Visit to John Hopkins

1. Visit to John Hopkins Aravinda Chakravarti and other researchers

2. People and labs • Aravinda Chakravarti - human geneticist specializing in complex traits. • Dan Arking much work with SNP arrays • Andy McCallion - Gene regulation, especially enhancers in zebrafish. • Akhilesh Pandey - runs human protein reference database. • Ada Hamosh - runs curation side of OMIM. • Joanna Amberger - curator • David Valle - psychiatric genetics • David Cutler - SNP haplotyping, phasing.

3. Some of Arivinda’s Projects • Likes projects that use a variety of techniques. Likes developing methods. • Hirschprung’s disease. • Cardiac sudden death & QT interval. • Hypertension • Autism

4. Hirschprung’s Disease • Lower parts of the gut, or in severe cases all of the gut lacks innervation. • Patients used to due from blocked gut during infancy. Surgery now helps. • 4x more common in males. • 1/5000 infants affected. • ~10% of siblings of affected are also affected.

5. Genetics of Hirschprung’s • Mutations in 6 genes significantly increase risk for Hirschprungs. • RET,PHOX2B,NRTN, L1CAM, GDNF, EDN3 • These genes identified since 90’s via linkage. • Aravinda’s lab sequenced RET in many patients. • They estimate that coding mutations in RET cause 3% of cases. Mutations here tend to be fairly penetrant. • A common SNP (~25% minor allele frequency) in a conserved noncoding region, increases Hirschprung’s risk by 4x, especially in males.

6. Sudden Cardiac Death & QT • Seemingly healthy individuals die suddenly from heart failure (VTach/V Fib). • ~2/3rds have some coronary artery disease but not enough to explain death • ~1/3rd are from people with no detectable heart disease. • Associated with long or very short QT interval (which can be observed in an EKG). • Hard to get samples from sudden death victims, since they are dead. • Initial study focused on QT interval as a quantitative trait. • Lots of data and DNA samples from Framingham Heart Study and others are available.

7. Genetic Analysis of QT Intervals • Nature Genetics article by Dan Arking et al. • Treated QT interval as a continuous trait. • Large association study using Affy 100k chip. • Took extreme 200 subjects showing most extreme QT’s out of 4000 total subjects. • Validated results on 4400 independent subjects. • Used simple ANOVA stats to calculate association at each SNP. • NOS1AP (CAPON) varients explain 1.5% of QT interval variation. • 3 SNPs in conserved noncoding regions, one of which likely explains this variation.

8. Genetic analysis of Sudden Death • Small samples of sudden death victims from ambulances are available. • Currently lab is doing an association study based on the Affy 500k chip. • At end of data gathering stage, just starting data analysis. • Evaluating algorithms, Abacus vs. BRLM • There is an annoying amount of variation between lots of Affy chips.

9. Hypertension • Also a quantitative trait. • Aravinda’s involved with many analysis • Meta-analysis of many linkage studies • Explaining differential effects of salt on hypertension in various populations to evolutionary history (salt/heat tolerant populations more susceptable to salt-sensitive hypertension). • Candidate gene approaches • Also has turned up regulatory mutants.

10. Aravinda’s Lab & Autism • Focusing on autistics with language difficulties. • Using affy 500k chip • Have family information • Use chip data first in linkage study, then use same data with transmission-disequilibrium-test for association study within candidate regions. • Have found some relatively common varients that contribute to risk. • Colleagues at UCLA have found rarer, higher risk variants.

11. Aravinda’s Thinking about Association vs. Linkage • Ultimately need to take kinship into account in both association and linkage studies. • For every region in the genome, given a population, can make a binary tree based on genetic similarity in that region. • In a sense are looking for regions where cases show up on one side of tree and controls on another. • There will be some such regions by chance common kinship *within*that*region. • The causative mutations should be in such a region as well. • A promising technique is to estimate the relatedness overall within the population, and use that to scale significance of associations.

12. Andy McClellan • Postdoc’d in Aravinda’s lab. • Has done functional assays of RET mutants in mouse and zebrafish. • Interested in transcriptional regulation in general, especially enhancers/suppressors. • Finding many mammalian enhancers work in zebrafish, even in absense of overt sequence conservation. • Doing zebrafish versions of many things Eddy Rubin & Len Pinnocio doing in mouse. • Higher throughput in zebrafish, and can observe embryo over time.

13. A technique Andy is examining: • Hypothesis - enhancers/repressors are brought into physical proximity with promoters they regulate. • Method: • Cross-link cells with formaldehyde • Digest DNA with restriction enzyme • Ligate with ligase • Sequences near each other in nucleus will form little circles. • Do PCR with primers from one sequence. Sequence PCR results and see what else is there. promoter fragment primer primer restriction &ligation site restriction &ligation site enhancer fragment

14. Akhilesh Pandey • Human Protein Reference Database. • http://www.hprd.org/ • Large scale effort curating human proteins and protein-protein interactions out of the literature. • Curation team was 70 at it’s peak, all PhDs in India. • Web works is also quite nice. • Contains much more pathway stuff than reactome. • Web works are also quite nice.

17. Ada Hamosh & OMIM • Pediatrician and geneticist • Took over running OMIM from Victor McKusick. • Software and web developmentfor OMIM is at NCBI. • Curation is mostly at John Hopkins with some additional contractors. McKusick still does some of the curation. Only ~7 curators.

18. OMIM continued • OMIM is 100% literature based. • Genetic varients in OMIM: • All varients in first paper describing gene/disease link. • Beyond this try to have most important and common disease-causing variants. • No shortcut to mapping variants to genome, all taken from literature directly, which is a hodge-podge. • Curators are skeptical of controlled vocabularies • Prefer medical thesaurus • http://www.nlm.nih.gov/research/umls/about_umls.html#Metathesaurus • Human disease phenotypes are especially a moving target because doctors intervene! Therapies generally improve over time.

19. David Valle • Pediatrician, works with OMIM • Discussed primarily psychiatric genetics. David Cutler • Implements software for working with Affymetrix chips, from gridding to calling. • His Abacus algorithm has been adopted by Affy now. • Also works on haplotype phasing.

20. Suggestions for hgGenome • Overall fewer than at King lab (reflecting hgGenome design for association studies….) • Support Merlin output, which gives chromosome/centimorgans as position in a number of different maps. • Support Affy ID’s as well as dbSNP. • Consider adding some optional smoothing.

21. Suggestion for track showing phased SNPs and copy number. s001 s002 s003 s004 s005 s006 s007 s008

22. Other suggestions • Ways to make it easier to find candidate genes within linkage/association peaks. • Making it more obvious that something has actually happened when you make a custom track in table browser. • Make it so that you can see OMIM ID from graphics page. • Make links into Human Protein Reference Database.

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