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Bioinformatics and Applications in Biotechnology
Published in Ram Chandra, R.C. Sobti, Microbes for Sustainable Development and Bioremediation, 2019
SNP is a single nucleotide DNA variation on the genomes of different members of a species and occurring ay specific positions. They occur due to substitutions, deletions, and insertions and give a fundamental insight into allelic variations and individual, ethnic predispositions. A major effort is being made to correlate the SNP variations and their contributions to disease and health in different individuals. The international HapMap project aims to develop a halotype map of human genome to find genetic variations responsible for disease, response to drugs, environmental factor, etc. In phase III of the project, 11 global ancestry groups have been assembled: ASW (African ancestry in Southwest USA); CEU (Utah residents with Northern and Western European ancestry from the CEPH collection); CHB (Han Chinese in Beijing, China); CHD (Chinese in Metropolitan Denver, Colorado); GIH (Gujarati Indians in Houston, Texas); JPT (Japanese in Tokyo, Japan); LWK (Luhya in Webuye, Kenya); MEX (Mexican ancestry in Los Angeles, California); MKK (Maasai in Kinyawa, Kenya); TSI (Tuscans in Italy); YRI (Yoruba in Ibadan, Nigeria) (Altshuler et al., 2010). We have also undertaken a study on genetic variations in Arg5Pro and Leu6Pro, which modulate the structure and activity of GPX1 and increase genetic risk for vitiligo (Mansuri et al., 2016). Bioinformatics tools of sequence alignment, modeling, etc. were extensively used in this study.
Nonparametric Finite Mixture of Gaussian Graphical Models
Published in Technometrics, 2018
However, it is very common in real-world applications that observed data come from different resources and may have heterogeneous dependencies across the whole population. For instance, genetic variations data and gene expression data of the international HapMap project (International HapMap 3 Consortium et al. 2010) consist of four representative populations in the world. Our research is motivated by exploring the heterogeneous dependencies of human brain fMRI data to study the attention deficit hyperactivity disorder (ADHD). The famous ADHD-200 Global Competition data (Biswal et al. 2010) aggregated across eight independent imaging sites. Thus, it is very important to estimate heterogeneous dependencies and discover subpopulation with certain commonality. In the current literature, there are two different arguments about whether the gender affects the ADHD. On the one hand, some studies show that there is a gender difference in ADHD (Sauver et al. 2004). On the other hand, other studies argue that the ADHD is not systematically different between boys and girls (Bauermeister et al. 2007). It is likely that there may exist two different subpopulations with hidden commonality among the whole population, corresponding to two existing arguments, respectively. Section 6 confirms this conjecture. More specifically, we show that both arguments may be explained through investigating heterogeneous brain functional connectivity using our proposed method.