The first wave of large-scale, high-density genome-wide association (GWA) studies has improved our understanding of the genetic basis of many complex traits 1. For several diseases, including type 1 (Refs 2,3) and type 2 diabetes 4–9, inflammatory bowel disease 10–14, prostate cancer 15–20 and breast cancer 21–23, there has been rapid expansion in the numbers of loci implicated in predisposition. For others, such as asthma 24, coronary heart disease 25–27 and atrial fibrillation 28, fewer novel loci have been found, although opportunities for mechanistic insights are equally prom-ising. Several common variants influencing important continuous traits, such as lipids 7,29–31, height 32–35 and fat mass 36–38, have also been found. An updated list of published GWA studies can be found at the National Cancer Institute (NCI)-National Human Genome Research Institute (NHGRI)’s catalog of published genome-wide association studies .
These findings are providing valuable clues to the allelic architecture of complex traits in general. At the same time, many methodological and technical issues that are relevant to the successful prosecution of large-scale association studies have been addressed. However, despite understandable celebration of these achieve-ments, sober reflection reveals many challenges ahead. Compelling signals have been found, often highlighting previously unsuspected biology, but, for most of the
traits studied, known variants explain only a fraction of observed familial aggregation 39, limiting the potential for early application to determine individual disease risk. Because current technology surveys only a lim-ited subset of potentially relevant sequence variation, this should come as no surprise. Much work remains to obtain a complete inventory of the variants at each locus that contribute to disease risk and to define the molecular mechanisms through which these variants operate. The ultimate objectives — full descriptions of the susceptibility architecture of major biomedical traits and translation of the findings into clinical practice — remain distant.
With completion of the initial wave of GW A scans, it is timely to consider the status of the field. This Review considers each major step in the implementation of a GW A scan, highlighting areas where there is an emerg-ing consensus over the ingredients for success, and those aspects for which considerable challenges remain.
Subject ascertainment and design
Although there is a growing focus on the application of GWA methodologies to population-based cohorts, most published GWA studies have featured case– control designs , which raise issues related to the optimal selection of both case and control samples.
*Wellcome T rust Centre for Human Genetics, University of Oxford, Oxford, UK. Correspondence to M.I.M e-mail: mark.mccarthy@https://www.doczj.com/doc/c67696718.html,
doi:10.1038/nrg2344
Published online 9 April 2008
Genome-wide association (GWA) studies
studies in which a dense array of genetic markers, which captures a substantial proportion of common
variation in genome sequence, is typed in a set of DNA
samples that are informative for a trait of interest. The aim is to map susceptibility effects through the detection of
associations between genotype frequency and trait status.
Genome-wide association studies for complex traits: consensus, uncertainty and challenges
Mark I. McCarthy*?, Gon?alo R. Abecasis §, Lon R. Cardon*||, David B. Goldstein ?, Julian Little #, John P . A. Ioannidis**?? and Joel N. Hirschhorn §§||||??
Abstract | The past year has witnessed substantial advances in understanding the
genetic basis of many common phenotypes of biomedical importance. These advances have been the result of systematic, well-powered, genome-wide surveys exploring the relationships between common sequence variation and disease predisposition. This approach has revealed over 50 disease-susceptibility loci and has provided insights into the allelic architecture of multifactorial traits. At the same time, much has been learned about the successful prosecution of association studies on such a scale. This Review highlights the knowledge gained, defines areas of emerging consensus, and describes the challenges that remain as researchers seek to obtain more complete descriptions of the susceptibility architecture of biomedical traits of interest and to translate the information gathered into improvements in clinical management.
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Case–control design
An association study design in which the primary comparison is between a group of individuals (cases), ascertained for the phenotype of interest and that are presumed to have a high prevalence of susceptibility alleles for that trait, and a second group (controls), not ascertained for the phenotype and considered likely to have a lower prevalence of such alleles. Selection bias
Bias arising from the fact that the samples ascertained for the study (particularly controls) might not be representative of the wider population that they are purported to represent. Misclassification bias
Bias resulting from the failure to correctly assign individuals to the relevant group in a case–control study; for example, the presence of some individuals who meet the criteria for being cases in a population-based control sample.
Population stratification The presence in study samples of individuals with different ancestral and demographic histories: if cases and controls differ with respect to these features, markers that are informative for them might be confounded with disease status and lead to spurious associations.Case selection. The principal issues with regard to case
ascertainment revolve around the extent to which selec-
tion should be driven by manoeuvres that are designed
to improve study power through enrichment for spe-
cific disease-predisposing alleles. These include efforts
to minimize phenotypic heterogeneity or to focus on
extreme and/or familial cases (defined, for example,
by early age of onset or ascertainment from multiplex
pedigrees). Because the genetic architecture of most
complex traits remains poorly understood, the value of
such efforts is hard to predict. In most circumstances,
and particularly when the total GWA sample size has
financial or operational constraints, efforts to enrich
case selection are likely to improve power. However,
there are situations in which selection of familial
cases or extreme individuals might have the opposite
effect40,41.
Control selection. optimal selection of control samples
remains more controversial, although the accumulating
empirical data indicate that many commonly expressed
concerns have been overstated. The Wellcome Trust
Case Control Consortium (WTCCC) study was able
to demonstrate the effectiveness of a ‘common control’
design in which 3,000 uK controls were compared
with 2,000 cases from each of 7 different diseases1. The
WTCCC also assuaged concerns about the potential
for selection bias when using non-population-based
controls1. Comparison of the genome-wide genotypic
distributions from the two constituents of the WTCCC
common-control resource (one derived from a popula-
tion-based birth cohort, the other from opportunistic
sampling of blood donors) revealed no excess of sig-
nificant associations, indicating that ascertainment,
selection and survival biases were, in this situation at
least, having minimal impact on genotype distributions.
Although each prospective control sample must be
critically evaluated, these findings suggest that a broad
range of ascertainment schemes are compatible with
GW A analysis.
one consequence of the common-control design is
the potential loss of power that is associated with the
inability to exclude latent diagnoses of the phenotype
of interest through intensive screening of controls.
Fortunately, the consequences of misclassification bias are
modest unless the trait is common, and any loss of power
is recoverable by increasing the sample size (BOX 1).
For common traits, such as obesity and hypertension, in
which the effect of misclassification on power is great-
est1, one remedy involves adopting a more stringent case
definition, for example, based on early age of onset or
ascertainment of a more extreme phenotype, while
still excluding monogenic cases. Although the most
powerful strategy for a given fixed sample size involves
a ‘hypernormal’ control group, it might be difficult to
identify such individuals without introducing inadvert-
ent selection effects. For instance, selecting extremely
low-weight individuals as controls for a case–control
study of obesity could result in overrepresentation of
alleles primarily associated with chronic medical dis-
ease or nicotine addiction rather than weight regulation
per se.
Other case–control design issues. Four other issues loom
large in the design of case–control studies. The first is
sample size, and with this issue the consensus view
is clear: the more samples the better1,34,35,38. The initial
wave of GW A studies has shown that, with rare excep-
tions, the effect sizes resulting from common SNP
associations are modest, and that sample sizes in the
thousands are essential1.
The second issue relates to the propensity for latent
population substructure (population stratification and
cryptic relatedness) to inflate the type 1 error rate
and generate spurious claims of association around
variants that are informative for that substructure42,43.
The evidence emerging from GWA studies is reassur-
ing: as long as cases and controls are well matched for
broad ethnic background, and measures are taken to
identify and exclude individuals whose GWA data
reveal substantial differences in genetic background,
the impact of residual substructure on type 1 error
seems modest1. Several statistical tools exist to detect
and adjust for residual stratification42,44, and invento-
ries of markers that are informative for the detection of
ethnic substructure are a useful by-product of current
scans1,45–47. These approaches can be used to adjust for
substructure even in populations with quite diverse
antecedents (such as european-descent populations
in North America)46,47 and with negligible impact on
power48. Analysis in African-descent populations is
complicated by their greater haplotypic diversity and
fine-scale geographical structure49, and by the exten-
sive admixture demonstrated by African-descent
populations that are resident in europe and North
America. Furthermore, it is important to note that the
tools mentioned above (particularly genomic-control
approaches44) correct for ‘average’ genome-wide meas-
ures of ethnic admixture, and will not always eliminate
spurious associations immediately adjacent to markers
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Cryptic relatedness evidence — typically gained from analysis of GWA data — that, despite allowance for known family relationships,
The third issue concerns the relative merits of family-
based and case–control association methods. Although
family-based association methods provide a robust strategy
for dealing with stratification, this typically comes at
has become less persuasive. Nevertheless, there are many
valuable clinical resources (for example, isolates) for
which pedigree information can be usefully exploited.
one option for the efficient use of family data in such a
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Pleiotropy
The phenomenon whereby a single allele can affect several distinct aspects of the phenotype of an organism, often traits not previously thought to be mechanistically related.
Linkage disequilibrium (LD). The nonrandom allocation of alleles at nearby variants to individual chromosomes as a result of recent mutation, genetic drift or selection, manifest as correlations between genotypes at closely linked markers.
Copy number variant (CNV). A class of DNA sequence variant (including deletions and duplications) in which the result is a departure from the expected diploid representation of DNA sequence.
DnA pooling approaches Association studies that are conducted using estimates of allele frequencies derived from pools of DNA compiled from multiple subjects rather than individual DNA samples.genotyping in the remaining members to propagate
genotypes through the family51.
The fourth question relates to the potential to use
historical control genotypes to substitute for, or supple-
ment, newly typed controls in future GW A studies. The
risks associated with this (particularly inflation of the
type 1 error) will clearly depend on the extent to which
there are disparities between the new cases and historical
controls with respect to population origins, DNA format
(whole-genome amplified DNA versus native DNA as
well as storage conditions)52,53 and genotyping imple-
mentation (platform, genotyping centre, generation of
chip or allele-calling software). The limits of acceptable
divergence are not yet known, but it seems safest that
studies intending to use historical control data also
type a sample of ethnically matched controls (including
a subset of the historical control samples, if available)
using the same assay as for the newly defined cases. This
should allow the detection of systematic effects that are
attributable to non-disease-related differences between
the historical and new data. An alternative approach
would involve the re-genotyping of any interesting GW A
signals in all samples using a dedicated assay.
From case–control to cohort studies. Increasingly, the
GWA approach is being extended from analysis of
case–control samples to population-based cohorts54–56.
Although typically underpowered for dichotomous phe-
notypes (given limited cases for any given disease), such
cohorts often offer a rich tapestry of longitudinal meas-
ures for a wide range of quantitative traits, and lifestyle
and exposure data can enable an evaluation of the joint
effects of genes and environment. These studies promise
new insights into the genetic basis of continuous traits
and enhanced opportunities for revealing pleiotropy57,
although low power remains an issue — especially
for the detection of non-additive gene–environment
interactions58,59. Whereas GWA data meta-analysis is
the obvious solution to overcome restrictions of sam-
ple size, such procedures are often complicated by the
lack of standardization that characterizes the meas-
urement of many key continuous biological traits and
environmental exposures60.
Implementation
Marker selection and assay design. The debates about
marker selection that dominated early discussions of
GWA approaches have now boiled down to choices
between a limited range of commodity genome-wide
chips61,62. This is not the place for a detailed discussion
of the relative merits of specific array-designs other than
to point out that, genotype for genotype, designs that
take linkage disequilibrium (lD) structure into account
when defining content will achieve greater coverage,
in the index population at least. However, they will also
be more vulnerable to loss of coverage when assays fail,
or when typing samples whose genetic ancestry differs
from that of the reference panel or panels used to guide
marker selection61,62. Although greater feature density,
which allows more variants to be typed in a single array,
increases coverage, this does not necessarily equate to
greater power. When funding is limited, but the avail-
ability of samples is not, overall power might be maxi-
mized by typing more individuals with a less dense and
less costly array. In populations of non-African ancestry,
some of the drive towards ever-increasing array density
has been blunted by the capability to impute genotypes
at untyped loci (discussed later)63,64.
A new option in array selection comes with the inclu-
sion on contemporary commodity arrays of additional
probes that are designed to type copy number variants
(CNvs)65. However, because the global inventory of
CNvs remains incomplete66, and with limited empirical
data currently available, the extent to which the cur-
rent round of products (such as the Affymetrix 6.0 and
Illumina Human1M arrays) captures the structural vari-
ome remains unclear. However, their use should provide
early insights into the contribution such variants make to
common phenotypes of biomedical importance67.
For any given study, the final choice of array platform
is often a pragmatic one, based not only on the number
and ethnic origin of the samples, and the overall research
objectives, but also on factors such as cost, array delivery
schedules and available genotyping capacity.
DNA pooling. The costs of well-powered GWA stud-
ies have reignited interest in the value of DNA pooling
approaches as a means to conduct more economical
genome-wide surveys for association68. However, even
though several studies have been completed69, the falling
costs of commodity genotyping and the intrinsic limita-
tions of the pooling approach (reduced power, loss of
individual genotype data and difficulties ensuring equi-
molar representation of samples) mean that the future of
this approach, for GW A studies at least, is uncertain.
Obtaining robust genotype data. experience from the
first wave of GW A studies has demonstrated that scru-
pulous attention to detail is required throughout because
each stage is fraught with the potential for error and
bias1,52,53,70,71. Many of these errors and biases have the
potential to generate extreme values for the association
test statistic; if uncorrected, these can dominate the tails
of the distribution, such that interesting true associa-
tions become lost in a sea of spurious signals. efforts to
prevent, detect and eradicate sources of bias and error
therefore remain a high priority in GW A studies1, despite
continuing improvements in genotyping performance.
These efforts start with careful attention to the qual-
ity and accurate quantification of the starting DNA.
Differences in extraction methods between cases
and controls can be an important source of bias52,53.
Implementation of genotyping-performance metrics allows
poorly performing arrays to be targeted for re-analysis
and deficient samples to be selected for replacement1.
Given the scale of data generation, conversion of raw
experimental data into genotypes has necessitated the
development of automated methods. Indeed, the very
idea of assigning a discrete genotype call has increasingly
been replaced by measures of the posterior probability of
each possible genotype, given the observed data1. Several
algorithms have been developed for defining the three
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Informative missingness
If patterns of missing data are nonrandom with respect to both genotype and trait status, then analysis of the available genotypes can result in misleading associations where none truly exists.
Signal intensity (cluster) plots
Plots of raw intensity data for individual variants that are generated by the genotyping platform and represent the extent to which the various genotypes can be discriminated: these provide a useful visual diagnostic for the genotyping data quality. Hardy–Weinberg equilibrium
(HWe). A theoretical description of the relationship between genotype and allele frequencies that is based on expectation in a stable population undergoing random mating in the absence of selection, new mutations and gene flow: in the context of genetic studies, departures from equilibrium can be used to highlight genotyping errors. Quantile-quantile plot
(Q-Q plot). In the context of GWA studies, a Q-Q plot is a diagnostic plot that compares the distribution of observed test statistics with the distribution expected under the null.
Cochran–Armitage test
A genotype-based contingency-table test for association that is well suited to the detection of trends across ordinal categories
(in this case, genotypes). Frequentist
A school of statistics that uses p values and combines them with hypothesis testing to make inferences.genotype clusters at a given SNP, and for assigning geno-
type calls to each1,53,72–74, with each generation of soft-
ware heralding steady improvements in both accuracy
and call rate. This last point is crucial because there is
no room for complacency regarding SNPs that display
low call rates1,53. Case–control studies, in particular, are
vulnerable to informative missingness, whereby spurious
associations arise through differences in the patterns of
missing data with respect to genotype.
Considerations such as these have highlighted a
tension between stringency and call rate, which has pre-
vented the promulgation of quality-control thresholds
with a universal value for defining the set of ‘clean’ geno-
types for analysis. If researchers aim to maximize accu-
racy by setting the threshold for calling genotypes too
high, the consequence for many SNPs will be a low call
rate (such that some true signals are discarded) and/or
a rise in type 1 error (due to informative missingness).
However, if a lower threshold is favoured, as some groups
have done1, call rates will be preserved (and informative
missingness minimized) at the expense of accuracy. Those
who adopt this more liberal strategy accept that a non-
trivial number of poorly performing SNPs will survive the
quality-control process and might be disproportionately
represented among the most extreme association signals.
If resources are not to be wasted in fruitless validation
and replication studies, it becomes essential to subject
interesting signals to individual reviews of quality-control
parameters (especially visualization of the signal intensity
(cluster) plots) (BOX 2) before proceeding. one corollary is
that GW A data-sharing efforts should extend to the pro-
vision of raw signal data as well as ‘finished’ genotypes.
once armed with a set of called genotypes, the
final phase of quality control beckons. experience has
shown that most SNPs showing extreme departures
from Hardy–Weinberg equilibrium (HWe) in controls can
be safely discarded1, although lesser (but nevertheless
quite marked) departures are to be expected under the
null hypothesis, given the number of tests performed.
Appropriate thresholds for any given study will depend
on the sample size and overall data quality, and might best
be defined by using the observed distribution of HWe
statistics (the WTCCC used this approach to set a thresh-
old at an exact p < 5.7 x 10–7 in controls)1. In any event,
HWe is an imprecise tool for quality control purposes.
Tests of departure from HWe are underpowered for the
detection of genotyping error75,76, whereas overenthusi-
astic use of HWe as a quality criterion can prove to be
counterproductive given that modest disequilibrium (in
cases particularly) can be a signature of true association.
Recent studies have emphasized the importance of
detecting individuals whose GW A data reveal an ancestry
that is discrepant with self-described labels, allowing them
to be removed from consideration1 or analysed separately.
Similarly, sample integrity needs to be confirmed using
recorded gender or previously obtained genotypes.
Inadvertent sample duplication and swaps, cross-contami-
nation and cryptic relatedness43 are frequently revealed by
analysis of GW A data, and, if unresolved, would violate
assumptions of statistical independence and introduce
misclassification effects. It is straightforward to identify
first- and second-degree relatives at least and exclude
one member of each pair from analysis. In the presence
of more remote relationships, options include explicit
modelling of those relationships, or adjustment through
genomic-control approaches43,44.
This sequence of manoeuvres has proved challenging
enough for experienced groups given the size of the data
sets concerned. However, all groups working with such
data, whether generated themselves or downloaded from
the web, need to appreciate the intricacies of data quality
control if they are to avoid potential misinterpretation.
The comments above refer to the implementation of SNP-
based scans, but the task of converting intensity traces
and SNP-based data into multi-allelic CNv genotypes is
far more demanding and is only now being tackled65.
Analysis and interpretation
Diagnostic plots. A limited number of formats have
emerged as standard tools for representing the data
emerging from a GWA scan, with the quantile-quantile
plot (Q-Q plot) among the most widely used52,77(BOX 2).
These plots help to indicate whether the study has gener-
ated more significant results than expected by chance and
to put such findings in context. undetected population
stratification or cryptic relatedness result in deviation
from the null across the entire distribution, whereas
large-effect susceptibility loci generate deviations at the
highly significant end of the range.
Single-point analyses. In most situations, the most power-
ful tool for the analysis of GW A data has been a single-
point, one degree of freedom test of association, such as the
Cochran–Armitage test. Such tests allow comparison of
the genotype distributions of cases and controls at each
SNP in turn, and can be conducted with or without
adjustment for relevant covariates, such as the principal
components of population substructure42. Although the
Cochran–Armitage method directly tests only one of sev-
eral possible genetic models, it has the merit of being robust
to modest deviations from additivity on the logistic scale
(at least to those most likely to be biologically relevant).
Furthermore, in situations in which the true model at the
causal variant is non-additive, even modest departures
from perfect lD will result in greatly reduced power to
detect that non-additivity at nearby variants: in the GW A
context therefore, in situations when few causal variants
will be directly typed, the additive model is likely to per-
form well. Whereas the use of alternative models (general,
dominant or recessive) could result in enhanced detection
of some signals78, the use of multiple correlated tests also
complicates computation of type 1 error rates and can
reduce the efficiency of subsequent follow-up efforts.
Historically, interpretation of genetic association find-
ings has adopted the standard frequentist approach to the
evaluation of significance. From such a view-point, GW A
results are compared against a single criterion of genome-
wide significance. Although several benchmarks have
been proposed, in european-descent GW A studies opin-
ion is coalescing around the need to adjust for 1–2 million
independent tests, which results in a target α (p value)of
~5 x 10–8(Refs 49,79,80). However, such an approach fails
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Quantile-quantile (Q-Q) plots provide a visual summary of the distribution of the observed test statistics generated by a genome-wide association (GWA) study 52,77. Typically, a single test statistic (for case–control studies, plots based on ~200 genotypes. In panel e the three clusters are well defined and individual genotypes are accurately called (as shown by the three colours). In panel f the clusters are well defined, but an error in Nature Reviews | Genetics C h r 1
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r 5
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Bayes’ factors
The Bayesian alternative to classical frequentist approaches to hypothesis testing, essentially equivalent to likelihood ratio tests: prior and posterior information are combined in a ratio that measures the strength of the evidence in favour of one model rather than the other. False-positive report probability
The probability that a reported association between a genetic variant and a trait of interest is not true.
Haplotype-based methods Association methods that rely on the relationship between the distribution of estimated haplotype frequencies and trait status, rather than each individual variant in turn. Imputation methods
A set of approaches for filling in missing genotype data using a sparse set of genotypes (for example, from a GWA scan) and a scaffold of linkage disequilibrium relationships (as provided by the HapMap).to account for factors such as the power of a study and the
number of likely true positives, which are important com-
ponents of any comprehensive evaluation of GW A find-
ings81. Consider a small case–control GW A performed
for a condition for which there is only weak evidence for
genetic involvement: any associations found are unlikely
to be genuine, whatever the p value obtained. Such
limitations have excited interest in Bayesian approaches,
which incorporate information on the likely number
of true associations, and the power of a given study to
detect associations of a given magnitude, into estimates of
credibility81,82. These methods, which generate measures
such as Bayes’ factors, or the false-positive report probability
rather than p values, avoid a single threshold for genome-
wide significance but depend on the ability to assign
plausible probabilities to each of the alternate hypoth-
eses (see Ref.1 for further discussion). Nevertheless,
both approaches conclude that only very low p values
equate to strong evidence for association (that is, are
associated with a low false-positive rate). Crucially, most
GW A signals that have attained such significance levels
have subsequently been confirmed by replication1.
Multi-marker analyses. As expected, given the incom-
plete coverage of common variation that is provided by
contemporary GWA platforms61,62, a modest boost to
power can be provided by computational approaches that
improve the detection of associations that are attribut-
able to variants that have not themselves been directly
typed83. Haplotype-based methods and imputation methods
are related but complementary approaches to achieve
this. In situations when haplotype-based analyses83,84
reveal evidence for association that exceeds that of any
directly typed SNP in the vicinity (after allowance for the
increased dimensionality), one can invoke either an effect
that is directly attributable to the haplotype (that is, inde-
pendent causal cis effects at multiple SNPs) or the expla-
nation that the haplotype tags more efficiently than any
individual genotyped SNP, an as yet untyped aetiological
variant. Imputation methods63,64 rely on information from
sets of resequenced and/or densely genotyped individuals
to infer missing genotypes at untyped variants. Because
data from the International HapMap Consortium49 are
typically used as the reference, imputation methods have
proved most powerful in recovering associations and
causal effects attributable to HapMap SNPs that are not
included on commercial arrays. Importantly, the use of
such methods is not restricted to samples drawn from
HapMap reference populations85.
Challenges. Immediate challenges in this area are numer-
ous. The imminent arrival of large-scale genome-wide
CNv data65 has focused attention on the development of
methods that are suited to the specific features of such
data (multiallelic, semi-quantitative and probably more
error prone) and methods that facilitate the integration of
CNv and SNP information. There is work to be done to
understand how best to incorporate the intrinsic uncer-
tainty that is associated with genotype calls derived from
both direct and, in particular, imputed data63,64, and in the
development of analysis tools that take account of, and/or
estimate, information on population history, which will
prove especially valuable for studies in genetic isolates86.
evaluation of the contribution of rare variants to com-
mon disease susceptibility raises issues related to detection
(rare variants are poorly captured by the standard GW A
arrays87) and functional assessment (the sheer number
of such variants and the limited power to test them for
association88). Finally, there is a need for improved meth-
ods to estimate the joint effects of multiple genes and/or
environmental exposures on disease predisposition. Such
analyses raise both computational and statistical issues,
related to the scale and complexity of the data and the
large number of hypotheses that could be addressed89.
Validation and replication
The importance of replication. The use of small sam-
ples, which are underpowered to detect loci of realistic
effect size, and over-liberal declarations of association
are the main reasons why so few of the complex-trait
associations that were claimed in the pre-GWA era
proved genuine81,90. This history, together with the high
dimensionality of GW A studies, their vulnerability to a
range of errors and biases, and the modest effect sizes
to be anticipated for most complex-trait susceptibility
alleles, help to explain the pre-eminent role of replication
in the evaluation of GW A findings91,92.
Terminology in this area can be confusing. Here,
we use ‘technical validation’ to refer specifically to the
reanalysis of original GWA samples using a second
genotyping platform. Technical validation allows early
detection of technical errors in typing or imputation that
might have generated a spurious association signal, and
is an important prelude to large-scale efforts to evalu-
ate selected signals in additional independent samples
(referred to here as ‘replication’).
Best practice. The aim of validation and replication is to
determine which of the findings arising from the primary
GW A reflect true reproducible associations. Accordingly,
the focus is not merely to provide additional evidence to
support or refute the original association, but also the
systematic appraisal of potential sources of error and bias
that could have been responsible. Hence, it is important
to use independent replication samples, and to use dis-
tinct genotyping assays to expose technical artefacts.
Credibility is increased when multiple investigative groups
find the same association in independent samples.
Claims of replication should be reserved for findings
involving the same allele or haplotype (or an established
proxy thereof), the same phenotype and the same genetic
model as the original signal. otherwise the risk of spurious
claims of association is increased by the testing of multiple
hypotheses. This has an important bearing on decisions
about the extent to which early replication efforts at a
given signal should focus exclusively on the index variant,
as opposed to including additional adjacent SNPs93. The
inclusion of adjacent SNPs runs the risk of generating a
profusion of apparent associations around spurious GW A
signals — complicating interpretation of the evidence
for replication — and can involve a substantial waste of
genotyping effort around the many false-positive signals.
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In most instances, therefore, it seems wise first to obtain definitive evidence of association at the index variant. So that failure to replicate can be meaningfully interpreted, the samples used in early rounds of replication should be broadly similar (with respect to ethnicity and ascertain-ment) to those of the original study. of course, as loci are validated as truly causal, extension into multiple ethnici-ties is highly desirable to test the generalizability and con-sistency of the proposed association, although such efforts will probably entail additional efforts at variant discovery given ethnic differences in lD.
Multi-stage designs. Several recent reports have empha-sized the potential advantages of multi-stage designs, in which signals from an initial, first-stage GW A are used to define a subset of SNPs that are retyped in additional second-stage samples94–96. Such designs have been seen as an effective way of retaining power while reducing geno-typing costs. However, the substantial price differential between commodity and custom genotyping means that those cost benefits can be less dramatic than comparisons of genotype numbers alone would suggest. In circum-stances in which the second-stage and GW A samples have similar provenance, so that the prospects for appreciable genetic heterogeneity between the stages are low, the best-powered analytical strategy involves a joint analysis (in which the distribution of test statistics across the data from both stages combined is considered), rather than the conventional replication design (which considers the second-stage results in isolation)94. Such considerations blur the boundaries of where exactly replication starts, but whichever analytical approach is taken, confirmation in many independent samples is important and it is the over-all strength of the evidence of association that matters.Power, sample size and heterogeneity. An appreciation of power and sample size is central to the design and interpre-tation of appropriate replication studies. Studies that lack the power to offer convincing support or refutation of the original finding can generate misleading inferences when considered in isolation, although combinations of such studies might be of value provided that all suitable studies have been included. Calculations of replication sample size need to consider the so-called ‘winner’s curse’ effect, whereby the original study will typically overestimate the true effect size97. Replication efforts that fail to make such allowance will probably be underpowered98,99.
If well-performed replication studies confirm the original findings, then the evidence in favour of associa-tion is enhanced (unless of course both the original and replication studies have succumbed to the same errors). Interpretation of a failure to replicate is more difficult. If it is clear that the replication studies were well powered and well performed, and that there is genuine divergence between the effect-size estimates (for example, no overlap of 95% confidence intervals), then there are two possible explanations. either the original finding was wrong, or the difference in findings is attributable to some source of heterogeneity100,101. The list of potential causes of hetero-geneity is long: it includes variable patterns of lD between the genotyped SNP and untyped causal alleles (although this is unlikely if the samples are of similar ancestry); dif-ferences in the distribution, frequency or effect size of the causal alleles at a given locus (due to, for example, drift or selection, or differences in case ascertainment); and the impact of non-additive interactions with other genetic variants or environmental exposures.
We should be wary of appealing to heterogeneity as a rationale for failure to replicate, as over-eagerness to deploy such an explanation would mean that no report of association could ever be refuted. Nevertheless, there are established instances in which the effects of proven associations can vary substantially across studies, so clearly circumstances exist where it can be justified. The role of variants in the fat mass and obesity associ-ated (FTO) gene on the risk of diabetes and obesity (BOX 3) provides one such example, illustrating how a clear case for heterogeneity can be made, especially when the initial association finding has been robustly replicated and the source of the heterogeneity is apparent1,5,36,37. Some sources of heterogeneity can be directly evaluated, including dif-ferences in lD structure between populations (particu-larly once the true causal allele has been identified), clear variation in case ascertainment that can be correlated with effect size or a highly significant interaction with a well-measured covariate. other sources of heterogeneity, such as an unmeasured or poorly defined environmental exposure, will be difficult or impossible to demonstrate. The number of associations for which heterogeneity has an important role cannot be reliably estimated from the evidence to date: because our inventory of ‘proven’ com-plex-trait associations is heavily biased towards variants that have shown consistent replication across studies (this being such an important factor in determining proof), there is likely to be a substantial under-representation
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heterogeneity 102. Moreover, estimation of between-study heterogeneity is highly imprecise when the number of studies is limited (less than 10) and/or their individual power is low. Systematic efforts to define the impact of gene–gene and gene–environment interactions, well-powered GW A studies in multiple ethnicities and com-prehensive exploration of variation around strong signals of association should all help to address this point.
Finding additional susceptibility genes
Meta-analysis. Individual GW As are underpowered to detect all but the biggest effects, and the susceptibility variants identified so far are probably only a subset of the loci that would be detectable using this approach if power were increased 39. Joint (meta) analysis of data
from comparable GWA scans 9,34,35,38,103 provides a low-cost approach to enhance power for both main and joint (gene–gene and gene–environment) effects, obtain in silico replication, inform SNP selection for subsequent replication efforts and explore potential sources of heter-ogeneity. In type 2 diabetes, joint analysis of three GW A scans (4,700 cases and 5,700 controls when combined)5–7,9 was central to the robust identification of several novel susceptibility variants, and allowed confirmation of the role of previously known susceptibility variants of modest effect size, such as those in KCNJ11 (potassium inwardly-rectifying channel, subfamily J, member 11) and PPARG (peroxisome proliferator-activated receptor gamma).
As increasingly large data sets are deployed to find signals for which smaller samples were underpowered, the average effect size of the novel variants that are dis-covered will decrease. For some purposes, such as pre-dictive diagnostics, this has implications for their likely translational impact. even so, it is worth remembering that owing to imperfect lD, a modest finding from the initial GWA might ultimately lead to fine mapping of variants with considerably larger effect sizes. When it comes to insights into disease pathogenesis, however, locus effect size is almost immaterial: even loci with modest effects, once they are established as genuine, can reveal novel causal mechanisms. Thus, discovery efforts are likely to continue to bear fruit as long as the clinical, logistical and financial resources are available to support them, and, as genotyping costs fall, ever larger studies will become possible.
The technical aspects of successful data combination have been widely discussed in the meta-analysis litera-ture 100,104. Clearly, researchers seeking to obtain an unbi-ased estimate of the significance of a given association are duty-bound to be as inclusive as possible 90. Although it might be tempting to exclude particular studies on the basis of some perceived failure of quality or potential het-erogeneity, any such decisions must be carefully justified. In the presence of heterogeneity, random-effects models provide more appropriate estimates of overall effect size, because heterogeneity violates the basic assumption of fixed-effects analysis 100. Although summary-level data will suffice for many meta-analysis purposes, access to primary individual-level data allows for more sophisti-cated reanalysis, including the capacity to undertake hap-lotype and conditional analyses, to perform imputation, to examine the joint effects of genes and environment, and to explore phenotypic heterogeneity.
Reintroducing biology. So far, most efforts at replication have concentrated, not unreasonably, on the signals for which the statistical evidence is strongest. However, the efficient identification of additional susceptibility loci with more modest effect sizes might benefit from the integration of statistical evidence with some assessment of functional candidacy. Several prioritization strategies for defining variant subsets with increased ‘prior’ odds for association can be envisaged, embracing aspects of bio-logical candidacy (for example, variants mapping to genes that interact with, or lie within the same pathways as, those
Box 4 | Strategies for resequencing within genome-wide association signals
Because genome-wide association (GWA) studies directly genotype only a small
proportion of the variants that segregate within the population examined, it is unlikely that the causal variant(s) will be among those for which genotype data are available. Imputation methods 63,64 allow association analysis to be extended to a larger set of variants (HapMap SNPs for which reliable imputation is possible), but this still comprises a minority of all common SNPs, and representation of rare variants is poorer still 87. Targeted resequencing allows recovery of a more complete inventory of sequence variation
within regions of interest, and enables systematic fine-mapping efforts to identify those putatively causal variants with the strongest effects on disease susceptibility.
For any given region, the extent and scope of resequencing efforts depends on the strategic goal. Consider the example of the fat mass and obesity associated (FTO ) region that is implicated in obesity and type 2 diabetes (see the figure)1,5,36. Association analysis using directly typed (black) and imputed (grey) SNPs localized the association signal to a 47 kb region (A–B) defined by flanking recombination hot spots (red trace), within intron 1 of the FTO gene. If the goal is to identify common causal variants that could underlie this index signal, it should suffice to resequence this ~50 kb interval in ~200 individuals (a total of ~10 Mb). If the aim is, instead, to identify all common variants contributing to regional susceptibility (including those independent of the index signal, so-far missed owing to incomplete coverage) then all sequence that is relevant to gene expression or function (arbitrarily, C–D) needs to be considered (~600 kb, hence a total of ~120 Mb). Should the aim extend to identification of rare variants with independent effects on disease risk, recovery of the full allelic spectrum of sequence variants will require deep resequencing in ~500–1000 individuals: in the first instance, such deep resequencing efforts might be preferentially targeted to exonic and conserved non-coding sequence. AKTIP , AKT interacting protein; CHD9, chromodomain helicase DNA binding protein 9; IRX3, iroquois homeobox 3; IRX5, iroquois homeobox 5; RBL2, retinoblastoma-like 2; RPGRIP1L , retinitis pigmentosa GTPase regulator interacting protein 1-like.
40800
5
10
15
2
4c M p e r M b
–l o g 10(P )
c M f r o m h i t S N P
Genes
51.5
52.0
52.5
53.0
53.5
Chromosomal position (Mb)
CHD9
AKTIP
IRX3
RPGRIP1L
FTO
RBL2
IRX5
AB C
D
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that were previously implicated in susceptibility to the dis-ease of interest) and genomic annotation (for example, all variants capturing variation in microRNA target sites).
Other populations. Among the GWA scans that have already been performed there has been a strong bias towards samples of North european origin1–38. There are excellent reasons for extending analyses to samples from populations with differing mutational and demo-graphical histories, including other major ethnic groups49 and population isolates86. each such sample offers new opportunities in terms of detectable susceptibility vari-ants, generating ethnic-specific patterns with respect to the identity of the loci themselves, the frequency and lD relationships of disease-susceptibility alleles, and the presence of additional genetic or environmental factors with which they might interact. Studies in other popula-tions are therefore capable of revealing novel suscepti-bility loci and aetiological pathways, as well as assisting with the fine-mapping of causal variants within those loci already confirmed105. Given the large sample sizes that are needed to achieve these ends, the ascertainment of GW A and replication sample sets from diverse ethnic groups is a priority.
Other sequence variants. Finally, it is worth reiterating that, so far, GW A scans have focused almost exclusively on the detection of effects that are attributable to com-mon SNPs. The first wave of GW A arrays offered limited power to capture structural variants106 and rare variants of any type87. The arrival of tools that are better-suited to the direct evaluation of these variant types is likely to provide the most immediate source of novel susceptibil-ity loci, although each brings its own set of technical and analytical challenges65.Following-up confirmed signals
The confirmed signals emerging from GWA scans and subsequent replication efforts are just that — associa-tion signals. The causal variants will only occasionally be among those that are directly typed in GWA scans, and the interval within which the aetiological variant(s) are expected to lie (typically defined in terms of flank-ing recombination hot spots) can be sizeable, often con-taining several genes1,24. even worse, because it seems likely that many complex-trait susceptibility effects are mediated through remote regulatory elements107, the coding exons of the susceptibility gene could lie well beyond the interval of maximal association.
Resequencing and fine mapping. The task of moving from confirmed association signal to complete enu-meration of the pattern of causal variants at a given locus poses significant challenges. on occasion, useful shortcuts to exhaustive fine mapping might be avail-able. The set of associated variants might include a number with particularly strong biological credentials — a non-synonymous coding SNP in a compelling bio-logical candidate, for example. Alternatively, clues can be gathered from expression studies108–110: the cluster of associated variants might display strong cis associations with expression of one of the nearby genes, transcript levels of which are themselves associated with the phe-notype of interest13,24. Although caution is warranted in placing weight on in silico or in vitro functional data (the concordance between epidemiological associa-tions and measurable functional effects has historically been poor111), such findings can provide a rapid route towards direct functional confirmation of the implicated molecular mechanisms.
In the absence of such insights, further progress will generally require exhaustive examination of the region, first, to generate a comprehensive inventory of regional variation, and second, to use fine-mapping approaches to define the signals with the strongest statistical claims112. Despite recent advances in sequencing and genotyping, both are daunting tasks.
Resequencing plans need to be tailored to the desired objective (BOX 4). Implementation raises a host of unanswered issues related to optimal study design and data interpretation. There are numerous choices to be made: first, about the samples to be resequenced, that is, the balance of HapMap individuals versus disease cases and whether to favour cases carrying the known susceptibility variant or haplotype; second, the depth of resequencing to be undertaken, which defines the allele spectrum recovered; and finally, the merits of extend-ing the search for rare variants beyond well-annotated sequence, given the difficulties associated with obtain-ing robust evidence for a statistical or functional effect for rare variants in unannotated sequence (BOX 4). Related challenges lie in the development of effi-cient strategies for fine mapping that take into account the desire both to discriminate between highly corre-lated variants (to determine which variants are causal) and to search for additional, independent signals. Given the relatively high price of custom genotyping,
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Mendelian randomization An analytical approach that allows one to test for a causal relationship between two phenotypes that show observational associations, but are subject to confounding: Mendelian randomization makes use of the random segregation of susceptibility alleles at meiosis to explore causality in a model that is freed from most sources of confounding.exhaustive fine-mapping efforts across multiple sig-
nals can cost far more than the original GWA scan.
Multi-stage approaches to fine mapping (homing
in on causal variants through successive rounds of
genotyping, whereby ever fewer SNPs are typed in
increasingly large samples) might seem efficient but
they can involve costly, time-consuming investment
in successive assay designs. Given inter-ethnic differ-
ences in patterns of lD and mutational histories (see
above), samples from other ethnicities (especially
those of African origin) should be extremely valuable
tools for fine mapping, and considerable effort is being
expended to identify samples on the scale required for
robust inference.
Function and translation. once all the statistical evi-
dence has been extracted and putative causal variants
are identified, substantial challenges remain (BOX 5).
Prominent among these is the need to obtain func-
tional confirmation that the variants implicated are
truly causal, and to reconstruct the molecular and
physiological mechanisms whereby they have an impact
on the phenotype of interest. Because many of the
complex-trait susceptibility variants so far identified map
to sequence of unknown function that is some distance
from the nearest coding sequence5–9,15,17,25–27, the design
of contextually appropriate functional assays is far from
straightforward. Improved tools for the functional anno-
tation of the genome (for example, from the eNCoDe
project)107 will be essential, but generating such annota-
tions for all tissues and/or cells of biomedical relevance
remains a monumental task.
It is also important to move beyond the selected
samples used in the discovery phase and evaluate the
impact of the variants on unbiased population sam-
ples. From an epidemiological perspective, consensus
guidelines have been proposed to grade the strength of
the epidemiological evidence for a given association113.
These take into account the extent of the evidence, the
consistency of the replication and the protection from
bias in the accumulated data. Population-based studies
will enable research to establish whether, using studies of
the joint effects of genes and environment and Mendelian
randomization approaches114, genetic discoveries can be
used to pinpoint modifiable environmental exposures.
Such analyses will require very large study samples for
which both genetic and environmental exposures are
accurately measured58,59.
Finally, the ultimate objective of genetic research
lies in the translation of the findings into advances in
clinical care (BOX 6). The mechanistic insights generated
by gene discovery might identify new therapeutic tar-
gets and lead to novel pharmaceutical and preventative
approaches. In addition, there is growing expectation
that individual patterns of genetic predisposition will
be of value in health-care delivery (personalized medi-
cine). For many, but not all115, diseases the modest effect
sizes of the variants emerging from GW A studies limits
the degree to which this is possible (BOX 7). Higher pen-
etrance, lower frequency variants that are not detected
by current GWA approaches but are amenable to new
high-throughput sequencing efforts might prove more
valuable in this respect116.
Reporting and deposition
Because wide availability of data is central to the success
of many of these endeavours, there has been substan-
tial investment in structures to support data-sharing
between investigators, such as the National Institutes
of Health (NIH)-supported dbGAP programme117, and
the european Bioinformatics Institute-based european
Genotyping Archive. Moreover, the NIH recently
announced a policy for sharing of data obtained in NIH
supported or conducted GW A studies. Although aggre-
gate or summary data (genotype frequencies and asso-
ciation p values by group as provided by the WTCCC
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and the National Cancer Institute Cancer Genetic Markers of Susceptibility (CGeMS) studies, for exam-ple 1,118) provide an excellent and convenient substrate for many data-sharing purposes, individual-level data (including raw signal-intensity information) provide a more valuable resource that supports a wider range of analyses (see above).
lack of transparency and incomplete reporting of both data and study methods have raised concerns in many health research fields 119–121, and poor reporting has been associated with biased estimates of effect 122. The importance of transparency was emphasized by the NCI-NHGRI Working Group on Replication in Association Studies 91. To help remedy this problem, some groups have advocated the development of evidence- based reporting guidelines similar to the consolidated standards of reporting trials (CoNSoRT) guidelines for the reporting of randomized clinical trials 123. In
have been identified, shedding light on the fundamental mechanisms that influence disease predisposition, and much is being learned about the complex relationships between changes in genome sequence and phenotypic variation.
However, we are far from the end of this particular voyage, and recent discoveries are nothing more than initial forays into the terra incognita of our genomes. We remain unable to explain more than a small proportion of observed familial clustering for most multifactorial traits, a fact that emphasizes the need to extend analy-sis to a more complete range of potential susceptibility variants, and to support more explicit modelling of the joint effects of genes and environment. Many of the greatest challenges to be faced in the years ahead lie not so much in the identification of the association signals themselves, but in defining the molecular mecha-nisms through which they influence disease risk and/or phenotypic expression.
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1. Wellcome T rust Case Control Consortium. Genome-
wide association study of 14,000 cases of seven
common diseases and 3,000 shared controls. Nature
447, 661–678 (2007).
In this study, high density, genome-wide association data on 17,000 individuals identified many novel
complex-trait susceptibility loci and explored key
methodological and technical issues relevant to the GWA approach.
2. T odd, J. A. et al. Robust associations of four new
chromosome regions from genome-wide analyses of
type 1 diabetes. Nature Genet. 39, 857–864 (2007).
3. Hakonarson, H. et al. A genome-wide association
study identifies KIAA0350 as a type 1 diabetes gene.
Nature 448, 591–594 (2007).
4. Sladek, R. et al. A genome-wide association study
identifies novel risk loci for type 2 diabetes. Nature
445, 881–885 (2007).
5. Zeggini, E. et al. Replication of genome-wide
association signals in UK samples reveals risk loci for
type 2 diabetes. Science 316, 1336–1341 (2007). 6. Scott, L. J. et al. A genome-wide association study of
type 2 diabetes in Finns detects multiple susceptibility variants. Science 316, 1341–1345 (2007).
7. Diabetes Genetics Initiative. Genome-wide association
analysis identifies loci for type 2 diabetes and
triglyceride levels. Science 316, 1331–1336 (2007).
8. Steinthorsdottir, V. et al. A variant in CDKAL1
influences insulin response and risk of type 2 diabetes.
Nature Genet. 39, 770–775 (2007).
9. Zeggini, E., Scott, L. J., Saxena, R., Voight, B. &
DIAGRAM Consortium. Meta-analysis of genome-wide
association data and large-scale replication identifies
several additional susceptibility loci for type 2 diabetes.
Nature Genet. 30 Mar 2008 (doi:10.1038/nrg.120). 10. Parkes, M. et al. Sequence variants in the autophagy
gene IRGM and multiple other replicating loci
contribute to Crohn’s disease susceptibility. Nature
Genet. 39, 830–832 (2007).
11. Duerr, R. H. et al. A genome-wide association study
identifies IL23R as an inflammatory bowel disease
gene. Science 314, 1461–1463 (2006).
12. Rioux, J. D. et al. Genome-wide association study
identifies new susceptibility loci for Crohn disease and
implicates autophagy in disease pathogenesis. Nature
Genet. 39, 596–604 (2007).
13. Libioulle, C. et al. novel Crohn disease locus identified
by genome-wide association maps to a gene desert on 5p13.1 and modulates expression of PTGER4. PLoS
Genet. 3, e58 (2007).
14. Hampe, J. et al. A genome-wide association scan of
nonsynonymous SnPs identifies a susceptibility
variant for Crohn disease in ATG16L1. Nature Genet.
39, 207–211 (2007).
15. Gudmundsson, J. et al. Genome-wide association
study identifies a second prostate cancer susceptibility variant at 8q24. Nature Genet. 39, 631–637 (2007).
16. Gudmundsson, J. et al. T wo variants on chromosome
17 confer prostate cancer risk, and the one in TCF2
protects against type 2 diabetes. Nature Genet. 39,
977–983 (2007).
This paper is one of the clearest demonstrations so far of the potential for pleiotropy: the same
variants in TCF2 influence risk to both type 2
diabetes and prostate cancer.
17. Yeager, M. et al. Genome-wide association study of
prostate cancer identifies a second risk locus at 8q24.
Nature Genet. 39, 645–649 (2007).
18. Thomas, G. et al. Multiple loci identified in a genome-
wide association study of prostate cancer. Nature
Genet. 40, 310–315 (2008).
19. Gudmundsson, J. et al. Common sequence variants
on 2p15 and Xp11.22 confer susceptibility to
prostate cancer. Nature Genet.40, 281–283 (2008).
20. Eeles, R. A. et al. Multiple newly identified loci
associated with prostate cancer susceptibility. Nature
Genet.40, 316–321 (2008).
21. Easton, D. F. et al. Genome-wide association study
identifies novel breast cancer susceptibility loci.
Nature 447, 1087–1093 (2007).
22. Hunter, D. J. et al. A genome-wide association study
identifies alleles in FGFR2 associated with risk of
sporadic postmenopausal breast cancer. Nature
Genet. 39, 870–874 (2007).
23. Stacey, S. n. et al. Common variants on chromosomes
2q35 and 16q12 confer susceptibility to estrogen
receptor-positive breast cancer. Nature Genet. 39,
865–869 (2007).
24. Moffatt, M. F. et al. Genetic variants regulating
ORMDL3 expression contribute to the risk of
childhood asthma. Nature 448, 470–473 (2007).25. Helgadottir, A. et al. A common variant on
chromosome 9p21 affects the risk of myocardial
infarction. Science 316, 1491–1493 (2007).
26. McPherson, R. et al. A common allele on chromosome
9 associated with coronary heart disease. Science
316, 1488–1491 (2007).
27. Samani, n. J. et al. Genomewide association analysis
of coronary artery disease. N. Engl. J. Med. 357,
443–453 (2007).
28. Gudbjartsson, D. F. et al. Variants conferring risk of
atrial fibrillation on chromosome 4q25. Nature 448,
353–357 (2007).
29. Willer, C. J. et al. newly identified loci that influence
lipid concentrations and risk of coronary artery
disease. Nature Genet. 40, 161–169 (2008).
30. Kathiresan, S. et al. Six new loci associated with blood
low-density lipoprotein cholesterol, high-density
lipoprotein cholesterol or triglycerides in humans.
Nature Genet. 40, 189–197 (2008).
31. Kooner, J. S. et al. Genome-wide scan identifies
variation in MLXIPL associated with plasma
triglycerides. Nature Genet. 40, 149–151 (2008).
32. Weedon, M. n. et al. A common variant of HMGA2 is
associated with adult and childhood height in the
general population. Nature Genet. 39, 1245–1250
(2007).
This paper demonstrates the power of the GWA
approach to identify genes influencing continuous
biomedical phenotypes, in this case, height.
33. Sanna, S. et al. Common variants in the GDF5-UQCC
region are associated with variation in human height.
Nature Genet. 40, 198–203 (2008).
34. Weedon, M. n. et al. Genome-wide association
analysis identifies 20 loci that influence adult height.
Nature Genet. (in the press).
35. Lettre, G. et al. Genome-wide association studies
identify 10 novel loci for height and highlight new
biological pathways in human growth. Nature Genet.
(in the press).
36. Frayling, T. M. et al. A common variant in the FTO
gene is associated with body mass index and
predisposes to childhood and adult obesity. Science
316, 889–894 (2007).
37. Scuteri, A et al. Genome-wide association scans shows
genetic variants in the FTO gene are associated with
obesity-related traits. PLoS Genet.3, e115 (2007).
38. Loos, R. J. F. et al. Association studies involving over
90,000 people demonstrate that common variants
near to MC4R influence fat mass, weight and risk of
obesity. Nature Genet. (in the press).
39. Altshuler, D. & Daly, M. Guilt beyond a reasonable
doubt. Nature Genet.39, 813–815 (2007).
40. Li, M., Boehnke, M. & Abecasis, G. R. Efficient study
designs for test of genetic association using sibship
data and unrelated cases and controls. Am. J. Hum.
Genet. 78, 778–792 (2006).
41. Howson, J. M., Barratt, B.J., T odd, J. A. &
Cordell, H. J. Comparison of population- and family-
based methods for genetic association analysis in
the presence of interacting loci. Genet. Epidemiol. 29,
51–67 (2005).
42. Price, A. L. et al. Principal components analysis
corrects for stratification in genome-wide association
studies. Nature Genet. 38, 904–909 (2006).
43. Voight, B. F. & Pritchard, J. K. Confounding from
cryptic relatedness in case–control association
studies. PLoS Genet.1, e32 (2005).
44. Zheng, G., Freidlin, B. & Gastwirth, J. L. Robust
genomic control for association studies. Am. J. Hum.
Genet. 78, 350–356 (2006).
45. Paschou, P. et al. PCA-correlated SnPs for structure
identification in worldwide human populations.
PLoS Genet. 3, e160 (2007).
46. Tian, C. et al. Analysis and application of European
genetic substructure using 300K SnP information.
PLoS Genet. 4, e4 (2008).
47. Price, A. L. et al. Discerning the ancestry of European
Americans in genetic association studies. PLoS Genet.
4, e236 (2008).
48. Fellay, J. et al. A whole-genome association study of
major determinants for host control of HIV-1. Science
317, 944–947 (2007).
49. International HapMap Consortium. A haplotype map
of the human genome. Nature 437, 1299–1320
(2005).
50. Laird, n. M. & Lange, C. Family-based designs in the
age of large-scale gene-association studies. Nature
Rev. Genet. 7, 385–394 (2006).
51. Chen, W. M. & Abecasis, G. R. Family-based
association tests for genomewide association scans.
Am. J. Hum. Genet. 81, 913–926 (2007).
52. Clayton, D. G. et al. Population structure, differential
bias and genomic control in a large-scale, case–control
association study. Nature Genet. 37, 1243–1246
(2005).
This paper presents a detailed description of the
potential for bias and error to complicate the
analysis of large-scale genetic association data.
53. Plagnol, V., Cooper, J. D., T odd, J. A. & Clayton D. G.
A method to address differential bias in genotyping in
large-scale association studies. PLoS Genet. 3, e74
(2007).
54. Cupples, L. A. et al. The Framingham Heart Study
100k SnP genome-wide association study resource:
overview of 17 phenotype working group reports.
BMC Med. Genet. 8, S1 (2007).
55. Ridker, P. M. et al. Rationale, design, and
methodology of the Women’s Genome Health Study:
A genome-wide association study of more than
25,000 initially healthy American women. Clin. Chem.
54, 249–255 (2008).
56. Li, S. et al. The GLUT9 gene is associated with serum
uric acid levels in Sardinia and Chianti cohorts. PLoS
Genet. 3, e194 (2007).
57. Cordell, H. J. & Clayton, D. G. Genetic association
studies. Lancet 366, 1121–1131 (2005).
58. Wong, M. Y., Day, n. E., Luan, J. A., Chan, K.P &
Wareham, n. J. The detection of gene–environment
interaction for continuous traits: should we deal
with measurement error by bigger studies or
better measurement? Int. J. Epidemiol. 32, 51–57
(2003).
59. Wong, M. Y., Day, n. E., Luan, J. A. & Wareham, n. J.
Estimation of magnitude in gene–environment
interactions in the presence of measurement error.
Stat. Med. 23, 987–998 (2004).
60. Burke, W., Khoury, M. J., Stewart, A., Zimmern, R. L.
& Bellagio Group. The path from genome-based
research to population health: development of
an international public health genomics network.
Genet. Med. 8, 451–458 (2006).
61. Barrett, J. C. & Cardon, L. R. Evaluating coverage of
genome-wide association studies. Nature Genet. 38,
659–662 (2006).
62. Pe’er, I. et al. Evaluating and improving power in
whole-genome association studies using fixed marker
sets. Nature Genet. 38, 663–667 (2006).
63. Marchini, J., Howie, B., Myers, S., McVean, G. &
Donnelly, P. A new multipoint method for genome-
wide association studies by imputation of genotypes.
Nature Genet. 39, 906–913 (2007).
64. Servin, B. & Stephens, M. Imputation-based analysis
of association studies: candidate regions and
quantitative traits. PLoS Genet. 3, e114 (2007).
65. McCarroll, S. A. & Altshuler, D. M. Copy-number
variation and association studies of human disease.
Nature Genet. 39, S37–S42 (2007).
This paper gives an excellent summary of the
challenges to be addressed if large-scale genetic
association studies are to be extended to CNVs.
66. Scherer, S. W. et al. Challenges and standards in
integrating surveys of structural variation. Nature
Genet. 39, S7–S15 (2007).
67. Weiss, L. A. et al. Association between microdeletion
and microduplication at 16p11.2 and autism. N. Engl.
J. Med. 358, 667–675 (2008).
68. Sham, P., Bader, J. S., Craig, I., O’Donovan, M. &
Owen, M. DnA pooling: a tool for large-scale
association studies. Nature Rev. Genet. 3, 862–871
(2002).
69. Cargill, M. et al. A large-scale genetic association
study confirms IL12B and leads to the identification of
IL23R as psoriasis-risk genes. Am. J. Hum. Genet. 80,
273–290 (2007).
70. Wang, W. Y., Barratt, B. J., Clayton, D. G. &
T odd, J. A. Genome-wide association studies:
theoretical and practical concerns. Nature Rev.
Genet. 6, 109–118 (2005).
71. Hirschhorn, J. n. & Daly, M. J. Genome-wide
association studies for common diseases and complex
traits. Nature Rev. Genet. 6, 95–108 (2005).
72. nicolae, D.L,. Wu, X., Miyake, K. & Cox, n. J.
GEL: a novel genotype calling algorithm using
empirical likelihood. Bioinformatics 22,
1942–1947 (2006).
73. Rabbee, n. & Speed, T. P. A genotype calling
algorithm for affymetrix SnP arrays. Bioinformatics
22, 7–12 (2006).
74. Xiao, Y., Segal, M. R., Yang, Y. H. & Yeh, R. F.
A multi-array multi-SnP genotyping algorithm for
Affymetrix SnP microarrays. Bioinformatics 23,
1459–1467 (2007).
368 | MAy 2008 | voluMe 9 https://www.doczj.com/doc/c67696718.html,/reviews/genetics
?2008Nature Publishing Group
75. Wittke-Thompson, J. K., Pluzhnikov, A. & Cox, n. J.
Rational inferences about departures from Hardy–
Weinberg equilibrium. Am. J. Hum. Genet. 76,
967–986 (2005).
76. Cox, D. G. & Kraft, P. Quantification of the power of
Hardy–Weinberg equilibrium testing to detect
genotyping error. Hum. Hered. 61, 10–14 (2006). 77. Smyth, D. J. et al. A genome-wide association study of
nonsynonymous SnPs identifies a type 1 diabetes
locus in the interferon-induced helicase (IFIH1) region.
Nature Genet. 38, 617–619 (2006).
78. Lettre, G., Lange, C. & Hirschhorn, J. n. Genetic model
testing and statistical power in population-based
association studies of quantitative traits. Genet.
Epidemiol. 31, 358–362 (2007).
79. Risch, n. & Merikangas, K. The future of genetic
studies of complex human diseases. Science273,
1516–1517 (1996).
80. Hoggart, C. J. et al. Genome-wide significance for
dense SnP and resequencing data. Genet. Epidemiol.
32, 179–185 (2008).
81. Wacholder, S., Chanock, S., Garcia-Closas, M.,
El Ghormli, L. & Rothman, n. Assessing the
probability that a positive report is false: an approach
for molecular epidemiology studies. J. Natl Cancer
Inst. 96, 434–442 (2004).
This is an influential paper setting out the rationale for a Bayesian interpretation of genetic association findings, focusing on methods for establishing the
confidence with which any given positive
association can be regarded.
82. Wakefield, J. A Bayesian measure of the probability
of false discovery in genetic epidemiology studies.
Am. J. Hum. Genet. 81, 208–227 (2007).
83. De Bakker, P. I. et al. Efficiency and power in genetic
association studies. Nature Genet. 37, 1217–1223
(2005).
84. Morris, A. P. A flexible Bayesian framework for
modeling haplotype association with disease, allowing
for dominance effects of the underlying causative
variants. Am. J. Hum. Genet. 79, 679–694 (2006). 85. De Bakker, P. I. et al. T ransferability of tag SnPs in
genetic association studies in multiple populations.
Nature Genet. 38, 1298–1303 (2006).
86. Service, S. et al. Magnitude and distribution of linkage
disequilibrium in population isolates and implications
for genome-wide association studies. Nature Genet.
38, 556–560 (2006).
87. Zeggini, E. et al. An evaluation of HapMap sample
size and tagging SnP performance in large-scale
empirical and simulated data sets. Nature Genet.
37, 1320–1322 (2005).
88. Easton, D. F. et al. A systematic genetic assessment
of 1,433 sequence variants of unknown clinical
significance in the BRCA1 and BRCA2 breast
cancer-predisposition genes. Am. J. Hum. Genet. 81,
873–883 (2007).
89. Marchini, J., Donnelly, P. & Cardon, L. R.
Genome-wide strategies for detecting multiple loci
that influence complex diseases. Nature Genet. 37,
413–417 (2005).
90. Hirschhorn, J.n., Lohmueller, K., Byrne, E. &
Hirschhorn, K. A comprehensive review of genetic
association studies. Genet. Med. 4, 45–61 (2002). 91. nCI-nHGRI Working Group on Replication in
Association Studies. Replicating genotype–phenotype
associations: what constitutes replication of a
genotype–phenotype association, and how best can it
be achieved? Nature 447, 655–660 (2007).
This feature article is a thoughtful summary of the
main issues relating to replication of genetic
association studies.
92. Lohmueller, K. E., Pearce, C. L., Pike, M., Lander, E. S.
& Hirschhorn, J. n. Meta-analysis of genetic
association studies supports a contribution of
common variants to susceptibility to common disease.
Nature Genet. 33, 177–182 (2003).
93. Clarke, G. M., Carter, K. W., Palmer, L. J., Morris, A. P.
& Cardon, L. R. Fine mapping versus replication in
whole-genome association studies. Am. J. Hum. Genet.
81, 995–1007 (2007).
94. Skol, A. D., Scott, L. J., Abecasis, G. R. & Boehnke M.
Optimal designs for two-stage genome-wide association studies. Genet. Epidemiol. 31, 766–788 (2007).95. Wang, H., Thomas, D. C., Pe’er, I. & Stram, D. O.
Optimal two-stage genotyping designs for genome-
wide association scans. Genet. Epidemiol. 30,
356–368 (2006).
96. Müller, H. H., Pahl, R. & Sch?fer, H. Including
sampling and phenotyping costs into the optimization
of two stage designs for genome wide association
studies. Genet. Epidemiol. 31, 844–852 (2007).
97. Zollner, S. & Pritchard, J. K. Overcoming the winner’s
curse: estimating penetrance parameters from
case–control data. Am. J. Hum. Genet. 80, 605–615
(2007).
98. Yu, K et al. Flexible design for following up positive
findings. Am. J. Hum. Genet. 81, 540–551 (2007).
99. Gorrochurn, P., Hodge, S. E., Heiman, G. A.,
Durner, M. & Greenberg, D. A. non-replication of
association studies: ‘pseudo-failures’ to replicate?
Genet. Med. 9, 325–331 (2007).
100. Ioannidis J. P., Patsopoulos, n. A. & Evangelou, E.
Heterogeneity in meta-analyses of genome-wide
association investigations. PLoS ONE 2, e841 (2007).
101. Ioannidis J. P. non-replication and inconsistency in the
genome-wide association setting. Hum. Hered. 64,
203–213 (2007).
102. Moonesinghe, R., Khoury, M. J., Liu, T. &
Ioannidis, J. P. Required sample size and
nonreplicability thresholds for heterogeneous
genetic associations. Proc. Natl Acad. Sci. USA 105,
617–622 (2008).
103. The GAIn Collaborative Research Group. new models
of collaboration in genome-wide association studies:
the Genetic Association Information network. Nature
Genet. 39, 1045–1051 (2007).
104. Egger, M., Schneider, M. & Davey Smith, G. Spurious
precision? Meta-analysis of observational studies.
BMJ 316, 140–144 (1998).
105. Helgason, A. et al. Refining the impact of TCF7L2 gene
variants on type 2 diabetes and adaptive evolution.
Nature Genet. 39, 218–225 (2007).
106. Locke, D. P., et al. Linkage disequilibrium and
heritability of copy-number polymorphisms within
duplicated regions of the human genome. Am. J. Hum.
Genet. 79, 275–290 (2006).
107. EnCODE Project Consortium. Identification and
analysis of functional elements in 1% of the human
genome by the EnCODE pilot project. Nature 447,
799–816 (2007).
This is a detailed examination of the functional
annotation of a subset of the human genome,
which reveals the complexity of genomic
organization.
108. Stranger, B. et al. Population genomics of human
gene expression. Nature Genet. 39, 1217–1224
(2007).
109. Dixon, A. L. et al. A genome-wide association study
of global gene expression. Nature Genet. 39,
1202–1207 (2007).
110. Goring, H. H. et al. Discovery of expression QTLs
using large-scale transcriptional profiling in human
lymphocytes. Nature Genet. 39, 1208–1216 (2007).
111. Ioannidis, J. P. & Kavvoura, F. K. Concordance of
functional in vitro data and epidemiological
associations in complex disease genetics. Genet. Med.
8, 583–593 (2006).
112. Lowe, C. E. et al. Large-scale genetic fine mapping and
genotype–phenotype associations implicate
polymorphism in the IL2RA region in type 1 diabetes.
Nature Genet. 39, 1074–1082 (2007).
113. Ioannidis, J. P. et al. Assessment of cumulative
evidence on genetic associations: interim guidelines.
Int. J. Epidemiol. 37, 120–132 (2008).
114. Davey Smith, G. & Ebrahim, S. ‘Mendelian
randomization’: can genetic epidemiology contribute
to understanding environmental determinants of
disease? Int. J. Epidemiol. 32, 1–22 (2003).
115. Zheng, S. L. et al. Cumulative association of five
genetic variants with prostate cancer. N. Engl. J. Med.
358, 910–919 (2008).
116. Stratton, M. R. & Rahman, n. The emerging
landscape of breast cancer susceptibility. Nature
Genet. 40, 17–22 (2008).
117. Mailman, M. D. et al. The nCBI dbGaP database
of genotypes and phenotypes. Nature Genet. 39,
1181–1186 (2007).
118. Zheng, S. L. et al. Association between two unlinked
loci at 8q24 and prostate cancer risk among European
Americans. J. Natl Cancer Inst. 99, 1499–1501 (2007).
119. Von Elm, E. & Egger, M. The scandal of poor
epidemiological research. BMJ 329, 868–869
(2004).
120. Brazma, A. et al. Minimum information about a
microarray experiment (MIAME) — toward standards
for microarray data. Nature Genet. 29, 356–371
(2001).
121. Altman, D. & Moher, D. Developing guidelines for
reporting healthcare research: scientific rationale and
procedures. Med. Clin. (Barc). 125, 8–13 (2005).
122. Gludd, L. L. Bias in clinical intervention research.
Am. J. Epidemiol. 163, 493–501 (2006).
123. Altman, D. G. et al. The revised COnSORT
statement for reporting randomized trials:
explanation and elaboration. Ann. Intern. Med.
134, 663–694 (2001).
124. Von Elm, E. et al. The strengthening the reporting of
observational studies in epidemiology (STROBE)
statement: guidelines for reporting observational
studies. Lancet 370, 1453–1457 (2007).
125. Seminara, D. et al. The emergence of networks in
human genome epidemiology: challenges and
opportunities. Epidemiology 18, 1–8 (2007).
126. Ge, D. et al. WGAViewer: a software for genomic
annotation of whole genome association studies.
Genome Res. 3 Mar 2008 (doi:10.1101/
gr.071571.107).
127. Janssens, A. C. J.W, Gwinn, M., Subramonia-Iyer, S. &
Khoury, M. J. Does genetic testing really improve the
prediction of future type 2 diabetes? PLOS Med. 3,
e114 (2006).
Acknowledgements
Preparation of this article was supported by funding from the
European Commission to the MolPAGE Consortium (LSHG-
CT-2004-512066: MMcC) and by research grants from the
national Institutes for Health (nHGRI and nHLBI; GRA). We
thank our colleagues — particularly P. Donnelly, J. Marchini,
J. Barrett, E. Zeggini, C. Lindgren, M. Boehnke, F. Collins,
C. Spencer and
D. Altshuler for discussions and the reviewers
for their comments.
Competing interests statement
The authors declare competing financial interests: see web
version for details.
NATuRe RevIeWS |genetics voluMe 9 | MAy 2008 |369
?2008Nature Publishing Group
2008年（下）高等教育自学考试全国统一命题考试 财务管理学试卷及参考答案 第一部分选择题 一、单项选择题（本大题共20小题，每小题1分，共20分）在每小题列出的四个备选项中只有一个是符合题目要求的，请将其代码填写在题后的括号内。错选、多选或未选均无分。 1．企业资金运动的中心环节是 ( ) A．资金筹集 B．资金投放 C．资金耗费 D．资金分配 2．以股东财富最大化作为公司财务管理目标时，主要的评价指标是 ( ) A．每股市价 B．每股利润 C．每股股利 D．每股净资产 3．后付年金终值系数的倒数是 ( ) 丸资本回收系数 B．偿债基金系数 C．先付年金终值系数 D．后付年金现值系数 4．下列可用于衡量投资风险程度的指标是 ( ) A．概率 B．预期收益 C．标准离差率 D．风险价值系数 5．与商业信用筹资方式相配合的筹资渠道是 ( ) A．银行信贷资金 B．政府财政资金
C．其他企业单位资金 D．非银行金融机构资金 6．某企业发行债券，面值为1 000元，票面利率为10 010，每年年末付息一次，五年到期还本。若目前市场利率为8%，则该债券是 ( ) A．平价发行 B．溢价发行 C．折价发行 D．贴现发行 7．企业为满足支付动机所持有的现金余额主要取决的因素是 ( ) A．企业临时举债能力 B．现金收支的可靠程度 C．企业的销售水平 D．企业可承担风险的程度 8．利用存货模型计算最佳现金持有量时，需要考虑的成本是 ( ) A．持有成本与转换成本 B．短缺成本与持有成本 C．取得成本与短缺成本 D．短缺成本与转换成本 9．固定资产按使用情况所作的分类是 ( ) A．生产用固定资产和非生产用固定资产 B．经营用固定资产和管理用固定资产 C．自有固定资产和融资租入固定资产 D．使用中的固定资产、未使用的固定资产和不需用的固定资产 10.下列各项中，属于非贴现投资评价指标的是 ( ) A．净现值 B．获利指数 C．净现值率 D．平均报酬率
成功的经典公关案例分析_经典危机公关案例分析 矛盾的80%来自与缺乏沟通，很多事只要能恰当的沟通都会顺利 解决。当企业发生公关危急时沟通就是最必要的工作之一。首先要 与企业全体员工进行沟通，让大家了解事件细节，以便配合进行危 机公关活动，比如保持一直的口径，一直的行为等。而后要马上与 受害者进行沟通，主动联系受害者，以平息其不满的情绪，比如开 通专线电话接听相关投诉，负责人亲自慰问与会见受害人等，最好 抢在媒体与当事人接触前先与当事人沟通。接下来就是与媒体进行 沟通，必须第一时间向媒体提供真实的事件情况及随时提供事件发 展情况，因为如果你不主动公布消息媒体和公众就会去猜测，而猜 测推断出的结论往往是负面的。这个时候消费者很敏感，信心也很 脆弱，看到负面的消息后很容易相信，甚至是放大这个消息的危害 程度。所以，这个时候必须及时坦诚的通过媒体向大众公布信息与 事件处理进展，这样可以有效填补此时舆论的“真空期”，因为这 个“真空期”你不去填补它，小道消息、猜测，甚至是竞争对手恶 意散布的消息会填满它。而后就是与政府及相关部门进行沟通，得 到政府的支持或谅解，甚至是帮助，对控制事态发展有很大的帮助。同时也要对企业的合作伙伴如供应商、经销商等进行沟通，以免引 起误解及不必要的恐慌，如前面提到的金正集团，因为缺乏与合作 伙伴的沟通，导致了各方的恐慌，使事态恶化。 案例： 点评： 及时的沟通，真诚的态度，使索尼轻松度过了这次危机，没有造成更大的负面影响，相信很多读者都没听过这个事件吧?这就证明索 尼此次的危机公关处理的十分成功，也没有影响到索尼与佳能等合 作伙伴的关系。全面快速的真诚沟通是此次事件圆满处理的最大功臣。
小学六年级语文考试试题及答案 第一部分：基础知识积累与运用(32分) 一、读拼音写字、词，请注意书写规范、美观。(3分) jiǎnyēdǐngkésòubiān 二、用“√”画出下面句子中加点字的正确读音。(2分) 1.听到这个噩耗，小刘颤(zhànchàn)栗，小陈颤(zhànchàn)抖。 2.我狼吞虎咽(yānyànyè)地把食物吞进咽(yānyànyè)喉，咽 (yānyànyè)得说不出话来。 三、下列各组成语中，没有错别字的一组是()(2分) A.恍然大悟德高望重可见一斑狂风怒号 B.囫囵吞枣流连忘反张冠李戴抑扬顿挫 C.排山倒海无影无踪滔滔不决不解之缘 D.窃窃私语自做自受兴高采烈悬崖峭壁 四、根据下列提示，给对联加上不同的标点表示不同的意思。(3分) 一个人总是挨官司。过年时，他家人说：“今年谁都不能打官司。”并且贴了一幅对联，上面写着“今年好晦气少不得打官司”只是没加标点。结果小儿子来探亲时读出了这幅对联，让全家人十分惊讶。 家人应该是这样读的： 小儿子应该是这样读的： 五、根据“生气”这个词语的不同意思，各写一句话。(3分) 因不合心意而不愉快：
指生命力或活力： 六、下面一组句子中，没有语病的一句是()(2分) A.在老师的帮助下，他的作文水平进步了。 B.只要你为大家做好事，才会得到大家的信任。 C.我们认真听取并讨论了校长的报告。 D.通过这次活动，我们增加了友谊。 七、请将左右两边相对应的内容用线段连接起来，并完成填空。(6分) 大漠沙如雪轻烟老树寒鸦李贺 孤村落日残霞要留清白在人间于谦 玉颗珊珊下月轮殿前拾得露华新白朴 粉骨碎身全不怕燕山月似钩皮日休 1.当看到兄弟不和，骨肉相残的现象时，我们常会想起曹植《七步诗》中的名句“。”(2分) 2.我们做事情应该一气呵成，正如《左传》中所说的：“”(2分) 八、判断下面句子的说法是否正确，正确的打“√”，错误的打“×”。(2分) 1.“舍本逐末”比喻做事不抓住主要问题，而专顾细枝末节。近义词是“本末倒置”。() 2.《白雪公主》《拇指姑娘》《丑小鸭》这三篇童话都是安徒生写的。() 3.最早的诗歌总集《诗经》，已有两千多年的历史。() 4.中国第一位获得奥运会金牌的男运动员是李宁。()
绝密★启用前 2008年普通高等学校招生全国统一考试 （海南卷） 本试卷分第Ⅰ卷（阅读题）和第Ⅱ卷（表达题）两部分，其中第Ⅰ卷第三、四题为选考题，其他题为必考题。考生作答时，将答案答在答题卡上，在本试卷上答题无效。考试结束后，将本试卷和答题卡一并交回。 注意事项 1、答题前，考生务必先将自己的姓名、准考证号填写在答题卡上，认真核对条形 码上的姓名、准考证，并将条形码粘贴在答题卡的指定位置上。 2、答题时使用0.5 毫米黑色中性(签字)笔或碳素笔书写,字体工整\笔迹清楚. 3、请按照题号在答题卡上各题的答题区域(黑色线框)内作答,超出答题区域书写 的答案无效. 4、保持卡面清洁,不折叠,不破损. 5、做选考题时,考生按照题目要求作答,并用2B铅笔在答题卡上把所选题目对应 的题号涂黑. 第Ⅰ卷阅读题 甲必考题 一、现代文阅读（9分，每小题3分） 阅读下面的文字，完成1～3题。 所谓“变形”，是相对于“常形”而言。“常形”是指显示生活中客观物象的正常自然形态；“变形”是指客观五香反映带艺术中的形态的改变。在显示生活中， 由于种种原因，物象的形态有时会出现变异，例如两头蛇、三脚鸡等，这种“变 形”虽然怪异，但不是艺术美学研究的对象。艺术美学所研究的，是正常的自然 形态在艺术边县中的变化及其美学意义。 艺术上的“变形”氛围广义和狭义两种。从广义上说，任何种类和流派的艺术，不论其创作思想和手法多么不同，它所塑造的形象较之原形都会有某些强调、 选择、集中乃至改变。从这个意义上说，变形乃是不是艺术反映生活的一种普遍 现象。不过一般地说，艺术上关于“变形”的观念是指狭义的“变形”，它表现为 客观物象的几何图形所发生的改变。例如杜甫的《古柏行》：“霜皮溜雨四十围，
可口可乐危机公关典型性分析： 可口可乐在灭顶之灾中的危机公关 1999年6月初，比利时和法国的一些中小学生饮用美国饮料可口可乐，发生了中毒。一周后，比利时政府颁布禁令，禁止本国销售可口可乐公司生产的各种品牌的饮料。 已经拥有113年历史的可口可乐公司，遭受了历史上鲜见的重大危机。 在现代传媒十分发达的今天，企业发生的危机可以在很短的时间内迅速而广泛地传播，其负面的作用是可想而知的。短时间内在全国甚至全球范围的影响，必将引起社会和公众的极大关注。稍有不慎，即对企业形象和品牌信誉造成毁灭性的打击，其无形资产在倾刻之间贬值。这对企业的生存和发展，都是致命的伤害。 一、可口可乐的危机公关从一般市场角度看不具备典型性，原因是时机把握的不好，对于危机公关时间是第一因素，必须充分把握时间，让危机成本降到最底，同时将公关效果达到最好，负面影响降到最底。 二、危机公关是双刃剑，危机公关处理好了，品牌度反而会提升，处理不好造成的损失是不可估量的，甚至导致企业丧命，中国的危机公关一般来说方法上还不够高明，如秦池酒标王、三株口服液等等，都是危机公关处理不好导致企业丧命，还有就是南京的冠生园，危机公关方法不够导致了企业丧命，品牌崩溃。所以危机公关应该防患于未然，前期应该做好危机公关的准备，甚至是相关模拟场景的假设，不少企业面临危机公关缩手无策，甚至成为了媒介的仇人，方法是绝对不科学的，如果采取人性化处理和科学策略应对，消除影响就是时间问题，相反品牌反而更加深入人心。如杨森是典型的表现，这种做法不仅体现了企业的态度，同时表现了企业实力，更直接的表达了企业文化的理性和人性道德，当然获得消费者的认同是肯定的，事情查清后，因为外部原因造成的，反而会获得消费者的理解和支持，同时品牌认同度会升级。当然由于企业内部原因如产品不过关，是属于违法行为，不属于危机公关是危难公关。可口可乐的危机公关方法是按一般程序走的，但在时间上滞后是第一失误、按一般程序走是第二失误、在公关推进的策略上没有创新突破性，仅仅是资本投入性运行是第三失误，从危机公关本质上讲，是资本营救了企业，方法不够高明。 第三、危机公关应该是主动性公关。主动面队，并积极应对。如可口可乐设立了专线面向社会沟通，这种做法是正确的，危机公关任何非主动行为都是逃避责任的行为，所以危机公关主动面对，尽快处理是企业社会责任的表现，积极争取消费者的支持这种公关本身是需要策划的，需要有方法，就象如何消除消费者误解和获得其原谅一样，需要方法，这种方法是需要大企业，无论是国际还是国内企业都需要探讨的，如肯德基的苏丹红危机公关，虽然事情过去了，但当时并没有主动公关策略，造成了其品牌一定的损失，这中负面影响将成为一个长时期的话题，当然如果是企业内部原因，应该是危难公关事件，非危机公关事件了，消除其影响需要更长的时间和更大的投入，再有类似情况出现，其后果不堪设想了。 第四、危机公关可以是广告策略，不少公司特意设立危机公关并广告到市场上，其目的是更体现自己品牌道德，产品优势，当然要把握好度，不能玩火自焚或受到同行业的攻击。我不提倡，并给予好的称呼说是反营销，反什么，反市场是不理性的，反消费者更是行不通的，反同行业更是不明智的。反向策略方法倒是可以用，但要控制好分寸，否则自己也成为反营
危机公关案例分析-- 态度比方法更重要 案例分析：两位在西雅图工作的网络顾问——汤姆?法默（Tom Farmer）和沙恩?艾奇逊（Shane Atchison）在美国休斯敦希尔顿酒店的双树旅馆（Double tree Club）预订了一个房间，并被告知预订成功。 尽管他们到饭店登记的时间是在凌晨两点，实在是个比较尴尬的时间段，但他们仍然很安心，因为他们的房间已经预订好了。但在登记时，他们立刻被泼了一桶凉水，一位晚间值班的职员草率地告诉他们，酒店客房已满，他们必须另外找住处。这两位住客不仅没有得到预订的房间，而且值班人员对待他们的态度也实在难以用言语表达——有些轻蔑，让人讨厌。甚至在他们的对话过程中，这个职员还斥责了客人。
这两位网络顾问当时离开了，然后制作了一个严厉的但又不失诙谐幽默的幻灯文件，标题是“你们是个糟糕的饭店”。在这个文件里记述了整个事件，包括与那名员工之间不可思议的沟通。他们把这个幻灯文件电邮给了酒店的管理层，并复制给自己的几位朋友和同事看。 这一幻灯文件立刻成为有史以来最受欢迎的电子邮件。几乎世界各地的电子邮箱都收到了这份文件，从美国休斯顿到越南河内，还有两地之间的所有地区。这份幻灯文件还被打印和复印出来，分发到美国各地的旅游区。双树旅馆很快成为服务行业内最大的笑话，成为商务旅行者和度假者避之不及的住宿地。传统媒体的评论员们也将这一消息载入新闻报道和社论中，借此讨论公司对消费者的冷漠和网络对于公众舆论的影响力。 接着，法默和艾奇逊收到了3000多封邮件，大部分都是支持他们的。对此，酒店的管理层也迅速有礼而大度地作出反应。
双树旅馆毫不迟疑地向他们俩道歉，并用两个人的名义向慈善机构捐献了1000美元作为双树旅馆的悔过之举。双树的管理层还承诺要重新修订旅馆的员工培训计划，以确保将此类事件再次发生的可能性降到最低。另外，双树旅馆的一位高级副总裁在直播网络上与法默和艾奇逊就此事展开讨论，以证明饭店认真对待此事。 评析 首先，互联网无孔不入的威力挑战着传统的口口相传的传播方式，挑战的程度在该“双树旅馆事件”中表现得再清楚不过了。互联网强大的传播能力已成不争的事实。对此我们要给予充分关注，对互联网所向披靡之势要有足够的心理准备。 其次，两位客人和负责预订房间的服务生的互动，证明了酒店雇员的个人行为会严重影响到公司的声誉。这显然是个别雇员恶劣服务的丑闻成了公众舆论的关注点而引起的公关危机事件。
六年级语文练习题及答案大全 1、形近字组词。 弈俱援盂 奕惧缓孟 2、按要求写四字词语。 意思与“专心致志”相近的四字词语： 仿照“沧沧凉凉”写叠词： 3、选字填空。 诲悔 我会牢记老师的教的。 对以前的事，他感到很后。 至致 他离开后今还没有来信。 由于他粗心大意，使公司损失一百万。 解释带点字的意思，并翻译句子。 弈秋，通国之善弈者也。 ＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿
＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿ 为是其智弗若与？ ＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿__＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿ 我以日始出时去人近，而日中时远也。 ＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿_____________________________________＿＿＿＿＿＿＿＿＿＿＿_ 孰为汝多知乎？ ＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿ 1、阅读《学弈》后，回答下面的问题。 写出“之”在句子中的意思。 ①一人虽听之，一心以为有鸿鹄将至，思援弓缴而射之。 ②虽与之俱学，弗若之矣。 课文记叙了两个人跟弈秋学下围棋，一个
________________________，一个__________________________，告诉我们_____________________________。 2、阅读《两小儿辩日》后，回答下面的问题。 找出文中的3对反义词，写下来。 ＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿ 抄写文中的一个比喻句，并写出这个句子把什么比作什么。 ＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿ 2、写话： 学习了《两小儿辩日》这篇课文，你喜欢文中的哪一个人物？为什么？ ＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿参考答案： 1、弈俱援盂奕惧缓孟
中国十大企业危机公关案例
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2007中国十大企业危机公关案例 来源：中国管理传播网 岁末年初，如果让我们回顾2007年中国企业危机公关事件的话，就不难发现：与前几年相比，在刚刚过去的2007年，更多的企业经历了危机公关之痛。从手机、汽车、IT到食品、服饰、超市等行业，众多企业经历了各式各样的公关危机。接下来，就让我们从诸多危机公关案例事件中筛选十大具有代表意义的典型案例，以全面还原2007年中国企业的危机公关现状。（以事件发生先后为序） 1、LG翻新事件 LG翻新事件起源于2006年，在2007年上半年愈演愈烈。2007年1月，在地下翻新工厂遭曝光后，LG声称背后有人敲诈；2月份又有媒体曝光工商局封存5台LG疑似翻新空调，随后LG承认更换部分产品包装；3月，湖南省消费者张洪峰披露了湖南省质量检验协会的鉴定结果，确认“其购买的五台LG空调都是翻新机器”，5月份张洪峰通过博客再次披露了LG空调的质量问题。LG翻新事件随着全国媒体的不断报道，从LG冰箱翻新、LG空调翻新到LG彩电翻新，不断有新的猛料被曝光，LG品牌一时陷入了空前的品牌危机。 点评：在系列产品的翻新事件被曝光之后，LG方面躲躲闪闪，没有承认自己的错误，未能采取有效的应对措施，再加上广大网友在网络上对LG翻新行为的声讨，其品牌形象与企业声誉大打折扣。由此我们也看出了，作为国际知名品牌的LG在危机公关方面的无知与短视。 2、摩托罗拉手机爆炸事件 2007年6月19日在甘肃金塔县发生了全国首例手机电池爆炸致死事件，作为问题手机的制造商——摩托罗拉未能在第一时间内采取积极的应对措施，在事发大约10天之后，以推卸事件责任为出发点，将这起爆炸事件的责任
古语有云：好事不出门，坏事传千里。在交通如此不发达的古代古人尚且能够得出此结论，更别说是现在这个信息爆炸的“地球村”互联网时代了。 【危机公关】危机公关可以起到亡羊补牢降低企业公关危害的作用，有时候一个很小的产品问题或者是服务问题都有可能引发危机公关事件。 企业危机公关的概念，目前并没有一个既定概念，一般认为企业危机公关指的是企业通过有计划地实行一系列相关联的行为，达到减少或者避免危机给公司带来的损害。 危机公关一直是不少企业的痛处，如果危机公关不能处理好，企业危机很有可能被夸大甚至往妖魔化方向发展。 危机公关可以起到亡羊补牢降低企业公关危害的作用，有时候一个很小的产品问题或者是服务问题都有可能引发危机公关事件。公关人员有效地处理公关危机对于一个企业的正常顺利发展具有十分重要的意义。 危机来临时刻越能考验一个公司的抗压能力，一个成熟的的企业与其他企业之间的差别在此展现。往往一个优秀的企业越是在危机的时刻，越能显示出这个公司的综合实力和整体素质。 危机公关如果处理得当，也有可能成为转机。公关人员在处理危机公关时，如果能迅速做出适当反应，采取及时的补救措施，主
动地以危机事件为契机，可以采取品牌自黑的模式，变好事为坏事，灵活运用网络语言化“危”为“机”，借题发挥的基础上在进行真诚道歉。 这样而言，不但可以扩大企业的知名度和美誉度，还可以显示出公司的综合实力和整体素质。 但是企业如果觉得兹事体小，而对投诉事件放任不管，危机雪球就会越滚越大，一件很小的事件就很有可能就演变成为“星星之火，可以燎原”。 本文从危机公关角度分析一下2019年的危机公关事件，希望对大家的公关思维有所启发。 “村里才通网”的奔驰公关 奔驰事件的爆发，对于舆论说，让舆论沸腾的点，往往就是给大品牌贴负面标签的行为。 比如“奔驰漏油”、“店大欺客”、“乱收服务费”。 实际上出瓜群众并不是想要看到奔驰倒下，而是抱着一种看热闹不嫌事大的八卦心理来看待奔驰事件爆发。 奔驰事件之所以会有这么大的传播力，一是因为奔驰公关能力和其在汽车界的业界地位并不成正比。
六年级上册语文测试卷及答案 一、 基础知识。（5小题，共26分。） 1、 读音节，找词语朋友。（10分） 2、 读一读，加点字念什么，在正确的音节下面画“＿”。（4分） 镌．刻（ju ān ju àn ） 抚摩．（m ó m ē） 扁． 舟（bi ān pi ān ） 阻挠． （n áo r áo ） 塑．料（su ò s ù） 挫．折（cu ō cu ò） 归宿． （s ù xi ǔ ） 瘦削． （xi āo xu ē） 3、 请你为“肖”字加偏旁，组成新的字填写的空格内。（4 分） 陡（ ）的悬崖 胜利的（ ）息 俊（ ）的姑娘 （ ）好的铅笔 弥漫的（ ）烟 畅（ ）的商品 （ ）遥自在的生 活 元（ ）佳节 4、 按要求填空，你一定行的。（4分） “巷”字用音序查字法先查音序（ ），再查音节（ ）。按 部首查字法先查（ ）部，再查（ ）画。能组成词语 t áo zu ì n ín ɡ zh òn ɡ w ǎn li án ēn c ì h ú l ún t ūn z ǎo ( ) ( ) ( ) ( ) ( ) z ī r ùn ku í w ú zh ēn zh ì mi ǎn l ì xu án y á qi ào b ì ( ) ( ) ( ) ( ) ( )
（）。 “漫”字在字典里的意思有：①水过满，向外流；②到处都是； ③不受约束，随便。 （1）我漫．不经心地一脚把马鞍踢下楼去。字意是（）（2）瞧，盆子里的水漫出来了。字意是（） 剩下一个义项可以组词为（） 5、成语大比拼。（4分） 风（）同（）（）崖（）壁（）（）无比和（）可（）（）扬顿（）（）高（）重 ( )不（）席张（）李（） 二、积累运用。（3小题，共20分。） 1、你能用到学过的成语填一填吗？（每空1分） 人们常用﹍﹍﹍﹍﹍﹍来比喻知音难觅或乐曲高妙，用﹍﹍﹍﹍﹍来赞美达芬奇的《蒙娜丽莎》，当我们面对一篇好文章时，我们可以说﹍﹍﹍﹍﹍。 2、补充古文名句。（每空1分） （1）鲁迅先生说过：“﹍﹍﹍﹍﹍﹍﹍﹍，俯首甘为孺子牛。”（2）﹍﹍﹍﹍﹍﹍﹍﹍，此花开尽更无花。 （3）﹍﹍﹍﹍必寡信。这句名言告诉我们﹍﹍﹍﹍﹍﹍﹍﹍﹍﹍﹍﹍。 （4）但存﹍﹍﹍﹍，留与﹍﹍﹍﹍。 （5）大漠沙如雪，﹍﹍﹍﹍﹍﹍。
2008年全国硕士研究生入学统一考试英语试题 Section I Use of English Directions: Read the following text.Choose the best word(s)for each numbered blank and mark A,B,C or D on ANSWER SHEET1.(10points) The idea that some groups of people may be more intelligent than others is one of those hypotheses that dare not speak its name.But Gregory Cochran is大1家to say it anyway.He is that大2家bird, a scientist who works independently大3家any institution.He helped popularize the idea that some diseases not大4家thought to have a bacterial cause were actually infections,which aroused much controversy when it was first suggested. 大5家he,however,might tremble at the大6家of what he is about to do.Together with another two scientists,he is publishing a paper which not only大7家that one group of humanity is more intelligent than the others,but explains the process that has brought this about.The group in大8家are a particular people originated from central Europe.The process is natural selection. This group generally do well in IQ test,大9家12-15points above the大10家value of100,and have contributed大11家to the intellectual and cultural life of the West,as the大12家of their elites,including several world-renowned scientists,大13家.They also suffer more often than most people from a number of nasty genetic diseases,such as breast cancer.These facts,大14家,have previously been thought unrelated.The former has been大15家to social effects, such as a strong tradition of大16家education.The latter was seen
群星学校六年级语文月考试卷 班级姓名分数 时量：120分钟满分：100分 一、这句话，你很熟悉。请你拼一拼，认真写出句子。（4分） suí fēng qián rù yè，rùn wù xì wú shēng。 ___________________________________________________________ 二、选择题（将正确答案的一个序号填在括号里，每题2分，共20分） 1．下面的几组词，带点字读音完全一样的一组是（） A 峰峦．难．处喃．喃阻拦．波澜．壮阔 B 大臣．丞．相继承．路程．墨守成．规 C 嘹．亮辽．阔聊．天疗．养寥．若星辰 2．下面的几组词，完全正确的一组是（） A 污辱清廉督都竣工菜羹迥然不同 B 慰籍筋脉慷慨磅礴妥贴自相矛盾 C 防御允诺咨询脾胃投掷迫不及待 3．下列词语使用正确的一组是（） A 一泓秋水一眼水井一盏明灯一记耳光一绺月光 B 窃窃私语婉婉动听咄咄逼人栩栩如声丝丝入扣 C 人生坎坷山道崎岖悬崖陡峭高山巍峨山脉蜿蜒 4．下列各词没按一定顺序排列正确的一项是（） A 顿号逗号分号句号省略号 B 冰冷水热水水蒸汽开水 C 寒冷凉爽温暖炎热酷热 三、按要求写词语（9分）
1．找规律写词： A．虚实、动静、、 B．千里迢迢、大名鼎鼎、 C．曲曲折折、支支吾吾、 2．在这六年中，同学们，。（写两个表示同学间情谊的成语） 3．写出三个含有动物的成语：、、 4．你对你的语文老师熟悉吗？请用两个成语说说他（她）给你的印象、。 四、根据要求写句子（4分） 1．詹天佑是杰出的爱国工程师（缩句）。 2．仿造句式，在横线上写上两句与前后意思连贯的句子。 让自己的生命为别人开一朵花：一次无偿的献血是一朵花，一句关切的问候是一朵花，，……能为别人开花的心是善良的，能为别人生活绚丽而付出的人是不寻常的人。 3．这个地方太小了。（改成夸张句） 4．我国古代有句名言，就是说坏事虽小，但不能去做，干多了就变成了大坏事；好事虽小，也不能因为它小就不做，再大的好事就是从点滴开始的。（2分） 5．下面的故事分别出自哪部书，请写出书名，并写出作者。（3分） 过五关，斩六将《》（） 西天取经《》（） 负荆请罪《》（） 五、回顾我们学过的课文，回答下面的问题（12分） 1．《将相和》一课中的“将”指的是，“相”指的是 将相不和的原因是，“相”认为“将相和”就会，“将相不和”就会。
绝密★启用前试卷类型：B 2008年普通高等学校招生全国统一考试 理科综合能力测试 本试卷共12页，满分360分，考试时间150分钟。 注意事项： 1.答卷前，考生务必将自己的姓名，准考证号填写在试题卷和答题卡上，并将准考证号条 形码粘巾在答题卡上指定位置。 2.选择题每小题选出答案后，用2B铅笔将答题卡上，对应题目的答案标号涂写，如写改 动，用橡皮擦干净后，再选涂其它答案标号，答在试题卷上无效。 3.非选择题用0.5毫米的黑色墨水签字夂答在答题卡上每题对应的答题区域内，答在试题 卷上无效。 4.考试结束，请将本试题卷和答题卡一并上交。 选择题共21小题，第小题6分，共126分。 以下数据可供解题时参考： 相对原子质量（原子量）：H l C 12 O 16 Na 23 K 39 Mn 55 Cu 64 Zn 65 Ag 108 Pb 207 一、选择题（本题共13小题，在每小题给出的四个选项中，只有一项是符合题目要求的。） 1.为了验证胰岛素具有降低血糖含量的作用，在设计实验方案时，如果以正常小鼠每次注射药物前后小鼠症状的变化为观察指标，则下列对实验组小鼠注射药物的顺序。正确的是 A.先注射胰岛素溶液，后注射葡萄糖溶液 B.先注射胰岛素溶液，再注射胰岛素溶液 C.先注射胰岛素溶液，后注射生理盐水 D.先注射生理盐水，后注射胰岛素溶液 2.某水池有浮游动物和藻类两个种群，其种群密度随 时间变化的趋势如图，若向水池中投放大量专食浮游 动物的某种鱼（丙），一段时期后，该水池甲、乙、丙 三个种群中公剩一个种群。下列关于该水池中上述三 个种群关系及变化的叙述，正确的是 A.甲和丙既有竞争关系又有捕食关系，最终仅剩下甲种群 B.甲和乙既有竞争关系又有捕食关系，最终仅剩下丙种群 C.丙和乙既有竞争关系又有捕食关系，最终仅剩下甲种群 D.丙和乙既有竞争关系又有捕食关系，最终仅剩下丙种群 3.下列关于细菌的叙述，错误 ．．的是 A.硝化细菌能以NH,作为氮源和能源物质 B.某些细菌可以利用光能因定CO2合成有机物 C.生长因子是某些细菌生长过程中需要额外补弃的营养物质 D.含伊红和美蓝试剂的培养基不能用来签别牛奶中的大肠杆菌 4.已知某种限制性内切酶在一线性DNA分子上有3个酶切位点，如图中箭头所指，如果该线性DNA分子在3个酶切位点上都被该酶切断，则会产 生a、b、c、d四种不同长度的DNA片段。现在多个上述 线性DNA分子，若在每个DNA分子上至少有1个酶切位 点被该酶切断，则从理论上讲，经该酶切后，这些线性
十大企业危机公关案例 The Standardization Office was revised on the afternoon of December 13, 2020
2007中国十大企业危机公关案例 2008/1/3/07:16 来源：中国管理传播网作者：未然 1、LG翻新事件 .............................................................................................. 错误!未定义书签。 2、摩托罗拉手机爆炸事件............................................................................. 错误!未定义书签。 3、戴尔断货诚信风波..................................................................................... 错误!未定义书签。 4、西门子贿赂丑闻......................................................................................... 错误!未定义书签。 5、森马广告风波............................................................................................. 错误!未定义书签。 6、家乐福群殴、踩踏事件............................................................................. 错误!未定义书签。 7、品客、乐事、依云遭遇“标准门”......................................................... 错误!未定义书签。 8、奔驰汽车安全风波..................................................................................... 错误!未定义书签。 9、华为等知名企业辞工潮............................................................................. 错误!未定义书签。 10、中石油社会责任风波............................................................................... 错误!未定义书签。 岁末年初，如果让我们回顾2007年中国企业危机公关事件的话，就不难发现：与前几年相比，在刚刚过去的2007年，更多的企业经历了危机公关之痛。从手机、汽车、IT到食品、服饰、超市等行业，众多企业经历了各式各样的公关危机。接下来，就让我们从诸多危机公关案例事件中筛选十大具有代表意义的典型案例，以全面还原2007年中国企业的危机公关现状。（以事件发生先后为序） 1、LG翻新事件 LG翻新事件起源于2006年，在2007年上半年愈演愈烈。2007年1月，在地下翻新工厂遭曝光后，LG声称背后有人敲诈；2月份又有媒体曝光工商局封存5台LG疑似翻新空调，随后LG承认更换部分产品包装；3月，湖南省消费者张洪峰披露了湖南省质量检验协会的鉴定结果，确认“其购买的五台LG空调都是翻新机器”，5月份张洪峰通过博客再次披露了LG空调的质量问题。LG翻新
小学毕业班语文测试卷 杭垓小学教育集团潘雪珍 一、抄写句子。做到笔画正确，结构匀称，书写工整，行款整齐。（2分） 什么是路，路是从荆棘的地方开辟出来的，从没有路的地方踩出来的。 二、看拼音写词语。要求把每个字写端正、匀称。（5分） duàn liàn fěi cuì kǔ xíng chōu yē méi guī （）（）（）（）（）三、按要求写词语或句子。（19分） 1．把词语补充完整并选择合适的词语填在下面的空格中。（6分） （）拥而至集思（）炎（）子孙（）无虚席 （）有趣味发愤（）强司空（）（）旁通 2010年5月1日，第41届世博会在中国上海隆重开幕。这是亿万的骄傲。在此之前各地举办的与世博会有关的讲座都是。开幕后，黄浦江边240多个的展馆，更是吸引了世界各地的游客。 2．根据提示，写出带有“海”字的成语。（2分） （1）泛指全国各地：（2）比喻危险之地： （3）形容东西难找：（4）比喻没有消息： 3．用“然”组词，分别填在括号里。（3分） （1）客家民居和傣家竹楼各有特点，建筑风格（）不同。 （2）两三百人聚集在一个会议室，秩序（），毫不混乱。 （3）那一座座碉堡，经历无数次地震撼动和风雨侵蚀而（）无恙。 4．用恰当的关联词把下面三个句子连起来。（2分） 桃花心木已经适应了环境种树的人不再来了桃花心木不会枯萎了 5．修改病句。（2分） 止咳祛痰片的主要成分是远志、桔梗、贝母、氯化铵等配制而成的。 6．根据“味道”一词的不同含义，写两句话（2分）
7．用最恰当的句式改写下面这句话。（2分） 为了生活，九岁的凡卡来到莫斯科当学徒。 四、按课文内容或要求填空。（19分） 1.“一人虽听之，，思援弓缴而射之。”句中的两个“之”分别指、；后一人的学习态度我们可以用、等成语来形容。 2.太阳他有脚啊，地挪移了；我也跟着旋转。 3.“‘’这一条意见，就是党外人士先生提出来的，他提得好，对人民有好处，我们就采用了。”这里用了论证的方法，说明 。 4.《卖火柴的小女孩》作者是，他被誉为“”。我还读过他写的童话。 5.名篇名著总能给我们带来人生的启迪，《百年孤独》告诉我们诚信的重要，书中那句话说得好“。” 6．《浣溪沙》这首词中，最能看出词人不服老，旷达乐观的句子是： 。 7. 通过本册课文的学习，我感受到了老北京春节的；认识了流落荒岛， 的鲁滨孙；还知道了镭的母亲就是居里夫人，她那的科学精神真让我敬佩。（填入四字词语） 五、阅读分析，完成练习。（22分） （一）句子（3分） “这点美丽的淡蓝色的荧光，融入了一个女子美丽的生命和不屈的信念。” 这里的“淡蓝色的荧光”指的是，说它融入了“美丽的生命，是因为，这句话是对居里夫人的赞颂。 (二)片断（4分）
2008年普通高等学校招生全国统一考试（安徽卷） 语文 第Ⅰ卷（选择题共30分） 一、（12分，每小题3分） 语言知识仍然是4题，语音在多年停考后又回归了，成语、病句还是必考题，第四题考查的应该是属于句式知识。总体难度不是很大。 1.下列各组词语中，斜线“/”前后加点字的读音完全相同的一组是 A．清澈/掣肘殷红/湮没瞠目/螳臂当车 B．箴言/斟酌蛊惑/商贾船舷/扣人心弦 C．联袂/抉择整饬/炽烈辍学/风姿绰约 D．徘徊/脚踝戏谑/琐屑惬意/锲而不舍 2.下列各句中，加点的成语使用恰当的一句是 A．时间真如行云流水，申奥成功的情景仿佛就在昨天，转眼间，举世瞩目的北京奥运会距离我们已经不到一百天了。 B．眼下，报刊发行大战硝烟渐起，有些报纸为了招徕读者而故意编造一些骇人听闻的消息，其结果却往往弄巧成拙。 C．著名学者季羡林先生学贯中西，兼容百家，在诸多研究领域都卓有建树，被人们誉为学界泰斗，真可谓实至名归。 D．有段时间，沪深股市指数波动非常大，有时一天上涨几百点，有时一天下跌几百点，涨跌幅度之大令人叹为观止。 3.下列各句中，没有语病、句意明确的一句是 A．诚信教育已成为我国公民道德建设的重要内容，因为不仅诚信关系到国家的整体形象，而且体现了公民的基本道德素质。 B．以“和谐之旅”命名的北京奥运火炬全球传递活动，激发了我国各族人民的爱国热情，也吸引了世界各国人民的高度关注。 C．今年4月23日，全国几十个报社的编辑记者来到国家图书馆，参观展览，聆听讲座，度过了一个很有意义的“世界阅日”。 D．塑料购物袋国家强制性标准的实施，从源头上限制了塑料袋的生产，但要真正减少塑料袋污染，还需消费者从自身做起。 4.下列各句中，语气最委婉的一句是 A．只关注孩子的学习成绩而忽视他们的心理健康，这是不是应该引起我们认真思考呢？B．只关注孩子的学习成绩而忽视他们的心理健康，这难道不应该引起我们认真思考吗？C．只关注孩子的学习成绩而忽视他们的心理健康，这无疑应该引起我们认真思考的。D．只关注孩子的学习成绩而忽视他们的心理健康，这恐怕不能不引起我们认真思考了。 二、（9分，每小题3分） 选文应该说是科学类文章，这是一大改变，前几年一直是社会科学类的文章。但文章总体阅读难度不打，也没有什么难以读懂的专业术语。题目设置的总体难度也不是很大，第6题稍微难一些。 阅读下面的文字，完成5-7题。 我们的宇宙外面是什么 在宇宙学中，有一个非常重要的常数、叫做宇宙学常数。这个常数在我们的宇宙中起着一种
2020年4月语文测试题（满分120分） 姓名：班级：总分： 一、基础知识（30分） 1、（9分）将下列每项中的一个错别字圈画出来，并改正在后面的括号里。 A、王侯鞭炮书藉万象更新（） B、截然彩绘骆驼万不得己（） C、肿涨搅和何曾焉知非福（） D、挪移妻凉染缸心平气和（） E、通霄游丝寂寞重见天日（） F、倒霉侵袭奈何排徊（） G、倾覆防卸忧伤宽慰（） H、汤匙不禁沸藤理智（） I、觉察甜腻栅拦摆摊儿（） 2、（3分）下列对病句的修改不正确的一项是（） A.目前，驰援武汉的 4000 多名军队医护人员仍然坚守，“不获全胜，决不收兵”。 “坚守”后面加“岗位”。 B.一个被困在疫区的女作家，把自己每天所听到的所见所闻所思，真实地告诉给我们。 去掉“所听到” C.中国将继续为国际社会抗击疫情保证大力支持。 把“保证”改成“提供”。 D.浙江26日宣布，对所有入境人员采取一律 14 天集中隔离医学观察措施。 去掉“措施” 3、（3 分）下面情境下，语言表达最准确、得体的一项是（） 【情境】停课不停学，进入网课学习后，同学们的学习时间骤然紧起来了，不少同学每晚伏案学习到半夜 12 点，这种勤奋的精神难能可贵。可是，白天在课堂上，相当一部分同学恹恹欲睡。怎样劝说这样的同学保证睡眠、劳逸结合、提高学习效率呢？ A.能学到那么晚真让我佩服，可是，充足的睡眠才能保证白天的学习效率，更有利于成绩的提高。 B.你真傻，白天睡觉晚上学。多得不偿失啊！ C.白天打不起精神，晚上回家再贪黑学，这也太不聪明了。 D.你每天都能学到半夜 12 点，太有毅力了，我一定要以你为榜样。 4、（3分）下列表述不正确的一项是（） A、“迢迢牵牛星，皎皎河汉女”这句诗描绘的是“牛郎织女”的神话传说。 B、《鲁滨逊漂流记》的作者是法国的丹尼尔?笛福 C、古代年龄表示法中，13岁也叫豆蔻年华。 D、《汤姆索亚历险记》表达了对自由的向往和对社会的讽刺，需要我们慢慢去发现，去品味。 5、（3分）2020年初，一场突如其来的疫情防控阻击战，在中华大地骤然打响。在这个没有硝烟的战场上，一群人，朝着同一个目的地──武汉进发，他们被誉为“最美逆行者”。请阅读下面三则材料，完成任务。 材料1：2003年，非典肆虐。67岁的他说：把最重的病人送到我这来。2020年，武汉疫情蔓延。84岁的他一边告诉公众“尽量不要去武汉”，一边自己登上去武汉的高铁，挂帅出征。 材料2：来自全国各地的医务工作者，他们在这个岁末逆路而行，请战书上言简意赅，指
全国2008年10月高等教育自学考试 中国近现代史纲要试题 课程代码：03708 一、单项选择题（本大题共30小题，每小题1分，共30分） 在每小题列出的四个备选项中只有一个是符合题目要求的，请将其代码填写在题后的括号内。错选、多选或未选均无分。 1.鸦片战争前中国封建社会的主要矛盾是（） A.地主阶级和农民阶级的矛盾 B.帝国主义和中华民族的矛盾 C.资产阶级和工人阶级的矛盾 D.封建主义和资本主义的矛盾 2.中国封建社会产生过诸多“盛世”，出现在清代的是（） A.文景之治 B.贞观之治 C.开元之治 D.康乾盛世 3.将中国领土台湾割让给日本的不平等条约是（） A.《南京条约》 B.《北京条约》 C.《马关条约》 D.《瑗珲条约》 4.西方列强对中国的侵略，首先和主要的是（） A.政治控制 B.军事侵略 C.经济掠夺 D.文化渗透 5.1839年组织编写成《四洲志》，向中国人介绍西方情况的是（） A.林则徐 B.魏源 C.马建忠 D.郑观应 6.19世纪末，在帝国主义列强瓜分中国的狂潮中提出“门户开放”政策的国家是（） A.俄国 B.日本 C.美国 D.德国 7.太平天国农民起义爆发的时间是（） A.1851年 B.1853年 C.1856年 D.1864年 8.太平天国由盛而衰的转折点是（） A.永安建制 B.北伐失利 C.天京事变 D.洪秀全病逝 9.最早对兴办洋务的指导思想作出完整表述的人是（） A.冯桂芬 B.马建忠 C.王韬 D.郑观应 10.洋务运动时期最早创办的翻译学堂是（） A.同文馆 B.广方言馆 C.译书局 D.译书馆 11.1898年发表《劝学篇》一文，对抗维新变法的洋务派官僚是（） A.李鸿章 B.左宗棠 C.张之洞 D.刘坤一