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Inferring population structure and relationship using minimal independent evolutionary markers in Y-chromosome: a hybrid approach of recursive feature selection for hierarchical clustering.

Srivastava AK, Chopra R, Ali S, Aggarwal S, Vig L, Bamezai RN - Nucleic Acids Res. (2014)

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: National Centre of Applied Human Genetics, School of Life Sciences, Jawaharlal Nehru University, New Delhi 110067, India.

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PCA plots reflecting population structure of samples included in the present study from North India (359 samples) and East India (71 samples), using 127, 32, 25, 15 and 12 Y-chromosome SNPs.
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Figure 7: PCA plots reflecting population structure of samples included in the present study from North India (359 samples) and East India (71 samples), using 127, 32, 25, 15 and 12 Y-chromosome SNPs.

Mentions: To show the applicability of our approach and systematically designed multiplexes, we also compared population structure and stratification in present dataset independently with different number of variables (evolutionary markers) in uniform sample size (430 samples). In the background of initial ancestry information obtained through genotyping of 133 Y-chromosomal markers, the structure of populations under present study was further dissected through PCA using 127 (six markers did not work in multiplex), 32, 25, 15 and 12 Y chromosome binary markers. In the study, despite substantial frequency difference in certain haplogroups among North Indian and East Indian populations, we did not observe stratification in different plots generated through PCA on the basis of 127, 32, 25, 15 and 12 markers (Figure 7). Except few outliers, samples were distributed in different clusters, each representing samples from the two studied populations and indicating a possible admixture event after initial settlement of both population groups in India. Utility of the set of 15 markers in dissecting population structure and arriving at similar conclusions, as with larger number of markers, was evident.


Inferring population structure and relationship using minimal independent evolutionary markers in Y-chromosome: a hybrid approach of recursive feature selection for hierarchical clustering.

Srivastava AK, Chopra R, Ali S, Aggarwal S, Vig L, Bamezai RN - Nucleic Acids Res. (2014)

PCA plots reflecting population structure of samples included in the present study from North India (359 samples) and East India (71 samples), using 127, 32, 25, 15 and 12 Y-chromosome SNPs.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4150763&req=5

Figure 7: PCA plots reflecting population structure of samples included in the present study from North India (359 samples) and East India (71 samples), using 127, 32, 25, 15 and 12 Y-chromosome SNPs.
Mentions: To show the applicability of our approach and systematically designed multiplexes, we also compared population structure and stratification in present dataset independently with different number of variables (evolutionary markers) in uniform sample size (430 samples). In the background of initial ancestry information obtained through genotyping of 133 Y-chromosomal markers, the structure of populations under present study was further dissected through PCA using 127 (six markers did not work in multiplex), 32, 25, 15 and 12 Y chromosome binary markers. In the study, despite substantial frequency difference in certain haplogroups among North Indian and East Indian populations, we did not observe stratification in different plots generated through PCA on the basis of 127, 32, 25, 15 and 12 markers (Figure 7). Except few outliers, samples were distributed in different clusters, each representing samples from the two studied populations and indicating a possible admixture event after initial settlement of both population groups in India. Utility of the set of 15 markers in dissecting population structure and arriving at similar conclusions, as with larger number of markers, was evident.

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: National Centre of Applied Human Genetics, School of Life Sciences, Jawaharlal Nehru University, New Delhi 110067, India.

Show MeSH