1. Genetic Approaches to Thinking, Moving and Feeling
Richard B. Lipton, M.D
Professor and Vice Chair, Neurology
Professor of Epidemiology and Population Health
Albert Einstein College of Medicine

2. Why consider genetic approaches?
Genetic factors play a crucial role in disease that affect thinking, feeling and moving
Genetic factors are relevant to potential shared mechanisms: vascular disease, inflammation, recovery from injury
Method for parsing the heterogeneity
Environmental risk factors interact
Identify molecular targets and treatments with pleiotropic effects

3. The best of times Human Genome Project
-Identify the 30,000 genes in the human genome
-Sequenced 3 billion base pairs(2003)
-<2% of the genome codes for protein
-3 million loci with single base pair differences
HapMap Project
-Systematic genotyping of 3 million SNPs
Available of high throughput technologies

4. The genome, the transcriptome and the proteome
The genome is static
The transcriptome is dynamic and tissue and cell specific. SNPs in a coding region may alter protein structure; SNPs in a promoter region may alter mRNA levels. Non-coding RNA can activate arrays of related genes.
The proteome reflects post-transcriptional processes including RNA spicing, post-translational modification

5. Mendelian vs. Complex Diseases and Traits
1. Mendelian (single gene)
a. Autosomal dominant (Huntington’s disease)
b. Autosomal recessive (Cystic fibrosis)
c. X-linked
2. Complex diseases and traits are multifactorial
a. Oligogenic or polygenic susceptibility genes
(No one gene is necessary or sufficient)
b. Locus heterogeneity (different genes
can cause same trait)
c. Pleiotropic effects
d. Environmental factors contribute

6. Causes of Complex Cognitive, Emotional
and Motor Traits
Genes +
Environment
Genes
Environment

7. Defining the phenotypes
Define the phenotype of interest
-Diseases-heterogeneity is the rule
-Single domain phenotypes: declarative memory, depression, walking speed
-Multiple domain phenotypes: executive function and walking speed
-Covariance shared by traits
-Use biological markers or endophenotypes
Once genes are identified contrast carriers and non-carriers to refine the phenotypes
Challenge: pleiotropy and polygenicity

8. Can a single gene influence thinking, feeling and moving
Yes for Huntington’s, TDP-43
Relevant genes might influence transmitters, ion channels, recovery from insults, plasticity, dendritic complexity, etc.
A gene may influence an area of the brain that has distal secondary affects
A gene may influence several areas of the brain each of which influences a distinct cognitive, emotional or motor process
A gene may influence several brain regions or processes each of which affects more than one domain (Kovas and Plomin, Trends in Cog Sci, 2006)

9. Approach to Complex Diseases
Assess the influence of genetic factors (heritability)
-Family studies
-Twin studies
Identify specific genetic factors
-Linkage analysis
-Allele sharing methods
-Case-control studies

10. Determining the Genetic Component
Familial aggregation
-Measure family relative risk.
-Suggests but does not prove genetic mechanism
-Look for aggregation of one domain (thinking) in relatives
of probands selected for another domain (moving)
Twin studies
-If concordance rate for MZ twins > DZ twins
some genetic influence
-Look for aggregation of one domain in MZ and DZ twins
with issues in another domain
Heritability: the proportion of the variance in a disease
or trait accounted for by genetic factors

11. Higher Concordance Rates for MZ vs. DZ
Twins Shows a Significant Genetic Component
Concordance
Trait MZ DZ
Schizophrenia
Insulin-dependent diabetes mellitus
PD < age 50*
PD ≥ age 50*
Survival** to 90 (females)
*Tanner, 1999
** Hjelmborg et al, 2006

12. Identification of Genetic Factors
in Complex Diseases
Linkage analysis: Identify extended families
where disease or an endophenotype appears
Mendelian (usually early-onset)
Allele sharing methods, genome wide screening in
affected sibling pairs (Identity by descent)
Association studies in human populations

13. Identification of Genetic Factors
in Complex Diseases: Linkage Analysis
Identify large families where the trait appears Mendelian
(Are there high density families with relevant phenotypes?)
Look for genes or DNA segments which segregate with the disease or trait.
Genes and DNA segments close to each other on a chromosome tend to be inherited together.
Look for coinheritance of polymorphic markers and disease.

14. Common Useful Genetic Markers
Simple Sequence Repeats
Tens of thousands in genome
Typically di-, tri- or tetra- nucleotide repeats
(GT)n
unique flanking DNA sequence
Single Nucleotide Polymorphisms (SNPs)
Millions distributed throughout genome
Single base pair substitutions A T
unique flanking DNA sequence

15. Association Studies
Comparison of allele frequencies between unrelated affected and unaffected individuals.
Case – Control
Unaffected
controls
Affected cases
Disease-marker association exists when alleles at the marker locus occur with different relative frequencies in affected and unaffected individuals.
Most important: Use unaffected individuals from the same population!

16. Association Study Approaches
Genome-wide scan
Dense set of markers throughout genome:
Candidate gene search
functional variants (if possible) in gene with biological relevance:
Single marker association
Define common haplotypes
Assess haplotypes for association

17. Gene Identification in Complex Traits using Candidate Gene Approaches
Select candidate genes based on biology and the availability of functional SNPs or SNP haplotypes
Select candidates for thinking, feeling and moving from the genes
-Expressed in brain
-Related to a specific domain
-Identified based on biology (brain recovery-ApOE, inflammation-CRP, vascular risk genes-lipid related, intracellular signal transduction genes, longevity, energy metabolism)
Options limited by current hypotheses

18. What is the Significance of a Population Association Between a Disease and a Particular Allele (Genetic Variant)?
Allele is directly involved in the pathogenesis of the disease
The result is a false positive due to statistical error
The result is a false positive due to inadequate matching of cases and controls (population stratification)
4) Linkage disequilibrium

19. Whole Genome Association (WGA):
In WGA, high density chip arrays containing hundreds of thousands of SNPs are used to screen the entire genome on a single array.
Using cases and controls, WGA association results in the generation of thousands of genotypes. Identify SNPs and SNP haplotypes associated with disease.
Then need to determine the disease causing SNPs among these, either coding sequence changes or possible promoter/enhancer etc variations.

20. Affymetrix Genotyping Technology
250 ng Genomic DNA
Nsp
RE Digestion
Adaptor
Ligation
PCR: One Primer Amplification
Complexity
Reduction
250,000 Genotypes
Fragmentation
and Labeling
AA BB AB
Hyb & Wash

21. Genetic approaches to thinking, feeling and moving: Centenarian studies
Only ~1/10,000 individuals is 100 years old
Exceptional longevity occurs with greater frequency in the siblings and offspring of Centenarians
Longevity genes may contribute to successful cognitive, motor and emotional aging
LonGenity PPG PI: Nir Barzilai focus on Ashkenazi Jews as a founder population

22. 104
90 years before 98 92 95
Barzilai et al, PLoS Biology 2006

23. A Major Barrier to Genetic Studies in Centenarians
What is the appropriate control group?

24. A Major Barrier to Genetic Studies in Centenarians
What is the appropriate control group?
Age mates of centenarians?

25. A Major Barrier to Genetic Studies in Centenarians
What is the appropriate control group?
Age mates of centenarians?
Alternative
Study centenarians their offspring and ages mates of their offspring
Hypothesis: Longevity gene frequency
Centenarian > Offspring > Controls

26. Offspring of Centenarians are Less Likely to Have Age-Related Diseases40
Cntnrn P 35 **
Offspring O C
Control 30 25
Prevalence in population 20 15
** p<0.01 10 ** ** 5 **
HTN (%)
DM (%)
MI (%)
Stroke (%)
JAGS 2004; 52:274

27. Modeling Changes in the Frequency of a Genotype as a Function of Age
Aging or “killing” genes
0.7
Longevity genes
0.6
Genes not contributing to life-span
0.5
Genotypic Frequency
0.4
0.3
0.2
0.1 60 65 70 75 80 85 90 95
100
Age

28. Favorable genotype in population (%)
Favorable Longevity-Associated Genotypes in Unrelated Year-Old Ashkenazi Individuals 35
ADIPOQ del/del 30
APOC3 CC 25
CETP VV
Favorable genotype in population (%) 20 15 10 5 65 75 85 95
105
Age (Year)

29. Of the three longevity genes identified to date
All are associated with large lipoprotein particle size
The favorable form of CETP is associated with high HDL levels and large lipoprotein particle sizes and with successful cognitive aging
The favorable form of adiponectin is associated with succesful motor aging

30. “Longevity Genotypes” are associated with HDL and LDL particle size* * 10 20 30 40 50 60 70 80 *
Large LDL (%) -- VV
I/V CC
C/A
A/-
*p<0.05
CETP
APOC3
ADIPOQ
(ug/mL)
(mg/dL)
(ug/mL)

31. CETP VV Genotype and Cognitive Function Barzilai et al, Neurology 2007
CETP VV frequency (%) 20 30 40 10
MMSE<25
MMSE ≥ 25 *
Centenarians
*p<0.049 10 15 20 5
Dementia
(n=31)
Non-demented
(n=129) *
EAS
Barzilai et al, Neurology 2007

32. Is Size of Lipoproteins Associated with Cognitive Function?* *
*P<0.003

33. ADIPOQ: A Longevity Genotype with a Successful Motor Aging Phenotype
The ADIPOQ del/del genotype is associated with longevity and a reduced risk of insulin resistance.
The less favorable forms of ADIPOQ genotype has links with insulin resistance and metabolic syndrome, pathways which may influence motor function.
Verghese et al. therefore examined the relationship between ADIPOQ del/del and gait performance in 322 subjects (mean age 78, 27% AJ, 63% women) who received quantitative gait measures.

34. Relationship between genotype and measures of gait
In linear regression analysis, adjusted for age and sex, the favorable form of the ADIPOQ gene (del/del) was associated with better performance on stance, swing, and double support phases.
These variables generally reflect balance and rhythm (Verghese et al. JNNP,2007).

35. Stride length variability, SD 4.3 ± 2.3 4.7 ± 3.3
Gait Variables
Del/Del
(n = 72)
Ins/Ins
(n = 80)
Velocity, cm/sec
92.2 ± 25.5
93.3 ± 24.3
Cadence, steps/ min
101.7 ± 12.4
102.0 ± 12.1
Stride length, cm
108.4 ± 19.5
108.5 ± 21.2
Stance, %
62.6 ± 2.9*
63.8 ± 3.8
Swing, %
37.4 ± 2.9*
36.2 ± 3.8
Double support time, %
25.0 ± 5.2*
27.6 ± 8.8
Stride length variability, SD
4.3 ± 2.3
4.7 ± 3.3
*p<0.05 (linear regression adjusted for age & sex)
322 subjects (27% AJ, 63% women)
Mean age 77.8y
Adiponectin del/del genotype is associated with better balance and
rhythm on gait

36. Summary
Define the phenotypes of interest with care considering the spatiotemporal expression
Consider family aggregation and twin studies to look at distribution within families of domains of interest.
Consider searching for genes that account for the covariance among traits

37. Summary Bank DNA (and other tissue?)
Consider candidate gene and WGAS in cross sectional and longitudinal studies (LonGenity focuses on AJs, EAS on the Bronx population)
Begin studies in midlife or early adult life to reduce influence of phenocopies
Use genes to refine phenotypes

38. Many thanks

39. Genetic Research Methods: Advantages and Disadvantages
Study the
Advantages
Identify single genes with large effects
Survey the whole genome
Disadvantages
Must be family-based
Limited power for complex disorders
Low resolution
Linkage
More power for complex diseases
Large samples for genes with small effect
Association
Customize choices
Find selected associations
Candidate genes ( SNPs)
Survey the genome
Expensive
Bioinformatic challenge
False positives
Whole genome ( SNPs)

40. Pair Wise Concordance in Survival to Age 90+ Among Swedish, Danish and Finnish TwinsMZ
Concordant Total
Pairs Pairs Concordance
Male %
Female %
Total % DZ
Concordant Total
Pairs Pairs Concordance
Male %
Female %
Total %
Concordance = C/C+D
Hjelmborg et al, Human Genetics, 2006

41. Can Plasma HDL Levels Predict Longevity?
Females 80
Spouse
Offspring
Proband * 70 60
HDL (mg/dl)
n=147
n=157 50 40
n=122
*p< vs. Others

42. These results support an association between specific genes and motor function.

44. Lipoprotein particle size as function of age
Heritability (h) of lipoprotein particle size
9.8
20.6
20.8
21.0
21.2
21.4
21.6
9.6
9.4
HDL Particle Size (nm)
LDL Particle Size (nm)
Control
9.2
Offspring
Probands
9.0
8.8
8.6
20.4 60 65 70 75 80 85 90 95
100< 60 65 70 75 80 85 90 95
100<
Age
Age
Barzilai et al JAMA 290:2030, 2003

45. Are lipoprotein sizes associated with protection from age-related diseases? (in offspring of centenarians) *
*P<0.003
Barzilai et al JAMA 290:2030, 2003

46. Lipoprotein and their size in healthy or subjects with the Metabolic Syndrome (MS)* *
*P<0.001
Barzilai et al JAMA 290:2030, 2003

47. Average Mini-Mental Score of Tertile HDL Groups*
(HDL 75±2 mg/dl) *
(HDL 51±2 mg/dl) *
(HDL 37±2 mg/dl) *
*p<0.04
J. Gerontol 57A, M712, 2002

48. Are “Longevity Genotypes” Associated with Clinically-Significant Phenotypes?2 4 6 8 10 12 14 16
(ug/mL) --
A/- *
*p<0.05
APOC3
(mg/dL) CC
C/A *
Levels * VV
I/V
CETP
ADIPOQ
(ug/mL)

49. Cross Sectional HDL Levels (Data from the Framingham study)
HDL (mg/dl) 50 50
Age

50. Genetic Research Methods: Advantages and Disadvantages
Study the
Advantages
Identify single genes with large effects
Survey the whole genome
Disadvantages
Must be family-based
Limited power for complex disorders
Low resolution
Linkage
More power for complex diseases
Large samples for genes with small effect
Association

51. Genetic Research Methods: Advantages and Disadvantages
Study the
Advantages
Identify single genes with large effects
Survey the whole genome
Disadvantages
Must be family-based
Limited power for complex disorders
Low resolution
Linkage

52. Levels of analysis
Genetics- study of single genes and their effects. Focus on the disease.
Genomics-study of the functions and interactions many genes, their transcription and regulation
Proteomics-Study of proteins
Metabolomics-Study of small molecules (physiologic indicators) in plasma, tissues or cells including peptides, lipids, carbs, drugs