Effect of Database Size in the Genetic Variants Calling

Sunhee Kim

, Young-suk Lee

and Chang-Yong Lee

The Department of Industrial and Systems Engineering, Kongju National University, Cheonan 330-717, South Korea

Center for RNA Research, Institute for Basic Science, Seoul 151-742, South Korea

Keywords:

Base Quality Score Recalibration, Single Nucleotide Polymorphism, Variant Calling, Database.

Abstract:

The base quality score recalibration (BQSR) is an important step in the variant calling from high-throughput

sequence data. Motivated by the fact that BQSR necessarily requires a database of known variants such as

the dbSNP, we present an extensive analysis on BQSR results for human and rice genome. We showed that

the recalibration results depended on the size of the database: the more variants are there in the database, the

larger averaged value of the recalibrated base quality scores is obtained. This implies that the recalibrated

quality score is lower than it should be when the number of variants in the database is not large enough. Based

on the ﬁnding that the size of the database should play a crucial role in BQSR, we proposed a method to create

a database when the size of a database is not large enough for BQSR results to be reliable. We demonstrated

that, in the case of human, the database constructed by the proposed method generated almost the same results

as the human dbSNP. In the case of rice, however, we showed that the proposed database is more reasonable

than the rice dbSNP.

1 INTRODUCTION

The high-throughput sequencing, such as the next

generation sequencer (NGS), generates unprecedent-

edly large amount of genetic data at a low cost (Met-

zker, 2010). Massive sequence data are mainly used

to identify various genetic variations including the

single nucleotide polymorphism (SNP) with the help

of relevant bioinformatic tools. The most well known

software for the SNP calling is the Genome Analy-

sis Toolkit (GATK), an open pipeline provided by the

Broad Institute (DePristo and et al., 2011; der Auwera

and et al., 2013).

While a NGS platform produces much more se-

quence data than the traditional Sanger sequencing

does, generated reads are shorter than the Sanger’s

in length and of inferior quality containing more se-

quencing errors. When a NGS platform calls a base,

they also provide an estimated quality of the base in

terms of Phred score (Ewing and et al., 1998; Ewing

and Green, 1998),

phred

= −10log

p(ε) , (1)

where p(ε) is the error rate, the probability of observ-

ing an incorrectly called base. Q

phred

is an integer and

known as the base quality score.

Studies have demonstrated that the Phred-scaled

base quality scores issued by a NGS platform are of-

ten inaccurate and deviate from true error rates (Li

and et al., 2009b; Brockman and et al., 2008). Base

quality scores are prone to various sources of system-

atic (i.e., non-random) and technical errors. Exam-

ples include the general trend of increased sequenc-

ing errors in later sequence cycles, dinucleotide con-

tents error, and errors due to manufacturing ﬂaws in

the equipment. In addition, each platform has a spe-

cialized base calling scheme in its sequencing work-

ﬂow that leads to sequencing errors. The accuracy of

a base quality score is important because the down-

stream analyses of the variant calling rely heavily on

the per-base quality score (DePristo and et al., 2011).

Thus, under- or over-estimated base quality scores

may result in inaccurate (i.e., false positive or false

negative) variant calls.

Because the correct estimate of the quality

score is essential in the variant calling, GATK

provides a recalibration tool for the base quality

score, named the base quality score recalibration

(BQSR) (GATK, 2018). As a data pre-processing

step of sequencing-by-synthesis reads in an aligned

sequence of SAM/BAM ﬁle (Li and et al., 2009a),

BQSR detects systematic errors in the estimated qual-

ity score of each base and recalibrates the base qual-

ity score, not the base call itself. BQSR is versa-

tile in that it can be applied to the sequencing data

Kim, S., Lee, Y. and Lee, C.

Effect of Database Size in the Genetic Variants Calling.

DOI: 10.5220/0007413402090215

In Proceedings of the 12th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2019), pages 209-215

ISBN: 978-989-758-353-7

209

of various platforms. Besides BQSR, the recalibrat-

ing quality of nucleotides (ReQON) (Cabanski and

et al., 2012) providesd by a package written in R (R-

project, 2018) claims that it also performs the recali-

bration, which also utilizes known SNP database. An-

other study related to the base quality recalibration

is RIG (Recalibration and Interrelation of Genomic

Sequence Data) that is a workﬂow to generate col-

lection of variants from various available genomic re-

sources (R. McCormick and Mullet, 2015). In addi-

tion, it was claimed that the recalibration was solved

without an external SNP database (Chung and Chen,

2017).

BQSR is a method of adjusting platform-provided

base quality scores to be more accurate by using an

external resource (or database) of known variants,

such as the dbSNP (Sherry and et al., 2001). It is a

method of adjusting Phred quality score to be more

accurate by examining every base in the BAM/SAM

ﬁle. The underlying assumption of BQSR is that any

mismatched base with the reference genome at a po-

sition not listed in the database is an error. That is,

BQSR assumes that a mismatched base listed in the

database is correctly sequenced real variants, whereas

a mismatch not listed in the database is regarded as a

sequencing error. In addition to the database, BQSR

groups bases into different categories with respect to

various covariates, such as the machine cycle, the

base position in a read, and the dinucleotide context,

then takes the mismatch rate for each category into

account in the recalibration.

The basic assumption of BQSR, any potential

variant in a read that is not listed in the database

is an error, is reasonable and valid in the statistical

sense only if we have enough and accurate informa-

tion about known variants in the database. If the size

of the database is smaller than it should be, the num-

ber of false negative (i.e., variants that are incorrectly

identiﬁed as errors) would increase. Thus, the useful-

ness of BQSR heavily relies on the number and qual-

ity of reported variants in the database. This means

that when the database is incomplete, mismatched

bases are less likely to be identiﬁed by the database;

as a result, the quality of the bases will be inferred to

be lower than it actually is (Wang and et al., 2015).

Thus, it may be ineffective to run BQSR when the

database of known variants is not comprehensive. In

this respect, it is imperative to ﬁnd a way of recal-

ibrating the base quality score for species of having

not enough information about known variants.

This naturally leads to the following questions. Is

the number of known variants in the database of an or-

ganism enough to trust BQSR results? What can we

do when we do not have enough known variants in the

database? In this study, we try to answer these ques-

tions. To this end, we used NGS data and the dbSNPs

of human and rice, and performed various empirical

studies for BQSR to investigate the above questions.

Although BQSR step in GATK provides a room to

add new covariates to the recalibration, for simplicity,

we do not add any new covariate besides default ones

provided by GATK.

2 MATERIALS AND METHODS

2.1 Data Acquisition

We used the genome-wide NGS data of

FASTQ (Cock and et al., 2010) ﬁles for human

and rice, each of which is obtained from the 1000

Genomes Project (Human-Genomes, 2015) and

the 3000 rice genomes project (Rice-Genomes,

2014), respectively. The 1000 Genomes Project

is an international research effort to establish a

detailed catalogue of human genetic variation by

resequencing about one thousand participants from

a number of different ethnic groups. The 3000 rice

genomes project is also an international effort of re-

sequencing a core collection of 3,000 rice accessions

from 89 countries. The data can be freely down-

loaded at http://www.internationalgenome.org/

for the 1000 Genomes Project, and

http://dx.doi.org/10.5524/200001 for the 3000

Rice Genomes Project. For the empirical studies,

we randomly selected 10 samples from each project.

The estimated average coverage over 10 resequenced

samples is about 5x and 9x for human and rice,

respectively.

Variants are called against the Nipponbare

IRGSP-1.0 reference genome for rice and the

GRCh38 for human. The GRCh38, the Genome

Reference Consortium Human genome build 38, is

built from reference sequences of different individu-

als, not just from one individual’s genome sequence.

Whereas, the IRGSP-1.0, built from one accession’s

genome, is assembled by the Oryza sativa Japonica

Group (Japanese rice) genome from National Insti-

tute of Agrobiological Sciences. The reference se-

quences for both can be obtained from Refs. (Human-

Reference, 2018) and (Rice-Reference, 2018), respec-

tively.

2.2 dbSNP

As for the database of known variants, we use the db-

SNP (Database, 2018) as GATK does. The dbSNP is

a repository of all known variants, such as SNPs and

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210

Figure 1: Plots of the average

Q over 10 individuals (or

accessions) versus the percentage of selected variants from

the dbSNP: the human () and the rice (). The error bars

are the corresponding standard deviations s

indels, open to the public. The dbSNP is a collec-

tion of VCF ﬁles generated from various resources.

Initially constructed for human, the dbSNP has ex-

tended to other organisms. We used the dbSNP build

151 for human (Homo sapiens) that consists of about

318,739,000 variants and the dbSNP build 151 for

rice (Oryza sativa) that consists of about 12,185,000

variants. These were the latest version when most of

data analyes were carried out.

A base in a BAM/SAM ﬁle whose poistion is

listed as a variant in the database is not considered

as a variant when the base matches with the reference

sequence. In this case, the variant in the database is

void in the sense that it does not play any role as far as

BQSR is concerned. On the other hand, the position

of a mismatched base can be in the list in the database

as a variant. In this case, we deﬁne the variant as an

“effective” variant. Thus, variants in the database can

be either effective or non-effective, and it is effective

variants that affect BQSR. Thus, from the perspective

of BQSR, variants in a database (e.g. dbSNP) can be

classiﬁed into two categories: either effective or not.

2.3 Characteristics of Mean

Recalibared Base Quality Score

To answer a question about the sufﬁciency of vari-

ants in the database, we investigated how the size of

a database affected BQSR results. This can be ac-

complished by examining the dependence of BQSR

results on the number of variants used as the database.

Speciﬁcally, we constructed 10 test databases of dif-

ferent sizes, each of which is composed of variants

randomly selected out of the dbSNP from 10% to

100% at a 10% interval. We then peformed BSQR

by using test databases of different number of vari-

ants. In this way, each database and BQSR result can

be identiﬁed by its selection ratio.

For a given selection ratio, let Q

be the recali-

brated quality score of base j of sample (individual

or accession) i. Then, the mean recalibrated quality

score

of sample i over all bases is given as

≡

∑

j=1

, (2)

where m

is the number of bases in sample i. With

’s, we can deﬁne the sample mean

Q and the sample

variance s

’s as

Q ≡

∑

i=1

and s

≡

n− 1

∑

i=1

−

, (3)

where n is the number of samples, and in our case,

n = 10.

The results of BQSR are shown in Fig. 1, in which

we plot

Q and s

(errorbars) in Eq. (3) versus the

selection ratio. For both human and rice, the mean

recalibrated quality score increases as the ratio in-

creases. This is expected because, according to the

underlying assumption of BQSR as stated in Section

1, the more variants we have in the database, the less

number of bases in the BAM/SAM ﬁle are regarded

as errors which results in higher recalibrated quality

scores.

In addition, we ﬁnd that s

, the sample standard

deviation of

’s, of rice is about two times larger than

that of human. This is mainly due to the characteristic

of the reference sequences and the selected samples.

As stated in Section 2.1, because the human reference

sequence built from sequences of different individu-

als, the reference sequences contains sequences from

different origins of country. As a result, the degree of

mismatches with the reference sequence spread more

or less uniformly across different origin of countries.

In contrast, the rice reference sequence is built from

a single accession of Nippobare Japonica. Thus, rice

samples of different variety groups, such as Indica an-

othe major rice cultivar, from Japonica may include

much more mismatches (and thus errors) than Japon-

ica accessions. This in turn leads to a large devia-

tion of the average quality scores over different ac-

cessions.

More importantly, from Fig. 1 we ﬁnd that the

mean recalibrated quality score of human gemome is

larger than that of rice genome for all selection ratios.

This result is in contradiction to the fact that, before

the recalibration, the average quality scores for both

human and rice are about the same: 33.02 ± 3.74 for

Effect of Database Size in the Genetic Variants Calling

211

human and 36.07± 0.63 for rice. Because there is no

reason to assume that the base quality scores of hu-

man genome are higher than those of rice genome, it

is not reasonable to have a noticable gap between the

mean recalibrated base quality scores of human and

rice.

One plausible explanation for the gap is that the

number of (effective) variants in the rice dbSNP is ei-

ther not large enough for BQSR to be valid, or far

smaller than that in the human dbSNP. Because the

larger is the size of the database, the higher the recal-

ibrated base quality scores are, BQSR scheme with a

database having not enough number of variants may

under-estimate the quality score in the recalibration

step. While the genome size of rice is about one

eighth of that of human, the number of variants in the

rice dbSNP is about one twenty ﬁfth of those in the

human dbSNP. This indirectly indicates that the size

of the rice dbSNP is signiﬁcantly small relatively to

that of the human dbSNP.

The above ﬁnding raises an important question:

what do we do if we do not have enough number of

variants as the database for the recalibration? In what

follows, we suggest a recalibration method when the

number of variants in a database is not enough.

2.4 Proposed Method of Creating

Database

We propose a method of constructing a database as

an alternative to an existing database for BQSR when

there is no database or the existing database does not

contain enough number of variants. The basic idea

of the proposed method is to create a new database,

similar to the suggestion by GATK forum (Bootstrap,

2018), as follows. We ﬁrst perform the variant calling

by using a variant calling pipeline, such as GATK,

without BQSR step to obtain a variant call format

(VCF) ﬁle that contains variants called by the pipeline

without BQSR step. We then perform again the vari-

ant calling including BQSR step by using the VCF ﬁle

as the database. In short, the VCF ﬁle obtained with-

out BQSR step plays a role of an alternative database

to the existing database, such as the dbSNP. As the

variant calling pipeline creates the database for itself,

we will call the VCF ﬁle as the dbSELF.

The above method can be generalized by an iter-

ative aggregation, in which the dbSELF is updated

by performing the variant calling pipeline repeatly.

There are two ways to update the dbSELF. The ﬁrst

method is to accumulate the variants called by the

pipeline to the current dbSELF, and the second is to

replace the current dbSELF with the most recent VCF

ﬁle. Thus, the dbSELF generated by the accumulation

Figure 2: The schematic ﬂow chart of current BQSR setp

(left) and the proposed step (right).

Figure 3: The relative frequency of ∆

’s for human and

rice. The plots are averaged distribution over 10 samples

with error bars representing the standard deviation.

method increases in size, whereas, in the replacement

method, the VCF ﬁle obtained from the nth variant

calling is the dbSELF for the (n + 1)th variant call-

ing. In general, the number of variants in the dbSELF

of the accumulation method is larger than that of the

replacement method. Note that the number of vari-

ants in the dbSELF depends on sample (individual or

accession). The procedure of the proposed method is

schematically depicted in Fig. 2.

3 EXPERIMENTAL RESULTS

AND DISCUSSION

In order to assess the proposed method, we adopted

GATK and performed the variant calling by using two

different databases: the dbSNP and the dbSELF. We

analyzed basewise difference in the recalibrated base

quality scores obtained by the two databases. The dif-

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212

Table 1: Statistics of dbSELF and dbSNP. N

total

and N

ef f

are the number of variants and the effective variants in the dbSNP,

respectively. N

ebase

and N

error

are the number of effective bases and errors, respectively; the error rate is deﬁned as the ratio

of the number of errors to the total number of bases in raw data. All quantities, except the number of variants in the dbSNP,

are averaged over 10 samples, and the corresponding standard devations are in the parentheses.

Database Genotype

Human N

total

ef f

ebase

error

Error rate (%)

dbSNP 318,739,162 5,965,063 (857,126) 17,919,487 (4,950,378) 14,570,351 (3,424,599) 0.11 (0.04)

dbSELF 2,824,914 (571,181) 2,822,702 (570,685) 14,316,273 (4,649,247) 18,173,565 (4,020,284) 0.14 (0.05)

Rice N

total

ef f

ebase

error

Error rate

dbSNP 12,185,568 1,411,561 (955,475) 8,936,172 (6,314,281) 7,410,641 (3,009,390) 0.24 (0.12)

dbSELF 1,505,469 (1,001,366) 1,464,242 (981,338) 10,815,057 (6,969,199) 5,531,758 (2,333,059) 0.18 (0.09)

ference is expressed as ∆Q

≡ Q

SNP

− Q

SELF

, where

SNP

and Q

SELF

are the recalibrated quality scores of

base j in sample i obtained by using the dbSNP and

the dbSELF, respectively. Note that ∆Q

is an inte-

gral value as the base quality score is an integer.

Figure 3 shows relative frequency distributions of

∆Q

’s for both human and rice averaged over 10

samples together with the standard deviations repre-

sented by the error bars. From Fig. 3, we see that the

disribution of ∆Q

’s for human is symmetric about

∆Q

= 0, and the majority of bases (about 64%)

have ∆Q

= 0 and more than 95% of bases have



∆Q



≤ 1. This means that more than 95% of the re-

calibrated base quality scores obtained by using two

different databases are the same or differ by one Phred

score. This result suggests that the dbSELF can serve

a reasonably good alternative to the dbSNP.

In the case of rice, however, whereas about 22%

of bases have ∆Q

= 0, more than 70% of bases

have their recalibrated quality scores obtained by the

dbSELF higher than those obtained by the dbSNP.

Considering that BQSR with the rice dbSNP under-

estimates the recalibrated quality score compared to

the human dbSNP as discussed in Section 2.3, the rice

dbSELF can alleviate, at least in part if not entirely,

the under-estimate of the recalibrated scores. In this

sense, the rice dbSELF may substitute for the rice db-

SNP for a better BQSR result.

As stated in Section 1, the genotype of a base is

regarded as an error when the base in a BAM/SAM

does not match with the reference at a position not

listed in the database. As a complementary to the er-

ror, we deﬁne an effective base as a mismatched base

that is identiﬁed by an effective variant listed in the

database. Thus, a mismatched base is either an error

or an effective base. In Table 1, we list the number

of variants and effective variants in the two databases,

together with the statistics of effective bases and er-

rors. In addition, we estimate the error rate, which is

the ratio of the number of errors to the total number

of genotyped bases in the raw data (i.e., FASTQ ﬁle).

Because all quantities, except the number of variants

in the dbSNP, depend on samples, we report in Table 1

the mean and the standard deviation of the quantities

over 10 samples. Note that the standard deviations

of the numbers of variants, effective variants, and ef-

fective bases for rice are larger than those for human

regardless of the database. This is due to the charac-

teristics of the reference sequence discussed in 2.3.

We see from Table 1 that, for both human and rice,

while the dbSELF contains less number of variants

than the dbSNP, almost all variants in the dbSELF

are effective variants. This is expected because the

dbSELF is nothing but a set of variants called from

samples without BQSR step. In the case of human,

we ﬁnd that the dbSELF contains far less number of

variants (about 0.8%) than the dbSNP does. However,

more than 99% of variants in the dbSELF are effec-

tive variants, whereas only about 2% in the dbSNP

are effective. More importantly, although the dbSELF

has less than a half as many effective variants as the

dbSNP has, the error rates obtained by using the two

databases differ by only 0.03%. This difference is not

a signiﬁcant compared to the difference in the number

of effective variants.

In the case of rice, we can see from Table 1 that

the dbSELF contains more effective variants (about

4%) than the dbSNP, although the dbSELF contains

less number of variants (about 12%) than the dbSNP.

While about 12% of variants in the dbSNP are effec-

tive, more than 97% of variants in the dbSELF are

effective. The fact that the rice dbSELF identiﬁes

more effective variants is a primary reason that BSQR

using the dbSELF gives higer recalibrated scores on

average than the dbSNP does. In addition, the db-

SELF generates more effective bases than the dbSNP

does; as a result, the error rate using the dbSELF

is smaller than that using the dbSNP. This basically

yields higher Q

SELF

on average than Q

SNP

Note that there is no reason in prior that the er-

ror rate of rice should be greater than that of human.

Rather, we should expect about the same error rate

for both human and rice. In this sense, the fact that

the error rate using the dbSELF is almost comparable

Effect of Database Size in the Genetic Variants Calling

213

Table 2: The results of the number of effective variants and the recalibrated base quality score from the iteration method. N

acc

and N

rep

stand for the number of effective variants from the accumulation and the replacement methods, respectively. V

acc

and

rep

represent the number of called variants in the VCF ﬁle from the accumulation and the replacement methods, respectively.

acc

and Q

rep

are the recalibrated quality scores for the accumulated and replaced database, respectively. All quantities are

the averaged quantities over 10 samples and the standard deviations are in parantheses.

Human

Iteration 1 2 3 4 5

acc

2,822,702 (570,685) 2,846,302 (548,120) 2,849,574 (546,712) 2,850,760 (546,425) 2,851,651 (545,974)

rep

2,822,702 (570,685) 2,572,609 (496,302) 2,588,531 (499,300) 2,591,406 (498,604) 2,591,033 (499,800)

acc

2,574,517 (496,981) 2,609,946 (500,232) 2,611,333 (502,151) 2,611,592 (499,787) 2,611,857 (500,988)

rep

2,574,517 (496,981) 2,590,469 (499,992) 2,593,340 (499,282) 2,592,973 (500,495) 2,595,153 (499,734)

acc

28.22 (1.38) 28.85 (1.71) 28.84 (1.72) 28.86 (1.69) 28.86 (1.72)

rep

28.22 (1.38) 28.61 (1.62) 28.62 (1.68) 28.64 (1.67) 28.66 (1.66)

Rice

Iteration 1 2 3 4 5

acc

1,464,242 (981,338) 1,465,483 (981,566) 1,465,981 (981,858) 1,466,151 (981,975) 1,466,228 (982,017)

rep

1,464,242 (981,338) 1,404,392 (913,189) 1,430,244 (946,967) 1,431,509 (948,436) 1,431,252 (948,166)

acc

1,441,154 (928,358) 1,471,463 (967,600) 1,471,935 (968,295) 1,472,026 (968,474) 1,471,632 (967,802)

rep

1,441,154 (928,358) 1,468,866 (964,458) 1,470,220 (966,029) 1,469,945 (965,743) 1,470,180 (965,956)

acc

27.02 (2.30) 28.68 (2.10) 28.63 (2.10) 28.65 (2.08) 28.63 (2.11)

rep

27.02 (2.30) 28.47 (2.18) 28.54 (2.15) 28.51 (2.15) 28.53 (2.16)

with that for human supports that the rice dbSELF is

a reasonably good alternative to the rice dbSNP.

We assessed two different iteration methods,

accumulation and replacement, by updating the

database.The results are shown in Table 2. From

Table 2 we found the following properties that both

human and rice have in common. First, the num-

ber of effective variants obtained from the accumu-

lation method increases steadily, without any sudden

change, which is expected. With the replacement

method, in contrast, the number of effective variants

decreases at the second iteration and increases back

at the third iteration. In particular, the ﬁrst itera-

tion produces the largest number of effective vari-

ants. Of course, in all iterations, the accumulation

method yields more variants than the replacement

method. Second, the average scores obtained by the

accumulation method are slightly higher than those

obtained by the replacement method for all iterations.

This is expected because, by deﬁntion, the accumu-

lation method yields more effective variants in the

database, although not much, than the replacement

method does.

4 CONCLUSIONS

In this study, we investigated the validity of the db-

SNP for BQSR. We found that the recalibration re-

sults were closely related to the size of dbSNP. This

implied that BQSR results might not be reliable when

the size of the database is not large enough. Based on

the ﬁnding that the size of the database should play a

crucial role in BQSR, we proposed a method to cre-

ate a database when the size of a database is not large

enough. We demonstrated that, in the case of rice, the

proposed method of construction a database is more

reasonable than the rice dbSNP. This suggests that the

propsed method can be applied to the variant callings

of other species for which the size of the database is

not large enough.

ACKNOWLEDGEMENTS

This work was supported by a Korea Research Foun-

dation Grant funded by the Korean Government

(MOEHRD) (NRF-2018R1D1A3B07042338).

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