Exploring correlations among copy number variants

doi:10.4236/ojgen.2012.23018

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Open Journal of Genetics, 2012, 2, 131-135 OJGen

http://dx.doi.org/10.4236/ojgen.2012.23018 Published Online September 2012 (http://www.SciRP.org/journal/ojgen/)

Exploring correlations among copy number variants

Joseph Abraham1*, Thomas LaFramboise2

1Programa do Pós Graduação em Genética, Faculdade de Medicina de Ribeirão Preto, Universidade de São Paulo, Ribeirão Preto,

Brazil

2Department of Genetics, Case Western Reserve University, Cleveland, USA

Email: *[email protected]

Received 16 May 2012; revised 16 June 2012; accepted 6 July 2012

ABSTRACT

There have been a great many recent studies investi-

gating the extent of Copy Number Variation in the

genomes of various species such as human, cattle,

dogs and many others. The results from these studies

indicate that the extent of the Copy Number Varia-

tion in the genome is considerable, and that in hu-

mans and in cattle, frequencies of different Copy

Number Variants may differ in different breeds/eth-

nicities. This is not entirely unexpected as allele fre-

quencies of certain loci vary with different breeds/

ethnicities/species and many known Copy Number

Variants behave similarly to ordinary markers as

regards Mendelian segregation. It is also well known

in many instances, species/breeds/ethnicities show

variation not only in marker allele frequencies, but

also in the extent of Linkage Disequilibrium between

markers. Thus it is worth investigating the extent of

association between Copy Number Variants in dif-

ferent populations. In this paper we will investigate

the extent of correlations between selected Copy

Number Variants in different human populations and

show that statistically significant correlations exist

and are strongly population dependent.

Keywords: Copy Number Variant; Pathway; Selection

1. INTRODUCTION

Over the past several years the genome sequences of

many species have been investigated in increasing detail

and with increasing precision. As a result much is now

known about the extent of variation in the genome se-

quences between different individuals belonging to the

same species. In particular it is known that there is, in

addition to point variation, also considerable structural

variation in the genome. One particular structural variant

which has attracted a lot of attention is Copy Number

Variation (CNV) [1-3] which can be interpreted as DNA

mutations arising from a gain or loss of a certain number

of contiguous base pairs during meioses. The distribution

of CNVs in the human genome is not random, and at-

tempts have been made to understand the evolutionary

events which account for the locations of Copy Number

Variants [4] along with the population genetics aspects of

Copy Number Variants [5,6]. In addition in humans the

association between Copy Number Variants and gene

expression level [7] has been studied, as well as the as-

sociation between Copy Number Variation and a number

of diseases of interest [8]. Apart from humans, Copy

Number Variation has been studied in a number of other

species such as cattle [9], chimpanzee [10], fruitfly [11]

among others. These studies have not only elucidated the

range and extent of Copy Number Variation, studies in

cattle [9] have also shown a clear association between

certain Copy Number Variants and breeds. Based on

these studies it is worth taking a closer look at the con-

nection between Copy Number Variation and ancestry in

humans. In this regard it is worth recalling that not only

marker allele frequencies but also the extent of Linkage

Disequilibrium between markers varies with ancestry, for

example Linkage Disequilibrium in African populations

typically extends over a shorter range than in European

and Asian populations. This suggests that the extent of

statistical correlations between Copy Number Variants

could vary in different populations. It is this issue which

is the main focus of this paper.

In order to investigate this question, what is needed is a

catalogue of Copy Number Variants observed in multiple

unrelated individuals in different populations. The copy

number catalogue we will use is in [12] where different

Copy Number Variants are typed in a number of indi-

viduals in various HapMap populations. The catalogue in

[12] contains information on 1320 Copy Number Variants

with a minor allele frequency larger than 1%, referred to

from now on as Copy Number Polymorhpisms (CNPs) to

analyze differences between populations.

2. MATERIALS AND METHODS

The data in [12] consist of records of over a thousand

*Corresponding author.

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J. Abraham, T. LaFramboise / Open Journal of Genetics 2 (2012) 131-135

132

CNPs in three HapMap populations, Yoruba YRI), Euro-

pean (CEU) and Japanese combined with Han Chinese

(CHBJPT). In [12], associated with each CNP is a nu-

merical identifier and also the different levels in which

that CNP appears in the populations. For example, a

CNP with levels (2,3,4) would be interpreted as appear-

ing with no insertions or deletions, one insertion or two

insertions. For each individual there is of course a unique

level; (2,3,4) would indicate that all the individuals con-

sidered have either 0, 1 or 2 insertions.

In each population there are 90 individuals, 30 parent

offspring trios in the case of Yoruba and European

populations and unrelated individuals in the case of the

Japanese Chinese population. In order to get statistics on

a sample of unrelated individuals, offspring data are re-

moved from the Yoruba and European populations. Fur-

thermore, we omit all 27 non autosomal Copy Number

Variants. Summary Statistics of the remaining 1292

CNVs is presented in the Table 1 below.

From Table 1 we see that there are 407 CNPs which

appear in just one level among all the unrelated YRI

parents. Of these 407, 163 of these are common to both

the unrelated YRI parents and the unrelated CEU parents

and 164 are common to YRI parents and the members of

the CHBJPT sample; the other entries can be interpreted

in a similar manner. In all three populations we observe

that CNPs with two levels are relatively common. Fur-

thermore, among CNPs which appear in two levels we

notice that there is relatively little overlap between the

three populations. For example, significantly less than

half of the CNPs which appear in two levels in the

CHBJPT sample also appear in two levels among the

unrelated parents of the YRI sample. Analyzing the sta-

tistics for CNPs which appear in three levels in at least

one of the populations, we see that there is much more

overlap between the different populations. For example

over half of the 238 polymorphisms which appear in two

levels in the CEU populations also appear in two levels

in the YRI population. Indeed the number of these

polymorphisms common to more than one population is

rather larger, even though the actual number of poly-

morphisms which appear in three levels is considerably

smaller than those which appear in two levels. The num-

ber of CNPs which appear in four or five levels is much

Table 1. Distribution of CNP levels.

No. of

Levels YRI CEU CHBJT YRI &

CEU

YRI &

CHBJPT

CEU &

CHBJPT

1 407 686 676 163 164 438

2 580 359 327 100 84 88

3 273 238 254 125 128 154

4 23 17 25 7 9 10

5 9 10 10 3 2 3

smaller and will be neglected.

As we are interested in comparing statistical correla-

tions between different polymorphisms we ignore those

polymorphisms which appear in a single level among all

unrelated individuals. Based on Table 1 it appears that

the polymorphisms which occur in two levels not only

occur in large number but also show the least overlap

between the three populations. From now on we will

focus from now exclusively on CNPs which appear in

two levels in at least one of the three populations under

consideration, in order to understand the difference in

statistical correlations between CNPs in different popula-

tions. As we will see, the corresponding χ2 test has a sin-

gle degree of freedom, which is useful when the sample

size is somewhat small affecting the power of the test.

As all CNPs under consideration appear in just two

levels, they will be treated from now on as binary geno-

types. As we would like to study differences in popula-

tions over and above those due to CNP frequencies, we

impose some restrictions on which CNPs we retain. For

CNPs where the sample frequency of the less frequent

level is less than 5%, the distributions in the three popu-

lations are very different. If we remove these CNPs from

the discussion, the frequency distributions become quite

similar as is seen in the Figure 1. In addition we we re-

move all CNPs where the missingness is larger than 5%.

This procedure is analogous to filtering SNPs based on

missingness and minor allele frequency. With this selec-

tion criterion, the mean and median minor level frequen-

cies in the three samples are very similar and not statis-

tically significantly different at the 5% significance level.

The CNP selection criteria can be summarized as fol-

lows:

 Choose only CNPs with two levels (based on the re-

sults of Table 1).

 Remove 2 level CNPs where the frequency of the less

frequent levels is less than 5%.

 From the surviving CNPs remove those with a miss-

ingness of larger than 5%.

The number of CNPs which survive the selection cri-

teria in the YRI, CEU and CHBJPT populations are 203,

119 and 77 respectively. The surviving CNP data for

each population in [12] can be recast in the form of a

matrix as shown below:

1 1 0 1 ...

0 1 1 0 ...

1 1 1 0 ...

. . . . ...

0 0 0 1 ...

J. Abraham, T. LaFramboise / Open Journal of Genetics 2 (2012) 131-135

133

Figure 1. Distribution of level frequencies.

In this matrix the rows, correspond to different CNPs

which survive the selection criteria and each column

corresponds to a distinct unrelated invividual. The 0 and

1 entries arise since we consider only those CNPs which

are present in just two levels in a given population. In a

given row, 0 might correspond to a single insertion and 1

to no insertions or deletions while in some other row 1

might correspond to an addition and 0 to neither insertion

or deletion. 1 & 0 in this data matrix are purely categori-

cal; there is no numerical significance attached to these

values. As each column corresponds to a distinct unre-

lated individual, the data matrices obtained from the

Yoruba and European individuals have 60 columns while

that from the combined Japanese Han-Chinese sample

has 90 columns. The number of rows is different in each

population, corresponding to the fact that the binary

CNPs are different in different populations.

where n11 counts the number of times where the same

individual has the CNP corresponding to 1 at both loci,

n10 the number of times the same individual has the CNP

corresponding to 1 at one locus and 0 at the other etc. If

the CNPs are on an average uncorrelated, then one

should find, after performing a Fisher’s Exact Test using

the counts in the contingency table, p-values that are not

particularly small. In particular this analysis can be ex-

tended to CNPs on different chromosomes, shedding

light on very long range correlations between CNPs.

In the absence of statistically significant correlations

between CNPs, any small p-values observed should be

artefacts of multiple testing, and not indicative of any

deeper structure underlying CNPs. To check that this is

not the case, we created 1000 permuted data matrices by

shuffling each row of the original data matrix independ-

ently. As the individuals are arranged in columns, in each

permutation any correlations between CNPs in the same

individual are broken up. In each permuted data matrix

we can compute the strength of the correlations between

distinct rows using Fishers Exact Test and the corre-

sponding p-values. The range of p-values obtained in this

manner can be used to decide which p-values are the

Considering an arbitrary pair of rows in any of the

three data matrices, it is possible to construct a (2 × 2)

contingency table of the form

n11 n10

n01 n00

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J. Abraham, T. LaFramboise / Open Journal of Genetics 2 (2012) 131-135

134

consequence of multiple testing.

3. RESULTS

The analysis described in the previous section was per-

formed on all three data sets. For a preliminary analysis

of the difference between the populations we focused on

p-values less than 0.005 and on correlations between

CNPs in different chromosomes. If the chromosomes are

different then the CNPs may be considered to be truly

independent and any correlation found is an indication of

the nonrandom nature of Copy Number Variants. It was

found that there were 33 such p-values in the YRI popu-

lation, 5 p-values in the CEU population and just 3 in the

CHBJPT population. The range of p-values is also dif-

ferent, in the YRI population the lowest p-value is 1.678

× 10−6, in the CEU population it is 1.127 × 10−3 and in

the CHBJPT population it is 1.169 × 10−3. It is also

noteworthy that the 7 lowest p-values in the YRI analysis

are smaller than the smallest p-values in the other popu-

lations. This is suggestive of the possibility that the cor-

relation structure between CNPs differs in different

populations. However, this might just be due to the fact

that the number of tests performed is much larger in the

YRI population than in the other two populations.

To rule out this possibility, the permutation test as de-

scribed in the previous section was carried out; to be

conservative in the permuted tests correlations between

CNPs both in the same chromosome and in different

chromosomes were included to decide which small

p-values could possibly be the consequence of multiple

testing. Assuming a significance level of 0.05, none of

the associations found in the CEU and CHBJPT sample

were, significant after compensating for multiple testing.

However, the most significant association found in the

YRI sample (p-value 1.678 × 10−6) remained significant

with a p-value of < 0.02 even after taking into account

multiple testing. This association is between polymor-

phisms on chromosome 6 (hg17 coordinates 202,353 to

326,149) and on chromosome 16 (hg 17 coordinates

33,208,395 to 33,618,281) with minor level frequencies

of 0.1 and 0.15. Such minor level frequencies are not

atypical of the other two populations, what distinguishes

these two CNPs in the YRI sample is the extent to which

Copy Numbers at these locations are correlated. These

CNPs have identifiers 902 & 2172 in [12]. Using 2 as the

baseline for defining no extra copies in [12] all the YRI

individuals could be considered have either one or two

extra copies at these locations. In the YRI population

unrelated individuals with two extra copies at one loca-

tion tend to have two extra copies at the other location.

4. DISCUSSION

Based on the discussion of the previous section we have

evidence for correlations in Copy Number Variants

which are statistically significant, and whose statistical

significance varies from population to population. This

represents a novel approach for analyzing Copy Number

Variants in the same population as well as for the con-

trasting the patterns of copy number variation in different

populations. Our methodology is conceptually similar to

using the structure of observed Linkage Disequilibrium

between markers and not just marker allele frequencies

in order to compare different populations. Furthermore,

the only significant long range correlations between

CNPs were found in the population where LD between

markers has the shortest range. It is also interesting to

note that no correlations of any significance were found

in the largest of all the samples, the CHBJPT sample.

This is not what one would expect if significant p-values

were determined only by sample sizes. Thus the differ-

ences observed between populations cannot be due to

different sample sizes, but may have their origins in the

differences in population histories. In Drosophila mela-

nogaster for example the pattern of Copy Number Varia-

tion is influenced by natural selection [11]. Selection can

also give rise to long range correlations between markers

[14]; this suggests that the strong correlations observed

in the YRI population could be driven by population ge-

netic events unique to that population. In this regard, it is

noteworthy that the region on chromosome 6 that we

identified overlaps with the location of the DUSP22 gene

which participates in the JnK signalling pathway [15]

whose role in cancer proliferation [16] is well docu-

mented. Any possible signals of selection in this region

region would be of considerable interest and worthy of

further study.

5. ACKNOWLEDGEMENTS

KJA was supported during the course of this investigation by the

United States Department of Agriculture, National Research Iniative

Grant USDA NRI-2009-03924 and also by the program Professor

Visitante do Exterior of Coordenação de Aperfeiçoamento de Pessoal

de Nível Superior (CAPES), Brasil. In addition, KJA wishes to thank

Prof. Cheryl Thompson for valuable and encouraging discussions.

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