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Dan Dediu 1
LSA2013Universality and Variability:
New Insights from Genetics29th of June, 2013
Dan Dediu
Language and GeneticsMax Planck Institute for Psycholinguistics
NijmegenThe Netherlands
1
Searching for the“language genes”
Dan Dediu 21Overview
● Heritability
● Linkage
● Association
● Sequencing
● Examples (hearing loss, dyslexia, SLI, stuttering)
● Conclusions
● Suggested readings
Overview
Dan Dediu 31Basic concepts
● Phenotype → observable/measurable properties
– height
Basic concepts
Dan Dediu 41Basic concepts
● Phenotype → observable/measurable properties
– height
– eye color
Basic concepts
Dan Dediu 51Basic concepts
● Phenotype → observable/measurable properties
– height
– eye color
– hard palate shape
Basic concepts
Dan Dediu 61Basic concepts
● Phenotype → observable/measurable properties
– height
– eye color
– hard palate shape
– rate of speech
Basic concepts
Dan Dediu 71Basic concepts
Basic concepts
● Phenotype → observable/measurable properties
– height
– eye color
– hard palate shape
– rate of speech
– vocabulary size
Dan Dediu 81Basic concepts
● Phenotype → observable/measurable properties
– height
– eye color
– hard palate shape
– rate of speech
– vocabulary size
– brain activation
Basic concepts
Dan Dediu 91Basic concepts
● Phenotype → observable/measurable properties
– height
– eye color
– hard palate shape
– rate of speech
– vocabulary size
– brain activation
– grammaticality judgments
Basic concepts
Dan Dediu 101Basic concepts
● Phenotype → observable/measurable properties
– height
– eye color
– hard palate shape
– rate of speech
– vocabulary size
– brain activation
– grammaticality judgments …..
Basic concepts
Dan Dediu 111Basic concepts
● Phenotype → variation
Basic concepts
Dan Dediu 121Basic concepts
● Phenotype → variation
– normal
Basic concepts
Height →
Fre
quen
cy →
Dan Dediu 131Basic concepts
● Phenotype → variation
– normal
– extreme/pathological
Basic concepts
Height →
Fre
quen
cy →
Dan Dediu 141Basic concepts
● Phenotype → variation
– normal
– extreme/pathological
→ measurement
Basic concepts
Dan Dediu 151Basic concepts
● Phenotype → variation
– normal
– extreme/pathological
→ measurement
Basic concepts
Dan Dediu 161Basic concepts
● Phenotype → variation
– normal
– extreme/pathological
→ measurement
Basic concepts
Height →
Fre
quen
cy →
Dan Dediu 171Basic concepts
● Genotype → abstract way
Basic concepts
Dan Dediu 181Basic concepts
● Genotype → abstract way
– gene → genetic locus
– alleles/variants
→ biallelic loci (A/a)
Basic concepts
Dan Dediu 191Basic concepts
● Genotype → abstract way
– gene → genetic locus
– alleles/variants
→ biallelic loci (A/a)
– CNVs
Basic concepts
Dan Dediu 201Basic concepts
● Genotype → abstract way
– gene → genetic locus
– alleles/variants
→ biallelic loci (A/a)
– CNVs, SNPs
Basic concepts
Dan Dediu 211Basic concepts
● Genotype → abstract way
– gene → genetic locus
– alleles/variants
→ biallelic loci (A/a)
– CNVs, SNPs, etc...
Basic concepts
Dan Dediu 221Basic concepts
● Notions of statistics
– (statistical) population ↔ sample & inference
Basic concepts
Dan Dediu 231Basic concepts
● Notions of statistics
– (statistical) population ↔ sample & inference
Basic concepts
Parameters(e.g., population mean μ)
Dan Dediu 241Basic concepts
● Notions of statistics
– (statistical) population ↔ sample & inference
Basic concepts
Parameters(e.g., population mean μ)?
Dan Dediu 251Basic concepts
● Notions of statistics
– (statistical) population ↔ sample & inference
Basic concepts
Parameters(e.g., population mean μ)
Estimates(e.g., sample mean x)?
Dan Dediu 261Basic concepts
● Notions of statistics
– (statistical) population ↔ sample & inference
Basic concepts
Parameters(e.g., population mean μ)
Estimates(e.g., sample mean x)?
Inference(confidence)
Dan Dediu 271Basic concepts
● Notions of statistics
– (statistical) population ↔ sample & inference
– mean (μ) and variation (σ)
Basic concepts
Dan Dediu 281Basic concepts
● Notions of statistics
– (statistical) population ↔ sample & inference
– mean (μ) and variation (σ)
Basic concepts
Height →
Fre
quen
cy →
Dan Dediu 291Basic concepts
● Notions of statistics
– (statistical) population ↔ sample & inference
– mean (μ) and variation (σ)
Basic concepts
Height →
Fre
quen
cy →
Dan Dediu 301Basic concepts
● Notions of statistics
– (statistical) population ↔ sample & inference
– mean (μ) and variation (σ)
Basic concepts
Height →
Fre
quen
cy →
Dan Dediu 311Basic concepts
Basic concepts
Height →
Fre
quen
cy →
● Notions of statistics
– (statistical) population ↔ sample & inference
– mean (μ) and variation (σ)
Dan Dediu 321Basic concepts
Basic concepts
Height →
Fre
quen
cy →
Weight →
Fre
quen
cy →
● Notions of statistics
– (statistical) population ↔ sample & inference
– mean (μ) and variation (σ)
Dan Dediu 331Basic concepts
Basic concepts
Weight →
Fre
quen
cy →
● Notions of statistics
– (statistical) population ↔ sample & inference
– mean (μ) and variation (σ)
Height →
Freq
uenc
y →
Dan Dediu 341Basic concepts
Basic concepts
● Notions of statistics
– (statistical) population ↔ sample & inference
– mean (μ) and variation (σ)
– correlation r (covariation cov)
Dan Dediu 351Basic concepts
Basic concepts
● Notions of statistics
– (statistical) population ↔ sample & inference
– mean (μ) and variation (σ)
– correlation r (covariation cov)
rheight , weight=covheight , weightσheight⋅σweight
Dan Dediu 361Basic concepts
Basic concepts
● Notions of statistics
– (statistical) population ↔ sample & inference
– mean (μ) and variation (σ)
– correlation r (covariation cov)
rheight , weight=covheight , weightσheight⋅σweight co
v = 12.8
r = 0.5
Dan Dediu 371Basic concepts
Basic concepts
● Notions of statistics
– (statistical) population ↔ sample & inference
– mean (μ) and variation (σ)
– correlation r (covariation cov)
rheight , weight=covheight , weightσheight⋅σweight co
v = 12.8
r = 0.5
regression line
Dan Dediu 381Heritability
Heritability
H 2=
Dan Dediu 391Heritability
Heritability
= proportion of phenotypic variation due to genotypic variation
H 2=
Dan Dediu 401Heritability
Heritability
= proportion of phenotypic variation due to genotypic variation
H 2=
σP2
Dan Dediu 411Heritability
Heritability
= proportion of phenotypic variation due to genotypic variation
H 2=
σG2
Dan Dediu 421Heritability
Heritability
= proportion of phenotypic variation due to genotypic variation
H 2= =σG
2
σP2
Dan Dediu 431Heritability
Heritability
= proportion of phenotypic variation due to genotypic variation
H 2= =
σG2
σ P2
H2 (broad sense heritability) ≠ h2 (narrow sense heritability)
Dan Dediu 441Heritability
Heritability
= proportion of phenotypic variation due to genotypic variation
H 2= =
σG2
σ P2
H2 (broad sense heritability) ≠ h2 (narrow sense heritability)
0 (genetic variation has no effect) → 1 (all phenotypic variation is genetic)
Dan Dediu 451Heritability
Heritability
Caveats in interpretation:
● Uniform traits
Dan Dediu 461Heritability
Heritability
Caveats in interpretation:
● Uniform traits
● Essential (fixed) genes
Dan Dediu 471Heritability
Heritability
Caveats in interpretation:
● Uniform traits
● Essential (fixed) genes
● Constant vs variable environment
Dan Dediu 481Heritability
Heritability
Caveats in interpretation:
● Uniform traits
● Essential (fixed) genes
● Constant vs variable environment
● Specific to population & environment
Dan Dediu 491Heritability
Heritability
Caveats in interpretation:
● Uniform traits
● Essential (fixed) genes
● Constant vs variable environment
● Specific to population & environment
● Changes with age
Dan Dediu 501Heritability
Heritability
Caveats in interpretation:
● Uniform traits
● Essential (fixed) genes
● Constant vs variable environment
● Specific to population & environment
● Changes with age
→ high heritability ≠ innateness/“genetic determinism”
Dan Dediu 511Heritability
Heritability
Estimation:
Twin studies vs. → h2=2(r M Z−r D Z )
Dan Dediu 521Heritability
Heritability
Twin studies vs. → h2=2(r M Z−r D Z )
Adoption studies vs. and
Estimation:
Dan Dediu 531Heritability
Heritability
Twin studies vs. → h2=2(r M Z−r D Z )
Adoption studies vs. and
Non-related samples (genome-wide complex trait analysis; GCTA)
Estimation:
Dan Dediu 541Heritability
Heritability
● Moderate → high heritabilities for:
- almost all aspects of speech and language
- normal and pathologic
Dan Dediu 551Heritability
Heritability
● Moderate → high heritabilities for:
- almost all aspects of speech and language
- normal and pathologic
● Examples:
- dyslexia (0.4 – 0.8), SLI (0.5), stuttering (0.7) …
- (I)SLA (0.7), conversational language (0.7), formal language (0.5), vocabulary size (0.7), “phonology & articulation” (0.7) …
Dan Dediu 561Heritability
Heritability
● Moderate → high heritabilities for:
- almost all aspects of speech and language
- normal and pathologic
● Examples:
- dyslexia (0.4 – 0.8), SLI (0.5), stuttering (0.7) …
- (I)SLA (0.7), conversational language (0.7), formal language (0.5), vocabulary size (0.7), “phonology & articulation” (0.7) …
Genetic correlations: Language
Maths
“Generalistgenes”
“Specialistgenes”
Dan Dediu 571Linkage studies
Linkage
● Genome: discrete linear molecules (chromosomes + mtDNA)
Dan Dediu 581Linkage studies
Linkage
● Genome: discrete linear molecules (chromosomes + mtDNA)
Dan Dediu 591Linkage studies
Linkage
● Genome: discrete linear molecules (chromosomes + mtDNA)
From mother
From father
Dan Dediu 601Linkage studies
Linkage
● Genome: discrete linear molecules (chromosomes + mtDNA)
From mother
From father
Dan Dediu 611Linkage studies
Linkage
● Genome: discrete linear molecules (chromosomes + mtDNA)
Dan Dediu 621Linkage studies
Linkage
● Genome: discrete linear molecules
→ independent loci (genes):
Peas plant
Dan Dediu 631Linkage studies
Linkage
● Genome: discrete linear molecules
→ independent loci (genes):
Peas plant
Color locus (R/r) Shape locus (Y/y)
Dan Dediu 641Linkage studies
Linkage
Peas plant
● Genome: discrete linear molecules
→ independent loci (genes):
Dan Dediu 651Linkage studies
Linkage
Peas plant
● Genome: discrete linear molecules
→ independent loci (genes):
gametes
Dan Dediu 661Linkage studies
Linkage
● Genome: discrete linear molecules
→ independent loci (genes):
Peas plant
Dan Dediu 671Linkage studies
Linkage
● Genome: discrete linear molecules
→ independent loci (genes):
Peas plant
Dan Dediu 681Linkage studies
Linkage
● Genome: discrete linear molecules
→ independent loci (genes)
→ non-independent loci → linkage + crossing over
recombinants
Dan Dediu 691Linkage studies
Linkage
● Genome: discrete linear molecules
→ independent loci (genes)
→ non-independent loci → linkage + crossing over
● recombination probability ~ physical distance
● 1 centiMorgan (cM) = 1% recombinants/generation
Dan Dediu 701Linkage studies
Linkage
● Genome: discrete linear molecules
→ independent loci (genes)
→ non-independent loci → linkage + crossing over
– LD map:
Gene structure
SNPs (landmarks)
Dan Dediu 711Linkage studies
Linkage
● Genome: discrete linear molecules
→ independent loci (genes)
→ non-independent loci → linkage + crossing over
– LD map:
Dan Dediu 721Linkage studies
Linkage
● Large pedigrees segregating the phenotype
The “KE” family
Dan Dediu 731Linkage studies
Linkage
● Large pedigrees segregating the phenotype
The “KE” familygenerations
Dan Dediu 741Linkage studies
Linkage
● Large pedigrees segregating the phenotype
The “KE” family
parents
children
Dan Dediu 751Linkage studies
Linkage
● Large pedigrees segregating the phenotype
The “KE” family
male female
Dan Dediu 761Linkage studies
Linkage
● Large pedigrees segregating the phenotype
The “KE” family
affected(disease) non-affected
(normal)
deceased
Dan Dediu 771Linkage studies
Linkage
● Large pedigrees segregating the phenotype
The “KE” family
twins
Dan Dediu 781Linkage studies
Linkage
● Large set (genome-wide) of landmarks
Dan Dediu 791Linkage studies
Linkage
● Large set (genome-wide) of landmarks
Dan Dediu 801Linkage studies
Linkage
● Large set (genome-wide) of landmarks
Loci
Dan Dediu 811Linkage studies
Linkage
● Large set (genome-wide) of landmarks
Alleles
.
.
.
Loci
.
.
Dan Dediu 821Linkage studies
Linkage
● Large set (genome-wide) of landmarks
Alleles
.
.
.
Loci
.
.
Dan Dediu 831Linkage studies
Linkage
● Large set (genome-wide) of landmarks
Alleles
.
.
.
Loci
.
.
Dan Dediu 841Linkage studies
Linkage
● Large set (genome-wide) of landmarks
Alleles
.
.
.
Loci
.
.
Dan Dediu 851Linkage studies
Linkage
● Large set (genome-wide) of landmarks
Alleles
.
.
.
Loci
.
.
Dan Dediu 861Linkage studies
Linkage
● Large set (genome-wide) of landmarks
Alleles
.
.
.
Loci
.
.
Dan Dediu 871Linkage studies
Linkage
● Large set (genome-wide) of landmarks
Alleles
.
.
.
Loci
.
.
Dan Dediu 881Linkage studies
Linkage
● Large set (genome-wide) of landmarks
Alleles
.
.
.
Loci
.
.
D7S513
2 8
3 8
χ2(1)=0.015,p=0.90
Dan Dediu 891Linkage studies
Linkage
● Large set (genome-wide) of landmarks
Alleles
.
.
.
Loci
.
.
D7S513
2 8
3 8
χ2(1)=0.015,p=0.90
D7S550
8 2
5 5
χ2(1)=0.88,p=0.35
Dan Dediu 901Linkage studies
Linkage
● Large set (genome-wide) of landmarks
Alleles
.
.
.
Loci
.
.
D7S513
2 8
3 8
χ2(1)=0.015,p=0.90
D7S550
8 2
5 5
χ2(1)=0.88,p=0.35
D7S527
0 10
10 0
χ2(1)=16.2,p<10-5
Dan Dediu 911Linkage studies
Linkage
● Large set (genome-wide) of landmarks
Alleles
.
.
.
Loci
.
.
D7S513
2 8
3 8
χ2(1)=0.015,p=0.90
D7S550
8 2
5 5
χ2(1)=0.88,p=0.35
D7S527
0 10
10 0
χ2(1)=16.2,p<10-5
D7S530
0 10
10 0
χ2(1)=16.2,p<10-5
Dan Dediu 921Linkage studies
Linkage
● Large set (genome-wide) of landmarks
Alleles
.
.
.
Loci
.
.
D7S513
2 8
3 8
χ2(1)=0.015,p=0.90
D7S550
8 2
5 5
χ2(1)=0.88,p=0.35
D7S527
0 10
10 0
χ2(1)=16.2,p<10-5
D7S530
0 10
10 0
χ2(1)=16.2,p<10-5
SPCH1
Dan Dediu 931Linkage studies
Linkage
● Large set (genome-wide) of landmarks
● In practice:
– LOD score (Logarithm of Odds) > 3
Dan Dediu 941Linkage studies
Linkage
● Large set (genome-wide) of landmarks
● In practice:
– LOD score (Logarithm of Odds) > 3
– Specialized software (MERLIN, GENEHUNTER)
Dan Dediu 951Linkage studies
Linkage
● Large set (genome-wide) of landmarks
● In practice:
– LOD score (Logarithm of Odds) > 3
– Specialized software (MERLIN, GENEHUNTER)
– Relatively low resolution
→ complementary techniques
Dan Dediu 961Linkage studies
Linkage
● Large set (genome-wide) of landmarks
● In practice:
– LOD score (Logarithm of Odds) > 3
– Specialized software (MERLIN, GENEHUNTER)
– Relatively low resolution
→ complementary techniques
Dan Dediu 971Linkage studies
Linkage
● Large set (genome-wide) of landmarks
● In practice:
– LOD score (Logarithm of Odds) > 3
– Specialized software (MERLIN, GENEHUNTER)
– Relatively low resolution
→ complementary techniques
here, a case (CS) unrelated to KE
– with same phenotype
– chromosomal abnormality affecting the SPCH1 interval on chromosome 7 → more precisely FOXP2
Dan Dediu 981Association studies
Association studies
● Large unrelated samples → variation in the phenotype of interest
Dan Dediu 991Association studies
Association studies
● Large unrelated samples → variation in the phenotype of interest
● Correlation between each marker and differences in phenotype
Dan Dediu 100
1Association studies
Association studies
Association with heightin 13,665 individuals
● Large unrelated samples → variation in the phenotype of interest
● Correlation between each marker and differences in phenotype
Dan Dediu 101
1Association studies
Association studies
Association with heightin 13,665 individuals
● Large unrelated samples → variation in the phenotype of interest
● Correlation between each marker and differences in phenotypeAssociation with BMIin 249,796 individuals
Dan Dediu 102
1Association studies
Association studies
● Large unrelated samples → variation in the phenotype of interest
● Correlation between each marker and differences in phenotype
● Issues:
– multiple testing correction (p < 5∙10-8)
Dan Dediu 103
1Association studies
Association studies
● Large unrelated samples → variation in the phenotype of interest
● Correlation between each marker and differences in phenotype
● Issues:
– multiple testing correction (p < 5∙10-8)
– replication
Dan Dediu 104
1Association studies
Association studies
● Large unrelated samples → variation in the phenotype of interest
● Correlation between each marker and differences in phenotype
● Issues:
– multiple testing correction (p < 5∙10-8)
– replication
– huge sample sizes (1,000+ → 100,000+) → tiny effect sizes
Dan Dediu 105
1Sequencing
Exome & genome sequencing
● Sequencing → full message instead of (dense) landmarks
Dan Dediu 106
1Sequencing
Exome & genome sequencing
● Sequencing → full message instead of (dense) landmarks
T C A G
Landmarks (SNPs)
(SNP) genotyping
Dan Dediu 107
1Sequencing
Exome & genome sequencing
● Sequencing → full message instead of (dense) landmarks
T C A G
Landmarks (SNPs)
? ? ? ? ?
(SNP) genotyping
Dan Dediu 108
1Sequencing
Exome & genome sequencing
● Sequencing → full message instead of (dense) landmarks
T C A G
Landmarks (SNPs)
? ? ? ? ?
(SNP) genotyping
Dan Dediu 109
1Sequencing
Exome & genome sequencing
● Sequencing → full message instead of (dense) landmarks
T C A G
Landmarks (SNPs)
? ? ? ? ?
(SNP) genotyping
T C A G
sequencing
CAGGATTACGATAA TTAGCGC AAATCGGCAT TAC
Dan Dediu 110
1Sequencing
Exome & genome sequencing
● Sequencing → full message instead of (dense) landmarks
T C A G
Landmarks (SNPs)
? ? ? ? ?
(SNP) genotyping
T C A G
sequencing
CAGGATTACGATAA TTAGCGC AAATCGGCAT TAC
Dan Dediu 111
1Sequencing
Exome & genome sequencing
● Sequencing → full message instead of (dense) landmarks
T C A G
sequencing
CAGGATTACGATAA TTAGCGC AAATCGGCAT TAC
Whole genome
Dan Dediu 112
1Sequencing
Exome & genome sequencing
● Sequencing → full message instead of (dense) landmarks
T C A G
sequencing
CAGGATTACGATAA TTAGCGC AAATCGGCAT TAC
Whole genome
Exome
Dan Dediu 113
1Sequencing
Exome & genome sequencing
● Sequencing → full message instead of (dense) landmarks
Whole genome
Exome
lots of data
Dan Dediu 114
1Sequencing
Exome & genome sequencing
● Sequencing → full message instead of (dense) landmarks
Whole genome
Exome
lots of data
Difficult to analyze:- heaps of genetic variation → what is relevant?
Dan Dediu 115
1Examples: hearing loss
Examples of genes
● Hearing loss
Dan Dediu 116
1Examples: hearing loss
Examples of genes
● Hearing loss → many types
Acquired
Congenital
Hearing lossHearing loss
Drugs Infections
Head injury Noise
Aging
Prenatalinfections
Genetics
Dan Dediu 117
1Examples: hearing loss
Examples of genes
Acquired
Congenital
Hearing lossHearing loss
Drugs Infections
Head injury Noise
Aging
Prenatalinfections
Genetics
● Hearing loss → many types → genetic component
Dan Dediu 118
1
Some examples: TECTA
Examples: hearing loss
Dan Dediu 119
1
Some examples: TECTA
Examples: hearing loss
Dan Dediu 120
1
Some examples: TECTA
● Tectorial membrane:
Examples: hearing loss
Dan Dediu 121
1
Some examples: TECTA
● Tectorial membrane:
→ collagen
Examples: hearing loss
Dan Dediu 122
1
Some examples: TECTA
● Tectorial membrane:
→ collagen
→ non-collagenous proteins
Examples: hearing loss
Dan Dediu 123
1
Some examples: TECTA
● Tectorial membrane:
→ collagen
→ non-collagenous proteins
→ α-tectorin
Examples: hearing loss
Dan Dediu 124
1
Some examples: TECTA
● Tectorial membrane:
→ collagen
→ non-collagenous proteins
→ α-tectorin
Examples: hearing loss
TECTA gene
Dan Dediu 125
1
Some examples: TECTA
● Tectorial membrane:
→ collagen
→ non-collagenous proteins
→ α-tectorin
Examples: hearing loss
TECTA gene (11q23.3)
Dan Dediu 126
1
Some examples: TECTA
● Tectorial membrane:
→ collagen
→ non-collagenous proteins
→ α-tectorin
Examples: hearing loss
TECTA gene (11q23.3)
mut
atio
ns
Dan Dediu 127
1
Some examples: TECTA
● Tectorial membrane:
→ collagen
→ non-collagenous proteins
→ α-tectorin
Examples: hearing loss
TECTA gene (11q23.3)
mut
atio
ns
Dan Dediu 128
1
Some examples: TECTA
● Tectorial membrane:
→ collagen
→ non-collagenous proteins
→ α-tectorin
Examples: hearing loss
TECTA gene
DFNA12 - dominant
Dan Dediu 129
1
Some examples: TECTA
● Tectorial membrane:
→ collagen
→ non-collagenous proteins
→ α-tectorin
Examples: hearing loss
TECTA gene
DFNA12 - dominant
Dan Dediu 130
1
Some examples: TECTA
● Tectorial membrane:
→ collagen
→ non-collagenous proteins
→ α-tectorin
Examples: hearing loss
TECTA gene
DFNA12 - dominant
Dan Dediu 131
1
Some examples: TECTA
● Tectorial membrane:
→ collagen
→ non-collagenous proteins
→ α-tectorin
Examples: hearing loss
TECTA gene
DFNA12 - dominant
DFNB21 - recessive
Dan Dediu 132
1
Some examples: TECTA
● Tectorial membrane:
→ collagen
→ non-collagenous proteins
→ α-tectorin
Examples: hearing loss
TECTA gene
DFNA12 - dominant
DFNB21 - recessive
Dan Dediu 133
1
Some examples: TECTA
● Tectorial membrane:
→ collagen
→ non-collagenous proteins
→ α-tectorin
Examples: hearing loss
TECTA gene
DFNA12 - dominant
DFNB21 - recessive
Dan Dediu 134
1
Some examples: TECTA
● Tectorial membrane:
→ collagen
→ non-collagenous proteins
→ α-tectorin
Examples: hearing loss
TECTA gene
DFNA12 - dominant
DFNB21 - recessive
Dan Dediu 135
1
Some examples: mitochondrial 12S rRNA
Examples: hearing loss
Dan Dediu 136
1
Some examples: mitochondrial 12S rRNA
Examples: hearing loss
Dan Dediu 137
1
Some examples: mitochondrial 12S rRNA
Examples: hearing loss
Dan Dediu 138
1
Some examples: mitochondrial 12S rRNA
Examples: hearing loss
Own genetic code
Dan Dediu 139
1
Some examples: mitochondrial 12S rRNA
Examples: hearing loss
Own genetic code
→ own translation machinery
Dan Dediu 140
1
Some examples: mitochondrial 12S rRNA
Examples: hearing loss
Own genetic code
→ own translation machinery
→ own ribosomes
Dan Dediu 141
1
Some examples: mitochondrial 12S rRNA
Examples: hearing loss
Own genetic code
→ own translation machinery
→ own ribosomes
Small subunit
ribosomal RNA
Dan Dediu 142
1
Some examples: mitochondrial 12S rRNA
Examples: hearing loss
Mitochondria → ancient bacteria
Dan Dediu 143
1
Some examples: mitochondrial 12S rRNA
Examples: hearing loss
Mitochondria → ancient bacteria (endosymbiotic theory)
Dan Dediu 144
1
Some examples: mitochondrial 12S rRNA
Examples: hearing loss
Mitochondria → ancient bacteria (endosymbiotic theory)
→ mitochondrial ribosomes are very similar to bacterial ribosomes
Dan Dediu 145
1
Some examples: mitochondrial 12S rRNA
Examples: hearing loss
Mitochondria → ancient bacteria (endosymbiotic theory)
→ mitochondrial ribosomes are very similar to bacterial ribosomes
even more similar
Mutations(e.g. A1555G)
Dan Dediu 146
1
Some examples: mitochondrial 12S rRNA
Examples: hearing loss
Mitochondria → ancient bacteria (endosymbiotic theory)
→ mitochondrial ribosomes are very similar to bacterial ribosomes
even more similar
Mutations(e.g. A1555G)
modifiers
Dan Dediu 147
1
Some examples: mitochondrial 12S rRNA
Examples: hearing loss
Mitochondria → ancient bacteria (endosymbiotic theory)
→ mitochondrial ribosomes are very similar to bacterial ribosomes
even more similar
Mutations(e.g. A1555G)
modifiers
Dan Dediu 148
1
Some examples: mitochondrial 12S rRNA
Examples: hearing loss
Hair cells are more affected by mitochondrial ribosomal impaired function more than other cells?
even more similar
Mutations(e.g. A1555G)
modifiers
Dan Dediu 149
1
Some examples: mitochondrial 12S rRNA
Examples: hearing loss
Hair cells are more affected by mitochondrial ribosomal impaired function more than other cells? → specific phenotype
even more similar
Mutations(e.g. A1555G)
modifiers
Dan Dediu 150
1
Congenital non-syndromic deafness
● Many loci (e.g., http://hereditaryhearingloss.org/main.aspx?c=.HHH&n=86163)
Examples: hearing loss
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Congenital non-syndromic deafness
● Many loci (e.g., http://hereditaryhearingloss.org/main.aspx?c=.HHH&n=86163)
● Autosomal dominant: DFNAnn (~25)
Examples: hearing loss
0% 50% 75%
100% 100% 100%
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Congenital non-syndromic deafness
● Many loci (e.g., http://hereditaryhearingloss.org/main.aspx?c=.HHH&n=86163)
● Autosomal dominant: DFNAnn, recessive: DFNBnn (~40)
Examples: hearing loss
0% 0% 25%
0% 50% 100%
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Congenital non-syndromic deafness
● Many loci (e.g., http://hereditaryhearingloss.org/main.aspx?c=.HHH&n=86163)
● Autosomal recessive: DFNB1 and DFNB3
Examples: hearing loss
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DFNB1A/B (GJB2 and GJB6)
● Autosomal recessive
● 13q12.11
Examples: hearing loss
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DFNB1A/B (GJB2 and GJB6)
● Autosomal recessive
● 13q12.11
● GJB2 (Gap junction beta-2; Connexin 26) and GJB6 (Gap junction beta-6; Connexin 30)
Examples: hearing loss
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DFNB1A/B (GJB2 and GJB6)
● Autosomal recessive
● 13q12.11
● GJB2 (Gap junction beta-2; Connexin 26) and GJB6 (Gap junction beta-6; Connexin 30)
Examples: hearing loss
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DFNB1A/B (GJB2 and GJB6)
● Autosomal recessive
● 13q12.11
● GJB2 (Gap junction beta-2; Connexin 26) and GJB6 (Gap junction beta-6; Connexin 30)
● GJB2: ~90 mutations → non-syndromic deafness
– other mutations: syndromes (skin + deafness)
Examples: hearing loss
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DFNB1A/B (GJB2 and GJB6)
● Autosomal recessive
● 13q12.11
● GJB2 (Gap junction beta-2; Connexin 26) and GJB6 (Gap junction beta-6; Connexin 30)
● GJB2: ~90 mutations → non-syndromic deafness
– other mutations: syndromes (skin + deafness) ● GJB6: some mutations → non-syndromic deafness
– other mutations: skin disorders
Examples: hearing loss
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DFNB1A/B (GJB2 and GJB6)
● Autosomal recessive
● 13q12.11
● GJB2 (Gap junction beta-2; Connexin 26) and GJB6 (Gap junction beta-6; Connexin 30)
● GJB2: ~90 mutations → non-syndromic deafness
– other mutations: syndromes (skin + deafness) ● GJB6: some mutations → non-syndromic deafness
– other mutations: skin disorders
→ potassium levels in the inner ear?
Examples: hearing loss
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DFNB3 (MYO15A)
● Autosomal recessive
● 17p11.2
Examples: hearing loss
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DFNB3 (MYO15A)
● Autosomal recessive
● 17p11.2
● MYO15A (unconventional myosin-15; myosin XVa)
Examples: hearing loss
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DFNB3 (MYO15A)
● Autosomal recessive
● 17p11.2
● MYO15A (unconventional myosin-15; myosin XVa)
Examples: hearing loss
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DFNB3 (MYO15A)
● Autosomal recessive
● 17p11.2
● MYO15A (unconventional myosin-15; myosin XVa)
→ sterocilia
Examples: hearing loss
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DFNB3 (MYO15A)
● Autosomal recessive
● 17p11.2
● MYO15A (unconventional myosin-15; myosin XVa)
→ sterocilia
Examples: hearing loss
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DFNB3 (MYO15A)
● Autosomal recessive
● 17p11.2
● MYO15A (unconventional myosin-15; myosin XVa)
→ sterocilia
Examples: hearing loss
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DFNB3 (MYO15A)
● Autosomal recessive
● 17p11.2
● MYO15A (unconventional myosin-15; myosin XVa)
→ sterocilia
Examples: hearing loss
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DFNB3 (MYO15A)
● Autosomal recessive
● 17p11.2
● MYO15A (unconventional myosin-15; myosin XVa)
→ sterocilia
Examples: hearing loss
transport
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DFNB3 (MYO15A)
● Autosomal recessive
● 17p11.2
● MYO15A (unconventional myosin-15; myosin XVa)
→ sterocilia
Examples: hearing loss
EPS8
transport
interaction
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Some examples: stuttering
Examples: stuttering
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Some examples: stuttering
Examples: stuttering
Digestive enzymes
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Some examples: stuttering
Examples: stuttering
Digestive enzymes
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Some examples: stuttering
Examples: stuttering
GNPT → 3 subunits (α, β, and γ)
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Some examples: stuttering
Examples: stuttering
GNPT → 3 subunits (α, β, and γ)
GNPTAB (chr 12)
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Some examples: stuttering
Examples: stuttering
GNPT → 3 subunits (α, β, and γ)
GNPTAB (chr 12)
GNPTG (chr 16)
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Some examples: stuttering
Examples: stuttering
GNPT → 3 subunits (α, β, and γ)
GNPTAB (chr 12)
GNPTG (chr 16)
Genome-wide linkage in many Pakistani families → mutation in GNPTAB
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Some examples: stuttering
Examples: stuttering
GNPT → 3 subunits (α, β, and γ)
GNPTAB (chr 12)
GNPTG (chr 16)
Genome-wide linkage in many Pakistani families → mutation in GNPTAB
+ identification of mutations in GNPTG and NAGPA
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Some examples: dyslexia
Examples: dyslexia
● Linkage in a Finnish family with severe dyslexia
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Some examples: dyslexia
Examples: dyslexia
● Linkage in a Finnish family with severe dyslexia +
● Non-related patient
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Some examples: dyslexia
Examples: dyslexia
● Linkage in a Finnish family with severe dyslexia +
● Non-related patient
→ ROBO1 gene on chromosome 3
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Some examples: dyslexia
Examples: dyslexia
● Linkage in a Finnish family with severe dyslexia +
● Non-related patient
→ ROBO1 gene on chromosome 3
● Association with NWR in normal population
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Some examples: dyslexia
Examples: dyslexia
● Linkage in a Finnish family with severe dyslexia +
● Non-related patient
→ ROBO1 gene on chromosome 3
● Association with NWR in normal population
ROBO1: axonal midline crossing
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Conclusions
Conclusions
● Just at the beginning
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Conclusions
Conclusions
● Just at the beginning
● Already some genes known
→ complex and fascinating mechanisms
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Conclusions
Conclusions
● Just at the beginning
● Already some genes known
→ complex and fascinating mechanisms
● “molecular windows” into the genetic architecture
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Conclusions
Conclusions
● Just at the beginning
● Already some genes known
→ complex and fascinating mechanisms
● “molecular windows” into the genetic architecture
● Varied methodology, multiple complementary approaches
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Conclusions
Conclusions
● Just at the beginning
● Already some genes known
→ complex and fascinating mechanisms
● “molecular windows” into the genetic architecture
● Varied methodology, multiple complementary approaches
● More work:
– (endo-)phenotyping (language scholars!)
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Conclusions
Conclusions
● Just at the beginning
● Already some genes known
→ complex and fascinating mechanisms
● “molecular windows” into the genetic architecture
● Varied methodology, multiple complementary approaches
● More work:
– (endo-)phenotyping (language scholars!)
– large samples (association)
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Conclusions
Conclusions
● Just at the beginning
● Already some genes known
→ complex and fascinating mechanisms
● “molecular windows” into the genetic architecture
● Varied methodology, multiple complementary approaches
● More work:
– (endo-)phenotyping (language scholars!)
– large samples (association)
– pedigrees (linkage)
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Conclusions
Conclusions
● Just at the beginning
● Already some genes known
→ complex and fascinating mechanisms
● “molecular windows” into the genetic architecture
● Varied methodology, multiple complementary approaches
● More work:
– (endo-)phenotyping (language scholars!)
– large samples (association)
– pedigrees (linkage)Thanks to: Alejandrina Cristia, Sarah Graham, Steve Levinson
Funding: Netherlands Organisation for Scientific Research ( ) Vidi grant 276-70-022
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Suggested reading
Suggested readings
● General books:
Jones, Steve (2000). The language of the genes. London: Flamingo.
Ridley, M. (2004). Nature via nurture: genes, experience and what makes us human. London: Harper Perennial.
● Introductions to genetics:Snustad, D. P., & Simmons, M. J. (2010). Principles of genetics. John Wiley & Sons, Inc.
Krebs, J. E., Kilpatrick, S. T., & Goldstein, E. S. (2013). Lewin’s Genes XI. Jones and Bartlett Publishers, Inc.
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Suggested reading
Suggested readings
● Heritability:Visscher, P. M., Hill, W. G., & Wray, N. R. (2008). Heritability in the genomics era–
concepts and misconceptions. Nat Rev Genet 9:255–266. doi:10.1038/nrg2322
Charney, E. (2012). Behavior genetics and postgenomics. Behavioral and Brain Sciences 35:331–358. doi:10.1017/S0140525X11002226
Stromswold, K. (2001). The Heritability of Language: A Review and Metaanalysis of Twin, Adoption, and Linkage Studies. Language 77:647–723.
● Linkage:Dawn Teare, M., & Barrett, J. H. (2005). Genetic linkage studies. The Lancet 366:1036–
1044. doi:10.1016/S0140-6736(05)67382-5
● Association:Hirschhorn, J. N., & Daly, M. J. (2005). Genome-wide association studies for common
diseases and complex traits. Nature Reviews Genetics 6:95–108. doi:10.1038/nrg1521
Balding, D. J. (2006). A tutorial on statistical methods for population association studies. Nature Reviews Genetics 7:781–791. doi:10.1038/nrg1916
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Suggested reading
Suggested readings
● Sequencing:Deriziotis, P., & Fisher, S. E. (2013). Neurogenomics of speech and language disorders:
the road ahead. Genome biology 14:204. doi:10.1186/gb-2013-14-4-204
O’Roak, B. J., Deriziotis, P., Lee, C., Vives, L., Schwartz, J. J., Girirajan, S., … Eichler, E. E. (2011). Exome sequencing in sporadic autism spectrum disorders identifies severe de novo mutations. Nature genetics 43:585–589. doi:10.1038/ng.835
● Genetics of language:Graham, S. A., & Fisher, S. E. (2013). Decoding the genetics of speech and language.
Current Opinion in Neurobiology 23:43-51. doi:10.1016/j.conb.2012.11.006
Kang, C., & Drayna, D. (2011). Genetics of speech and language disorders. Ann rev genomics hum genet 12:145–164. doi:10.1146/annurev-genom-090810-183119
Smith, S. D., Grigorenko, E., Willcutt, E., Pennington, B. F., Olson, R. K., & DeFries, J. C. (2010). Etiologies and molecular mechanisms of communication disorders. Journal of developmental and behavioral pediatrics 31, 555–563. doi:10.1097/DBP.0b013e3181ee3d9e
Bishop, D. V. M. (2009). Genes, cognition, and communication: insights from neurodevelopmental disorders. Ann N Y Acad Sci 1156:1–18. doi:10.1111/j.1749-6632.2009.04419.x
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