Visit to John Hopkins People in addition to labs Some of Arivinda’s Projects Hirschprung’s Disease Genetics of Hirschprung’s

Visit to John Hopkins People in addition to labs Some of Arivinda’s Projects Hirschprung’s Disease Genetics of Hirschprung’s

Visit to John Hopkins People in addition to labs Some of Arivinda’s Projects Hirschprung’s Disease Genetics of Hirschprung’s

Vickery, Lou, Morning Show Co-Host has reference to this Academic Journal, PHwiki organized this Journal Visit to John Hopkins Aravinda Chakravarti in addition to other researchers People in addition to 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 P in addition to ey – runs human protein reference database. Ada Hamosh – runs curation side of OMIM. Joanna Amberger – curator David Valle – psychiatric genetics David Cutler – SNP haplotyping, phasing. 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

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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. Genetics of Hirschprung’s Mutations in 6 genes significantly increase risk as long as 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. 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 in addition to DNA samples from Framingham Heart Study in addition to others are available.

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. 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. 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). C in addition to idate gene approaches Also has turned up regulatory mutants.

Aravinda’s Lab & Autism Focusing on autistics with language difficulties. Using affy 500k chip Have family in as long as mation Use chip data first in linkage study, then use same data with transmission-disequilibrium-test as long as association study within c in addition to idate regions. Have found some relatively common varients that contribute to risk. Colleagues at UCLA have found rarer, higher risk variants. Aravinda’s Thinking about Association vs. Linkage Ultimately need to take kinship into account in both association in addition to 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 as long as regions where cases show up on one side of tree in addition to 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, in addition to use that to scale significance of associations. Andy McClellan Postdoc’d in Aravinda’s lab. Has done functional assays of RET mutants in mouse in addition to 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, in addition to can observe embryo over time.

A technique Andy is examining: Hypothesis – enhancers/repressors are brought into physical proximity with promoters they regulate. Method: Cross-link cells with as long as maldehyde Digest DNA with restriction enzyme Ligate with ligase Sequences near each other in nucleus will as long as m little circles. Do PCR with primers from one sequence. Sequence PCR results in addition to see what else is there. primer primer enhancer fragment promoter fragment restriction & ligation site restriction & ligation site Akhilesh P in addition to ey Human Protein Reference Database. Large scale ef as long as t curating human proteins in addition to 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.

Ada Hamosh & OMIM Pediatrician in addition to geneticist Took over running OMIM from Victor McKusick. Software in addition to web development as long as OMIM is at NCBI. Curation is mostly at John Hopkins with some additional contractors. McKusick still does some of the curation. Only ~7 curators. 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 in addition to 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 Metathesaurus Human disease phenotypes are especially a moving target because doctors intervene! Therapies generally improve over time.

David Valle Pediatrician, works with OMIM Discussed primarily psychiatric genetics. David Cutler Implements software as long as working with Affymetrix chips, from gridding to calling. His Abacus algorithm has been adopted by Affy now. Also works on haplotype phasing. Suggestions as long as hgGenome Overall fewer than at King lab (reflecting hgGenome design as long as 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. Suggestion as long as track showing phased SNPs in addition to copy number. s001 s002 s003 s004 s005 s006 s007 s008

Other suggestions Ways to make it easier to find c in addition to idate 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. xyz XYZ

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