1. A Connected Digital Biomedical Research Enterprise with Big Data
Belinda Seto, Ph.D.
Deputy Director
National Eye Institute

2. What is it?
Digital research assets: data, workflow, publications, software
To connect these assets
Unique identifiers or tags
Annotation
Community-developed standards
Interfaces

3. Benefits Increase scientific productivity Enhance collaborations
Foster creativity: new tools, algorithms, methods, modeling
Enable new discoveries
Improve interoperability
Facilitate reproducibility

4. Gene Expression Data Volume Velocity Variety
Distribution of the number and types of selected studies released by GEO each year since inception. Users can explore and download historical submission numbers using the ‘history’ page at as well as constructing GEO DataSet database queries for specific data types and date ranges using the ‘DataSet type’ and ‘publication date’ fields as described at
Published by Oxford University Press 2012.
Barrett T et al. Nucl. Acids Res. 2013;41:D991-D995

5. Gene Expression Omnibus
A public repository (NLM) of microarray, next generation sequencing and functional genomic data
Web-based interface and apps for query and data download

6. Myriad Data Types Genomic Other ‘Omic Imaging Phenotypic Exposure
Within biomedical research, many data types
Victims of our own success
Data production outstrips data handling and analysis
Major long-term changes are needed
Exposure
Clinical

7. Making Big Data Functional
Engender interdisciplinary approach to data collection and analysis by integrating scientific, algorithmic, and computational work
Drive functional data collection and analysis that has practical value in determining risk alleles

8. Integration of Data
Opportunities: Understanding biology across scales, from molecules to population
Challenges: need access to primary data and processed data, machine-readable metadata, tools to reduce dimensionality

9. Integration of Disparate Data Types: Brain Images with Genomic

10. Brain measures versus epidemiological studies to find genetic variants that directly affect the brain
difficult
May require 10,000-30,000 people
e.g., the Psychiatric Genetics Consortium studies
Gene variants (SNP’s) may affect brain measures directly, many brain measures relate to disease status.
easier?

11. Finding Genetic Variants Influencing Brain Structure
CTAGTCAGCGCT
CTAGTCAGCGCT
Intracranial Volume …
CTAGTAAGCGCT
The way you do this is relatively simple, obtain a large group of subjects and for each person you measure a phenotype.
You then obtain DNA, find a specific location in the genome and determine the letter or genotype of each person. You then attempt to see if there is a relationship between the genotype and the phenotype. In this toy example, you can see there’s a clear additive effect of the A allele on intracranial volume. The more A alleles you have, the bigger your intracranial volume.
CTAGTAAGCGCT
CTAGTCAGCGCT
C/C
A/C
A/A
SNP
Phenotype
Genotype
Association

12. Genome-Wide Association Studies (GWAS)
Identify loci for phenotypes or diseases using genotyping arrays throughout entire genome
Study association of polymorphisms with complex human traits
Meta-analysis across multiple studies

13. Genome-wide Association Study
One SNP
“Candidate gene” approach
e.g., BDNF
Screening
500,000 SNPs – 2,000,000 SNPs
-log10(P-value)
Intracranial Volume
Position along genome
The 3 billion base pair genome though has millions of variants. Genome-wide association provides a means for studying a large portion of the common variation. Genome-wide association looks at millions of SNPs at a time, testing each individually for their association to a phenotype of interest. It is importantly an unbiased search where you do not put in your hopes and desires about the genome, the genome guides you to the area of association.
When you conduct a genome-wide association study
An unbiased searchWhere in the genome is a variant associated with a trait. Need P-values < 5x10^-8 to achieve genome wide signifiance. Change picture here
NIH-funded database of genotypes and phenotypes enabling searches to find where in the genome a variant is associated with a trait.
C/C
A/C
A/A

14. Applications of GWAS
Identify genetic variants that affect brain measures: volumetric, fiber integrity, connectivity
Risk genes
Early biomarkers of disease

15. What is a risk gene? - A common genetic variant related to a brain measure, or a disease, or a trait such as obesity, found by searching the genome
99.9% of DNA is the same for all people - DNA variation causes
changes in predisposition to disease, and brain structure.
One type of variation is a single nucleotide polymorphism (SNP)- Single letter change in the DNA code
23 pairs of chromosomes
In a particular part of the chromosome 5 there are many genes
Within a gene there are exons, introns, and SNPs
Single Nucleotide Polymorphism (SNP)

16. GRIN2B Risk Allele Glutamate receptor, signaling pathway
Genetic polymorphism of GRIN2B gene
Associated with reductions of brain white matter integrity
Bipolar disorder
Obsessive compulsive disorder

17. GRIN2b genetic variant is associated with
2.8% temporal lobe volume deficit
GRIN2b is over-represented in AD
- could be considered an Alzheimer’s disease risk gene
- needs replication
Jason L. Stein1, Xue Hua PhD1, Jonathan H. Morra PhD1, Suh Lee1, April J. Ho1, Alex D. Leow MD PhD1,2, Arthur W. Toga PhD1, Jae Hoon Sul3, Hyun Min Kang4, Eleazar Eskin PhD3,5, Andrew J. Saykin PsyD6, Li Shen PhD6, Tatiana Foroud PhD7, Nathan Pankratz7, Matthew J. Huentelman PhD8, David W. Craig PhD8, Jill D. Gerber8, April Allen8, Jason J. Corneveaux8, Dietrich A. Stephan8, Jennifer Webster8, Bryan M. DeChairo PhD9, Steven G. Potkin MD10, Clifford R. Jack Jr MD11, Michael W. Weiner MD12,13, Paul M. Thompson PhD1,*, and the ADNI (2010). Genome-Wide Analysis Reveals Novel Genes Influencing Temporal Lobe Structure with Relevance to Neurodegeneration in Alzheimer's Disease, NeuroImage 2010.

18. GRIN2b genetic variant associates with brain volume
in these regions; 2.8% more temporal lobe atrophy
Jason L. Stein1, Xue Hua PhD1, Jonathan H. Morra PhD1, Suh Lee1, April J. Ho1, Alex D. Leow MD PhD1,2, Arthur W. Toga PhD1, Jae Hoon Sul3, Hyun Min Kang4, Eleazar Eskin PhD3,5, Andrew J. Saykin PsyD6, Li Shen PhD6, Tatiana Foroud PhD7, Nathan Pankratz7, Matthew J. Huentelman PhD8, David W. Craig PhD8, Jill D. Gerber8, April Allen8, Jason J. Corneveaux8, Dietrich A. Stephan8, Jennifer Webster8, Bryan M. DeChairo PhD9, Steven G. Potkin MD10, Clifford R. Jack Jr MD11, Michael W. Weiner MD12,13, Paul M. Thompson PhD1,*, and the ADNI (2010). Genome-Wide Analysis Reveals Novel Genes Influencing Temporal Lobe Structure with Relevance to Neurodegeneration in Alzheimer's Disease, NeuroImage, 2010.

19. Alzheimer’s risk gene carriers (CLU-C) have lower fiber integrity even when young (N=398), 50 years before disease typically hits
Effects occurred in multiple regions, including several known to degenerate in AD. Such regions included the corpus callosum, fornix, cingulum, SLF and ILF (Liu et al., 2009; Stricker et al., 2009). This suggests that the CLU-C related variability found here might create a local vulnerability important for disease onset.
Voxels where CLU allele C (at rs ) is associated with lower FA after adjusting
for age, sex, and kinship in 398 young adults
(68 T/T; 220 C/T; 110 C/C). FDR critical p = Left hem. on Right
Braskie et al., Journal of Neuroscience, May

20. Effect is even stronger for carriers of a
schizophrenia risk gene variant, trkA-T (N=391 people)
NTRK1 is a high affinity receptor for the neurotrophin NGF. We found that healthy T-allele carriers at rs6336 in the NTRK1 gene had lower FA broadly throughout the brain. In a recent meta-analysis, this allele was associated with a 1.64 times greater probability of developing schizophrenia in Caucasians versus the C allele . We found significant effects of NTRK1-T regions including the ILF, IFO, cingulum, and genu of the corpus callosum, which most consistently showed lower FA in schizophrenia patients versus controls in a recent meta-analysis (Ellison-Wright I, Bullmore E. Meta-analysis of diffusion tensor imaging studies in schizophrenia. Schizophr Res. 2009;108(1-3):3-10).
a. p values indicate where NTRK1 allele T carriers (at rs6336) have lower FA after adjusting for age, sex, and kinship in 391 young adults (31 T+; 360 T-).
FDR critical p =
b. Voxels that replicate in 2 independent halves of the sample (FDR-corrected). Left is on Right.
Braskie et al., Journal of Neuroscience, May 2012

21. Neural Fiber Integrity Fractional Anisotropy
Applied to diffusion tensor MRI
Eigen = 0 means diffusion is totally unrestricted
Eigen = 1 means diffusion is restricted to only one direction
FA measures fiber density, axonal diameter, or myelination of white matter

22. SNP’s can predict variance in brain integrity
Neuro-chemical genes
COMT
NTRK1
ErbB4
BDNF
Neuro-developmental genes
HFE
CLU
Neuro-degenerative risk genes
A significant fraction of variability in white matter structure of the corpus callosum (measured with DTI) is predictable from SNPs;
Kohannim O, et al. Predicting white matter integrity from multiple common genetic variants. Neuropsychopharmacology 2012, in press.

23. Big Data 26,000 whole brain MR images
> 500,000 single nucleotide polymorphism (SNP)
Analyze each voxel of the entire brain and search for genetic variants of the whole genome at each brain voxel
Select only the most associated SNP at each voxel, by analyzing P-values through an inverse beta transformation

24. Genetic clustering boosts GWAS power
Many top hits now reach genome-wide significance (N=472) and replicate
Several SNPs affect multiple ROIs
Can form a network of SNPs that
affect similar ROIs
It has a small-world, scale-free topology
(for more, see Chiang et al., J. Neurosci., 2012)

25. Population level Data Integration: Electronic Medical Records, Genotypes and Phenotypes

26. eMERGE
Goal: research to combine DNA biorepositories with EMR for large-scale association studies of genetics and phenotypes; to incorporate genetic variants into EMG for use in clinical care

27. Network Members

28. eMERGE Innovation
Algorithms for electronic phenotyping of clinical conditions identified in EMR
Discoveries of genetic variants in biorepository samples

29. Big Data to Knowledge