Research Talks – CGSI

Roel Ophoff | Genomic Studies of Psychiatric Disorders | CGSI 2017

Duke Hong — Thu, 21 Sep 2017 07:00:00 +0000

08/09/2017
UCLA Undergraduate Research Program in Neurogenetics
12:00pm in the MacDonald Research Labs First Floor Conference Room

Research Talk by Roel Ophoff
Genomic Studies of Psychiatric Disorders

Jonathan Flint | Genes can “Influence” Personality and Behavior | CGSI 2017

Duke Hong — Thu, 21 Sep 2017 07:00:00 +0000

07/26/2017
UCLA Undergraduate Research Program in Neurogenetics
12:00pm in the Gonda First Floor Conference Room

Research Talk by Jonathan Flint
Genes can “Influence” Personality and Behavior

Nelson Freimer | The Depression Grand Challenge | UCLA’s Big Data Initiative for Brain Health | CGSI 2017

Duke Hong — Thu, 21 Sep 2017 07:00:00 +0000

07/19/2017
UCLA Undergraduate Research Program in Neurogenetics
12:00pm in the Gonda First Floor Conference Room

Research Talk by Nelson Freimer
The Depression Grand Challenge: UCLA’s Big Data Initiative for Brain Health

Barbara Engelhardt | Joint analysis of gene expression levels and histological images identifies genes associated with tissue morphology | CGSI 2017

Duke Hong — Wed, 26 Jul 2017 07:00:00 +0000

07/26/2017 @ 10:30-11:00
Research Talk by Barbara Engelhardt
Joint analysis of gene expression levels and histological images identifies genes associated with tissue morphology
1. Heard, N.A., Holmes, C.C. and Stephens, D.A., 2006. A quantitative study of gene regulation involved in the immune response of anopheline mosquitoes: An application of Bayesian hierarchical clustering of curves. Journal of the American Statistical Association, 101(473), pp.18-29.
2. Qin, Z.S., 2006. Clustering microarray gene expression data using weighted Chinese restaurant process. Bioinformatics, 22(16), pp.1988-1997.
3. McDowell, I.C., Manandhar, D., Vockley, C.M., Schmid, A., Reddy, T.E. and Engelhardt, B., 2017. Clustering gene expression time series data using an infinite Gaussian process mixture model. bioRxiv, p.131151.

Fereydoun Hormozdiari | Discovery of genetic variants and modules in neurodevelopmental disorders | CGSI 2017

Duke Hong — Tue, 25 Jul 2017 07:00:00 +0000

07/25/2017 @ 13:30-14:15
Research Talk by Fereydoun Hormozdiari
Discovery of genetic variants and modules in neurodevelopmental disorders
1. O’Roak, B.J., Vives, L., Girirajan, S., Karakoc, E., Krumm, N., Coe, B.P., Levy, R., Ko, A., Lee, C., Smith, J.D. and Turner, E.H., 2012. Sporadic autism exomes reveal a highly interconnected protein network of de novo mutations. Nature, 485(7397), pp.246-250.
2. Parikshak, N.N., Gandal, M.J. and Geschwind, D.H., 2015. Systems biology and gene networks in neurodevelopmental and neurodegenerative disorders. Nature Reviews Genetics, 16(8), pp.441-458.
3. Turner, T.N., Hormozdiari, F., Duyzend, M.H., McClymont, S.A., Hook, P.W., Iossifov, I., Raja, A., Baker, C., Hoekzema, K., Stessman, H.A. and Zody, M.C., 2016. Genome sequencing of autism-affected families reveals disruption of putative noncoding regulatory DNA. The American Journal of Human Genetics, 98(1), pp.58-74.
4. Hormozdiari, F., Penn, O., Borenstein, E. and Eichler, E.E., 2015. The discovery of integrated gene networks for autism and related disorders. Genome research, 25(1), pp.142-154.
5. Gilman, S.R., Iossifov, I., Levy, D., Ronemus, M., Wigler, M. and Vitkup, D., 2011. Rare de novo variants associated with autism implicate a large functional network of genes involved in formation and function of synapses. Neuron, 70(5), pp.898-907.

Kirk Lohmueller | Variation in positive and negative selection across the Tree of Life | CGSI 2017

Duke Hong — Tue, 25 Jul 2017 07:00:00 +0000

07/25/2017 @ 10:30-11:30
Research Talk by Kirk Lohmueller
Variation in positive and negative selection across the Tree of Life
1. Galtier, N., 2016. Adaptive protein evolution in animals and the effective population size hypothesis. PLoS Genet, 12(1), p.e1005774.
2. Kim, B.Y., Huber, C.D. and Lohmueller, K.E., 2016. Inference of the distribution of selection coefficients for new nonsynonymous mutations using large samples. bioRxiv, p.071431.

Sriram Sankararaman | Probabilistic PCA for large-scale genetic data | CGSI 2017

Duke Hong — Tue, 25 Jul 2017 07:00:00 +0000

07/25/2017 @ 09:00-10:00
Research Talk by Sriram Sankararaman
Probabilistic PCA for large-scale genetic data

Fabio Vandin | Computational methods for survival analysis in genome-wide cancer studies | CGSI 2017

Duke Hong — Mon, 24 Jul 2017 07:00:00 +0000

07/24/2017 @ 13:30-14:15
Research Talk by Fabio Vandin
Computational methods for survival analysis in genome-wide cancer studies
1. Vandin, F., Upfal, E. and Raphael, B.J., 2011. Algorithms for detecting significantly mutated pathways in cancer. Journal of Computational Biology, 18(3), pp.507-522.
2. Raphael, B.J., Dobson, J.R., Oesper, L. and Vandin, F., 2014. Identifying driver mutations in sequenced cancer genomes: computational approaches to enable precision medicine. Genome medicine, 6(1), p.5.
3. Leiserson, M.D., Vandin, F., Wu, H.T., Dobson, J.R., Eldridge, J.V., Thomas, J.L., Papoutsaki, A., Kim, Y., Niu, B., McLellan, M. and Lawrence, M.S., 2015. Pan-cancer network analysis identifies combinations of rare somatic mutations across pathways and protein complexes. Nature genetics, 47(2), pp.106-114.
4. Vandin, F., Papoutsaki, A., Raphael, B.J. and Upfal, E., 2015. Accurate computation of survival statistics in genome-wide studies. PLoS Comput Biol, 11(5), p.e1004071.
5. Hansen, T. and Vandin, F., 2016. Finding Mutated Subnetworks Associated with Survival in Cancer. arXiv preprint arXiv:1604.02467.

Jingyi Jessica Li | Neyman-Pearson (NP) classification algorithms and NP Receiver Operating Characteristic (NP-ROC) curves | CGSI 2017

Duke Hong — Mon, 24 Jul 2017 07:00:00 +0000

07/24/2017 @ 10:30-11:15
Research Talk by Jessica (Jingyi) Li
Neyman-Pearson (NP) classification algorithms and NP Receiver Operating Characteristic (NP-ROC) curves
1. Tong, X., Feng, Y. and Li, J.J., 2016. Neyman-Pearson (NP) classification algorithms and NP receiver operating characteristic (NP-ROC) curves. arXiv preprint arXiv:1608.03109.
2. Li, J.J. and Tong, X., 2016. Genomic Applications of the Neyman–Pearson Classification Paradigm. In Big Data Analytics in Genomics (pp. 145-167). Springer International Publishing.

Sagiv Shifman | Genomics approaches to study neurodevelopmental disorders | CGSI 2017

Duke Hong — Fri, 21 Jul 2017 07:00:00 +0000

07/21/2017 @ 13:30-14:15
Research Talk by Sagiv Shifman
Genomics approaches to study neurodevelopmental disorders
1. Ben-David, E. and Shifman, S., 2012. Networks of neuronal genes affected by common and rare variants in autism spectrum disorders. PLoS Genet, 8(3), p.e1002556.
2. Ben-David, E. and Shifman, S., 2013. Combined analysis of exome sequencing points toward a major role for transcription regulation during brain development in autism. Molecular psychiatry, 18(10), p.1054.
3. Shohat, S., Ben-David, E. and Shifman, S., 2016. Varying intolerance of gene pathways to mutational classes explain genetic convergence across neuropsychiatric disorders. bioRxiv, p.054460.
4. Fromer, M., Pocklington, A.J., Kavanagh, D.H., Williams, H.J., Dwyer, S., Gormley, P., Georgieva, L., Rees, E., Palta, P., Ruderfer, D.M. and Carrera, N., 2014. De novo mutations in schizophrenia implicate synaptic networks. Nature, 506(7487), pp.179-184.
5. Parikshak, N.N., Luo, R., Zhang, A., Won, H., Lowe, J.K., Chandran, V., Horvath, S. and Geschwind, D.H., 2013. Integrative functional genomic analyses implicate specific molecular pathways and circuits in autism. Cell, 155(5), pp.1008-1021.
6. Zhang, B. and Horvath, S., 2005. A general framework for weighted gene co-expression network analysis. Statistical applications in genetics and molecular biology, 4(1), p.1128.
7. Xu, X., Wells, A.B., O’Brien, D.R., Nehorai, A. and Dougherty, J.D., 2014. Cell type-specific expression analysis to identify putative cellular mechanisms for neurogenetic disorders. Journal of Neuroscience, 34(4), pp.1420-1431.
8. Study, D.D.D., 2017. Prevalence and architecture of de novo mutations in developmental disorders. Nature, 542(7642), pp.433-438.