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Accepted Manuscript

Title: Identification of genome regions determining semen quality in Holstein-Friesian bulls using information theory

Authors: Alicja Borowska, Tomasz Szwaczkowski, Stanisław

Kaminski,´ Dorota M. Hering, Władysław Kordan, Marek







To appear in:

Animal Reproduction Science

Received date:


Revised date:


Accepted date:


Please cite this article as: Borowska A, Szwaczkowski T, Kaminski´ S, Hering DM, Kordan W, Lecewicz M, Identification of genome regions determining semen quality in Holstein-Friesian bulls using information theory, Animal Reproduction Science (2010),

This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Identification of genome regions determining semen quality in Holstein-Friesian bulls

using information theory

Alicja Borowska1, Tomasz Szwaczkowski2,

Stanisław Kamiński3, Dorota M. Hering3,

Władysław Kordan4, Marek Lecewicz4





1Division of Horse Breeding, Poznan University of Life Sciences, Wolynska st. 33, 60-637

Poznan, Poland





2Department of Genetics and Animal Breeding, Poznan University of Life Sciences,

Wolynska st. 33, 60-637 Poznan, Poland




3Department of

Animal Genetics, University

of Warmia and Mazury in Olsztyn,


Oczapowski st. 5,

10-718 Olsztyn, Poland





Department of Animal Biochemistry and Biotechnology, University of Warmia and Mazury

in Olsztyn, M. Oczapowski st. 5, 10-718 Olsztyn, Poland


*Corresponding author at: Department of Genetics and Animal Breeding, Poznan University of Life Sciences, Wolynska st. 33, 60-637 Poznan, Poland; E-mail:



Use of information theory can be an alternative statistical approach to detect genome regions and candidate genes that are associated with livestock traits. The aim of this study was to verify the validity of the SNPs effects on some semen quality variables of bulls using entropy analysis. Records from 288 Holstein-Friesian bulls from one AI station were included. he following semen quality variables were analyzed: CASA kinematic variables of sperm (total motility, average path velocity, straight line velocity, curvilinear velocity, amplitude of lateral head displacement, beat cross frequency, straightness, linearity), sperm membrane integrity (plazmolema, mitochondrial function), sperm ATP content. Molecular data included 48192 SNPs. After filtering (call rate = 0.95 and MAF= 0.05), 34794 SNPs were included in the entropy analysis. The entropy and conditional entropy were estimated for each SNP. Conditional entropy quantifies the remaining uncertainty about values of the variable with the

knowledge of SNP. The most informative SNPs for each variable were determined. The

located on Chromosomes: 3, 4, 5 and 16. The results from the study indicate that important genome regions and candidate genes that determine semen quality variables in bulls are located on a number of chromosomes. Some detected clusters of SNPs were located in RNA (U6 and 5S rRNA) for all the variables for which analysis occurred. Associations between

ACCEPTEDcomputations were performed using the R statistical package. A majority of the loci had relatively small contributions. The most informative SNPs for all variables were mainly

RK2 as well GALNT13 genes and some semen characteristics were also detected.

Keywords: Bull; Semen quality; Single nucleotide polymorphism; Entropy analysis


1. Introduction

Long-term selection directed towards an increase of production traits has been negatively associated with reproductive characteristics. Undesirable trends for reproduction have been reported for a number of livestock populations, including dairy cattle (Berry et al.,

2003).During the same period when these reproductive trends have become apparent, artificial

dairy production programs. Accordingly, sperm quality is an important economic trait in cattle, as it affects the conception rate among other factors. Fertility traits increasingly contribute in the total merit index in the Holstein populations, for example in the Holstein French breed (Boichard et al., 2015). There have been some reports (Jamrozik et al., 2005; Druet et al., 2009) indicating marked decreases in conception rate in the Holstein population in most countries. From the breeder’s perspective, this leads to a considerable reduction in reproduction capacity and consequently economic losses.

insemination (AI) has been widely applied MANUSCRIPTin cattle populations around the world. n an era of globalization, use of AI technologies have a key role in the realized genetic improvement in

ACCEPTEDhere are a number of variables describing semen quality (Druet et al., 2009; Hering et al., 2014; Kamiński et al., 2016) and as reviewed by Chenoweth (2005) many genetic

sperm defects occur. Fertility (including semen quality) isaffected by a complex set of traits related to genetic and environmental factors. Semen quality is controlled by many genes with small contributions to this variability being associated with each gene (Druet et al., 2009). Fertility traits are usually considered to be lowly heritable (Amer et al., 2016). Hence, selection efficiency for these traits based on classical polygenic methodology is limited.

Several methods to evaluate the importance of polymorphic nucleotides and the associated clusters have been previously described. One of the most popular approaches is the classical genome-wide association study introduced by Meuwissen et al (2001). This


approach allows for detection of genetic markers by determining statistically significant differences in variables associated with the frequency of the polymorphic variants. These regions may be situated in both gene-coding sequences as well as elsewhere in the genomic space between the genes.

Recently, alternative statistical approaches to detect the effects of SNPs on complex traits have been described in the literature. For example, Yao et al. (2013) applied a machine- learning method to identify additive and non-additive effects of SNPs on residual feed intake in dairy cattle. Entropy analysis has also been used to identify genome regions that are associated with performance traits. Although the theoretical background of this methodology was introduced by Shannon (1948), there continues to be wider application in studies on associations between genome regions and specific genetic traits (Moore et al. 2006, Bertram and Gorelick, 2009). The information theory is especially suitable in epidemiological research (Lavender et al., 2012). The methodology has been successfully implemented in the areas of animal breeding and genetics (Borowska et al., 2014; Graczyk et al. 2017). The entropy

approachACCEPTEDhas some advantages. It allows for searching for SNPs according to the “information impact” on the trait of interest. Ruiz-Marin et al. (2010) concluded the entropy-based method

can be especially useful in the detection of rare effects of low frequency genotypes. The main advantage, however, is an opportunity for evaluation of the interactions between SNPs (Kang et al., 2008, Chen et al., 2011).

he application of alternative methods to analyze the same data allows for validation of results. Moreover, it supplies an inference about the sensitivity of methods used.

Semen quality has been the subject of genome-wide studies but mostly for basic variables of fresh semen, such as sperm concentration (Aston et al., 2009, 2010, Hering et al. 2014a), sperm motility (Hering et al., 2014b), semen volume and total number of sperm


(Hering et al., 2014c), concentration of hormones involved in bull fertility (Fortes et al., 2012. 2013) or combined analysis of several semen production variables (Suchocki and Szyda 2015; Qin et al. 2016). The main objective of the present study was to detect the genome regions that are associated with eleven semen quality variables of bulls, using entropy analysis. The interactions of pairs of loci that associated with this variable were also determined.

2. Material and methods



2.1. Data


The data included came from 288 Holstein-Friesian bulls from one AI station in Poland. The bulls were at a similar age (12 to 18 months) and uniform feeding and housing management facilities were used for all bulls. Bulls were housed in similar environmental conditions. All bulls underwent routine evaluation of testis and none had clinical symptoms affecting basic semen production variables. Semen samples were collected at about 6 months

of age. The following variables were analysed: TM - Total otility (%), VAP - Average Path

ACCEPTEDVelocity (Um/s), VSL - Straight Line Velocity (um/s), VCL - Curvilinear Velocity (um/s), LH - Amplitude of Lateral Head Displacement (um), BCF - Beat Cross Frequency (Hz),

VSL/VCL - STR Straightness (%), VSL/VCL - LIN – Linearity (%), Sperm Membrane integrity (%): PLAZ - plazmolema and MIT - mitochondrial, ATP - sperm ATP content. Sperm motility was evaluated using the computer assisted sperm analysis (CASA) system (VideoTesT Sperm 2.1, St. Petersburg, Russia). Sperm plasma membrane integrity was assessed using dual fluorescent staining, SYBR-14 and PI (LIVE/DEAD™ Sperm Viability Kit, Invitrogen, Molecular Probes, Eugene, USA) as described by Garner and Johnson (1995), with slight modifications. The sperm mitochondrial function was assessed using dual staining with fluorescent probes, 5,5’,6,6’-tetrachloro-1,1’,3,3’-tetraethylbenzimidazolylcarbocyanine


iodide, JC-1 (Invitrogen, Molecular Probes, Eugene, USA) with propidium iodide (PI, Sigma Chemical Co., St. Louis, MO, USA), according to a previously described method (Thomas et al. 1998), with some modifications (Dziekońska et al. 2009). The ATP content was determined using the Bioluminescence Assay Kit CLSII (Roche Diagnostics GmbH, Mannheim, Germany). The number of motile sperm of all the bulls was at least 70%. More details on the collection of phenotypic data and genomic analysis were previously reported by Kamiński et al. (2016). Statistical description of the variables is presented in Table 1.

2.2. Statistical analysis

Entropy analysis is based on discrete variables. Hence, the values for the variables were divided into quartiles (see e.g. Borowska et al., 2017). Molecular data included 48 192 SNPs. Two selection criteria for the data were applied: call rate = 0.95 and minor allele frequency = 0.05. The 34 794 SNPs were subsequently included in the analysis.

The effects of SNPs on the variables assessed in this study were estimated by the so- called portion of information (PI) with the following formula: PI = H(T)-H(T/S) where: H(T) is the entropy of the variable; H(T/S) is the conditional entropy. More information about entropy variables has been previously reported (Borowska et al., 2017).

Interaction between pairs of SNPs (say Sand S2) was described by mutual information MI((S1|S2) = H(S1) + H(S2) - H(S2,S1where: H(S1), H(S2are the entropies of the SNP S1/Sestimated using the previously described formula; H(S1, S2is the joint entropy between SNP Sand Scalculated as: H(S1, S2) = H(S2) + H(S1|S2).

For this analysis the most informative SNPs were selected. Initially, SNPs with the smallest values for conditional entropy (from the first quartile) were chosen. Subsequently, a quarter of the most informative SNPs for each chromosome were subjected to an interaction


analysis (about 2 000 SNPs for each trait). All calculations were conducted using the R 3.1.2 program with the 1.2.0 ‘infotheo’ package 2014 being utilized (Meyer 2008).

The location of SNPs and genes was subsequently verified using: the Ensembl genome database ( and the National Center for Biotechnology Information (NCBI) database (

3. Results and discussion

3.1. Variation in the recorded variables

The statistical description of the variables is listed in Table 1. The values for these variables are similar to those reported in previous studies (Druet et al 2009, Suchocki and Szyda 2015). The variability coefficients, however, fluctuated among variables, from 2.76% (for MIT) to 15.68% (for VSL). All the bulls were certified by the AI company to be producing semen of standard quality. It should be noted that both genetic polymorphism and

large variabilities for the variables that were studied are desirable for effective detection of

importantACCEPTEDgenome regions. From this perspective, five variables (BCF, STR, LIN, PLAZ, MIT) were characterized with relatively little variability.

3.2. Classification of loci and chromosomes

The portion of information (PI) with the most informative SNPs is visualized on a genome-wide scale in Figures 1-2. As expected, the associations of the loci with the variables that were analyzed varied. A majority of these SNPs had relatively small associations with the variable. he SNPs with the greatest associations with the variables there were studied were widely dispersed on multiple chromosomes and 28% of these SNPs were for genes with known functions. There were, however, some tendencies for clustered distributions of SNPs. A list of the most saturated chromosomes for the SNPs - at least 400 SNPs (in parenthesis)


that were associated with the reproductive variables that were studied are provided in Table 2. For each variable, the greatest number of informative SNPs (at least 500) was located on Chromosome 1. Other important clusters for variables were located on Chromosomes: 2, 3, 4 (eight variables per chromosome), 6 (seven variables), 7 (six variables), 8 (four variables), 5 (three variables), 10 and 11 (two variables) and 9 (LIN, only). These findings may indicate a potential source of genetic effects on these variables.

Some detected clusters of SNPs were located in RNA (U6 and 5S rRNA) for all the reproductive variables that were analyzed. Because these genes undergo constitutive and stable expression in almost any cell, results of the present study seem to reflect and confirm the importance of these genes on the variation of sperm motility and other functions. With multiple copies of U6 and rRNA genes being located across the entire bovine genome, there is an increased risk that these genes can be considered too many times in affecting the same trait. Findings in the present study are consistent with the GWAS results obtained by Suchocki and Szyda (2015) where there was analysis of semen production variables (including sperm motility measured by traditional microscopy) in bulls of different ages from different AI centres. Also Okamura et al. (2012) reported that there was a role of small RNAs in the proliferation of germ cells of mouse embryos.

Some associations between the PARK2 gene (including important SNPs) and total sperm motility, amplitude of lateral head displacement, mitochondrial integrity and ATP content were observed in the present study. This gene, when transcribed, encodes for a protein, parkin, which has a role in the cellular functions. Some mutations in this gene affect the PARK2 encoded protein leading to the loss of its function and rapid degradation. A number of these mutations lead to Parkinson’s disease in humans. This corresponds with some functions of GALNT13 gene, including the prominent SNPs detected in the present


study that are associated with the following variables: TM, ALH, BCF, LIN and ATP. The GALNT13 gene is encodes for an enzyme that is involved in regulation of concentrations of manganese and calcium of cells. As reported by Penagaricano et al. (2012), the calcium channels mediate the influx of calcium ions into the cell upon membrane polarization. Furthermore, calcium flux through these channels has a key role in the acrosome reaction, which allows spermatozoa to penetrate the zona pellucida and fuse with the oocyte membrane (Ikawa et al, 2010). Average path velocity and straight-line velocity of sperm cells are associated with clusters of SNPs located near are within the ACCNI gene, and the functions of proteins that are encoded by this gene are also involved in calcium metabolism. It appears as though these functions can partially explain the biological pathway between the expression of this gene and the kinematic variables associated with sperm motility.

Two variables of sperm membrane integrity have been evaluated on the semen of the same bulls used to conduct the present research (Kamiński et al., 2016). Based on the classical genome-wide association study conducted previously, it was reported that there were

significantACCEPTEDeffects of SNPs (located on Chromosome 6) for sperm plasma membrane integrity. The results of the present study are consistent with the findings of Kamiński et al. (2016),

because a number of the most informative SNPs identified by entropy analysis were also located in hromosome 6 (see Table 2). For example, the CTNN2 SNP was detected with both analyses as being the most saturated for the MIT and PLAZ variables. This probably results from the TNN2 protein being involved in aging of spermatozoa; with the amount of this protein being 1.7-fold less in aged sperm cells in comparison to sperm cells that have more recently been produced via spermatogenesis. Furthermore, the CTNN2 protein is associated with other proteins contributing to cell aging (Paul and Robaire 2013).


For mitochondrial functions of bull sperm, however, results of the present study indicate there are associations with SNPs that were not reported by Kamiński et al. (2016) where there were not any SNPs that were associated with mitochondrial function (corrected by the Bonferroni test).

If the candidate genes detected in the present study with the entropy analysis are compared with candidate genes from the previous study using GWAS analyses (Kamiński et al., 2016), there were two common SNPs associated with sperm plazmolema integrity. hese two markers, namely BTB-01365922 and ARS-BFLG-NGS-59175 are both located on Chromosome 6 in positions 18 332 881 bp and 20 507 429 bp, respectively. There are two genes located in close proximity to these markers, LEF1 and NPNT, coding for the lymphoid enhancer binding protein1 and nephronetctin ( The function of these genes is not known to be directly associated with sperm functions or semen integrity, but a role for these proteins that is related to semen quality cannot be dismissed. Another example of a common candidate gene associated with bull sperm quality is the SOX5 (SRY- box5) gene that encodes for the sex determining Y chromosome box5 protein ( This protein binds specifically to the DNA sequence and activates or represses target genes, including genes important for sperm hyper-activation, flagellar motility and maintenance of the axoneme structure of mature mammalian sperm (Kiselak et al. 2010, Mata-Rocha et al. 2014). There were associations reported from a previous study between missense mutations within the SOX5 gene and sperm motility and sperm concentration in fresh semen of 368 Holstein bulls (Hering and Kamiński, 2016). More details on molecular characteristics of the primary sperm variables (TM, PLAZ, MIT, ATP) are provided as supplementary material for the present reporting of the findings in this study.

These findings in the present study are encouraging and provide for an interest in conducting a simultaneous analysis of the same dataset using different statistical approaches,


entropy analysis included, to ascertain the most reliable markers or candidate genes and confirm the biological importance of these genes in associations with important sperm variables for bulls.

There are a few previous reports on comparative analysis of GWAS and entropy theory. For example, an investigation conducted by Graczyk et al. (2017) and Reyer et al. (2015) on chicken data led to the identification of relatively similar genome regions for specific biological actions using both approaches.

Estimates of genetic parameters (including estimates of single locus effects) for a given variable vary among populations depending on statistical methods and models used in the studiesThe possible effects of inter-locus interactions, therefore, were not considered using these analyses. This can lead to an incorrect estimation of the effects of single SNPs.

3.3. Relationships between pairs of loci

The combined information of SNP pair associations with various variables are

ACCEPTEDdepicted in Figures 3 and 4 (light areas - the most relevant interactions). The parameter was computed to obtain relationships between all pairs of SNPs and computations revealed

potential regions in the genome that effect sperm quality of bulls. Analyses for total sperm motility resulted in the greatest values of mutual information for SNPs located on hromosomes: 2, 3, 7, 10, 12; Average Path Velocity: 2, 3, 12, 14, 21 and 22; Straight Line Velocity: 2, 5, 11, 12 and 15; Curvilinear Velocity: 2, 6, 7, 8, 21 and 22; Amplitude of Lateral Head Displacement: 2, 3, 5, 11, 13, 14 and 22; Beat Cross Frequency: 2, 3, 6, 7, 8, 12 and 18; Straightness: 1, 2, 6, 7, 10, 11, 13 and 22; Linearity: 1, 2, 4, 9, 10, 13, 14, 22 and 23; Sperm membrane integrity – plazmolema: 1, 2, 5, 6, 8, 13, 14 and 18 whereas for mitochondrial: 1,


Conflict of interest:

2, 7, 8 and 12; sperm ATP content: 2, 3, 5, 6, 8, 9 and 10. In general, there were similar tendencies for interactions between SNPs.

4. Conclusions

The results of the present study indicate there are important genome regions that are

bulls. The most informative SNPs for all the variables are located on hromosomes: 2, 3, 4, 5 and 16. Whereas the largest amount of mutual SNP information related to sperm variables was from Chromosomes: 2, 3, 6, 8, 10 and 11.

associated with semen quality traits in bullsMANUSCRIPTand that these are distributed on a number of chromosomes. A majority of the loci had relatively small contributions to sperm quality of

All authors of the submitted paper disclose any actual or potential conflict of interest including any financial, personal or other relationships with other people or organizations that could be inappropriately influence, or be perceived to influence, their work described in the submitted paper.


We gratefully acknowledge the helpful comments of editor-in-chief and anonymous reviewers who contributed to the improvement of this manuscript. This study was financially supported by the National Center of Scientific Research, grant no N N311 524940 and UWM Olsztyn grant no 0105-804.



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List of Figures

Fig. 1. Localization of the most informative SNPs for total motility (TM), average path velocity (VAP), straight line velocity (VSL), curvilinear velocity (VCL), amplitude of lateral head displacement (ALH), beat cross frequency (BCF), straightness (STR) and linearity (LIN). [PI-portion of information]

Fig. 2. Localization of the most informative SNPs for mitochondrial sperm membrane integrity (MIT), plazmolema sperm membrane integrity (PLAZ) and sperm ATP content (ATP); [PI - portion of information].


Fig. 3a. Mutual information between SNPs for total motility (TM), average path velocity (VAP), curvilinear velocity (VCL) and straight line velocity (VSL)

Fig. 3b. Mutual information between SNPs for amplitude of lateral head displacement (ALH),beat cross frequency (BCF), straightness (STR) and linearity (LIN)

Fig. 4. Mutual information between SNPs for mitochondrial sperm membrane integrity (MIT), plazmolema sperm membrane integrity (PLAZ) and sperm ATP content (ATP)




Table 1

Statistical description of the variables recorded (= 288) .



Coefficient of










TM (%)







VAP (Um/s)







VSL (um/s)







VCL (um/s)







ALH (um)







BCF (Hz)





















PLAZ (%)







MIT (%)







ATP (%)







Note on symbols: TM- Total motility, VAP – Average Path Velocity, VSL – Straight Line Velocity, VCL – Curvilinear Velocity, ALH -Amplitude of Lateral Head Displacement, BCF

Beat Cross Frequency, VSL/VCL – STR Straightness, VSL/VCL – LIN – Linearity, Sperm membrane integrity: PLAZ - plazmolema and MIT - mitochondrial, ATP - sperm ATP content


Table 2

Location of the most important SNPs (from 1 quartile of conditional entropy)







The most saturated*





































1 (557) 2

(504) 4

U6 (15) FAM155A (10)



2 210

4 948

PARK2 (8) GALNT13 (7)








THSD7B (7)























U6 (27) GALNTL6 (13)








5S rRNA (12) KCNIP4



2 270

4 855

1 (511)


(504) 3

(11) ACCN1 (8) ACOXL





(7) C4orf22 (7)












FAM155A (7) PARK2





















1 (599)


(481) 4

U6 (21) ACCN1 (10)



2 312

4 851

(479) 11 (472) 10

EPHA6 (7) EXOC6B (7)






SNX29 (7)













1 (536)


(505) 3

U6 (21) KCNIP4 (12)





(445) 2 (444) 6



2 218

4 918

EPHA6 (7) GRIK2 (7)


(444) 10 (425) 4





PLEX2 (7) RORA (7)

























1 (572)


(456) 3

U6 (14) GALNT (13)





PARK2 (12) 5S_rRNA



2 195

4 850

(455) 6 (442) 4


(12) GALNTL6 (8)










KCNIP4 (8)




















1 (631)


(464) 6

U6 (22) 5S_rRNA (10)



2 284

4 819



(442) 11 (415) 8

(7) NTRK (2) PRKG1 (7)
































1 (605)


(522) 4

U6 (19) 5S_rRNA (9)



2 231

4 978






2(454) 7 (425)

KCNIP4 (8) NSG1 (8)










SLC9A9 (8)











2 238

4 935

1 (674)


(467) 3

U6 (19) GALNTL6 (190





5S_rRNA (80)




























(438) 9(406) 6(404)

DIAPH39(8) GRIK2 (8)






NSG1 (8)











1(707) 6 (5160

U6 (27) GPC5 (10)



2 317

4 762

CTNNA2 (9) RBMS3(9)


4(474) 7(416)





5S_RRNA (8) GPC6 (8)
















1 (593) 2 (489) 4

U6 (12) 5S_rRNA (11)





PARK2 (10) ACOXL (8)



2 346

4 774

(460) 6 (436) 7


CTNNA2(8) THSD7B (8)










UNC13C (8)
















1 (555), 4 (460), 5

U6 (6), GALNT13 (6),





(456), 8 (435), 7



2 295

4 926

PARK2 (6), PRKCE (6),


(430), 2 (404), 3





RELN (6) 5S rRNA (6)

















*in parenthesis - number of SNPs located on the most saturated chromosomes/genes

Note on symbols: *in parenthesis - the number of SNPs across the most saturated chromosomes / genes, TM- Total motility (%), VAP – Average Path Velocity (Um/s), VSL – Straight Line Velocity (um/s), VCL – Curvilinear Velocity (um/s), ALH -Amplitude of Lateral Head Displacement (um), BCF – Beat Cross Frequency (Hz), VSL/VCL – STR Straightness (%), VSL/VCL – LIN – Linearity (%), Sperm membrane integrity (%): PLAZ - plazmolema and MIT - mitochondrial, ATP - sperm ATP content