Objective: In this study we analyze the PCOS phenotype-genotype relationship in silico, using SNPs of representative genes for analysis of genetic clustering and distance, to evaluate the degree of genetic similarity. Data Source: 1000 Genomes, HapMap, and Human Genome Diversity Project databases were used as source of allele frequencies of the SNPs, using data from male and female individuals grouped according to their geographical ancestry. Setting and Design: Genetic clustering was calculated from SNPs data by Bayesian inference. The inferred ancestry of individuals was matched with PCOS phenotype data, extracted from a previous meta-analysis. The measure of genetic distance was plotted against the geographic distance between the populations. Results: The individuals were assigned to five genetic clusters, matching with different world regions (Kruskal-Wallis/Dunn's post test; P < .0001), and converging in two main PCOS phenotypes in different degrees of affinity. The overall genetic distance increased with the geographic distance among the populations (linear regression; R2 = 0.21; P < .0001), in a phenotype-unrelated manner. Conclusions: Phenotype-genotype correlations were demonstrated, suggesting that PCOS genetic gradient results from genetic drift due to a serial founder effect occurred during ancient human migrations. The overall prevalence of the disease supports intralocus sexual conflict as alternative to the natural selection of phenotypic traits in females.

The polycystic ovary syndrome evolutionary paradox: a genome-wide association studies-based, in silico, evolutionary explanation / Casarini, Livio; Brigante, Giulia. - In: THE JOURNAL OF CLINICAL ENDOCRINOLOGY & METABOLISM. - ISSN 1945-7197. - STAMPA. - 99:(2014), pp. 2412-2420. [10.1210/jc.2014-2703]

The polycystic ovary syndrome evolutionary paradox: a genome-wide association studies-based, in silico, evolutionary explanation.

CASARINI, Livio;BRIGANTE, Giulia
2014

Abstract

Objective: In this study we analyze the PCOS phenotype-genotype relationship in silico, using SNPs of representative genes for analysis of genetic clustering and distance, to evaluate the degree of genetic similarity. Data Source: 1000 Genomes, HapMap, and Human Genome Diversity Project databases were used as source of allele frequencies of the SNPs, using data from male and female individuals grouped according to their geographical ancestry. Setting and Design: Genetic clustering was calculated from SNPs data by Bayesian inference. The inferred ancestry of individuals was matched with PCOS phenotype data, extracted from a previous meta-analysis. The measure of genetic distance was plotted against the geographic distance between the populations. Results: The individuals were assigned to five genetic clusters, matching with different world regions (Kruskal-Wallis/Dunn's post test; P < .0001), and converging in two main PCOS phenotypes in different degrees of affinity. The overall genetic distance increased with the geographic distance among the populations (linear regression; R2 = 0.21; P < .0001), in a phenotype-unrelated manner. Conclusions: Phenotype-genotype correlations were demonstrated, suggesting that PCOS genetic gradient results from genetic drift due to a serial founder effect occurred during ancient human migrations. The overall prevalence of the disease supports intralocus sexual conflict as alternative to the natural selection of phenotypic traits in females.
2014
99
2412
2420
The polycystic ovary syndrome evolutionary paradox: a genome-wide association studies-based, in silico, evolutionary explanation / Casarini, Livio; Brigante, Giulia. - In: THE JOURNAL OF CLINICAL ENDOCRINOLOGY & METABOLISM. - ISSN 1945-7197. - STAMPA. - 99:(2014), pp. 2412-2420. [10.1210/jc.2014-2703]
Casarini, Livio; Brigante, Giulia
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11380/1073740
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