Post on 17-Aug-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
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. Key Concepts 2. Mendelian Genes 3. Risk Genes: Common Variants 4. Risk Genes: Rare Variants 5. Genomic Approaches
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
... 100. 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
FMR1-related disorders
Hagerman et al, Pediatrics 2009
FMR1-related disorders
Genetic Architecture of Complex Diseases
Manolio et al 2009
• FMR1 (Fragile X) • MECP2 (Rett) • TSC1/TSC2 (Tuberous Sclerosis) • CACNA1C (Timothy) • Dup15q • 22q11.2 DS
Genetic Risk and ASD
0
25
50
75
100
Mendelian forms (10%)
Estimated Heritability (80%)
Outline
1. Key Concepts 2. Mendelian Genes 3. Risk Genes: Common Variants 4. Risk Genes: Rare Variants 5. Genomic Approaches
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
... 100. 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
... 100. 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
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
Genetic Architecture of Complex Diseases
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
Genetic Risk and ASD
0
25
50
75
100
Common variation (1%)
Estimated Heritability (80%)
Mendelian forms (10%)
?
Nature 2008
Genetic Architecture of Complex Diseases
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
Genetic Risk and ASD
0
25
50
75
100
Common variation (20%??)
Estimated Heritability (80%)
Mendelian forms (10%)
estimated
Genetic Architecture of Complex Diseases
Manolio et al 2009
hundreds of common
variants with small effect s
[hundredsof rare variants with moderate
effect size
Outline
1. Key Concepts 2. Mendelian Genes 3. Risk Genes: Common Variants 4. Risk Genes: Rare Variants 5. Genomic Approaches
Rare Copy Number Variants (CNVs)
rare CNVs
Cooper et al, Nat Genet 2011
Genetic Architecture of Complex Diseases
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
rare CNVs
Genetic Risk and ASD
0
25
50
75
100
Common variation (20%??)
Estimated Heritability (80%)
Mendelian forms (10%)
rare CNVs (7%)
estimated
Genetic Architecture of Complex Diseases
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
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/)
PFBC: CAUSAL MUTATIONSExome Sequencing
www.my46.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
CHD8
Bernier et al, Cell 2014
Genetic Risk and ASD
0
25
50
75
100
Common variation (20%??)
Estimated Heritability (80%)
Mendelian forms (10%)
rare CNVs (7%)
rare de novo events (10%?)
estimated
Genetic Risk and ASD
0
25
50
75
100
Common variation (20%??)
Estimated Heritability (80%)
Mendelian forms (10%)
rare CNVs (7%)
rare de novo events (10%?)
estimated
20%
33%
47% explained or estimated
missing
non-genetic
Outline
1. Key Concepts 2. Mendelian Genes 3. Risk Genes: Common Variants 4. Risk Genes: Rare Variants 5. Genomic Approaches
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
genomics: WGCNA
Parikshak et al, Cell 2013
genomics: WGCNA
Parikshak et al, Cell 2013
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: dhg@ucla.eduhttp://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
gcoppola@ucla.edu