Day 2 :
Keynote Forum
Jun Zhu
Zhejiang University, China
Keynote: Mixed linear model approaches of association mapping for complex traits based on omics variants
Time : 09:05-10:05
Biography:
Jun Zhu has completed his PhD co-major in Statistics and Genetics from NC State University and Post-doctoral studies from NC State University Department of Statistics. He is now the Director of Institute of Bioinformatics, and has been serving as Dean of College and Vice-President in Zhejiang University. He has published more than 260 papers with more than 9000 citations in reputed journals.
Abstract:
Complex traits are controlled by 4 omics variants of SNPs, transcripts, proteins, and metabolites. We proposed mixed linear model approaches for association mapping SNPs (QTSs), transcripts (QTTs), proteins (QTPs) and metabolites (QTMs) to complex traits. Precise prediction for genetic architecture of complex traits has been impeded by the limited understanding on genetic effects of complex traits, especially on locus-by-locus interaction (GxG) and locus-by-environment interaction (GxE). The analysis of large omics datasets, especially two-loci interaction analyses, involves intensive computation. A GPU-based mapping software (QTXNetwork) has been developed for detecting multiple loci on large-scale omics data, and for estimating variance components of genetic effects. By analyzing datasets of SNPs and transcripts for mouse and drosophila datasets, we demonstrated that unbiased estimation could be obtained for genetic effects of causal loci. Transcript association can efficiently detect causal transcript loci on complex traits (QTTs), and on other transcripts (tQTTs). Complicated genetic networks of transcripts controlled by other omics variables can also be revealed for SNPs (tQTSs), proteins (tQTPs) and metabolites (tQTMs). Association mapping for startle in Drosophila revealed high heritability for 85 QTTs (0.996) and 48 QTSs (0.935). The QTTs were also controlled by other 86 tQTTs (0.804~0.998) and 25 tQTSs (0.115~0.423). Both real data analyses and Monte Carlo simulations demonstrated that genetic effects and environment interaction effects could be estimated with no bias and high statistical power by using the proposed approaches.
Keynote Forum
Lyudmila Gulyaeva
Russian Novosibirsk State University, Russia
Keynote: MicroRNA expression profile in breast cancer
Time : 10:05-11:05
Biography:
Lyudmila Gulyaeva, PhD, Doctor of Biology is currently a Professor of Molecular Biology and Biochemistry at Novosibirsk State University, Head of the Molecular Carcinogenesis Laboratory of Institute of Molecular Biology and Biophysics, Novosibirsk, Russia. She got her PhD in Biochemistry in 1986 and Doctor Degree in 2000. She has spent most of her scientific carrier studying drug-metabolizing enzymes and their associations with cancer. A special interest of her research is receptor-mediated mechanism of hormone-related cancer and role of microRNA in regulation of target genes. The results of her scientific activity were published in over 180 scientific papers.
Abstract:
MicroRNAs (miRs) play an essential role in epigenetic alterations of gene expression. Dysregulation of microRNAs expression was observed in many types of cancer including breast cancer (BC). We aimed to determine the microRNA expression in breast tumor samples (n=149) with various receptor phenotypes. Bioinformatics analyses with help of the “Pathway Studio” program determined potential microRNAs: miR-21, 221, 200а, 146, 17, 27, 155, 125 and 16, which are significantly correlated with the expression of ER, PR and HER2. Our study has shown that expression of oncogenic miR-21 and miR-221 increased mainly in the triple negative BC (TNBC), whereas miR-200a and miR-146 were highly expressed in the ER-positive BC. The decreased expression of miR-17 was found in TNBC. There was no significant correlation between miR-27,-155 expression and BC phenotypes. For the first time, we have shown that the elevated level of miR-16 expression is correlated with high expression of ER and HER2. The in silico analysis has shown that studied oncogenic miRs can target ERα, PR, CYP19. Moreover, the expression profile of miR-21, 221, 155, 222 and 205 can change under neoadjuvant radio- and chemotherapy of BC patients. To understand the reasons for the changes in the miR profile, we have studied the expression of studied miRs together with miR-326,-3573,-483,-126a,--1843a,-190a,-6327, their “host” genes and target mRNAs, involved in cell proliferation and differentiation in liver, breast, ovaries and uterus of rats treated with DDT and benzo(a)pyrene. Tissue-specific expression of studied miRs was found, that could be explained by the tissue-specific activation of nuclear receptors by xenobiotics. Thus, the results confirmed the role of microRNAs in carcinogenesis of mammary gland that may open new strategies in breast cancer diagnostics and treatment.