驯养、选育条件下尼罗罗非鱼群体的选择压力分析

Analysis of selective pressure on Nile tilapia(Oreochromis niloticus) populations during domestication/selective breeding

家养动物是研究长期人工选择对动物基因组产生选择效应机制的独特对象,尼罗罗非鱼(Oreochromis niloticus)是一种受人工干预(驯养、选育)历史较短的优良养殖对象,可作为研究新近发生的人工干预对动物基因组产生影响的遗传机制的良好模型。本研究以1个尼罗罗非鱼埃及野生群体为对照组,以4个"新吉富"罗非鱼选育系群体、2个企业自主选育群体和5个驯养群体为实验组,采用3种模型分析方法(岛屿模型、分级岛屿模型和贝叶斯似然法),在12个微卫星位点上进行F_(ST)-离群值点检测(F_(ST)-outlier test)。结果显示,在本研究所分析的12个微卫星位点中,4个"新吉富"罗非鱼选育系群体在2个微卫星位点(OMO043,OMO114)受到了显著的正向选择压力(P<0.01),2个企业自主选育群体在另外2个微卫星位点(OMO049,OMO100)受到显著的正向选择压力(P<0.01),而5个驯养群体只在1个微卫星位点(OMO013)受到了显著的正向选择压力(P<0.01)。由此可见,选育群体受到的正向选择位点数明显多于驯养群体,选育群体与驯养群体受到正向选择的位点各异,不同选育群体间受到正向选择的位点也各不相同。本研究结果表明,不同的人工干预途径从不同的方向上对尼罗罗非鱼基因组产生了影响。

Domesticated animals provide a unique opportunity to identify genomic targets for artificial selection to a captive environment. Nile tilapia （Oreochromis niloticus） is a useful model species for studying the genetic basis of recent, ongoing domestication, as reared Nile tilapia strains have experienced intense artificial selection for only a short period of time. In this study, we screened 12 microsatellite loci from 11 independently reared strains （6 used for selec-tive breeding and 5 for ranching） and their wild progenitor population of Nile tilapia to identify recent selection foot-prints related to domestication and selective breeding.FST-outlier tests were implemented using three different genetic software programs （LOSITAN, ARLEQUIN, and BAYESCAN） to identify loci under positive selection. All approaches assumed that directional selection increases genetic differentiation between populations and reduces variability at linked loci. However, because all of the tests are based on different assumptions, identifying outlier loci simultaneously using all approaches provides additional support for the candidate status of a particular locus. The LOSITAN program uses coalescent simulations to generate a neutral joint distribution ofFSTand heterozygosity; loci with the highestFST：het-erozygosity ratios are candidates for having experienced selection. Coalescent simulations were performed using 12 samples and a sample size of 30, assuming island and stepwise mutation models. The meanFSTvalue was used with other values close to the mean to obtain half of the data points above and half of those below the median, as suggested in the software manual. The second method to detect selection footprints was based on the hierarchical island model and was implemented in ARLEQUIN software. Coalescent simulations were performed using 12 samples and a sample size of 30, assuming a stepwise mutation model. R-project software was used to generate a neutral joint distribution ofFST and heterozygosity. The third method to detect selection footprints also identified loci that exhibit extreme differentia-tion compared with the remainder of the genome using the Bayesian likelihood method implemented via the reversible jump Markov chain Monte Carlo method. This approach was implemented in BayeScan software.FSTwas modeled using a logistic regression model and locus and population effects byrelaxing the assumption of the symmetrical island model and allowing for asymmetries in population structure. The estimates of the model parameters were automatically adjusted during short pilot runs （10 pilot runs with 5000 iterations each）. We used a burn-in of 50, 000 iterations anda total chain length of 500000 iterations to identify loci under selection. BayeScan estimates the posterior probability that a locus is under selection by calculating the Bayes factor, which is given by the ratio of the posterior model probabilities of two models（selected/neutral）, given the data. According to Jeffreys’ interpretation, if the Bayes factor BF is〉10[lg（BF）〉1], “strong evidence” favors one model over the other and corresponds to a posterior probabil-ity〉0.91.The results showed that two loci （OMO043 and OMO114） provided significant evidence of positive artificial selection in four genetically improved NEW GIFT strain populations. Another two loci （OMO049 and OMO100） showed significant evidence of positive artificial selection in two selective breeding stocks produced by three compa-nies. However, only one loci （OMO013） experienced positive artificial selection in five domesticated populations. The number of loci detected under selection in selective breeding populations was larger than that in domesticated popula-tions. The loci detected under selection varied among the selective breeding and domesticated populations. These re-sults indicate that the Nile tilapia genome has been affected by artificial selection in different directions.