Biomedical subjects

Analabha Basu

Publications and source records attributed to Analabha Basu.

4 recordsLinked to original sources

PubMed2024

Multivariate genetic architecture reveals testosterone-driven sexual antagonism in contemporary humans.

Sex difference (SD) is ubiquitous in humans despite shared genetic architecture (SGA) between the sexes. A univariate approach, i.e., studying SD in single traits by estimating genetic correlation, does not provide a complete biological overview, because traits are not independent and are genetically correlated. The multivariate genetic architecture between the sexes can be summarized by estimating the additive genetic (co)variance across shared traits, which, apart from the cross-trait and cross-sex covariances, also includes the cross-sex-cross-trait covariances, e.g., between height in males and weight in females. Using such a multivariate approach, we investigated SD in the genetic architecture of 12 anthropometric, fat depositional, and sex-hormonal phenotypes. We uncovered sexual antagonism (SA) in the cross-sex-cross-trait covariances in humans, most prominently between testosterone and the anthropometric traits - a trend similar to phenotypic correlations. 27% of such cross-sex-cross-trait covariances were of opposite sign, contributing to asymmetry in the SGA. Intriguingly, using multivariate evolutionary simulations, we observed that the SGA acts as a genetic constraint to the evolution of SD in humans only when selection is sexually antagonistic and not concordant. Remarkably, we found that the lifetime reproductive success in both the sexes shows a positive genetic correlation with anthropometric traits, but not with testosterone. Moreover, we demonstrated that genetic variance is depleted along multivariate trait combinations in both the sexes but in different directions, suggesting absolute genetic constraint to evolution. Our results indicate that testosterone drives SA in contemporary humans and emphasize the necessity and significance of using a multivariate framework in studying SD.

Humans↗

PubMed2005

Identification of polymorphic motifs using probabilistic search algorithms.

The problem of identifying motifs comprising nucleotides at a set of polymorphic DNA sites, not necessarily contiguous, arises in many human genetic problems. However, when the sites are not contiguous, no efficient algorithm exists for polymorphic motif identification. A search based on complete enumeration is computationally inefficient. We have developed probabilistic search algorithms to discover motifs of known or unknown lengths. We have developed statistical tests of significance for assessing a motif discovery, and a statistical criterion for simultaneously estimating motif length and discovering it. We have tested these algorithms on various synthetic data sets and have shown that they are very efficient, in the sense that the "true" motifs can be detected in the vast majority of replications and in a small number of iterations. Additionally, we have applied them to some real data sets and have shown that they are able to identify known motifs. In certain applications, it is pertinent to find motifs that contain contrasting nucleotides at the sites included in the motif (e.g., motifs identified in case-control association studies). For this, we have suggested appropriate modifications. Using simulations, we have discovered that the success rate of identification of the correct motif is high in case-control studies except when relative risks are small. Our analyses of evolutionary data sets resulted in the identification of some motifs that appear to have important implications on human evolutionary inference. These algorithms can easily be implemented to discover motifs from multilocus genotype data by simple numerical recoding of genotypes.

Africa↗

PubMed2003

Ethnic India: a genomic view, with special reference to peopling and structure.

We report a comprehensive statistical analysis of data on 58 DNA markers (mitochondrial [mt], Y-chromosomal, and autosomal) and sequence data of the mtHVS1 from a large number of ethnically diverse populations of India. Our results provide genomic evidence that (1) there is an underlying unity of female lineages in India, indicating that the initial number of female settlers may have been small; (2) the tribal and the caste populations are highly differentiated; (3) the Austro-Asiatic tribals are the earliest settlers in India, providing support to one anthropological hypothesis while refuting some others; (4) a major wave of humans entered India through the northeast; (5) the Tibeto-Burman tribals share considerable genetic commonalities with the Austro-Asiatic tribals, supporting the hypothesis that they may have shared a common habitat in southern China, but the two groups of tribals can be differentiated on the basis of Y-chromosomal haplotypes; (6) the Dravidian tribals were possibly widespread throughout India before the arrival of the Indo-European-speaking nomads, but retreated to southern India to avoid dominance; (7) formation of populations by fission that resulted in founder and drift effects have left their imprints on the genetic structures of contemporary populations; (8) the upper castes show closer genetic affinities with Central Asian populations, although those of southern India are more distant than those of northern India; (9) historical gene flow into India has contributed to a considerable obliteration of genetic histories of contemporary populations so that there is at present no clear congruence of genetic and geographical or sociocultural affinities.

Chromosomes, Human, Y↗

PubMedDate not supplied

A comparison of two popular statistical methods for estimating the time to most recent common ancestor (TMRCA) from a sample of DNA sequences.

We have compared two statistical methods of estimating the time to most recent common ancestor (TMRCA) from a sample of DNA sequences, which have been proposed by Templeton (1993) and Bandelt et al. (1995). Monte-Carlo simulations were used for generating DNA sequence data. Different evolutionary scenarios were simulated and the estimation procedures were evaluated. We have found that for both methods (i) the estimates are insensitive to demographic parameters and (ii) the standard deviations of the estimates are too high for these methods to be reliably used in practice.

Algorithms↗