title: Inferring population structure and relationship using minimal independent evolutionary markers in Y-chromosome: a hybrid approach of recursive feature selection for hierarchical clustering.
abstract: Inundation of evolutionary markers expedited in Human Genome Project and 1000 Genome Consortium has necessitated pruning of redundant and dependent variables. Various computational tools based on machine-learning and data-mining methods like feature selection/extraction have been proposed to escape the curse of dimensionality in large datasets. Incidentally, evolutionary studies, primarily based on sequentially evolved variations have remained un-facilitated by such advances till date. Here, we present a novel approach of recursive feature selection for hierarchical clustering of Y-chromosomal SNPs/haplogroups to select a minimal set of independent markers, sufficient to infer population structure as precisely as deduced by a larger number of evolutionary markers. To validate the applicability of our approach, we optimally designed MALDI-TOF mass spectrometry-based multiplex to accommodate independent Y-chromosomal markers in a single multiplex and genotyped two geographically distinct Indian populations. An analysis of 105 world-wide populations reflected that 15 independent variations/markers were optimal in defining population structure parameters, such as FST, molecular variance and correlation-based relationship. A subsequent addition of randomly selected markers had a negligible effect (close to zero, i.e. 1 × 10(-3)) on these parameters. The study proves efficient in tracing complex population structures and deriving relationships among world-wide populations in a cost-effective and expedient manner.
authors.affiliation: National Centre of Applied Human Genetics, School of Life Sciences, Jawaharlal Nehru University, New Delhi 110067, India.
authors.forename: Amit Kumar
authors.initials: AK
authors.lastname: Srivastava
authors.affiliation: National Centre of Applied Human Genetics, School of Life Sciences, Jawaharlal Nehru University, New Delhi 110067, India.
authors.forename: Rupali
authors.initials: R
authors.lastname: Chopra
authors.affiliation: National Centre of Applied Human Genetics, School of Life Sciences, Jawaharlal Nehru University, New Delhi 110067, India.
authors.forename: Shafat
authors.initials: S
authors.lastname: Ali
authors.affiliation: National Centre of Applied Human Genetics, School of Life Sciences, Jawaharlal Nehru University, New Delhi 110067, India.
authors.forename: Shweta
authors.initials: S
authors.lastname: Aggarwal
authors.affiliation: School of Computational and Integrative Sciences, Jawaharlal Nehru University, New Delhi 110067, India.
authors.forename: Lovekesh
authors.initials: L
authors.lastname: Vig
authors.affiliation: National Centre of Applied Human Genetics, School of Life Sciences, and School of Computational and Integrative Sciences, Jawaharlal Nehru University, New Delhi 110067, India bamezai@hotmail.com.
authors.forename: Rameshwar Nath Koul
authors.initials: RN
authors.lastname: Bamezai
mesh_terms: D041322:Chromosomes, Human, Y / Q000737:chemistry*; D016000:Cluster Analysis; D019143:Evolution, Molecular; D005819:Genetic Markers; D005828:Genetics, Population / Q000379:methods; D060005:Genotyping Techniques; D006239:Haplotypes; D006801:Humans; D007194:India; D008297:Male; D010802:Phylogeny; D025341:Principal Component Analysis; D044465:White People / Q000235:genetics
chemical_list: D005819:Genetic Markers