

25

50

75

100




rare CNVs (7%)

estimated


Manolio et al 2009



rare CNVsrare sequence

variants?

June 26, 2000

Bamshad et al, Nat Rev Genet 2011;12:745

Exome Sequencing

@6:6:1355:6985:YGCTGTTTCTGCAGACAGGACCTCAATAGTTCTGGTGAGCTGCTCACTGGGCAAGTAACTACCATCCTGAGGGGGCA

+6:6:1355:6985:Y?<>B><2BB@BBB/@>BBBBB?BB@@B6B@@@@@0B-@BBBBBB@B>BBAB3@@A>><@>,@B7BBBB;@9:<9=?

@6:6:1356:4867:YCATTTCATGGAGTATCTAGGACCTTACCCAGCGAGGCCACAAGTGCGAAGTTGTCTAGCATCACGCGGCGGTACAG

+6:6:1356:4867:YIIIIIIIIIIIIIIII=B=BBBBB@6::??BBBBBBBBAB@<6>>B@B@@@B=@B@/<@@@@@@@B@#########

@6:6:1357:2232:YACCGCAGTGGATGCGGTGCAACACGGGTTTCGTACCATCGTCGTGCGCGAATGCGTCGGCGAACGCCACCCGGCGG

+6:6:1357:2232:Y############################################################################

NGS Data

sanger-fastq format

NGS Data Alignment to Reference Genome

NextGen Sequencing: Main Platforms

Roche 454

Illumina HiSeq2500

Gartner Inc.

NextGen Sequencing: the Hype Cycle

2005 2014

Genome Length 3 billion

Positions Called 2.8 billion (~93%)

Average Depth of Coverage 61

Number of Heterozygotes 2.4 million (0.09%)

Variants in Coding Regions 20,696

Predicted deleterious 800-1,800

HGMD 726

never seen in EVS 1,724 (~8%)

Genome Sequencing some numbers

EVS: Exome Variant Server (evs.gs.washington.edu/EVS/) HGMD: Human Gene Mutation Database (www.hgmd.org/)

http://evs.gs.washington.edu/EVS/

http://www.hgmd.org/

PFBC: CAUSAL MUTATIONSExome Sequencing

www.my46.org

www.1000genomes.org

Whole-Genome Sequencing

http://www.1000genomes.org

http://evs.gs.washington.edu/EVS/

Exome Variant Server

http://www.1000genomes.org

ExAC Database

http://exac.broadinstitute.org

http://exac.broadinstitute.org

Simons Simplex Collection

http://sfari.org/resources/simons-simplex-collection

The Simons Simplex Collection (SSC) is a core project and resource of the Simons Foundation Autism Research Initiative (SFARI). The SSC achieved its primary goal to establish a permanent repository of genetic samples from 2,600 simplex families, each of which has one child affected with an autism spectrum disorder, and unaffected parents and siblings.

Exome Sequencing

O’Roak et al, Nature 2012 Iossifov et al, Neuron 2012 Sanders et al, Nature 2012

~200 ASD genes

Excess of de novo events from older fathers Chen et al 2015

ASD genes

gene.sfari.org

http://gene.sfari.org

CHD8

Bernier et al, Cell 2014


0

25

50

75

100




rare CNVs (7%)

rare de novo events (10%?)

estimated


0

25

50

75

100




rare CNVs (7%)

rare de novo events (10%?)

estimated

20%

33%

47% explained or estimated

missing

non-genetic

Outline


DNA

RNA

Protein

transcription

translation

High-throughput genotyping

Genome Sequencing

Gene Expression

Epigenetics

Proteomics

Microarrays

NextGen Sequencing

Genome Sequencing Projects

Green et al, Nature 2011;470:204

• Understand disease pathogenesis at the global level

• Characterize susceptibility to complex diseases

• Characterize and understand drug response (personalized treatment)

Promise of Genomic Medicine

Green et al Nature 2011;470:204

Green et al Nature 2011;470:204

Promise of Genomic Medicine

Non-Genetic Factors

common variant

common variant

Imaging features

common variant

Rare CNV Rare variant

Rare variantRare variant

CNV CNV

common variant

Gene Expression

Epigenetics

Towards a Personalized Genetic Risk Map

common variant

common variant

common variant

-OMICs Studies - Conventional Approach

genomics

genomics: transcription outliers

Voineagu et al, Mol Psychiatry 2012

Mike Oldham Steve Horvath

Differential Expression vs. Differential Co-Expression

Weighted Gene Coexpression Network Analysis (WGCNA)

Steve Horvath, PhD

**Slide courtesy of A Barabasi

Flight connections and hub airports

The nodes with the largest number of links (connections) are most important!

Steve Horvath

Construct a network Rationale: make use of interaction patterns between genes

Identify modules Rationale: module (pathway) based analysis

Relate modules to external information Array Information: Clinical data, SNPs, proteomics Gene Information: gene ontology, EASE, IPA Rationale: find biologically interesting modules

Find the key drivers in interesting modules Tools: intramodular connectivity, causality testing Rationale: experimental validation, therapeutics, biomarkers

Study Module Preservation across different data Rationale: • Same data: to check robustness of module definition • Different data: to find interesting modules.

Steve Horvath

Transcriptional networks in ASD brain

genomics: WGCNA

Parikshak et al, Cell 2013

Integrative Functional GenomicAnalyses Implicate Specific MolecularPathways and Circuits in AutismNeelroop N. Parikshak,1,2 Rui Luo,3,4 Alice Zhang,2 Hyejung Won,1 Jennifer K. Lowe,1,4 Vijayendran Chandran,5

Steve Horvath,3,6 and Daniel H. Geschwind1,2,3,4,5,*1Program in Neurobehavioral Genetics, Semel Institute, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles,CA 90095, USA2Interdepartmental Program in Neuroscience, University of California, Los Angeles, Los Angeles, CA 90095, USA3Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA4Center for Autism Treatment and Research, Semel Institute, David Geffen School of Medicine, University of California, Los Angeles,Los Angeles, CA 90095, USA5Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles,CA 90095, USA6Department of Biostatistics, University of California, Los Angeles, Los Angeles, CA 90095, USA*Correspondence: [email protected]://dx.doi.org/10.1016/j.cell.2013.10.031

SUMMARY

Genetic studies have identified dozens of autismspectrumdisorder (ASD) susceptibility genes, raisingtwo critical questions: (1) do these genetic lociconverge on specific biological processes, and (2)where does the phenotypic specificity of ASD arise,given its genetic overlap with intellectual disability(ID)? To address this, we mapped ASD and ID riskgenes onto coexpression networks representingdevelopmental trajectories and transcriptional pro-files representing fetal and adult cortical laminae.ASD genes tightly coalesce in modules that implicatedistinct biological functions during human corticaldevelopment, including early transcriptional regula-tion and synaptic development. Bioinformatic ana-lyses suggest that translational regulation by FMRPand transcriptional coregulation by common tran-scription factors connect these processes. At a cir-cuit level, ASD genes are enriched in superficialcortical layers and glutamatergic projection neurons.Furthermore, we show that the patterns of ASD andID risk genes are distinct, providing a biologicalframework for further investigating the pathophysi-ology of ASD.

INTRODUCTION

Autism spectrum disorder (ASD) is a heterogeneous neurodeve-lopmental disorder in which hundreds of genes have been impli-cated (Berg and Geschwind, 2012; Geschwind and Levitt, 2007).Analysis of copy number variation (CNV) and exome sequencinghave identified rare variants that alter dozens of protein-coding

genes in ASD, none of which account for more than 1% ofASD cases (Devlin and Scherer, 2012). This and the fact that asignificant fraction (40%–60%) of ASD is explained by commonvariation (Klei et al., 2012) point to a heterogeneous geneticarchitecture.These findings raise several issues. Based on the background

human mutation rate (MacArthur et al., 2012), most genesaffected by only one observed rare variant to date are likely falsepositives that do not increase risk for ASD (Gratten et al., 2013). Itis therefore essential to develop approaches that prioritizesingleton variants, especially missense mutations. Furthermore,given the heterogeneity of ASD, it would be valuable to identifycommon pathways, cell types, or circuits disrupted within ASDitself. Recent studies combining gene expression, protein-protein interactions (PPIs), and other systematic gene annotationresources suggest some molecular convergence in subsets ofASD risk genes (Ben-David and Shifman, 2013; Gilman et al.,2011; Sakai et al., 2011; Voineagu et al., 2011). Yet, it remainsunclear how the large number of genes implicated throughdifferent methods may converge to affect human brain develop-ment, which is critical to a mechanistic understanding of ASD(Berg andGeschwind, 2012). Additionally, ASD has considerableoverlapwith ID at the genetic level, so identifyingmolecular path-ways and circuits that confer the phenotypic specificity of ASDwould be of considerable utility (Geschwind, 2011; Matson andShoemaker, 2009).Here, we took a stepwise approach to determine whether

genes implicated in ASD affect convergent pathways duringin vivo human neural development and whether they are en-riched in specific cells or circuits (Figure 1A). First, we con-structed transcriptional networks representing genome-widefunctional relationships during fetal and early postnatal braindevelopment and mapped genes from multiple ASD and IDresources to these networks. We then assessed shared neurobi-ological function among these genes, including coregulatoryrelationships and enrichment in layer-specific patterns from

1008 Cell 155, 1008–1021, November 21, 2013 ª2013 Elsevier Inc.

http://geschwindlab.neurology.ucla.edu/sites/all/files/networkplot/ParikshakDevelopmentalCortexNetwork.html

genomics: WGCNA

Genetics

Gene Expression

Imaging

Clinical Phenotype

EpigeneticsGenotyping Sequencing

Transcriptome Proteome

Methylome Histone Modifications

• Structural • Functional

Neuropathology

OMICs Approaches to Human CNS Disease

• Binary • Quantitative

Genetics

Gene Expression

Imaging

Clinical Phenotype

EpigeneticsGenotyping Sequencing

Transcriptome Proteome

Methylome ...

Structural Functional

Neuropathology

OMICs Approaches to Human CNS Disease

unidimensional approach

systems biology approach

Geschwind and Konopka, Nature 2010

Genetics

meth1

meth2

meth3

trait

traittrait

Network Edge Orienting (NEO)

0.6

3.5

Steve Horvath Aten et al, BMC Systems Biol 2008

Conclusions

1. The genetic map for ASD and ID and the role of common and rare variation are increasingly characterized

2. Two technological advances (microarrays and sequencing) have facilitated progress over the past 10 years

3. Whole-genome sequencing will clarify the role of non-coding variation

4. Replication and functional validation pose significant challenges

Thank you

[email protected]

UCSB Summer Institute in Cognitive Neuroscience, June 29 2015

Science

Transcript of UCSB Summer Institute in Cognitive Neuroscience, June 29 2015