Qualidade espermática genétca cai com idade (em humanos e gado bovino)
https://www.sciencedaily.com/releases/2006/06/060606091933.htm
https://www.sciencedirect.com/science/article/pii/S0022030211004279
Animais no envelhecimento caindo qualidade espermática
https://www.sciencedirect.com/science/article/abs/pii/0531556595000194
humanos idosos e qualidade espermática
https://www.sciencedirect.com/science/article/abs/pii/S0378432018300113?via%3Dihub
Entropia nas populações dos seres vivos
https://pubmed.ncbi.nlm.nih.gov/29116373/
Accepted Manuscript
Title: Identification of genome regions determining semen quality in
Authors: Alicja Borowska, Tomasz Szwaczkowski, Stanisław
Kaminski,´ Dorota M. Hering, Władysław Kordan, Marek
Lecewicz
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DOI: |
https://doi.org/10.1016/j.anireprosci.2018.03.012 |
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ANIREP 5790 |
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Animal Reproduction Science |
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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
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Identification of genome regions determining semen quality in
using information theory
Alicja Borowska1, Tomasz Szwaczkowski2, |
Stanisław Kamiński3, Dorota M. Hering3, |
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Władysław Kordan4, Marek Lecewicz4 |
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1Division of Horse Breeding, Poznan University of Life Sciences, Wolynska st. 33, |
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2Department of Genetics and Animal Breeding, Poznan University of Life Sciences, |
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3Department of |
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of Warmia and Mazury in Olsztyn, |
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Department of Animal Biochemistry and Biotechnology, University of Warmia and Mazury |
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in Olsztyn, M. Oczapowski st. 5, |
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*Corresponding author at: Department of Genetics and Animal Breeding, Poznan University of Life Sciences, Wolynska st. 33,
1
ABSTRACT
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
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
2
1. Introduction
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
3
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
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
approachACCEPTEDhas some advantages. It allows for searching for SNPs according to the “information impact” on the trait of interest.
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
4
(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 |
MANUSCRIPT |
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2.1. Data |
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The data included came from 288
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,
5
iodide,
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 =
Interaction between pairs of SNPs (say S1 and S2) was described by mutual information MI((S1|S2) = H(S1) + H(S2) - H(S2,S1) where: H(S1), H(S2) are the entropies of the SNP S1/S2 estimated using the previously described formula; H(S1, S2) is the joint entropy between SNP S1 and S2 calculated 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
6
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 (http://www.ensembl.org/) and the National Center for Biotechnology Information (NCBI) database (http://www.ncbi.nlm.nih.gov/).
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
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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
8
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
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
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
9
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
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,
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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
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,
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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.
Acknowledgements
We gratefully acknowledge the helpful comments of
12
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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).
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].
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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)
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Tables:
Table 1
Statistical description of the variables recorded (n = 288) .
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Coefficient of |
Variable |
Mean |
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variability |
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TM (%) |
62.70 |
11.43 |
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VAP (Um/s) |
30.36 |
15.23 |
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VSL (um/s) |
27.86 |
15.68 |
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|
|
|
VCL (um/s) |
54.59 |
12.89 |
|
||
|
|
|
ALH (um) |
1.20 |
12.63 |
|
||
|
|
|
BCF (Hz) |
7.54 |
5.47 |
|
||
|
|
|
VSL/VCL – STR (%) |
87.75 |
5.10 |
|
||
|
|
|
VSL/VCL – LIN (%) |
49.83 |
6.55 |
|
||
|
|
|
PLAZ (%) |
72.14 |
3.63 |
|
||
|
|
|
MIT (%) |
71.29 |
2.76 |
|
||
|
|
|
ATP (%) |
15.75 |
14.38 |
|
||
|
|
|
Note on symbols: TM- Total motility, VAP – Average Path Velocity, VSL – Straight Line Velocity, VCL – Curvilinear Velocity, ALH
–Beat Cross Frequency, VSL/VCL – STR Straightness, VSL/VCL – LIN – Linearity, Sperm membrane integrity: PLAZ - plazmolema and MIT - mitochondrial, ATP - sperm ATP content
26
Table 2
Location of the most important SNPs (from 1 quartile of conditional entropy)
|
Intron |
Intergenic |
|
|
The most saturated* |
||
|
|
|
|
|
|||
Variable |
|
|
|
|
|
|
|
|
variant |
Variant |
Chromosomes |
Genes |
|||
|
|
|
|||||
|
|
|
|
|
|
|
|
|
|
|
1 (557) 2 |
(504) 4 |
U6 (15) FAM155A (10) |
||
TM |
2 210 |
4 948 |
PARK2 (8) GALNT13 (7) |
||||
|
|
|
(496) |
7 |
(402) |
THSD7B (7) |
|
|
|
|
|
|
|
||
|
|
|
|
|
|
|
|
|
|
|
|
|
|
U6 (27) GALNTL6 (13) |
|
|
|
|
|
|
|
5S rRNA (12) KCNIP4 |
|
VAP |
2 270 |
4 855 |
1 (511) |
2 |
(504) 3 |
(11) ACCN1 (8) ACOXL |
|
(480) |
6 |
(407) |
(7) C4orf22 (7) |
||||
|
|
|
|||||
|
|
|
|
|
|
FAM155A (7) PARK2 |
|
|
|
|
|
|
|
(7) |
|
|
|
|
|
|
|
|
|
|
|
|
1 (599) |
3 |
(481) 4 |
U6 (21) ACCN1 (10) |
|
VSL |
2 312 |
4 851 |
(479) 11 (472) 10 |
EPHA6 (7) EXOC6B (7) |
|||
|
|
|
(411) |
SNX29 (7) |
|||
|
|
|
|
|
|
|
|
|
|
|
1 (536) |
7 |
(505) 3 |
U6 (21) KCNIP4 (12) |
|
|
|
|
(445) 2 (444) 6 |
||||
VCL |
2 218 |
4 918 |
EPHA6 (7) GRIK2 (7) |
||||
(444) 10 (425) 4 |
|||||||
|
|
|
PLEX2 (7) RORA (7) |
||||
|
|
|
(415) |
5 |
(410) |
||
|
|
|
|
||||
|
|
|
|
|
|
|
|
|
|
|
1 (572) |
2 |
(456) 3 |
U6 (14) GALNT (13) |
|
|
|
|
PARK2 (12) 5S_rRNA |
||||
ALH |
2 195 |
4 850 |
(455) 6 (442) 4 |
||||
(12) GALNTL6 (8) |
|||||||
|
|
|
(440) |
||||
|
|
|
KCNIP4 (8) |
||||
|
|
|
|
|
|
||
|
|
|
|
|
|
|
|
|
|
|
1 (631) |
3 |
(464) 6 |
U6 (22) 5S_rRNA (10) |
|
BCF |
2 284 |
4 819 |
FAM155A (7) GALNTL6 |
||||
(442) 11 (415) 8 |
(7) NTRK (2) PRKG1 (7) |
||||||
|
|
|
(414) |
5 |
(404) |
||
|
|
|
TRHDE (7) |
||||
|
|
|
|
|
|
||
|
|
|
|
|
|
|
|
|
|
|
1 (605) |
6 |
(522) 4 |
U6 (19) 5S_rRNA (9) |
|
STR |
2 231 |
4 978 |
(459) |
8 |
(457) |
CCSER1(9) CTNNA2(8) |
|
2(454) 7 (425) |
KCNIP4 (8) NSG1 (8) |
||||||
|
|
|
|||||
|
|
|
3(407) |
SLC9A9 (8) |
|||
|
|
|
|
|
|
|
|
LIN |
2 238 |
4 935 |
1 (674) |
2 |
(467) 3 |
U6 (19) GALNTL6 (190 |
|
|
|
|
5S_rRNA (80) |
||||
|
|
|
|
|
|
||
|
|
|
|
|
|
|
|
|
|
|
|
|
|
27 |
|
|
|
(438) 9(406) 6(404) |
DIAPH39(8) GRIK2 (8) |
|
|
|
|
|
NSG1 (8) |
|
|
|
|
|
|
|
|
|
|
1(707) 6 (5160 |
U6 (27) GPC5 (10) |
|
PLAZ |
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) |
||
MIT |
2 346 |
4 774 |
(460) 6 (436) 7 |
||
CTNNA2(8) THSD7B (8) |
|||||
|
|
|
(423) |
||
|
|
|
UNC13C (8) |
||
|
|
|
|
||
|
|
|
|
|
|
|
|
|
1 (555), 4 (460), 5 |
U6 (6), GALNT13 (6), |
|
|
|
|
(456), 8 (435), 7 |
||
ATP |
2 295 |
4 926 |
PARK2 (6), PRKCE (6), |
||
(430), 2 (404), 3 |
|||||
|
|
|
RELN (6) 5S rRNA (6) |
||
|
|
|
(403) |
||
|
|
|
|
||
|
|
|
|
|
*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
28