This document outlines an overview of genetic approaches for identifying genes involved in brain development and developmental disorders. It discusses key concepts such as phenotypes and heritability. It covers identifying Mendelian genes, common risk variants identified through genome-wide association studies, and rare variants including rare copy number variants implicated in these disorders. The goal is to elucidate the genetic architecture involving hundreds of common and rare variants contributing to conditions like autism spectrum disorder.

1. 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

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

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

4. 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

5. 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

6. 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

7. Genetic Risk and ASD
0
25
50
75
100
Estimated Heritability (80%)

8. 3. Genetic Architecture of Complex Diseases
Manolio et al 2009
Genome
Sequencing
High-throughput
genotyping
Genome
Sequencing

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

10. 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 Controls
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. 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

11. Genetic Architecture of Complex Diseases
Manolio et al 2009
Mendelian mutations

12. Hagerman et al, Pediatrics 2009
FMR1-related disorders

13. • 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

14. Hagerman et al, Pediatrics 2009
FMR1-related disorders

15. Genetic Architecture of Complex Diseases
Manolio et al 2009
• FMR1 (Fragile X)
• MECP2 (Rett)
• TSC1/TSC2 (Tuberous Sclerosis)
• CACNA1C (Timothy)
• Dup15q
• 22q11.2 DS

16. Genetic Risk and ASD
0
25
50
75
100
Mendelian forms (10%)
Estimated Heritability (80%)

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

18. 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 Controls
1. 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

19. • Assumption
• Principle
• Technology
Genome-Wide Association Studies (GWAS)
Genetic component
Linkage disequilibrium
Microarrays

20. From Lichten Nature 2008;454:421
GWAS - rationale
meiotic recombination

21. Cardon & Bell Nat Rev Genet 2001;2:91
GWAS

22. Kruglyak Nat Rev Genet 2008;9:314
GWAS

23. Genotyping using Microarrays
www.affymetrix.com

24. Corvin et al 2010
GWAS
analysis steps

25. Pearson & Manolio, JAMA 2008;299:1335
Manhattan Plot

26. https://www.genome.gov/26525384

27. 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

28. GWAS in ASD
Weiss et al, Nature 2009
Wang et al, Nature 2009

29. GWAS in ASD
Wang et al, Nature 2009

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

31. GWAS confounders - population stratification
Novembre et al, Nature 2008;456:98

32. !
Pop
Structure
GWAS confounders: population stratification

33. 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

34. Genetic Risk and ASD
0
25
50
75
100
Common variation (1%)
Estimated Heritability (80%)
Mendelian forms (10%)
?

35. Nature 2008

36. Genetic Architecture of Complex Diseases
Manolio et al 2009
hundreds of
common
variants with
small effect s

37. GWAS in Psychiatric Disease
Sullivan et al, Nat Rev Genet 2012

38. Why Do We Need So Many Samples?
Altshuler et al, 2008

39. 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.

40. Allen et al Nature 2010
What to Expect: Insights from Other Complex Traits

41. Genetic Risk and ASD
0
25
50
75
100
Common variation (20%??)
Estimated Heritability (80%)
Mendelian forms (10%)
estimated

42. Genetic Architecture of Complex Diseases
Manolio et al 2009
hundreds of
common
variants with
small effect s
[hundreds
of rare variants
with moderate
effect size

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

44. Rare Copy Number Variants (CNVs)

45. rare CNVs
Cooper et al, Nat Genet 2011

46. 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

47. Genetic Risk and ASD
0
25
50
75
100
Common variation (20%??)
Estimated Heritability (80%)
Mendelian forms (10%)
rare CNVs (7%)
estimated

48. 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
rare sequence
variants?

49. June 26, 2000

50. Bamshad et al, Nat Rev Genet 2011;12:745
Exome Sequencing

51. @6:6:1355:6985:Y
GCTGTTTCTGCAGACAGGACCTCAATAGTTCTGGTGAGCTGCTCACTGGGCAAGTAACTACCATCCTGAGGGGGCA
+6:6:1355:6985:Y
?<>B><2BB@BBB/@>BBBBB?BB@@B6B@@@@@0B-@BBBBBB@B>BBAB3@@A>><@>,@B7BBBB;@9:<9=?
@6:6:1356:4867:Y
CATTTCATGGAGTATCTAGGACCTTACCCAGCGAGGCCACAAGTGCGAAGTTGTCTAGCATCACGCGGCGGTACAG
+6:6:1356:4867:Y
IIIIIIIIIIIIIIII=B=BBBBB@6::??BBBBBBBBAB@<6>>B@B@@@B=@B@/<@@@@@@@B@#########
@6:6:1357:2232:Y
ACCGCAGTGGATGCGGTGCAACACGGGTTTCGTACCATCGTCGTGCGCGAATGCGTCGGCGAACGCCACCCGGCGG
+6:6:1357:2232:Y
############################################################################
NGS Data
sanger-fastq format

52. NGS Data
Alignment to Reference Genome

53. NextGen Sequencing: Main Platforms
Roche 454
Illumina HiSeq2500

54. Gartner Inc.
NextGen Sequencing: the Hype Cycle
2005 2014

55. 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/)

56. PFBC: CAUSAL MUTATIONS
Exome Sequencing
www.my46.org

57. www.1000genomes.org
Whole-Genome Sequencing

58. http://evs.gs.washington.edu/EVS/
Exome Variant Server

59. ExAC Database
http://exac.broadinstitute.org

60. 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.

61. Exome Sequencing
O’Roak et al, Nature 2012
Iossifov et al, Neuron 2012
Sanders et al, Nature 2012

62. ~200 ASD genes
Excess of de novo events from older fathers
Chen et al 2015

63. ASD genes
gene.sfari.org

64. CHD8
Bernier et al, Cell 2014

65. 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

66. 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

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

68. DNA
RNA
Protein
transcription
translation
High-throughput
genotyping
Genome
Sequencing
Gene
Expression
Epigenetics
Proteomics
Microarrays
NextGen Sequencing
Genome Sequencing
Projects

69. Green et al, Nature 2011;470:204

70. • 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

71. Green et al Nature 2011;470:204
Promise of Genomic Medicine

72. 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

73. -OMICs Studies - Conventional Approach

74. genomics

75. genomics: transcription outliers
Voineagu et al, Mol Psychiatry 2012

76. Mike Oldham
Steve Horvath
Differential Expression vs. Differential Co-Expression

77. Weighted Gene Coexpression Network Analysis (WGCNA)
Steve Horvath, PhD

78. **Slide courtesy of A Barabasi
Flight connections and hub airports
The nodes with the largest number of links
(connections) are most important!
Steve Horvath

79. 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

80. Transcriptional networks in ASD brain

81. genomics: WGCNA
Parikshak et al, Cell 2013

82. genomics: WGCNA
Parikshak et al, Cell 2013

83. genomics: WGCNA
Parikshak et al, Cell 2013

84. genomics: WGCNA
Parikshak et al, Cell 2013

87. Integrative Functional Genomic
Analyses Implicate Specific Molecular
Pathways and Circuits in Autism
Neelroop 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, USA
2Interdepartmental Program in Neuroscience, University of California, Los Angeles, Los Angeles, CA 90095, USA
3Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
4Center for Autism Treatment and Research, Semel Institute, David Geffen School of Medicine, University of California, Los Angeles,
Los Angeles, CA 90095, USA
5Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles,
CA 90095, USA
6Department of Biostatistics, University of California, Los Angeles, Los Angeles, CA 90095, USA
*Correspondence: dhg@ucla.edu
http://dx.doi.org/10.1016/j.cell.2013.10.031
SUMMARY
Genetic studies have identified dozens of autism
spectrum disorder (ASD) susceptibility genes, raising
two critical questions: (1) do these genetic loci
converge 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 risk
genes onto coexpression networks representing
developmental trajectories and transcriptional pro-
files representing fetal and adult cortical laminae.
ASD genes tightly coalesce in modules that implicate
distinct biological functions during human cortical
development, including early transcriptional regula-
tion and synaptic development. Bioinformatic ana-
lyses suggest that translational regulation by FMRP
and transcriptional coregulation by common tran-
scription factors connect these processes. At a cir-
cuit level, ASD genes are enriched in superficial
cortical layers and glutamatergic projection neurons.
Furthermore, we show that the patterns of ASD and
ID risk genes are distinct, providing a biological
framework 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 sequencing
have identified rare variants that alter dozens of protein-coding
genes in ASD, none of which account for more than 1% of
ASD cases (Devlin and Scherer, 2012). This and the fact that a
significant fraction (40%–60%) of ASD is explained by common
variation (Klei et al., 2012) point to a heterogeneous genetic
architecture.
These findings raise several issues. Based on the background
human mutation rate (MacArthur et al., 2012), most genes
affected by only one observed rare variant to date are likely false
positives that do not increase risk for ASD (Gratten et al., 2013). It
is therefore essential to develop approaches that prioritize
singleton variants, especially missense mutations. Furthermore,
given the heterogeneity of ASD, it would be valuable to identify
common pathways, cell types, or circuits disrupted within ASD
itself. Recent studies combining gene expression, protein-
protein interactions (PPIs), and other systematic gene annotation
resources suggest some molecular convergence in subsets of
ASD risk genes (Ben-David and Shifman, 2013; Gilman et al.,
2011; Sakai et al., 2011; Voineagu et al., 2011). Yet, it remains
unclear how the large number of genes implicated through
different methods may converge to affect human brain develop-
ment, which is critical to a mechanistic understanding of ASD
(Berg and Geschwind, 2012). Additionally, ASD has considerable
overlap with ID at the genetic level, so identifying molecular path-
ways and circuits that confer the phenotypic specificity of ASD
would be of considerable utility (Geschwind, 2011; Matson and
Shoemaker, 2009).
Here, we took a stepwise approach to determine whether
genes implicated in ASD affect convergent pathways during
in 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-wide
functional relationships during fetal and early postnatal brain
development and mapped genes from multiple ASD and ID
resources to these networks. We then assessed shared neurobi-
ological function among these genes, including coregulatory
relationships and enrichment in layer-specific patterns from
http://geschwindlab.neurology.ucla.edu/sites/all/files/networkplot/ParikshakDevelopmentalCortexNetwork.html
genomics: WGCNA

88. Genetics
Gene Expression
Imaging
Clinical Phenotype
Epigenetics
Genotyping
Sequencing
Transcriptome
Proteome
Methylome
Histone Modifications
• Structural
• Functional
Neuropathology
OMICs Approaches to
Human CNS Disease
• Binary
• Quantitative

89. Genetics
Gene Expression
Imaging
Clinical Phenotype
Epigenetics
Genotyping
Sequencing
Transcriptome
Proteome
Methylome
...
Structural
Functional
Neuropathology
OMICs Approaches to
Human CNS Disease
unidimensional approach
systems biology approach
Geschwind and Konopka, Nature 2010

90. Genetics
meth
1
meth
2
meth
3
trait
traittrait
Network Edge Orienting (NEO)
0.6
3.5
Steve Horvath
Aten et al, BMC Systems Biol 2008

91. 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

92. Thank you
gcoppola@ucla.edu