GEOVISUALIZATION AND EPIDEMIOLOGY A GENERAL DESIGN FRAMEWORK

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Genetic Epidemiology34:479–491(2010)Design of Association Studies with Pooled or Un-pooledNext-Generation Sequencing DataSu Yeon Kim,1ÃYingrui Li,2Yiran Guo,2Ruiqiang Li,2Johan Holmkvist,3Torben Hansen,3,4Oluf Pedersen,3,5,6Jun Wang,2,7and Rasmus Nielsen1,2,71Departments of Integrative Biology and Statistics,UC Berkeley,Berkeley,California2Beijing Genomics Institute,Shenzhen,China3Hagedorn Research Institute,Gentofte,Denmark4Faculty of Health Science,University of Southern Denmark,Odense,Denmark5Faculty of Health Science,University of Aarhus,Aarhus,Denmark6Institute of Biomedical Science,University of Copenhagen,Denmark7Department of Biology,University of Copenhagen,Copenhagen,DenmarkMost common hereditary diseases in humans are complex and rge-scale genome-wide association studies based on SNP genotyping have only identified a small fraction of the heritable variation of these diseases.One explanation may be that many rare variants(a minor allele frequency,MAF o5%),which are not included in the common genotyping platforms,may contribute substantially to the genetic variation of these diseases.Next-generation sequencing,which would allow the analysis of rare variants,is now becoming so cheap that it provides a viable alternative to SNP genotyping.In this paper,we present cost-effective protocols for using next-generation sequencing in association mapping studies based on pooled and un-pooled samples,and identify optimal designs with respect to total number of individuals,number of individuals per pool,and the sequencing coverage.We perform a small empirical study to evaluate the pooling variance in a realistic setting where pooling is combined with exon-capturing.To test for associations,we develop a likelihood ratio statistic that accounts for the high error rate of next-generation sequencing data.We also perform extensive simulations to determine the power and accuracy of this method.Overall,our findings suggest that with a fixed cost,sequencing many individuals at a more shallow depth with larger pool size achieves higher power than sequencing a small number of individuals in higher depth with smaller pool size,even in the presence of high error rates.Our results provide guidelines for researchers who are developing association mapping studies based on next-generation sequencing.Genet.Epidemiol. 34:479–491,2010.r2010Wiley-Liss,Inc.Key words:pooled samples;association mapping;rare allele;optimal design;next-generation sequencingContract grant sponsors:The Lundbeck Foundation Centre of Applied Medical Genomics in Personalized Disease Prediction,Prevention and Care(LuCAMP);NIH;Contract grant numbers:R01-HG003229;R01-MH084691.Additional Supporting Information may be found in the online version of this article.ÃCorrespondence to:Su Yeon Kim,Departments of Integrative Biology and Statistics,University of California,Berkeley,3060Valley Life Sciences Bldg]3140,Berkeley CA94720.E-mail:suyeonkim@Received2October2009;Revised28January2010;Accepted21February2010Published online15June2010in Wiley InterScience().DOI:10.1002/gepi.20501INTRODUCTION Determining the genetic basis of complex genetic diseases is one of the main challenges in human genetics [Altshuler et al.,2008;Frazer et al.,2009;Kruglyak,2008; McCarthy and Hirschhorn,2008;McCarthy et al.,2008].association mapping studies(GWAs) revealed many susceptibility loci for complex diseases such as diabetes,hypertension,bipolar,Crohn’s disease and others[Barrett et al.,2008;Hindorff et al.,2009; Thomas et al.,2009;Wellcome Trust Case Control,2007]. However,despite many successes,a majority of the additive generic variance still remains unexplained [Altshuler et al.,2008;Frazer et al.,2009;Kruglyak,2008; McCarthy and Hirschhorn,2008].One explanation for this result is that rare variants might play a significant role in complex diseases.Since genotyping platforms include SNPs that are discovered by sequencing a small panel, most of the included SNPs are common variants [MAF45%;Frazer et al.,2007]in previous GWAs.The importance of rare variants in understanding complex traits has been discussed in a number of recent studies [Azzopardi et al.,2008;Bodmer and Bonilla,2008;Cohen et al.,2004,2006;Gorlov et al.,2008;Iyengar and Elston, 2007;Ji et al.,2008;McClellan et al.,2007;Polychronakos, 2008].For example,in a re-sequencing study of three genes,Ji et al.[2008]found several rare mutations related to hypertension,appearing in the heterozygous state. The cost of re-sequencing has dropped dramatically over the past few years[Bentley et al.,2008;Wang et al., 2008;Wheeler et al.,2008].This recent development has made next-generation re-sequencing a viable alternative to SNP genotyping based on chips or other platformsr2010Wiley-Liss,Inc.[Bentley et al.,2008;Hillier et al.,2008;Van Tassell et al., 2008].However,in most studies full genome-wide re-sequencing at a high depth is still prohibitively expensive. Instead,two strategies are being used to reduce costs:(1) sequencing of the exome only—using various exon-capturing techniques and(2)the use of pooled samples. With recent developments in exon-capturing techniques, most of the human exons can be efficiently captured [Albert et al.,2007;Gnirke et al.,2009;Hodges et al.,2007; Krishnakumar et al.,2008;Okou et al.,2007;Porreca et al., 2007].While many functional variant may lie in non-coding regions,many of the mutations with the best potential for translational medicine are in coding regions,providing an extra motivation for focusing on the exome[Jones et al., 2009;Kryukov et al.,2009;Raymond et al.,2009].The use of pooled samples is particularly attractive in two-stage designs[Chi et al.,2009;Skol et al.,2006;Wang et al.,2006;Zuo et al.,2006,2008].In the first stage of a two-stage design,a large number of markers are geno-typed,or re-sequencing is being used,on a moderate number of individuals.In the second stage,the most promising markers from the first stage are further examined by genotyping in a larger set of individuals. To reduce the cost of large-scale association studies,pools of DNA from many individuals have been successfully used in the first stage of the two-stage design[Bansal et al., 2002;Boss et al.,2009;Nejentsev et al.,2009;Norton et al., 2004;Sham et al.,2002].The use of pooled samples reduces cost,particularly when combined with techniques such as exon-capturing,which has a cost associated with each pool to which it is applied.With the advent of cheap re-sequencing techniques,a viable,an economically attractive protocol for GWAs may include an initial stage of exon-capturing and re-sequencing in pooled samples [Druley et al.,2009;Ingman and Gyllensten,2008;Jones et al.,2009;Lavebratt and Sengul,2006],and a second stage based on genotyping of the best candidate SNPs from the first stage.Such a protocol is cost effective and has the potential to detect rare SNPs that would not be captured by any of the major genotyping platforms. Here,we examine the feasibility of this protocol,using both pooled and un-pooled samples.There are a number of issues related to this protocol that warrants further research,including the effects of sequencing errors and pooling variance,and the consequences of these factors for the choice of optimal experimental protocol.We first make a limited experimental study based on Nimblegen s exons capturing,and sequencing using the Illumina genome analyzer s,to determine the feasibility of studies based on a combination of these techniques.We then use the results of this study to model the protocol using realistic parameters.We perform simulations to determine power and to identify optimal designs.METHODSEXPERIMENTAL DESIGNOur analyses assume a two-stage design,where the first stage may include exon-capturing and/or pooled samples, and will involve next-generation re-sequencing.The second stage is based on traditional SNP genotyping of the most promising variants from the first stage.While the properties and design of the second stage have been explored extensively in the previous work[Skol et al.,2006;Zuo et al.,2008],the use of next-generation sequencing for the first stage[e.g.,Nejentsev et al.,2009] has not previously been explored to the same degree.EXPERIMENTAL DATAA small empirical study was conducted to evaluate feasibility of this novel approach and assess the variability introduced in real experiments.Genomic DNA was purified from blood leucocytes from five Danish volun-teers recruited at Steno Diabetes Center,Denmark.The volunteers gave informed consent and the research protocol,which is focused on studies of the genetics of metabolic disorders,was approved by the local Ethical Committee of Copenhagen.Two pools were constructed, one with two individuals and the other with five individuals.Pooling DNA of individuals was done following the pre-PCR protocol described in Lavebratt and Sengul[2006]with some small modification.In our procedure,individual DNA was diluted to10ng/m l instead to5ng/m l before pooling.Our pooling procedure is summarized in Supplementary Figure1.Exon-capturing was performed using a NimbleGen chip s[Albert et al., 2007],which captures6,726‘‘exonic’’regions covering $5Mb sequence.The DNA sample in each pool was fragmented to an average size of500bp,end repaired, ligated to Solexa adaptors s,hybridized and captured using the high-density oligonucleotide microarray.Finally, the DNA sample was amplified using PCR,and the eluted sample from it was sequenced using Solexa Genome Analyzer II s.Solexa sequence reads were aligned to the reference human genome(NCBI build36)using read-alignment program SOAP[Li et al.,2008].Pooling efficacy.In our design,reads from indivi-duals in pools could be identified after re-sequencing because they were re-sequenced both individually and in pools.The individual re-sequencing allowed the identifi-cation of unique mutations specific to each individual. Pooling efficacy was then measured by the degree to which the pooled DNA was composed of an equal contribution of DNA from each individual.Consider a pool with P s number of individuals.When using pooled samples,individual DNA samples are measured and pooled,then a fixed amount of pooled DNA is prepared for sequencing(Supplementary Fig.1). Therefore,the sequencing depth of each individual is not controlled directly,but is a function of the proportion of DNA from each individual and the total sequencing depth. However,the mean depth for an individual is expected to be proportional to the amount of DNA of that individual in the pool.Therefore,one way to model the pooling efficacy is as follows:Y i;B;Y i;b$PoissonðmÂR iÞ;where m¼D p2andR i¼W iP j W j;W i$G a p;1a p ;ð1Þwhere Y i;B and Y i;b are the counts of read bases generated from each of the two alleles(B and b)in individual i,D p is the pool depth and R i is the proportion of the DNA from each individual.W i is the DNA amount of individual i. Note that if pooling is perfect,then W i is the same across individuals and R i is expected to be1=P s.In reality, however,W i is likely to vary,and one way to model this480Kim et al. Genet.Epidemiol.variation is to use a gamma distribution,as the gamma distribution provides a very flexible family of continuous positive variables using only two parameters.The gamma distribution is a favorite choice in statistics for modeling such distributions and the properties of the distribution are well known.In our case,when W i follows a gamma distribution with a shared rate parameter,ðR 1;...;R P s Þ$Dirichlet ða p Þ.Note that the mean and the variance of each component in the Dirichlet distribution is:E ðR i Þ¼1P sand Var ðR i Þ¼P s À1P 2s Âða p P s 11Þð2ÞThe parameter a p controlling the pooling variance isestimated from the data using the maximum likelihood method.Exon-capturing efficacy.Variation in exon-captur-ing efficacy introduces extra variation in read depth across the sites.High depth implies efficient exon-capturing,and low depth implies poor exon-capturing performance.We model the exon-capturing efficacy as a gamma distribution:Y j $Poisson ðm t ÂC j Þand C j $G a c ;1a c;ð3Þwhere Y j is the observed total depth at site j and m t is the expected total sequence depth given by experimental design.C j controls the exon-capturing efficacy at site j .Large values of C j imply efficient capturing on site j and small values C j imply poor exon-capturing efficacy .Exon-capturing might fail for some sites.In that case,the capturing efficacy can be modeled as a mixture of a point mass at zero and a gamma distribution.We estimate the sequencing error rate conservatively as the average mismatch rate.Specifically,all the counts of pairwise mismatches between the reference human gen-ome and aligned reads were averaged across targeted regions as well as across the aligned reads.EXPERIMENTAL COSTIn order to illustrate how to make cost effective choices regarding optimal design,we will make assumptions regarding experimental cost.Obviously,costs can change rapidly and design of individual studies should take current costs into consideration.It should be noticed that our general conclusions are not dependent on the specificassumptions regarding costs made here.In the most general pooled design,the total cost of an experiment has four components:cost of obtaining DNA samples,cost of pooling DNA samples,exon-capturing,and sequencing.We will in the following ignore the cost of obtaining DNA samples and assume that such samples are available before the onset of the study.The DNA pooling cost mainly depends on the total number of individuals that are pooled.The key step in pooling is to dilute and accurately quantify the DNA concentration of each individual sample.We will here assume,for the sake of example,that initial preparation for each individual in a pool costs $2.We will assume that exon-capturing cover-ing all human exons ($30Mb)costs $3,500for each pool.The cost of sequencing is divided into two parts,a cost of preparation of the DNA sample which we will assume is $200,and a cost associated with the production of sequencing reads.We will assume a cost of $500for Solexa-sequencing of a 30Mb exonic region at a depth of 2Â(for details,see Supplementary Methods).The assumed experimental costs are summarized in Table I.Notice that for individual sequencing,the exon-capturing cost rapidly increases with the number of individuals,even if the sequencing cost is fixed.The use of pooled samples allows deeper sequencing at the same cost.LIKELIHOOD RATIO TESTThe data produced by next-generation sequencing differ from that of SNP-chips.Genotyping,in principle,reveals the two alleles in each individual in each targeted nucleotide site.Next-generation re-sequencing produces large amounts of short reads.After mapping to the reference genome,an alignment of reads across the targeted regions is obtained.A schematic example of re-sequencing data at a single site is shown in Figure 1.In this particular example,cases and controls each consist of two pools with two individuals.Each nucleotide site in each individual is represented in reads a random number of times.It is generally unknown which of the two alleles in an individual is represented in a particular read.Also,there is a high probability of sequencing errors.One of the main challenges in the use of next-generation sequencing data in association mapping studies aimed at detecting associations with rare SNPs is to distinguish between true SNPs and false SNPs caused by sequencingTABLE I.Examples of experimental costs for re-sequencing of pooled or un-pooled DNA samples in targeted regionsNumber of Pool Depth Experimental cost (unit:$1,000)cases/controls size per indiv.Pooling Exon-capturingSequencing Total 50018Â03,5002,2005,700100014Â07,0002,4009,400200012Â014,0002,80016,80050018Â03,5002,2005,702100024Â43,5002,2005,704200042Â83,5002,2005,708400081Â163,5002,2005,716100054Â41,4002,0803,484100058Â41,4004,0805,4841000516Â41,4008,0809,484The cost settings are obtained by assuming Solexa-sequencing of pooled or unpooled DNA samples on the human exome ($30Mb),captured using Nimblegene arrays.481Design of Association Studies Genet.Epidemiol.errors.The nature of the data is such that,even after basic bioinformatic quality control procedures,sequencing errors remain a serious problem.Power can be gained by incorporating the possibility of errors into the statistical framework used for detecting associations.There is a trade-off between eliminating too many putative SNPs as sequencing errors,thereby eliminating a number of true SNPs,and eliminating too few putative SNPs,and then suffer a serious multiple testing problem.A full likelihood approach incorporated into the association mapping testing procedure may help to directly identify the optimal balance between eliminating too few or too many putative SNPs in a particular design,and to select the putative SNPs that most likely are associated with the trait.We have developed a simple version of such a likelihood approach.We have also extended this method so it can be applied to provide a more powerful method for pooling designs.In the following we will discuss how the method can be developed into a likelihood ratio test,which we apply for each individual site to test the difference in minor allele frequencies:H0:p1¼p2ð¼p0Þvs.H A:p1¼p2,where p1 and p2are the minor allele frequency in cases and controls, respectively.We test each site,j,independently of each other. Suppose that for both cases and controls,there are N pool pools with P s individuals per pool.After re-sequencing, for each pool m,an alignment of reads OðmÞ¼ðXðmÞ1;...; XðmÞVðmÞÞ0is obtained,where XðmÞk is the k th read among the total VðmÞreads in locus j.Let GðmÞbe the number of A (minor)alleles in pool m.(Note that GðmÞis not observed.) Each observed read is a copy of one of the alleles in a pool, but copying is potentially made with errors.Note that below,we assume a relatively simple structure in which e¼Pðread¼A j allele¼aÞ¼Pðread¼a j allele¼AÞ.How-ever,our statistical model can easily be extended to incorporate a more complicated error structure.In Equation(4)below,instead of a binomial distribution modeling the conditional read counts a multinomial distribution with probability that takes each type-specific error rate(e b;b0)into account could be used:e b;b0¼Pðread¼b j allele¼b0Þ,where b;b02ðA;C;G;TÞ.Our likelihood ratio statistic(LRT)is computed as: LRT¼À2logLð^p0j O;eÞ12¼À2log Q2N pool m¼1PðOðmÞj^p0;eÞQ N pool m¼1PðOðmÞj^p1;eÞQ2N pool m¼N pool11PðOðmÞj^p2;eÞ0@1A;and,the likelihood for each pool m is computed as: PðOðmÞj p0;eÞ¼X GðmÞPðGðmÞj p0ÞPðOðmÞj GðmÞ;eÞ¼X S pool k¼0PðGðmÞ¼k j p0ÞY VðmÞr¼1PðXðmÞr j GðmÞ¼k;eÞ!¼X S pool k¼0 Binomðk;S pool;p0ÞBinomðnðmÞA;VðmÞ;kS poolð1ÀeÞ11Àk Spoole ;ð4Þwhere nðmÞA is the number of A in reads.The last equation is motivated as follows:GðmÞfollows a binomial distribution with S pool(52P s)number of trials each with probability p0 (assuming no population structure).Given that there are k minor alleles(A)in a pool,each read becomes of type A with probability k/S pool if there are no errors.If errors occur,the probability iskS poolð1ÀeÞ11Àk SpooleAssuming homogeneity in sequencing efficiency across chromosomes(i.e.,the number of sequenced reads for each allele follows the same distribution)and independence, the number of A reads in the pool(nðmÞA)follows a binomial distribution.Notice that the total number of reads in each pool(VðmÞ)is assumed to be given.Cases ControlsGenotype ReadbaseGenotype ReadbaseFig.1.Schematic illustration of the next-generation sequencing data at a single position.Each case and control sample consist of two pools with two individuals in each pool.The two alleles of each individual are shown in the‘‘Genotype’’column.There are two types of alleles(A and a).Each allele appears a random number of times,and may have been affected by sequencing errors.482Kim et al.Genet.Epidemiol.The performance of the likelihood ratio statistic for hypothesis testing based on individual sites could be evaluated in terms of Type I and Type II errors in a classical sense.However,our objective is to evaluate design issues relating to a two-step design in which next-generation sequencing is used in the first step of the design.We therefore evaluate the efficacy of the statistic in selecting a causal SNP among a pre-determined number of selected sites.Accordingly,the power is determined as the prob-ability of including the causative SNP among the set of selected sites(e.g.,the probability of including the causal SNP among50sites selected from a total of300,000sites).We do not evaluate the Type I and Type II error of the test in the classical sense,although we will present simulation results showing that P-values calculated based on the likelihood ratio test statistic under suitable conditions follow a uniform distribution,and hence,that the test will have a Type I error rate equal to the nominal significance level. Maximization of the likelihood function was done using a bounded version of the Broyden-Fletcher-Goldfarb-Shanno(BFGS)algorithm[Broyden,1970;Fletcher,1970; Goldfarb,1970;Shanno,1970]or the Simplex method [Nelder and Mead,1965].Overall,the Simplex method algorithm performed better,presumably because the computational expense in numerically approximating the gradient in the BFGS algorithm overwhelmed the faster convergence of this algorithm for this low-dimensional puting our LRT statistic for1,000sites takes 69sec with the Simplex algorithm,but136sec with BFGS algorithm on a standard desktop computer,for a total of 10,000reads from500cases and500controls in1,000pools.G-TESTWith genotyping data and under the Hardy-Weinberg assumption,one natural way to test for differences in allele frequencies between cases and controls is to use G-tests.G-tests are likelihood-ratio tests,which are commonly used to test independence in contingency tables.When counts are generated by independent trials, the test statistic asymptotically follows a w2distribution. The test statistic,G-statistic,is computed as:G¼2X i;j O i;j log O i;j E i;j ð5Þwhere O i;j is the frequency observed in a cell,and E i;j is the frequency expected under the null hypothesis in which rows and columns are independent in a contingency table. The well-known Pearson’s w2test is asymptotically equivalent to G-test.With next-generation sequencing data,the G-statistic can be calculated based on the read counts.However,note that then G-statistic does not take pooling structure as well as sequencing errors into account.Also,the read counts are not generated independently,since a single allele can be copied multiple times as read bases.As such,the likelihood function on which the G-statistic is based is mis-specified.In our study,we examined G-test mainly to compare the performance with our LRT statistic.SIMULATING DATAAssuming a single causative SNP,we performed extensive simulations to examine the statistical properties of the method.In these simulations,the power was evaluated as the probability of including the causative SNP among the set of SNPs selected for the second stage in a two-stage design. We then varied(1)the sequencing error rate per base pair, (2)the number of cases and control individuals,(3)the pool size(the number of individuals per pool),and(4)the sequencing depth per individual.To simplify the simulation procedure,we do not attempt to take population structure and linkage disequilibrium across loci into account.The general conclusions regarding relative power of different designs should not be affected by the strength of LD.For simplicity,we use equal numbers of cases and controls for all of our simulations,so,in the subsequent description,the number of total individuals is twice the number of cases(if not otherwise stated).Each data set consists of aligned reads from a300Kb region,which contains one causative SNP.The null set consists of two subsets,a set of true SNPs and a set of ‘‘false’’SNPs.False SNPs are sites that are invariable in the sample but appear polymorphic due to sequencing errors. Based on the results of the empirical study,we will assume an error rate of1%,if not otherwise stated.We compute the number of true SNPs in the region assuming a mutation rate of5Â10À4,as estimated in coding regions by Wang et al.[2008].For example,with1,000cases and 1,000controls,the number of SNPs is computed as 5Â10À4ÂP2;000À1i¼11iÂ300;000¼1;227.For each true SNP under the null,the minor allele frequency(MAF)is drawn from the distribution of sample frequencies under the assumption of a standard coalescence model.Geno-types are simulated assuming Hardy-Weinberg equili-brium and the reads are generated by copying each allele a Poisson distributed number of times with mean equal to half the per-individual depth.False SNPs are simulated by assuming a MAF of zero.Since every site has high sequencing depth and the next-generation sequencing error is relatively high,approx.1%,almost all the invariable sites appear as false SNPs in the read alignment. The causative SNP is simulated similarly,but with different MAFs for cases and controls computed using a multiplicative disease model.When necessary,we also simulate data with variation in pooling efficacy and exon-capturing efficacy.When both effects are introduced,we use the following model:Y i;j;b$PoissonðmÂR iÂC jÞð6Þm¼D p;R i¼W iP i i;W i$Gða p;1a pÞ;C j$Gða c;1a cÞ;where Y i;j;b is the count of the reads generated from an allele in individual i at locus j.D p is the depth of each pool, and R i is the proportion of the amount of DNA from individual i within a pool.W i models the amount of DNA from individual i and C j models exon-capturing efficacy at site j.a p and a c controls the pooling variance and capturing variance,respectively,the pooling variance equals1/a p, and the capturing variance equals1/a c.Notice that Equations(1)and(3)are simplified versions of this model, in which only one of the two effects is introduced.When the exon-capturing efficacy is constant among sites,the model simplifies to Equation(1).When the exon-capturing efficacy varies among sites,the expected read count of each allele,and thus the total read count,change in proportion to C j.483Design of Association StudiesGenet.Epidemiol.RESULTSEXPERIMENTAL DATAWe examined seven DNA samples,consisting of data from five Danish individuals,sequenced individually,and in two pools:a pool with two individuals and another pool with five individuals.The individual sequencing data were used to detect unique mutations,which allow us to identify individuals of the corresponding reads.Exon-capturing was performed for each of the seven DNA samples,using a NimbleGen chip,and was subse-quently sequenced using Solexa-sequencing (for details,see Methods).We evaluated (a)the performance of DNA pooling,(b)exon-capturing efficacy,and (c)sequencing error rate.Both pools show good pooling efficacy,i.e.,an approxi-mately equal amount of DNA from each individual in the pool (Fig.2).We first identified mutations that are specific to each ing such mutations,we then estimated the relative amount of DNA from each individual by averaging the number of reads from the unique mutations across the sequenced region.In the pool with five individuals,28,026reads can be uniquely assigned to an individual based on 3,217diagnostic mutations.The estimated proportion of DNA from each individuals was 0.186,0.210,0.191,0.211,and 0.202,respectively,showing that the proportions differ only slightly.Assuming the model in Equation (1),the estimated pooling variance (1/a p )is $1/300,which is very small.The pool with two individuals shows even better performance (data not shown).The exon-capturing efficacy varies significantly across the genome compared to the Poisson distribution expected under a constant capturing efficacy across the genome (Supplementary Fig.2).The estimated exon-capturing variance is approx.1/2(i.e.,a c is 2)(see Equation 3).Nonetheless,exon-capturing efficacy across samples were homogeneous (Supplementary Fig.3).On average,a sequencing error rate of 0.9%was estimated in the Illumina/Solexa raw reads (Table II)using the method described in Methods.DISTRIBUTION OF THE LRTWe examined the distribution of the likelihood ratio statistic using simulations.As suggested by standard asymptotic theory,the distribution of the test statistic closely follows a w 2distribution with one degree of freedom,except in cases in which the parameters are close to the boundary (Supplementary Fig.4).Across a range of MAFs covering 0.05–0.5%,the distribution of p -values computed based on the w 2distribution is nearly uniform.However,when the MAF is very small relative to the assumed sequencing error rate,a sharp peak near 1.0appears in the distribution of P -values,implying an excess of test statistics with nearly zero values.This is due to difficulty in distinguishing true SNPs with very low minor alleles from false ones,and thus MAFs under both the null and the alternative hypothesis are estimated to be zero.We also varied the number of individuals in a pool (pool size)as well as the sequencing depth per individual.Again,the distribution of the LRT statistic closely follows the w 2distribution across a range of pool sizes andI1I2I3I4I50123456Individiualsl o g 2 (r e a d c o u n t o f u n i q u e m u t a t i o n )Fig.2.Pooling variance observed in the empirical study in the pool of five ing individual sequencing data,unique mutations for each of the five individuals were obtained,and the number of reads with each mutation was counted in the pooled sample.The box plots shows the lower quartile,median (black line),and upper quartile of the log2of the number of reads with each mutations (y -axis)for each individual (x -axis).The variation among individuals is rela-tively small compared to the variation within individuals.TABLE II.Summary of the empirical data consisting of sequences from seven individuals in 5MB of exonic DNAIndiv.1Indiv.2Indiv.3Indiv.4Indiv.5Pool 1112Pool 2112131415Sequencing error rate (%)0.930.890.890.860.96 1.050.86Generated reads (Mb)92.986.9111.5112.1104.2110.0122.9%of reads mapped to exons47.444.647.647.052.649.849.6Average coverage for the targeted region18.0116.8621.621.7320.2221.2721.24The seven samples consist of five individual samples (Indiv 1-Indiv 5)and two pooled samples,one with a pool size of two (Pool 1)and the other with a pool size of five (Pool 2).Sequencing error rates are conservatively estimated as the average mismatch rate.‘‘Generated reads’’is the number of the reads after filtering out contaminated reads.‘‘The percentage of reads mapped to exons’’is the fraction of the reads mapped to exons among the generated reads.‘‘Average coverage for the targeted region’’is the average number of reads per base-pair across targeted exon regions.484Kim et al.Genet.Epidemiol.。

小学上册英语第3单元真题试卷(有答案)英语试题一、综合题(本题有100小题，每小题1分，共100分.每小题不选、错误，均不给分)1.The _______ of an object can be tested by dropping it.2.The center of an atom is called the __________.3.Herbaceous plants are different from ______ (木本植物).4.The ______ (猴子) swings from tree to tree.5.Acids turn blue litmus paper ______.6.My ________ (玩具) makes a cool noise when I spin it.7.What do you call a person who studies rocks?A. BiologistB. GeologistC. MeteorologistD. Archaeologist答案:B8.The flowers are _______ (在盛开).9.The ________ (光合作用) process is vital.10.The ____ is a small animal that enjoys exploring its surroundings.11.My grandma loves to tell __________ (有趣的故事).12.Planting trees can combat ______ (气候变化).13.What do we call the process of photosynthesis?A. Conversion of light into energyB. Absorption of lightC. Dispersal of lightD. Reflection of light答案: A14.The sun is very _____ (热).15.The __________ (河流) flows into the ocean.16.Planting _____ (药用植物) can benefit health and wellness.17.The _____ (景观) can include many different plants.18.She has a red ______ (backpack).19.The invention of the printing press allowed for the mass production of _______.20.My dad is a __________ (飞行员) and travels a lot.21. A sloth spends most of its life hanging _______ ( upside down).22.What is the name of the famous ancient city in Italy?A. PompeiiB. RomeC. VeniceD. Florence答案:A.Pompeii23.An owl is a wise _______ that comes out at night.24.________ (植物资源利用) can enhance livelihoods.25.The _____ (computer/smartphone) is useful.26. A dog barks and a cat _______.27.The ancient Romans built ________ to transport water.28. A substance that increases the rate of a chemical reaction without undergoing permanent change is a _____ (catalyst).29.I have a toy _____ that can sing.30.The __________ is known for its vibrant nightlife.31. A ______ (植物的利用) can lead to innovative products.32. A ____ is known for its elaborate courtship dance.33.What is the first month of the year?A. FebruaryB. MarchC. JanuaryD. April答案: C34.She is _____ (playing) the flute.35.The ______ is important for clean air.36.What is the name of the fairy tale character who had long hair?A. CinderellaB. RapunzelC. Snow WhiteD. Belle答案: B37.I need to _____ (wash/clean) my room.38.The ____ has a cheerful song and is often heard in the morning.39.The __________ (历史的回响) resonates with humanity.40.The girl sings very ________.41.I have a _______ (subscription) to the magazine.42.I like to go to the ______ (电影院) to watch new movies. It’s always an exciting experience.43.The _____ (海豚) is known for its playful behavior.44. A ______ is a huge ball of ice and dust that orbits the sun.45.Let’s ________ a game after school.46.The __________ is where the Earth's crust is thinnest.47.My uncle is a _______ (职业). 他在 _______ (地点)工作.48.My friend is __________ (具有前瞻性).49.The kitten is ________ and soft.50.My dad is a ______. He enjoys woodworking.51. A _______ is a mixture that appears uniform throughout.52.I can sing songs with my musical ________ (玩具名称).53.The ________ (仙人掌) can survive in dry places with little water.54.The atmosphere contains gases that are essential for ______.55.My ________ (玩具名称) helps me learn about animals.56.My favorite game is ______ (Scrabble).57.Did you ever watch a _______ (小老虎) in the zoo?58.The owl hoots _______ (在夜间).59.What is the name of the famous American landmark known for its statue of a man on a horse?A. Mount RushmoreB. Lincoln MemorialC. Statue of LibertyD. Washington Monument答案: A. Mount Rushmore60.What animal barks?A. CatB. DogC. CowD. Sheep答案:b61.My ________ (弟弟) loves to draw pictures of animals.62.How many sides does a triangle have?A. 2B. 3C. 4D. 5答案:b63. A chemical reaction can produce heat, light, or _____.64. A rainy day means I can stay ______ (舒适).65.What do we call the small pieces of glass used for decoration?A. MarbleB. BeadsC. GemsD. Mosaic答案:D66.I have a _____ (collection) of postcards.67. A pelican has a large ________________ (喙).68.I often write emails to my ____.69.He likes to __________ books.70.My favorite color is ________ (blue).71.What is the capital of France?A. HamburgB. ParisC. RomeD. Madrid答案: B72.My uncle drives a ____ (bus) for the city.73.The hummingbird can hover in ______ (空中) thanks to its wings.74.The _______ can be found in many colors.75.What do we call the outer layer of the Earth?A. CrustB. MantleC. CoreD. Shell答案:A76.The capital of Italy is __________.77. A __________ is a type of bird known for its beautiful song.78.n Wall came down in ________ (1989). The Berl79.The boiling point of water is __________ degrees Fahrenheit.80. Carta was signed in __________ (1215). The Magn81. A displacement reaction involves one element replacing ______.82.The weather is _______ (很适合户外活动)。

参考文献卷号和期号怎么看【参考文献卷期怎么写】参考文献格式及参考文献期号卷号标注参考文献格式参考文献采用顺序编码制。

按文章正文部分引用的文献出现的先后顺序用［1］、［2］……连续编码。

引用多篇文献时，只须将各篇文献的序号在方括号内全部列出，各序号间用“，”。

如遇连续序号，可标注起讫序号.放在“参考文献”中时，用［1］、［2］…顺序编号。

作者姓名采用姓前名后的写法，外文姓全大写，名可缩写并省略缩写点；3人和3人以下作者全部列出，3人以上作者只列前3人，后写“等”字。

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电子文献载体和标志：磁带［MT］,磁盘［DK］，光盘［CD］，联机网络［OL］。

期刊类文献著录格式作者.题名［J］.期刊名(外文可缩写，并省略缩写点)，出版年，卷号(期号)：起止页码. 如期刊无卷号，则为：“出版年(期号)：起止页码.”。

专著类文献著录格式作者.书名［M］.其他责任者.版次(第1版不标注).出版地：出版者，出版年:起止页码.论文集类文献著录格式论文作者.论文题目［C］//编者.论文集名：其他题名信息.出版地（或会议城市）：出版者，出版年:起止页码.学位论文类著录格式作者.题名［D］.保存地点（城市）：保存单位，年份. 科技报告著录格式作者.题名［R］.地点（城市）：主办单位，年份.专利文献著录格式专利申请者或所有者.题名：专利国别，专利号［P］.公告日期或公开日期［引用日期］.获取和访问路径（联机文献必备）.网络文献著录格式作者.题名［文献类型标志/文献载体标志］. 出版地：出版者，出版年(更新日期)［引用日期］.获取和访问路径.参考文献的标注必须规范，否则会直接影响论文的发表。

J o u r n a l o f Sh an do ng Un ive rs ity of S c i e n c e a n d T e c h n o l og y第10卷第4期V01．10N o．42008年8月Aug．2008Sociai I Science50cia,sciences 编者按：认知科学(Cog niti ve Science)是目前世界科技革命中的标志性新兴交叉学科。

作为研究人类心智机制的前沿学科，认知科学已经引起了全世界科学家与哲学家的广泛关注。

人工智能、心理学和语言学等普遍被视为认知科学的核心学科，不过，基于人类智能和认知研究的复杂性和系统性，哲学在认知科学研究中发挥着独特的作用。

然而，由于对此缺乏详细的分析与说明，导致人们常常轻视甚至忽略了哲学在认知科学中的作用。

1998年，蒂姆·范·戈尔德(Tim Gelder)发表于《哲学心理学》(第11卷第2期，第117页一第136页)的《哲学在认知科学中的作用》，通过哲学家使用的独特方法，勾勒了哲学家在认知科学中的诸种角色，对哲学在认知科学中的作用作了生动和深刻的说明。

戈尔德系澳大利亚哲学家和认知科学家，1989年在美国科学哲学重镇匹兹堡大学获哲学博士学位。

戈尔德的研究工作集中于分布式表征、认知动力学理论以及推理技巧等方面。

1991年发表于《哲学杂志))(Journal of Philoso phy)的《如果不是计算，认知可能是什么?》(Whatm i g h t co g—nition be，if n o t computation?)被视为认知科学哲学研究的经典论文。

此外，他还为威尔逊(R．Wilson)等主编的《MIT认知科学百科全书)(Th e MI T Encyclo ped ia of Co gni ti ve S cien ces)撰写了“认知的动力学进路”的著名词条。

哲学在认知科学中的作用(澳]蒂姆·范·戈尔德著；孟伟，杨业玲译箩’嘴攘要：哲学到底对认知科学研究有哪些帮助?通过描述哲学家在钛知科学研究中扮演的莱些角色，我们哥以蔺鼍’l按地回答这个阚题。

期刊全称JCR 期刊简称ISSN所属小类所属小类(中文)小类分区Annual Review of Marine Science ANNU REV MA 1941-1405GEOCHEMIST 地球化学与地球1Annual Review of Marine Science ANNU REV MA 1941-1405OCEANOGRAP 海洋学1Annual Review of Marine Science ANNU REV MA 1941-1405MARINE & FR 海洋与淡水生物1ATMOSPHERIC CHEMISTRY AND PHYSICS A TMOS CHEM 1680-7316METEOROLOG 气象与大气科学1BULLETIN OF THE AMERICAN METEOROLO B AM METEOR 0003-0007METEOROLOG 气象与大气科学1CLIMATE DYNAMICS CLIM DYNAM 0930-7575METEOROLOG 气象与大气科学2EARTH AND PLANETARY SCIENCE LETTER EARTH PLANE 0012-821XGEOCHEMIST 地球化学与地球2EARTH-SCIENCE REVIEWS EARTH-SCI RE 0012-8252GEOSCIENCES 地球科学综合1Geochemical Perspectives GEOCHEM PER 2223-7755GEOCHEMIST 地球化学与地球1GEOCHIMICA ET COSMOCHIMICA ACTA GEOCHIM COS 0016-7037GEOCHEMIST 地球化学与地球2GEOLOGICAL SOCIETY OF AMERICA BULL GEOL SOC AM 0016-7606GEOSCIENCES 地球科学综合1GEOLOGY GEOLOGY 0091-7613GEOLOGY 地质学1Geoscientific Model Development GEOSCI MODE 1991-959XGEOSCIENCES 地球科学综合1GONDWANA RESEARCH GONDWANA R 1342-937XGEOSCIENCES 地球科学综合1Journal of Advances in Modeling Earth SystemsJ ADV MODEL 1942-2466METEOROLOG 气象与大气科学2JOURNAL OF CLIMATE J CLIMATE 0894-8755METEOROLOG 气象与大气科学2JOURNAL OF PETROLOGY J PETROL 0022-3530GEOCHEMIST 地球化学与地球1Nature Geoscience NAT GEOSCI 1752-0894GEOSCIENCES 地球科学综合1PRECAMBRIAN RESEARCH PRECAMBRIA 0301-9268GEOSCIENCES 地球科学综合1QUATERNARY SCIENCE REVIEWS QUATERNARY 0277-3791GEOSCIENCES 地球科学综合1QUATERNARY SCIENCE REVIEWS QUATERNARY 0277-3791GEOGRAPHY,自然地理2REVIEWS OF GEOPHYSICS REV GEOPHYS 8755-1209GEOCHEMIST 地球化学与地球1AMERICAN JOURNAL OF SCIENCE AM J SCI 0002-9599GEOSCIENCES 地球科学综合2APPLIED CLAY SCIENCE APPL CLAY SC 0169-1317MATERIALS S 材料科学：综合3APPLIED CLAY SCIENCE APPL CLAY SC 0169-1317MINERALOGY 矿物学3APPLIED CLAY SCIENCE APPL CLAY SC 0169-1317CHEMISTRY, P 物理化学3Atmospheric Measurement Techniques ATMOS MEAS 1867-1381METEOROLOG 气象与大气科学2BASIN RESEARCH BASIN RES 0950-091XGEOSCIENCES 地球科学综合2BULLETIN OF VOLCANOLOGY B VOLCANOL 0258-8900GEOSCIENCES 地球科学综合3CHEMICAL GEOLOGY CHEM GEOL 0009-2541GEOCHEMIST 地球化学与地球2Climate of the Past CLIM PAST 1814-9324GEOSCIENCES 地球科学综合2Climate of the Past CLIM PAST 1814-9324METEOROLOG 气象与大气科学2CONTRIBUTIONS TO MINERALOGY AND PE CONTRIB MIN 0010-7999GEOCHEMIST 地球化学与地球2CONTRIBUTIONS TO MINERALOGY AND PE CONTRIB MIN 0010-7999MINERALOGY 矿物学2Cryosphere CRYOSPHERE 1994-0416GEOSCIENCES 地球科学综合2Cryosphere CRYOSPHERE 1994-0416GEOGRAPHY,自然地理2DEEP-SEA RESEARCH PART I-OCEANOGRA DEEP-SEA RES 0967-0637OCEANOGRAP 海洋学2EARTH SURFACE PROCESSES AND LANDFO EARTH SURF P 0197-9337GEOSCIENCES 地球科学综合2EARTH SURFACE PROCESSES AND LANDFO EARTH SURF P 0197-9337GEOGRAPHY,自然地理3Earth System Dynamics EARTH SYST D 2190-4979GEOSCIENCES 地球科学综合2Elements ELEMENTS 1811-5209GEOCHEMIST 地球化学与地球2Elements ELEMENTS 1811-5209MINERALOGY 矿物学2GEOCHEMISTRY GEOPHYSICS GEOSYSTEM GEOCHEM GE 1525-2027GEOCHEMIST 地球化学与地球2GEOMORPHOLOGY GEOMORPHOL 0169-555XGEOSCIENCES 地球科学综合2GEOMORPHOLOGY GEOMORPHOL 0169-555XGEOGRAPHY,自然地理3GEOPHYSICAL JOURNAL INTERNATIONAL GEOPHYS J IN 0956-540XGEOCHEMIST 地球化学与地球3GEOPHYSICAL RESEARCH LETTERS GEOPHYS RES 0094-8276GEOSCIENCES 地球科学综合2GEOSTANDARDS AND GEOANALYTICAL RE GEOSTAND GE 1639-4488GEOCHEMIST 地球化学与地球3HOLOCENE HOLOCENE 0959-6836GEOSCIENCES 地球科学综合2HOLOCENE HOLOCENE 0959-6836GEOGRAPHY,自然地理2HYDROLOGY AND EARTH SYSTEM SCIENC HYDROL EAR 1027-5606GEOSCIENCES 地球科学综合2HYDROLOGY AND EARTH SYSTEM SCIENC HYDROL EAR 1027-5606WATER RESOU 水资源1INTERNATIONAL GEOLOGY REVIEW INT GEOL REV 0020-6814GEOLOGY 地质学2INTERNATIONAL JOURNAL OF CLIMATOLO INT J CLIMAT 0899-8418METEOROLOG 气象与大气科学3JOURNAL OF GEODESY J GEODESY 0949-7714GEOCHEMIST 地球化学与地球2JOURNAL OF GEODESY J GEODESY 0949-7714REMOTE SENS 遥感2JOURNAL OF GEODYNAMICS J GEODYN 0264-3707GEOCHEMIST 地球化学与地球3JOURNAL OF GEOLOGY J GEOL 0022-1376GEOLOGY 地质学2JOURNAL OF GEOPHYSICAL RESEARCH J GEOPHYS RE 0148-0227GEOSCIENCES 地球科学综合2JOURNAL OF GLACIOLOGY J GLACIOL 0022-1430GEOSCIENCES 地球科学综合2JOURNAL OF GLACIOLOGY J GLACIOL 0022-1430GEOGRAPHY,自然地理3JOURNAL OF HYDROLOGY J HYDROL 0022-1694GEOSCIENCES 地球科学综合2JOURNAL OF HYDROLOGY J HYDROL 0022-1694ENGINEERING 工程：土木1JOURNAL OF HYDROLOGY J HYDROL 0022-1694WATER RESOU 水资源2JOURNAL OF HYDROMETEOROLOGY J HYDROMETE 1525-755XMETEOROLOG 气象与大气科学2JOURNAL OF METAMORPHIC GEOLOGY J METAMORPH 0263-4929GEOLOGY 地质学1JOURNAL OF PHYSICAL OCEANOGRAPHY J PHYS OCEAN 0022-3670OCEANOGRAP 海洋学2JOURNAL OF QUATERNARY SCIENCE J QUATERNAR 0267-8179GEOSCIENCES 地球科学综合2JOURNAL OF QUATERNARY SCIENCE J QUATERNAR 0267-8179GEOGRAPHY,自然地理3JOURNAL OF SYSTEMATIC PALAEONTOLO J SYST PALAE 1477-2019PALEONTOLO 古生物学2JOURNAL OF SYSTEMATIC PALAEONTOLO J SYST PALAE1477-2019EVOLUTIONA进化生物学4J ATMOS SCI0022-4928METEOROLOGJOURNAL OF THE ATMOSPHERIC SCIENCES3气象与大气科学JOURNAL OF THE GEOLOGICAL SOCIETYJ GEOL SOC LO0016-7649GEOSCIENCES地球科学综合20024-3590OCEANOGRAP LIMNOLOGY AND OCEANOGRAPHY LIMNOL OCEA海洋学20024-3590LIMNOLOGY湖沼学1 LIMNOLOGY AND OCEANOGRAPHY LIMNOL OCEA2 LITHOS LITHOS0024-4937GEOCHEMIST地球化学与地球LITHOS LITHOS0024-4937MINERALOGY矿物学130027-0644METEOROLOG MONTHLY WEATHER REVIEW MON WEATHE气象与大气科学OCEAN MODELLING OCEAN MODE1463-5003OCEANOGRAP海洋学23 OCEAN 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GEOTECHNICAL JOURNAL CAN GEOTECH 0008-3674GEOSCIENCES 地球科学综合4CANADIAN GEOTECHNICAL JOURNAL CAN GEOTECH 0008-3674ENGINEERING 工程：地质4CANADIAN JOURNAL OF EARTH SCIENCES CAN J EARTH 0008-4077GEOSCIENCES 地球科学综合4CANADIAN MINERALOGIST CAN MINERAL 0008-4476MINERALOGY 矿物学4CARBONATES AND EVAPORITES CARBONATE E0891-2556GEOLOGY地质学41634-0744GEOLOGY地质学4 CARNETS DE GEOLOGIE CARNETS GEO1634-0744PALEONTOLO古生物学4 CARNETS DE GEOLOGIE CARNETS GEOCentral European Journal of Geosciences CENT EUR J G2081-9900GEOSCIENCES地球科学综合440009-2819GEOCHEMIST地球化学与地球CHEMIE DER ERDE-GEOCHEMISTRY CHEM ERDE-G4 CHINESE JOURNAL OF GEOPHYSICS-CHINE CHINESE J GE0001-5733GEOCHEMIST地球化学与地球0254-4059OCEANOGRAPCHINESE JOURNAL OF OCEANOLOGY AND C HIN J OCEAN海洋学40254-4059LIMNOLOGY湖沼学4 CHINESE JOURNAL OF OCEANOLOGY AND C HIN J OCEANCLAY MINERALS CLAY MINER0009-8558GEOSCIENCES地球科学综合4 CLAY MINERALS CLAY MINER0009-8558MINERALOGY矿物学4 CLAY MINERALS CLAY MINER0009-8558CHEMISTRY, P物理化学40009-8604GEOSCIENCESCLAYS AND CLAY MINERALS CLAY CLAY 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浅谈科学与非科学的划界问题摘要：科学与非科的划问题的实质，是要分析清楚科学不同于其他任何非科学的观念形式的基本性质是什么，或者说，是要划出一个界线来回答“科学是什么”。

对这个问题的思考，会引发出科学哲学中的许多相关问题，因而具有重大的理论意义，而且对科学的正常发展具有重大的现实意义。

要全面、较正确地区分科学与非科学，我们必须保持一个辩证的态度。

在9周的《科学方法》公共课学习后，我就科学与非科学的划界问题浅谈一下自己总结后的看法。

正文：现代社会，科学迅猛发展，自然科学和社会科学相互影响、相互渗透，科学技术的社会作用日益增强。

什么是科学，成为科学观中的一个最基本的问题，具有重要的理论意义和实际意义。

这时就出现了一个“划界”问题，即科学与非科的划界问题。

一、科学与非科学的含义科学一词，英文为 science，源于拉丁文的 scio，其本意是“知识、学问”，1999 年版《辞海》中将科学定义为“运用范畴、定理、定律等思维形式反映现实世界各种现象的本质的规律的知识体系。

”法国《百科全书》中认为“科学首先不同于常识，科学通过分类，以寻求事物之中的条理。

此外，科学通过揭示支配事物的规律，以求说明事物。

”而英国科学委员会则将科学定义为“以日程现象为基础，用系统的方法对知识的追求、对大自然的理解以及对社会的理解。

”我们可以这样说，科学是一种解决问题分析问题方法但绝对不是全部。

科学是关于自然、社会和思维的知识体系。

或者说，科学是以范畴、定理和定律形式反映世界本质和运动规律的知识体系。

人们能够通过生产实践和学科实验而获得知识，但是，零散的经验知识还不是科学，科学是关于事物本质和普遍规律的理论知识。

科学研究一要强调逻辑分析，二要强调实证检验，两者相结合才能得出规律性的认识。

非科学是指科学以外的所有知识体系或观念。

有的学者将其扩大为科学之外的其它社会要素部分，包括文学、艺术、宗教、民俗、政治、经济、军事活动中的经验和谋略、生产、管理、医疗诊断等活动中的经验诀窍，甚至科学发现和技术发明过程中的直觉思维活动等。