HMG Advance Access published October 21, 2003
Impact of selection, mutation rate and genetic drift on human genetic variation
Shamil Sunyaev(1,2), Fyodor A. Kondrashov(3,4), Peer Bork(2,5), Vasily Ramensky(6)
(1) Corresponding author: Genetics Division, Department of Medicine, Brigham & Women's Hospital and Harvard Medical School, Boston, MA 02115, Tel: +1-617-525-6675, Fax: +1-617-732-5123, Email: ssunyaev@https://www.doczj.com/doc/198293066.html,
(2) European Molecular Biology Laboratory (EMBL), Meyerhofstr. 1, 69117 Heidelberg, Germany
(3) National Center for Biotechnology Information, National Institutes of Health, Bethesda, MD 20894, USA
(4) Section of Evolution and Ecology, University of California at Davis, Davis, CA 95616, USA
(5) Max-Delbrueck Center for Molecular Medicine, Robert-Roessle-Strasse 10, 13122 Berlin, Germany
(6) Engelhardt Institute of Molecular Biology, Vavilova 32, 119991 Moscow Russia
Copyright ? 2003 Oxford University Press
ABSTRACT
The accumulation of genome-wide information on single nucleotide polymorphisms in humans provides an unprecedented opportunity to detect the evolutionary forces responsible for heterogeneity of the level of genetic variability across loci. Previous studies have shown that history of recombination events has produced long haplotype blocks in the human g enome, which contribute to this heterogeneity. Other factors, however, such as natural selection or the heterogeneity of mutation rates across loci may also lead to heterogeneity of genetic variability. We compared synonymous and nonsynonymous variability within human genes to their divergence from murine orthologues. We separately analysed the nonsynonymous variants predicted to damage protein structure or function and the variants predicted to be functionally benign. The predictions were based on comparative sequence analysis and, in some cases, on the analysis of protein structure. A strong correlation between nonsynonymous, benign variability and nonsynonymous human-mouse divergence suggests that selection played an important role in shaping the pattern of variability in coding regions of human genes. However, the lack of correlation between deleterious variability and evolutionary divergence shows that a substantial proportion of the observed nonsynonymous single nucleotide polymorphisms reduce fitness and never reach fixation. Evolutionary and medical implications of the impact of selection on human polymorphisms are discussed.
INTRODUCTION
Since Darwin, biologists understood the importance of heritable variability for the evolution in natural populations; more recently, it has become clear that population variability is also crucial for the study of heritable diseases. Single nucleotide polymorphisms (SNPs) are the most frequent type of genetic variation among humans and are responsible for most of th e variation in human phenotypes. SNPs have been described by Kreitman in the fruit fly Drosophila melanogaster (1). In a subsequent study by McDonald and Kreitman, SNPs of different functional categories have been related to the rate of divergence between species, in order to test for the presence of natural selection (2). Here, we compare the densities of various functional categories of SNPs in different human genes to the divergence between these genes and their murine orthologues. This analysis allows us to identify the evolutionary forces responsible for the observed heterogeneity in polymorphism levels across human genes.
The polymorphism rate has been estimated for many human genes (3). Some genes are highly polymorphic, whereas other genes display almost no polymorphism across the human population (4-6). Theoretically, several evolutionary forces, including random drift (coalescence), heterogeneity of mutation rates, and negative and positive natural selection can lead to this heterogeneity in the polymorphism rate. In addition to satisfying theoretical interests, understanding the heterogeneity of the polymorphism rate is important for the optimisation of the design of association studies aimed at the identification of alleles responsible for human phenotypes (7-10).
Recent data demonstrated wide alternations in the polymorphism density along the genome and postulated existence of stable haplotype blocks (11,12). Recombination hot spots have been hypothesised to contribute greatly to the heterogeneity in polymorphism density along the genome (11-15). Recombination hot spots split the genome into regions with different coalescent histories, i.e. the genome can be represented as a mosaic of blocks, each consisting of sites with a common genealogy, not broken by recombination in most individuals. In selectively neutral regions of the genome, differences in the coalescent history of genes are due to random genetic drift. In protein-coding regions, however, natural selection impacts genetic variation, which causes the patterns to be more complex.
Our aim was to focus on the coding regions and study all of the human genes in public databases that had SNP information and orthologous murine sequences available, in order to: 1) analyse the impact of population history, the mutation rate, and negative (stabilising) selection on the structure of human genetic variation, and 2) study the distribution of deleterious alleles among monomorphic and variable genes.
RESULTS
The variability of a population is shaped by an interplay of several evolutionary forces. Consequently, several not mutually exclusive explanations may account for different polymorphism rates across genes (Table 1). Mutation with heterogeneous rates, population history (coalescence), and selection may all independently affect the polymorphism rate at each individual locus. We attempted to disentangle the impacts of these factors using data on intraspecies polymorphism and interspecies divergence. The
correlation of the polymorphism and evolution rates o f human genes establishes the role of factors that also affect interspecies divergence, and the correlations of the polymorphism rates between different functional categories of SNPs make it possible to distinguish the effects of mutation or coalescence from that of selection.
This analysis relies on two assumptions. First, it assumes that evolutionary constraints are similar for within population variability and between species divergence. For example, it was shown that majority of deleterious amino acid substitutions destabilise proteins (16). Stability requirements are unlikely to differ substantially for orthologous proteins of closely related species.
The second assumption is that the mutation processes are similar for human and mouse orthologues. The mutation rate at a nucleotide site depends on a number of factors, including DNA methylation sequence contexts (17,18) and other weaker factors (19,20). The nucleotide content at a locus is not significantly different in humans and mice (21); thus, we expected that the per locus mutation rate is similar in the course of the evolution of these two species.
The mutation rate influences all functional categories of substitutions equally and affects intraspecies polymorphism and interspecies divergence in the same way (22). Therefore, if mutation rate heterogeneity were a strong force responsible for the heterogeneity of the polymorphism rate across genes, the abundance of all functional types of SNPs in a gene should be strongly correlated with the number of substitutions between this human gene and its mouse orthologue (Table 1). Also, the numbers of polymorphic sites of different functional types would be correlated with each other.
To detect the impact of mutation rate heterogeneity, we need to eliminate the effects of selection and coalescent history. The only correlation affected by heterogeneity in the mutation rate, but not by selection or coalescent history is the correlation of neutral polymorphism and divergence rates. Thus, we analysed the correlation of the number of synonymous substitutions K s between human and murine genes with the number of synonymous polymorphisms. Polymorphism rate is expected to correlate with divergence in neutral sites. However, if the heterogeneity of the mutation rate among loci is within a relatively small range compared to other factors influencing the polymorphism rate, this correlation would not determine most of differences in SNP density and therefore it would not be highly significant. As shown in Figure 1, genes with high K s show a tendency to have a higher synonymous polymorphism rate (also, a correlation coefficient of approximately 0.2 was reported recently in a related study (23)). This tendency is stronger for small values of K s, which is probably due to the inaccuracy of the K s values in genes with a high synonymous divergence rate. However, the dependence of number of synonymous SNPs on K s is overall not highly significant. This suggests, in agreement with (12,20,24), that mutation rate heterogeneity plays only a minor role in creating the observed heterogeneity in polymorphism rates across genes. Alternatively, fluctuations of the mutation rate along the genome are not conserved between the human and the mouse genomes.
All contemporary alleles at each locus have descended from a single allele (22). If a locus has had a long coalescent history, i. e. if the ancestral allele existed many generations ago, many sites at the locus, both functional and neutral, are expected to be polymorphic. Thus, the impact of different coalescent histories at different loci, as well as
of loci-specific mutation rates, will create a correlation of densities of functional and neutral SNPs at a locus. We observe a strong and highly significant correlation between the density of synonymous and nonsynonymous SNPs in a gene (Figure 2). Therefore, the correlation between the densities of functional and neutral SNPs cannot be explained by mutation rate heterogeneity and must be due to different coalescent histories of different loci.
Heterogeneity in the coalescent histories of loci may be caused by genetic drift and recombination, or by selection through background selection (25) or hitchhiking (26). We assume that positive selection and, therefore, hitchhiking is too rare to substantially alter the polymorphism rate at many loci simultaneously. Then, the cause of heterogeneous coalescent histories of human loci can be either genetic drift or differences in the strength of negative selection. If background selection has influenced polymorphism rates of modern SNPs, then genes under stronger selective constraint should have a lower density of synonymous SNP.
To determine the existence of the influence, we related the density of synonymous SNPs to the strength of negative selection, estimated as a ratio of nonsynonymous to synonymous substitution rates K A/K S. Correlation of the density of synonymous SNPs with K A/K S ratio was not observed (Figure 3). This suggests that genetic drift, not background selection, is primarily responsible for differenc es in coalescent history.
The expected effect of selection on the density of SNPs is greatly dependent on their contribution to fitness. With respect to fitness four types of changes are possible in a protein sequence: benign (neutral), slightly deleterious, strongly deleterious and beneficial. Very deleterious and beneficial SNPs are expected to be observed only at low
rates in a population because very deleterious SNPs are prevented from becoming common by negative selection and beneficial substitutions achieve fixation quickly and are not generally observed in polymorphism state. Thus, the majority of the SNPs observed in a population are probably neutral or slightly deleterious.
The impact of negative selection on a gene depends on its overall contribution to fitness and on the fraction of sites (possible mutations) that are functionally important for this gene. Both the rate of nonsynonymous substitution (K A) and the number of benign SNPs are proportional to the fraction of sites that are under selective constraint, therefore, a correlation between the density of benign SNPs and K A was expected. Any neutral variation is expected to correlate with divergence, however, if different loci show a higher heterogeneity in protein evolution rates than that in mutation rates, then they would show a more profound difference in benign amino acid variation than in synonymous variation.
Slightly deleterious mutations, those with a coefficient of selection against them below 1/4N e (where N e is the effective population size) contribute to both fixations and polymorphism (27). In contrast, substantially deleterious mutations with coefficients of selection above 1/4N e almost never reach fixation. Such mutations may be present at low frequencies and, thus, contribute to polymorphism, unless they are very deleterious (with selection coefficients above, approximately 100/4 N e). Among mutations that may affect a protein, the fraction of substantially deleterious mutations with selection coefficients within the range 1/4 N e - 100/4 N e is likely to be substantial (28) because this range spans two orders of magnitude. Regardless of this fraction, we did not expect the density of substantially deleterious SNPs to correlate with K A.
We observed a strong correlation between K A and the density of nonsynonymous SNPs predicted to be benign and only a very weak correlation between K A and the density of SNPs predicted to be damaging for protein structure or function (Figure 4). We predicted that some polymorphisms are damaging based on evolutionary sequence conservation and biochemical and protein structure characteristics of the substitution (see Methods). The polymorphisms that we predicted to be damaging by our routine may, nevertheless, prove not to affect fitness because what is “damaging” from the point of view of protein structure, or even evolutionary conservation, may not have a profound effect on fitness in the modern species. However, the fact that the correlation between K A and the density of damaging SNPs is very weak demonstra tes that most of the SNPs that we predict to be damaging do have a deleterious effect on fitness strong enough to prevent their fixations (29).
Since only the density of substantially deleterious SNPs should fail to correlate with K A, these results demonstrate that substantially deleterious alleles are present in the human population in large numbers; it has been suggested (16,29-31) that as many as 1/5 of all missense SNPs may be deleterious. Because the number of deleterious SNPs depends only slightly on the rate of protein evolution, estimated by interspecies sequence divergence, deleterious SNPs are evenly distributed across human genes,
DISCUSSION
Here we compared the contributions of genetic drift, mutation and selection on the distribution of human polymorphisms. Our results confirm the importance of the coalescent history, genetic drift, to the structure of human haplotypes (12). However, at
least in protein coding regions, negative selection also has a profound effect on the density of polymorphisms. We found that selection lowers genetic variability by eliminating deleterious variants and the magnitude of this effect is relatively strong in comparison to genetic drift. The correlation of the nonsynonymous sequence divergence to the density of damag ing SNPs shows that the accumulation of deleterious SNPs also has a considerable effect on the pattern in human polymorphism such that many SNPs in the human genome appear to be substantially deleterious. While previous analyses detected high levels of del eterious SNPs in the human population (4, 16, 29-34) here we show that selection is strong enough to prevent their fixation.
Comparing polymorphisms and sequence divergence also has its implications for medical geneticists looking for allelic variants involved in human disorders. Sequence conservation between species has been proposed to be a useful marker to identify potentially important allelic variants. Although there is little doubt that this strategy is useful where individual amino acid sites are concerned, our study shows that the overall number of deleterious variants does not depend on the conservation of a gene as a whole (Figure 4).
The common disease/common variant (CD/CV) hypothesis claims that genetic diseases are caused by frequent alleles (7). On the other hand, it is also possible that common multifactorial genetic diseases are mostly caused by a high number of rare alleles; this is known as the common disease/rare variant hypothesis (CD/RV) (10). From the evolutionary point of view, the CD/RV hypothesis can be explained in terms of the mutation accumulation model, which postulates that large number of deleterious alleles are involved in the inheritance of phenotypes. On the other hand, the CD/CV hypothesis
would be most easily supported by pleiotropic models (10). Although multiple studies on specific complex phenotypes will be needed to decisively discriminate between these hypotheses, statistical analysis of allelic variation can provide indirect evidence supporting either of them. Our re sults support the mutation accumulation model with respect to significant number of substantially deleterious allelic variants accumulated in individual human genomes.
Both the direct and the indirect association studies assume that common diseases are caused by common SNPs (8). In addition, indirect association studies are dependent upon the existence of stable haplotype blocks in the genome and the association of common neutral polymorphisms with the disease causing variants in the haplotype blocks (8). If many deleterious SNPs contribute to the common complex phenotypes of medical interest, association studies might be ineffective (10) and novel strategies are to be sought to identify the genetic basis of complex diseases.
MATERIALS AND METHODS
Protein sequences for human and mouse genes were obtained by extracting all entries from the SWALL database (35) annotated as “Homo sapiens” and “Mus musculus” in the organism field. Orthologous pairs between these two species were identified via BLAST (36) as b idirectional best hits (37) that spanned at least 80% of the length of the longest protein and had showed at least 60% amino acid sequence identity. This procedure yielded 11,597 orthologous pairs. For each gene in each orthologous pair, a nucleotide sequence was obtained by comparing the amino acid sequences of genes in our dataset with the coding sequences obtained from the complete human (38) and mouse (21)
genomes and from complete mRNA sequences from GenBank. Only genes with greater than 99% similarity (excluding gaps longer than 20 nucleotides or probable alternative isoforms) were used to reconstruct the nucleotide sequence, resulting in 2,592 gene pair alignments. For each orthologous gene pair,a nucleotide alignment was reconstructed from an amino acid alignment made with CLUSTAL (39) using default parameters.
We estimated the rate of synonymous (K S) and non-synonymous (K A) sequence divergence using the Li-Pamilo-Bianchi method (40,41). To demonstrate the independence of our results and the method o f estimating K S and K A values, we repeated all computations using K S and K A estimates obtained using the Yang and Nielsen method as implemented in the PAML package (42, 43) (data not shown).
SNPs from the HGVbase database, Release 13 (44) were mapped onto protein sequences using the snp2prot program (45) and were classified into three functional categories (synonymous, nonsynonymous damaging and nonsynonymous benign) using PolyPhen software (45). The mapping resulted in 5,923 nsSNPs and 5,856 sSNPs observed in 4,332 human proteins.
To evaluate the statistical significance of the observed dependences, we grouped the genes according to both variables to form a 4x3 contingency table with equally populated bins. A contingency table χ2 test of independence was applied to all statistical dependences considered. Since the data sample size and the number of degrees of freedom were kept constant, χ2 values and corresponding p-values were directly comparable for all tests, except of the dependence shown in Figure 2. Figure 2 presents only TSC (The SNP Consortium) (46) data corresponding to the systematic study, which used a fixed population sample for SNP discovery. Several χ2–based contingency
measures corrected for the sample size depende nce are common. We used the contingency coefficient ()
N C +=22
χχ , where N is the sample size. We relied on χ2 statistics on grouped data because it is distribution free, i.e. does not depend on a specific hypothesis on the original distribution of the data. The results are robust with respect to the data grouping. We also repeated the analysis using
correlation coefficient and ANOVA. The results were shown to be independent of the method for detection of statistical dependence. In ANOVA, genes were grouped
according to the number of SNPs per gene. For the dependence of benign nsSNPs density on K A, ANOVA resulted in a p-value of 4.62x10-27 (4.21x10-23 for the χ2 test); the same test for damaging nsSNPs had a p-value of 0.03 (0.046 for the χ2 test); and the ANOVA p-value for the dependence of sSNP rate on K S was 0.64 (0.088 for the χ2 test). In order to verify that our results were not due to the comparative sequence analysis method implied, we confirmed all of the results using a set of functional
predictions made on the basis of 3D-structure alone. To prove that the qualitative results were not seriously affected by erroneous SNPs in the database, by possible bias due to non-uniform and generally unknown sample size, all results of the analysis have been repeated on subsets of the database corresponding to SNP data, annotated as “validated” in dbSNP (47) and also on the data obtained by systematic genome-wide SNP screens in a fixed sample of individuals provided by The SNP Consortium (TSC) and Japanese SNP database (JSNP) (48).
The data on human -mouse sequence divergence and SNP distribution in genes are available via ftp at https://www.doczj.com/doc/198293066.html,/Sunyaev/snp2div/snp2div.xls.
ACKNOWLEDGEMENTS
We are grateful to Alexey Kondrashov and Alison Wellman for the careful reading of the manuscript and providing us with their valuable comments. REFERENCES
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LEGENDS TO FIGURES
Figure 1
Dependence of the number of synonymous SNPs (sSNPs) per gene on K S between human and murine orthologues as estimated with the Li-Pamilo-Bianchi method. The genes were binned according to K S values so that every bin contains 250 observations. Mean number of synonymous SNPs for each bin is plotted. The dependence was not significant according to the χ2 test (p-value of the χ2 test is 0.088, although p < 0.05 if only K S values less than 0.5 are considered). Normalisation of the number of synonymous SNPs to number of synonymous sites in the gene does not change the character of the dependence. The result also does not change if only a subset of available SNPs corresponding to TSC or JSNP data is considered. The result is also robust to the choice of the method of estimation of K S; i.e. data obtained using Li-Pamilo-Bianchi method qualitatively agree with the data obtained using Yang-Nielsen method.
Figure 2
Correlation of the number of synonymous SNPs per gene with the density of non-synonymous SNPs. The scatter plot presents an average number of non-synonymous SNPs per gene, normalised to the total number of non-synonymous sites, versus the number of synonymous SNPs per gene. Only TSC data were used to eliminate impact of sample size. The correlation is highly significant (P-value of the χ2 test is 8.57x10-5). The contingency coefficient of 0.18 is very close to the contingency coefficient of 0.21 for the dependence of benign nsSNPs on K A (Figure 4).
Figure 3
Number of synonymous SNPs per gene plotted versus the ratio K A/K S. K A and K S values were estimated using the Li-Pamilo-Bianchi method. The genes were binned according to K A/K S values so that every bin contains 250 observations. Mean number of synonymous SNPs for each bin is plotted.The impact of background selection is not detectable
求生之路2秘籍大全|牺牲消逝秘籍怎么用教程|M60 1.使用“~”键在控制台输入秘籍，而不是使用“Y”键。（控制台开启方法：进入游戏依次选择选项-键盘/鼠标-允许使用开发者控制台-已开启） 2.使用“MAP”命令开启战役，单人战役无法使用秘籍（MAP建图命令在第二页直接跳到第二页）。 3.使用秘籍前在控制台输入“Sv_Cheats1”开启作弊。 求生之路2秘籍m60说明： m60需要在求生之路2牺牲以上的版本才可获得，低版本无效，无法获得的同学可以下载本站的最新版游戏。 m60秘籍：giverifle_m60 联机指令：sv_lan 常用秘籍 sv_cheats0关闭作弊(默认) sv_cheats1打开作弊 sv_alltalk0打开全局语音通话(默认) sv_alltalk1关闭全局语音通话 *命令中"1"表示打开"0"表示关闭 --------- god1无敌 Buddha打不死 noclip穿墙 impulse10全部武器，子彈 sv_infinite_ammo1无限弹药不换弹夹 givehealth加满血 giveammo加满弹夹 respawn死亡后复活仅限于复活自己，且在出生点复活 ------------------------------------------------------------------------------ say内容说话[say大家好]玩家会见到对话框入面:[Concole:大家好] say_team内容同上 kick玩家名踢人 z_difficultyNormal游戏难度(Easy,Normal,Hard,Impossible) differences查看你的伺服器设定与伺服器设定的预设值有什么不同 restart重新开始游戏 pause暂停游戏 kill自杀 -------------------------------------------------- setinfo name* 更改玩家的名字（*代表你的名字改中文名要加""号） cl_drawhud 0 消除抬头状态列HUD r_drawviewmodel 0 使你的武器隐形 spectate切换到旁观
先打sv_cheats 1 在打god 1 无敌 无限子弹sv_infinite_ammo 1 give pistol 手枪 give pumpshotgun 散弹枪give autoshotgun 连散 give rifle M4/M16 give smg 乌兹 give hunting_rifle 狙击 give pipe_bomb 土制炸弹give molotov 燃烧瓶 give oxygentank 氧气瓶 give propanetank 煤气罐give gascan 油桶 give pain_pills 药瓶 give first_aid_kit 急救包 give health 满血100 give ammo 弹药 秘籍大全及地图名 god 无敌 noclip 穿墙 sv_infinite_ammo 1 无限弹药give pistol 手枪 give pumpshotgun 散弹枪give autoshotgun 连散 give rifle M4/M16 give smg 乌兹 give hunting_rifle 狙击 give pipe_bomb 土制炸弹give molotov 燃烧瓶 give oxygentank 氧气瓶
give propanetank 煤气罐 give gascan 油桶 give pain_pills 药瓶 give first_aid_kit 急救包 give health 满血100 give ammo 弹药 z_spawn hunter(创造Hunter) z_spawn smoker(创造smoker) z_spawn boomer (创造boomer) z_spawn tank (创造tank) z_spawn witch(创造witch) ent_fire !self setteam 1 变目击者 ent_fire !self setteam 2 变幸存者 ent_fire !self setteam 3 被感染 changelevel 地图名快速改变地图 give物品名制造指定物品（武器等等） nb_blind 1 让僵尸看不到你，不过碰到僵尸的话他们还是会发现你nb_delete_all 清除所有被感染的头目及幸存者 z_add 增加一只僵尸在游戏中 z_spawn bossname 增加一只Boss怪物，bossname分别是：tank, boomer, smoker, witch, hunter cl_drawhud 0 消除抬头状态列HUD r_drawviewmodel 0 使你的武器隐形 sv_lan 0 启用Internet网络; 在"Campaign Lobby" 界面开启
单机也可以,但不是按单人模式那个.是用控制台~ map开图,开图的话是map c1m1(比如).然后进入游戏后按~,打上,sv_cheats 1,之后可以打秘籍,那些CS枪只能在第二关刷到,希望对你有帮助 下面就是命令代码： god 1 无敌 noclip 穿墙 sv_infinite_ammo 1 无限弹药不换弹夹 give health 加满血 give ammo 加满弹夹 --------------------------------------------- upgrade_add Incendiary_ammo 获得燃烧子弹的升级效果 upgrade_add explosive_ammo 获得爆炸子弹的升级效果 upgrade_add laser_sight 获得激光瞄准的升级效果 ----------------------------------------------------------- give adrenaline 肾上腺素针 give defibrillator 电震仪器 give first_aid_kit 医药包 give pain_pills 药丸 give gascan 汽油红桶 give propanetank 煤气罐 give oxygentank 氧气瓶 give pipe_bomb 炸弹 give molotov 燃烧酒瓶 give vomitjar 胆汁瓶 give autoshotgun 1代的连发散弹枪 give shotgun_spas 2代的连发散弹枪 give pumpshotgun 1代的单发散弹枪 give shotgun_chrome 2代的单发散弹枪 give hunting_rifle 1代的连狙 give sniper_military 2代的连狙 give rifle M16步枪 give rifle_ak47 AK47步枪 give rifle_desert SCAR步枪 give smg 小型冲锋枪 give smg_silenced 消声器小型冲锋枪 give pistol 手枪 give pistol_magnum 玛格南手枪 give crowbar 铁撬棍(仅限第1、2、4大关战役可用) give fireaxe 斧头(仅限第1、2、4大关战役可用) give katana 东洋武士刀(仅限第1、2、4大关战役可用) give weapon_chainsaw 电锯 give weapon_grenade_launcher 榴弹发射器 give cricket_bat 板球棒(仅限第1、3大关战役可用) give baseball_bat 棒球棍(不可用或未知)
秘籍作用 god <0 或1> 无敌模式 give health 生命 noclip 开/关穿墙模式 sv_infinite_ammo <0 或1> 开/关无限弹药 give ammo 弹药全满 map 地图名选关 changelevel 地图名选关 sv_cheats 0 关闭所有秘籍效果 kill 自杀 quit 退出游戏 melee_range 70 (预设为70)近战武器的伤害范围数值越高能砍得越远sb_dont_bash 1 电脑队友不用手推 sb_dont_shoot 1 电脑队友不开枪 sb_takecontrol # 游戏中在4个人物之间切换控制 (#代表Ellis,Nick,Rochelle,Coach也可以不要后缀为随机切换) sb_move 0 所有电脑队友停止移动 sb_escort 1 所有电脑队友保护你紧跟在你身边 sb_open_fire 1 所有电脑队友不停的开火 sb_crouch 1 所有电脑队友蹲下 sb_flashlight 1 所有电脑队友使用手电筒(-1为不使用) sb_give 物品名给予所有电脑物品（参见下面的物品名) sb_give_random_weapon 给每个电脑随机分配一把武器 cl_showfps 1 显示帧数 (=显示帧数和地图名 2=显示帧数和平滑率 3=服务器信息 4=显示帧数和日志文件) thirdpersonshoulder 第三人称方式(再输入一次可还原为第一人称) nb_delete_all 踢掉所有电脑队友和附近的僵尸和所有的特殊僵尸(但是所有的僵尸还是会刷新) nb_blind 1 所有电脑僵尸都看不到你(但是撞到僵尸还是会攻击你) cl_drawhud 0 关闭所有的接口包括准星(现实方式) setinfo name 新名更改玩家的名字（改中文名要加""号） sv_lan 1 仅限局域网联机(0=外网可联机)
秘籍?全求?之路2：牺牲 打开控制台输?S v_C h e a t s1 下?就是命令代码： g o d1?敌 n o c l i p穿墙 s v_i n?n i t e_a m m o1?限弹药不换弹夹 g i v e h e a l t h加满? g i v e a m m o加满弹夹 m e l e e_r a n g e70(预设为70)近战武器的伤害范围数值越?能砍得越远 s b_d o n t_b a s h1强制电脑队友不??推 s b_d o n t_s h o o t1强制电脑队友不开枪 s b_t a k e c o n t r o l*游戏中在4个?物之间切换控制(*代表 E l l i s,N i c k,R o c h e l l e,C o a c h也可以不要后缀为随机切换) s b_m o v e0所有电脑队友停?移动 s b_e s c o r t1强制所有电脑队友保护你紧跟在你?边 s b_o p e n_?r e1强制所有电脑队友不停的开? s b_c r o u c h1强制所有电脑队友蹲下 s b_?a s h l i g h t1强制所有电脑队友使??电筒(-1为强制不使?) s b_g i v e*给予所有电脑?个道具或武器(*代表物品名如?r e a x e参见下?的道具参数) s b_g i v e_r a n d o m_w e a p o n给每个电脑随机分配?把武器 c l_s h o w f p s1显?帧数(1=显?帧数和地图名2=显?帧数和平滑率3=服务器信息4=显?帧数和?志?件) t h i r d p e r s o n s h o u l d e r第三?称模式(再输??次可还原为第??称) n b_d e l e t e_a l l踢掉所有电脑队友和附近的僵?和所有的特殊僵?(但是所有的僵?还是会刷新) n b_b l i n d1所有电脑僵?都看不到你(但是撞到僵?还是会攻击你) c l_ d r a w h u d0关闭所有的界?包括准星(现实模式) ---------------------------------------------
《求生之路2》秘籍 准备工作 游戏中按P 或～开启控制台，输入sv_cheats 1 开启控制台（此步至关重要，必须输入秘籍方有效） (成功后会有提示信息Server cvar 'sv_cheats' changed to 1 ) 然后输入以下秘籍可得到对应效果： 注意：使用秘籍会导致无法获得成就 秘籍作用 god <0 或1> 无敌模式 give health 生命 noclip 开/关穿墙模式 sv_infinite_ammo <0 或1> 开/关无限弹药 give ammo 弹药全满 map 地图名选关 changelevel 地图名选关 sv_cheats 0 关闭所有秘籍效果 kill 自杀 quit 退出游戏 melee_range 70 (预设为70)近战武器的伤害范围数值越高能砍得越远 sb_dont_bash 1 电脑队友不用手推 sb_dont_shoot 1 电脑队友不开枪 sb_takecontrol # 游戏中在4个人物之间切换控制 (#代表Ellis,Nick,Rochelle,Coach也可以不要后缀为随机切换) sb_move 0 所有电脑队友停止移动 sb_escort 1 所有电脑队友保护你紧跟在你身边 sb_open_fire 1 所有电脑队友不停的开火 sb_crouch 1 所有电脑队友蹲下 sb_flashlight 1 所有电脑队友使用手电筒(-1为不使用) sb_give 物品名给予所有电脑物品（参见下面的物品名) sb_give_random_weapon 给每个电脑随机分配一把武器 cl_showfps 1 显示帧数 (=显示帧数和地图名 2=显示帧数和平滑率 3=服务器信息 4=显示帧数和日志文件) thirdpersonshoulder第三人称方式(再输入一次可还原为第一人称) nb_delete_all 踢掉所有电脑队友和附近的僵尸和所有的特殊僵尸
你先在主菜单按键盘左上角的~ 键，从而打开控制台建立一个对抗地图，比如你玩死亡中心第一关的对抗模式，你就输入map c1m1_hotel versus， 注意：map 地图名versus 对抗模式(4001版本不可用) map 地图名realism 写实模式(4001版本不可用) map 地图名survival 生存模式(4001版本不可用) map 地图名scavenge 清道夫模式(4001版本不可用) 建好地图以后，两个选择： 1：如果你出来是僵尸，你就在控制台输入sv_cheats 1，再输入sb_all_bot_team 1，这样就不会因为没有玩家加入人类而关闭服务器，4个电脑会一直自己走到安全门过关。同时也会有3个电脑扮演僵尸。 如果你是人类方，想要变感染就再次~键打开输入jointeam 3 z_add 创造一个普通僵尸 z_spawn jockey 创造一个Jockey骑头怪 z_spawn charger 创造一个Charger小坦克 z_spawn spitter 创造一个Spitter口水婆 z_spawn hunter 创造一个Hunter猎人怪 z_spawn smoker 创造一个Smoker烟怪 z_spawn boomer 创造一个Boomer爆炸怪 z_spawn tank 创造一个Tank大坦克 z_spawn witch 创造一个Witch女巫 2：如果你是人类，你想玩僵尸，你就按上面的输入sv_cheats 1后，再输入m换队伍，注意是有次数限制的（貌似是2次）。再和上面一样输入sb_all_bot_team 1。就好了。这样的指令只对这一关有效。到了下一关要重新输入。 不过用这种方法，电脑不会开机关，收集油桶等等 另外附送求生之路2地图代码（不包括资料片）： c1m1_hotel 死亡中心1旅馆 c1m2_streets 死亡中心2街道 c1m3_mall 死亡中心3购物中心 c1m4_atrium 死亡中心4中厅 c2m1_highway 黑色狂欢节1高速公路 c2m2_fairgrounds 黑色狂欢节2游乐场 c2m3_coaster 黑色狂欢节3过山车 c2m4_barns 黑色狂欢节4谷仓 c2m5_concert 黑色狂欢节5音乐会 c3m1_plankcountry 沼泽激战1乡村 c3m2_swamp 沼泽激战2沼泽 c3m3_shantytown 沼泽激战3贫民窟 c3m4_plantation 沼泽激战4种植园 c4m1_milltown_a 暴风骤雨1密尔城
求生之路2秘籍大全 发布时间:2010-11-23 作者：游戏堡浏览次数:次 求生之路2设置 1.选项中改为"可使用控制台" 2.注：单人游戏作弊无效所以必须在控制台里开地图 在主界面按"~"键进入控制台.输入map加地图名字(map后面要有一个空格) 以下是开启控制台的地图： c1m1_hotel 1 死亡中心1旅馆 c1m2_streets 1 死亡中心2街道 c1m3_mall 1 死亡中心3购物中心 c1m4_atrium 1 死亡中心4中厅 c2m1_highway 1 黑色狂欢节1高速公路 c2m2_fairgrounds 2 黑色狂欢节2游乐场 c2m3_coaster 2 黑色狂欢节3过山车 c2m4_barns 2 黑色狂欢节4谷仓 c2m5_concert 2 黑色狂欢节5音乐 c3m1_plankcountry 3 沼泽激战1乡村 c3m2_swamp 3 沼泽激战2沼泽 c3m3_shantytown 3 沼泽激战3贫民窟 c3m4_plantation 3 沼泽激战4种植园
c4m1_milltown_a 4 暴风骤雨1密尔城 c4m2_sugarmill_a 4 暴风骤雨2糖厂 c4m3_sugarmill_b 4 暴风骤雨3逃离工厂 c4m4_milltown_b 4 暴风骤雨4重返小镇 c4m5_milltown_escape 暴风骤雨5逃离小镇 c5m1_waterfront 5 教区1码头 c5m2_park 5 教区2公园 c5m3_cemetery 5 教区3墓地 c5m4_quarter 5 教区4特区 c5m5_bridge 5 教区5桥 c6m1_riverbank 和一代重逢1需求生2追加内容许可 c6m2_bedlam 和一代重逢2 需求生2追加内容许可 c6m3_port 和一代重逢3 需求生2追加内容许可 求生之路2秘籍大全 发布时间:2010-11-23 作者：游戏堡浏览次数:次载入地图完毕后，输入“sv_cheats 1”打开作弊模式 输入以下秘籍即可 god 1 无敌
求生之路秘籍大全 操作方法：开启控制台，输入Sv_Cheats 1 回车确认开启秘籍模式。最后游戏中按～开启控制台，输入以下秘籍回车确认可得到对应效果god 1 无敌 noclip 穿墙 sv_infinite_ammo 1 无限弹药 give pistol * give pumpshotgun 散弹枪 give autoshotgun 连散 give rifle M4/M16 give smg 乌兹 give hunting_rifle 狙击 give pipe_bomb 土制炸弹 give molotov 燃烧瓶 give oxygentank 氧气瓶 give propanetank 煤气罐 give gascan 油桶 give pain_pills 药瓶 give first_aid_kit 急救包 give health 满血100 give ammo 弹药 z_spawn hunter(创造Hunter) z_spawn smoker(创造smoker) z_spawn boomer (创造boomer) z_spawn tank (创造tank) z_spawn witch(创造witch) ent_fire !self setteam 1 变目击者 ent_fire !self setteam 2 变幸存者 ent_fire !self setteam 3 被感染 changelevel 地图名快速改变地图 give物品名制造指定物品（武器等等） nb_blind 1 让僵尸看不到你，不过碰到僵尸的话他们还是会发现你 nb_delete_all 清除所有被感染的头目及幸存者 z_add 增加一只僵尸在游戏中 z_spawn bossname 增加一只Boss怪物，bossname分别是： tank, boomer, smoker, witch, hunter cl_drawhud 0 消除抬头状态列HUD r_drawviewmodel 0 使你的武器隐形 sv_lan 0 启用Internet网络; 在"Campaign Lobby" 界面开启 map地图名启动网络服务器; 在"Campaign Lobby" 界面开启 openserverbrowser 显示可用的服务器 z_difficulty "Easy" 游戏难度简单
求生之路2秘籍使用方法及作弊码大全 求生之路2秘籍使用方法及作弊码大全使用方法: 先在选项里面把控制台调成“已启用”,不能在“单人游戏”里面用秘籍,只能在控制台输入“MAP+地图名称” 进入地图,输入地图完毕后,输入“sv_cheats（空格）1”打开作弊模式 地图输入范例: map c1m1_hotel 之后输入那些秘籍貌似就可以了 代码大全: 秘籍作用 god 无敌模式 give health 生命 noclip 开/关穿墙模式 sv_infinite_ammo 开/关无限弹药 give ammo 弹药全满 map 地图名选关 changelevel 地图名选关 sv_cheats 0 关闭所有秘籍效果 kill 自杀 quit 退出游戏 melee_range 70 (预设为70)近战武器的伤害范围数值越高能砍得越远 sb_dont_bash 1 电脑队友不用手推 sb_dont_shoot 1 电脑队友不开枪 sb_takecontrol # 游戏中在4个人物之间切换控制 (#代表Ellis,Nick,Rochelle,Coach也可以不要后缀为随机切换) sb_move 0 所有电脑队友停止移动 sb_escort 1 所有电脑队友保护你紧跟在你身边 sb_open_fire 1 所有电脑队友不停的开火 sb_crouch 1 所有电脑队友蹲下 sb_flashlight 1 所有电脑队友使用手电筒(-1为不使用) sb_give 物品名给予所有电脑物品（参见下面的物品名) sb_give_random_weapon 给每个电脑随机分配一把武器 cl_showfps 1 显示帧数 (=显示帧数和地图名 2=显示帧数和平滑率 3=服务器信息 4=显示帧数和日志文件) thirdpersonshoulder 第三人称方式(再输入一次可还原为第一人称) nb_delete_all 踢掉所有电脑队友和附近的僵尸和所有的特殊僵尸 (但是所有的僵尸还是会刷新) nb_blind 1 所有电脑僵尸都看不到你(但是撞到僵尸还是会攻击你) cl_drawhud 0 关闭所有的接口包括准星(现实方式) setinfo name 新名更改玩家的名字（改中文名要加”“号） sv_lan 1 仅限局域网联机(0=外网可联机) 物品相关 秘籍作用 give defibrilator 除颤器(医用)
打开控制台输入Sv_Cheats 1 下面就是命令代码： god 1 无敌 noclip 穿墙 sv_infinite_ammo 1 无限弹药不换弹夹 give health 加满血 give ammo 加满弹夹 melee_range 70 (预设为70)近战武器的伤害范围数值越高能砍得越远 sb_dont_bash 1 强制电脑队友不用手推 sb_dont_shoot 1 强制电脑队友不开枪 sb_takecontrol * 游戏中在4个人物之间切换控制(*代表Ellis,Nick,Rochelle,Coach也可以不要后缀为随机切换) sb_move 0 所有电脑队友停止移动 sb_escort 1 强制所有电脑队友保护你紧跟在你身边 sb_open_fire 1 强制所有电脑队友不停的开火 sb_crouch 1 强制所有电脑队友蹲下 sb_flashlight 1 强制所有电脑队友使用手电筒(-1为强制不使用) sb_give * 给予所有电脑一个道具或武器(*代表物品名如fireaxe参见下面的道具参数) sb_give_random_weapon 给每个电脑随机分配一把武器 cl_showfps 1 显示帧数(1=显示帧数和地图名2=显示帧数和平滑率3=服务器信息4=显示帧数和日志文件) thirdpersonshoulder 第三人称模式(再输入一次可还原为第一人称) nb_delete_all 踢掉所有电脑队友和附近的僵尸和所有的特殊僵尸(但是所有的僵尸还是会刷新) nb_blind 1 所有电脑僵尸都看不到你(但是撞到僵尸还是会攻击你) cl_drawhud 0 关闭所有的界面包括准星(现实模式) --------------------------------------------- z_add 创造一个普通僵尸 z_spawn jockey 创造一个Jockey骑头怪 z_spawn charger 创造一个Charger小坦克 z_spawn spitter 创造一个Spitter口水婆 z_spawn hunter 创造一个Hunter猎人怪 z_spawn smoker 创造一个Smoker烟怪 z_spawn boomer 创造一个Boomer爆炸怪 z_spawn tank 创造一个Tank大坦克 z_spawn witch 创造一个Witch女巫 z_speed 250 普通僵尸的移动速度 z_health 50 普通僵尸的生命值 z_tank_health 4000 Tank大坦克的生命值 z_tank_speed 210 Tank大坦克的移动速度 z_witch_speed 300 Witch女巫的移动速度 z_witch_health 1000 Witch女巫的生命值 z_witch_damage 100 Witch女巫的伤害值 z_exploding_health 50 Boomer爆炸怪的生命值
游戏中按P 或～开启控制台，输入sv_cheats 1 开启控制台（此步至关重要，必须输入秘籍方有效） (成功后会有提示信息Server cvar 'sv_cheats' changed to 1 ) 然后输入以下秘籍可得到对应效果： 注意：使用秘籍会导致无法获得成就 秘籍作用 god <0 或1> 无敌模式 give health 生命 noclip 开/关穿墙模式 sv_infinite_ammo <0 或1> 开/关无限弹药 give ammo 弹药全满 map 地图名选关 changelevel 地图名选关 sv_cheats 0 关闭所有秘籍效果 kill 自杀 quit 退出游戏 melee_range 70 (预设为70)近战武器的伤害范围数值越高能砍得越远 sb_dont_bash 1 电脑队友不用手推 sb_dont_shoot 1 电脑队友不开枪 sb_takecontrol # 游戏中在4个人物之间切换控制 (#代表Ellis,Nick,Rochelle,Coach也可以不要后缀为随机切换) sb_move 0 所有电脑队友停止移动 sb_escort 1 所有电脑队友保护你紧跟在你身边 sb_open_fire 1 所有电脑队友不停的开火 sb_crouch 1 所有电脑队友蹲下 sb_flashlight 1 所有电脑队友使用手电筒(-1为不使用) sb_give 物品名给予所有电脑物品（参见下面的物品名) sb_give_random_weapon 给每个电脑随机分配一把武器 cl_showfps 1 显示帧数 (=显示帧数和地图名 2=显示帧数和平滑率 3=服务器信息 4=显示帧数和日志文件) thirdpersonshoulder 第三人称方式(再输入一次可还原为第一人称) nb_delete_all 踢掉所有电脑队友和附近的僵尸和所有的特殊僵尸 (但是所有的僵尸还是会刷新) nb_blind 1 所有电脑僵尸都看不到你(但是撞到僵尸还是会攻击你) cl_drawhud 0 关闭所有的接口包括准星(现实方式)
F:\Program Files\yxbird\left4dead2\left4dead2.exe D:\left4dead2\left4dead2.exe versus sv_cheats 1 sb_all_bot_team 1游戏中按P 或～开启控制台，输入sv_cheats 1 开启控制台（此步至关重要，必须输入秘籍方有效） (成功后会有提示信息Server cvar 'sv_cheats' changed to 1 ) 然后输入以下秘籍可得到对应效果：饮水思源.农夫三拳.吃井不忘挖水人！ setinfo name 注意：使用秘籍会导致无法获得成就 秘籍 作用 god <0 或1> 无敌模式 give health 生命 noclip 开/关穿墙模式 sv_infinite_ammo <0 或1> 开/关无限弹药 give ammo 弹药全满 map 地图名 选关 changelevel 地图名 选关 sv_cheats 0 关闭所有秘籍效果 kill 自杀 quit
退出游戏 melee_range 70 (预设为70)近战武器的伤害范围数值越高能砍得越远 sb_dont_bash 1 电脑队友不用手推 sb_dont_shoot 1 电脑队友不开枪 sb_takecontrol # 游戏中在4个人物之间切换控制 (#代表Ellis,Nick,Rochelle,Coach也可以不要后缀为随机切换) sb_move 0 所有电脑队友停止移动 sb_escort 1 所有电脑队友保护你紧跟在你身边 sb_open_fire 1 所有电脑队友不停的开火 sb_crouch 1 所有电脑队友蹲下 sb_flashlight 1 所有电脑队友使用手电筒(-1为不使用) sb_give 物品名 给予所有电脑物品（参见下面的物品名) sb_give_random_weapon 给每个电脑随机分配一把武器 cl_showfps 1 显示帧数 (=显示帧数和地图名 2=显示帧数和平滑率 3=服务器信息 4=显示帧数和日志文件) thirdpersonshoulder