Visit to John Hopkins

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Visit to John Hopkins

• Aravinda Chakravarti and other researchers

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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.

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Some of Arivindas Projects

• Likes projects that use a variety of techniques.
  Likes developing methods.
• Hirschprungs disease.
• Cardiac sudden death QT interval.
• Hypertension
• Autism

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Hirschprungs 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.

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Genetics of Hirschprungs

• Mutations in 6 genes significantly increase risk
  for Hirschprungs.
• RET,PHOX2B,NRTN, L1CAM, GDNF, EDN3
• These genes identified since 90s via linkage.
• Aravindas 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
  Hirschprungs risk by 4x, especially in males.

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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.

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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
  QTs 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.

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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.

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Hypertension

• Also a quantitative trait.
• Aravindas 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.

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Aravindas 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.

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Aravindas 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 withinthatregion.
• 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.

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Andy McClellan

• Postdocd in Aravindas 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.

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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
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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 its peak, all PhDs in
  India.
• Web works is also quite nice.
• Contains much more pathway stuff than reactome.
• Web works are also quite nice.

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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.

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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.ht
  mlMetathesaurus
• Human disease phenotypes are especially a moving
  target because doctors intervene! Therapies
  generally improve over time.

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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.

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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 IDs as well as dbSNP.
• Consider adding some optional smoothing.

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