Hasil untuk "Genetics"

A. Vignal, D. Milan, M. Sancristobal et al.

During the last ten years, the use of molecular markers, revealing polymorphism at the DNA level, has been playing an increasing part in animal genetics studies. Amongst others, the microsatellite DNA marker has been the most widely used, due to its easy use by simple PCR, followed by a denaturing gel electrophoresis for allele size determination, and to the high degree of information provided by its large number of alleles per locus. Despite this, a new marker type, named SNP, for Single Nucleotide Polymorphism, is now on the scene and has gained high popularity, even though it is only a bi-allelic type of marker. In this review, we will discuss the reasons for this apparent step backwards, and the pertinence of the use of SNPs in animal genetics, in comparison with other marker types.

A. Wilson, R. Cann, R. Cann et al.

Chris Gunter

D. Goldman, G. Oroszi, F. Ducci

L. Bogorad, F. Constabel, S. Baron et al.

J. Mattei, I. Hanski, O. Gaggiotti

A. Rafalski

A. Silman, J. E. Pearson

Chapter summary The prevalence of rheumatoid arthritis (RA) is relatively constant in many populations, at 0.5–1.0%. However, a high prevalence of RA has been reported in the Pima Indians (5.3%) and in the Chippewa Indians (6.8%). In contrast, low occurrences have been reported in populations from China and Japan. These data support a genetic role in disease risk. Studies have so far shown that the familial recurrence risk in RA is small compared with other autoimmune diseases. The main genetic risk factor of RA is the HLA DRB1 alleles, and this has consistently been shown in many populations throughout the world. The strongest susceptibility factor so far has been the HLA DRB1*0404 allele. Tumour necrosis factor alleles have also been linked with RA. However, it is estimated that these genes can explain only 50% of the genetic effect. A number of other non-MHC genes have thus been investigated and linked with RA (e.g. corticotrophin releasing hormone, oestrogen synthase, IFN-γ and other cytokines). Environmental factors have also been studied in relation to RA. Female sex hormones may play a protective role in RA; for example, the use of the oral contraceptive pill and pregnancy are both associated with a decreased risk. However, the postpartum period has been highlighted as a risk period for the development of RA. Furthermore, breastfeeding after a first pregnancy poses the greatest risk. Exposure to infection may act as a trigger for RA, and a number of agents have been implicated (e.g. Epstein–Barr virus, parvovirus and some bacteria such as Proteus and Mycoplasma). However, the epidemiological data so far are inconclusive. There has recently been renewed interest in the link between cigarette smoking and RA, and the data presented so far are consistent with and suggestive of an increased risk.

W. Ewens

S. Faruque, M. Albert, J. Mekalanos

C. Feschotte, Ning Jiang, S. Wessler

Andrew Storfer, M. Murphy, J. Evans et al.

R. Varshney, Spurthi N. Nayak, G. May et al.

E. Fearon

J. Shulman, P. D. De Jager, M. Feany

W. Ewens

A. E. Desjardins

M. Civelek, A. Lusis

Bahareh Rabbani, M. Tekin, N. Mahdieh

Leonardo Di Bari, Thierry Mora, Andrea Pagnani et al.

Generative models derived from large protein sequence alignments define complex fitness landscapes, but their utility for accurately modeling non-equilibrium evolutionary dynamics remains unclear. In this work, we perform a rigorous comparative analysis of three simulation schemes, designed to mimic evolution in silico by local sampling of the probability distribution defined by a generative model. We compare standard independent Markov Chain Monte Carlo, Monte Carlo on a phylogenetic tree, and a population genetics dynamics, benchmarking their outputs against deep sequencing data from four distinct in vitro evolution experiments. We find that standard Monte Carlo fails to reproduce the correct phylogenetic structure and generates unrealistic, gradual mutational sweeps. Performing Monte Carlo on a tree inferred from data improves phylogenetic fidelity and historical accuracy. The population genetics scheme successfully captures phylogenetic correlations, mutational abundances, and selective sweeps as emergent properties, without the need to infer additional information from data. However, the latter choice come at the price of not sampling the proper generative model distribution at long times. Our findings highlight the crucial role of phylogenetic correlations and finite-population effects in shaping evolutionary trajectories on fitness landscapes. These models therefore provide powerful tools for predicting complex adaptive paths and for reliably extrapolating evolutionary dynamics beyond current experimental limitations.