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ASSOCIATIONS AMONG BETA-LACTOGLOBULIN GENOTYPES AND SOME PRODUCTION TRAITS IN SHEEP: A SYSTEMATIC REVIEW AND META-ANALYSIS.

Byline: M. Ozdemir and N. Esenbuga

Keywords: Sheep, Meta-analysis, [beta]lg polymorphism, genetic model, production traits.

INTRODUCTION

Various studies have investigated the association between different forms of milk protein genes and economic yield traits; one of the the most important among these genotypic forms is the beta-Lactoglobulin([beta]lg). The association between [beta]lg variants and certain production traits has been examined, and the possibility to use it as a polymorphic genetic marker has been reported in different studies(Martinez et al., 1993; Dario et al., 2008; Ramos et al., 2009; Yousefi et al., 2013; Giambra et al., 2014; Ozmen and Kul, 2016). Nevertheless, the differences in the association in question have been determined to be insignificant by some researchers(Esenbuga, 1995; Giaccone et al., 2000; Mroczkowski et al., 2004; Michalcova and Krupova, 2009; Giambra et al., 2014; Rozbicka-Wieczorek et al., 2015; Triantaphyllopoulos et al., 2017)(Supplementary file).

Although numerous studies have been published on the association between [beta]lg gene polymorphism and some economic yield traits in sheep; the results are inconsistent due to reasons such as sample sizes used in studies, breed differences, the effect of the changing environment, the interaction between genotype and environment. Therefore, the results of such contradictory studies, which are frequently repeated, do not provide any benefit to producers.

The meta-analysis is a test method that uses the results of many different studies conducted in a specific area and provides the effect of the samples with a more powerful analysis. It may be necessary to apply the meta-analysis in a specific area, especially on a number of studies are repeated and of which sample numbers are different. By combining individual studies with this analysis, insufficiencies experienced due to the limitations of the randomized design and sufficient sample size are not observed, and it is possible to achieve stronger results(DeCoster, 2004; Borenstein et al., 2009).

Furthermore, the meta-analysis performed represents a good example of obtaining clearer results by the combination and analysis of the study results, which are independent of each other in a particular field, and of contributing to explaining similar or different findings in the studies.

The aim of the present study was to conduct a meta-analysis on the results of the previous studies on sheep in the literature to analyze the overall association between [beta]lg gene and specific yield traits.

MATERIALS AND METHODS

Materials: Scanning of the studies examining the association among [beta]lg polymorphism and yield traits in databases(Web of Science, Science Direct, Google Scholar) was performed, and 22 original publications were collected from 1993 to 2017. All the articles selected had 3 criteria:

* Studies on the association between [beta]lg polymorphism and yield traits(for example; lactation milk yield, fat content, protein content, lactose content, casein content),

* The number of animals and the [beta]lg genotype,

* The mean of the relevant yield trait of each genotype and the standard deviation or standard error.

Data extraction: We extracted the data using a standard form of the content of each study prepared independently of each other in MicroSoft Excel. The content of the studies covered the name of the first author, the date and country of publication, the breed of animals, the number of animals examined, the number of genotypes, the Hardy-Weinberg Equilibrium, the mean of the relevant yield traits of genotypes, the standard deviation of the means and the statistical significance level of the association.

Statistical analysis: The data set was prepared separately for each yield trait and the following methods were followed:

1. In the meta-analysis, the analysis of the differences between the means was carried out according to the random effect model or fixed effect model. The model selection is made depending on whether the effects of studies are homogeneous or heterogeneous(Hintze, 2007). Accordingly, the fixed effect model was used when the differences of the means used in the study were homogeneous, and the random effect model was used when they were heterogeneous. The assumption of heterogeneity was calculated based on the I2 statistics(for the significance level of heterogeneity test(p), the level-0.10 was used).

2. The form of inheritance of the alleles to be used in the study was examined as dominant(AA+AB versus BB), complete over-dominant(AA+BB versus AB), recessive(AA versus AB+BB), co-dominant(AA versus AB, AA versus BB, and AB versus BB)(Ozdemir et al., 2018).

3. In the analysis of the factors, [beta]lg genotype differences on the related yield trait were evaluated according to the breeding, types of the sheep breed(Dairy Subgroup: Dairy sheep breeds and/The Others Subgroup: Dual purpose breeds and various crossbreeds) and were evaluated as overall.

4. Standard mean differences(SMD) and standard errors were calculated with 95% CI to assess the power of the association between the means of the yield traits of the [beta]lg gene variants examined. This procedure was used to compare all paired(multiply pairwise) variants. In the SMD calculation, the Cohen method(Cohen, 1988) was used when the number of the studies examined was large(>10), and the Hedges method(Hedges, 1981) was used when the number of the studies was low. If the number of studies >10, the Cohen method for standardized mean differences is advantageous. There is an overestimation of the effect size in case of a small number of studies. The Hedges method for standardized mean differences has advantages in the case of a small number of studies(DeCoster, 2004).

5. STATA version 11.0(Stata Corp. 2009, Stata Statistical Software) was used in all statistical analyses.

An example for the dataset: The association among the [beta]lg genotypes and the total milk yield is presented in Table 1, as an example of the dataset utilized in the present study. All the data set used as material in the study can be seen as supplemental files.

RESULTS AND DISCUSSION

The data set utilized in the present study were organized separately according to economic production traits, loci, and genetic models. Prior to performing the statistical evaluation, the dominant(AA+AB versus BB), complete over-dominant(AA+BB versus AB), recessive(AA versus AB+BB), co-dominant(AA versus AB, AA versus BB and AB versus BB) statuses of the alleles were considered(Table 2). According to the results of the analysis, generally the co-dominant feature as genetic model was displayed, so the association analyzes were performed according to this model.

Tables 2 and 3 contain information on the number of studies and the results of the meta-analysis in which different methods(Cohen or Hedges) were employed depending on the status of whether the studies were homogeneous or heterogeneous. The tables contain information on the Heterogeneity test, SMD, and 95% CI values, its% weight, P values of the pairwise comparisons.

When the genetic model analyses conducted on [beta]lg genotypes and yield traits were examined, it was found out that the genotype mean differences in terms of the total milk yield, fat, protein, and lactose content were not statistically significant in the Dominant and Recessive genetic models, and the mean differences of the fat and casein content were significant only in the Complete over-dominant model and generally AB genotype was determined as superior in the dairy subgroups(Table 2).

In the meta-analysis using 14 studies on the total milk yield, the associations between [beta]lg genotypes(AA vs AB, AA vs BB, and AB vs BB) and the total milk yield were found to be significant in all the sheep and subgroups(p<0.05). While the difference between AB vs BB genotypes in the other subgroup was found to be significant(p<0.05), the difference between the means of AA vs AB genotypes in the dairy subgroup was found to be significant(p<0.05), the difference between the means of AA vs AB and AB vs BB genotypes in the dairy subgroup was found to be significant(p0.05). It was determined that the genotypes were ranked as AA>BB>AB in terms of the total milk yield(Table 3).

In the individual studies conducted, while some researchers reported that the total milk yield means between [beta]lg genotypes were significant(Martinez et al., 1993; Ramos et al., 2009), the others reported that the mean differences were insignificant(Mroczkowski et al., 2004; Michalcova and Krupova, 2009; Giambra et al., 2014; Rozbicka-Wieczorek et al., 2015; Triantaphyllopoulos et al., 2017).

Table 1. Studies investigating the association among [beta]lg polymorphism and total milk yield in sheep.

First Author###Breeds###N###Country###Type###AA###SD1###N1###AB###SD2###N2###BB###SD3###N3###P

Corral et al., 2010###Merino###529###USA###other###40.24###18.71###301###39.93###18.76###200###47.46###19.21###28

###*

Erdogan and Cemal, 2010###CineCapari###40###Turkey###dairy###104.69###10.61###4###64.25###5.28###21###76.39###5.91###15

Mroczkowski et al., 2004###Merino###770###Poland###other###30.67###11.81###193###31.03###12.60###376###31.64###11.77###201###ns

###*

###Serra da###1025###Portugal###dairy###118.46###58.23###406###124.31###2.82###470###120.41###44.43###149

Ramos et al., 2009###Estrela

###*

Ramos et al., 2009###Merino###314###Portugal###other###77.16###25.10###66###83.15###26.88###132###86.39###25.74###116

Giambra et al., 2014###Lacaune###749###Switzerland###dairy###371.00###115.33###133###372.00###118.03###387###367.00###136.19###229###ns

###East###394###Germany###dairy###284.00###107.99###238###589.00###124.90###156###ns

Giambra et al., 2014###Friesian

###***

Martinez et al., 1993###Manchega###3672###Spain###dairy###58.67###4.64###1364###59.29###4.15###1748###54.96###2.36###560

Michalcova and Krupova,###Impr.###67###Slovenia###other###100.50###5.28###17###10.28###3.65###34###102.45###4.08###16###ns

2009###valachian

Michalcova and Krupova,###Czigaia###45###Slovenia###other###86.56###7.56###16###88.28###5.52###19###97.93###7.62###10###ns

2009

###Morkarama 113###Turkey###other###73.27###35.29###20###69.75###37.65###65###69.94###35.82###28###ns

Esenbuga, 1995###n

Esenbuga, 1995###vesi###95###Turkey###dairy###105.03###35.16###33###114.87###36.40###53###95.66###35.10###9###ns

Triantaphyllopoulos et###Chios and###217###Greece###other###904.90###201.65###17###1058.34###84.26###134###770.11###115.81###66###ns

al.,2017###Karag.

Rozbicka-Wieczorek et###Heath and###60###Poland###other###37.93###13.408###10###43.44###25.44###36###38.57###15.865###14###ns

al.,2015###Lowl.

In the analysis of 21 studies, no significant association was found between [beta]lg genotypes and fat content in all sheep and milk-type sheep group(P>0.05). While some researchers reported that the fat content means between [beta]lg genotypes were significant(Dario et al., 2008; Ramos et al., 2009; Yousefi et al., 2013; Giambra et al., 2014; Ozmen and Kul, 2016). on the other hand, they reported that the mean differences were insignificant(Esenbuga, 1995; Giaccone et al., 2000; Michalcova and Krupova, 2009; Rozbicka-Wieczorek et al., 2015; Triantaphyllopoulos et al., 2017).

In the analysis of 20 studies, while no significant association was determined between [beta]lg genotypes and the protein content in all sheep(p>0.05), in the dairy group, the protein content means of AA vs AB genotypes were found to be statistically different(p<0.05) and AB genotype was found to be superior. While there was no statistically significant association between the protein content means of AA vs AB and AA vs BB genotypes in the other subgroup, a significant difference(p0.05). While some authors reported in the individual studies conducted that the lactose content means were significant between [beta]lg genotypes and AB genotype was superior(Celik and Ozdemir, 2006; Yousefi et al., 2013; Rozbicka-Wieczorek et al., 2015; Triantaphyllopoulos et al., 2017);the others reported that the mean differences were insignificant(Mroczkowski et al., 2004; Michalcova and Krupova, 2009; Ozmen and Kul, 2016).

In the analysis of 5 studies used to test the association between [beta]lg genotypes and the casein content, while there was a statistically significant association(p0.05). A statistically significant association was determined between the casein content values of all [beta]lg genotypes in the other subgroup, and the ranking of the genotypes was BB>AB>AA(Table 3). No significant relationship was determined between the casein content means of all genotypes in the dairy sub-group(p>0.05). While some researchers reported in the individual studies conducted that the casein content means were significant between [beta]lg genotypes(Martinez et al., 1993; Rozbicka-Wieczorek et al., 2015), the others reported that the mean differences were insignificant(Mele et al., 2007; Lupolov and Petcu, 2013).

The results of association studies among [beta]lg polymorphism and economic yield traits are conflicting. These may be due to differences in the sample size, breeds studied, environmental effects, especially genotype-environmental interactions. Breeders should take into account the environmental conditions, animal breeds and genotype-environmental interactions and should use this information for breeding purposes(Supplementary file).

Conclusion: The association between [beta]lg gene and certain economic yield traits was investigated and assessed in accordance with a number of genetic models in the meta-analysis. The Meta-analysis shows that the association of [beta]lg gene must generally be investigated in accordance with the co-dominant genetic model. A significant association was found among [beta]lg genotypes and total milk yield, protein content and casein content studied with the meta-analysis. The [beta]lg gene AA genotype for total milk yield, AB genotype for protein content and casein content have significant superiority especially in dairy subgroup, but important candidate genotype for fat content and lactose content could not be determined. It is suggested that [beta]lg major gene will be beneficial for the improvement of the economic yield traits studied, and it is possible to utilize it as major gene and a molecular marker.

Conflict of Interest: We certify that there is no conflict of interest with any financial organization regarding the material discussed in the manuscript.

Table 2. Genetic model analyses of the genotypes related to [beta]lg.

###AA+AB versus BB, Dominant Model###AA versus AB+BB, Resesive Model###AA+BB versus AB, Copmlete over Domn. Model

Traits###Type###n###I2###SMD###95%###CI###%Weight###P###I2###SMD###95%###CI###%Weight###P###I2###SMD###95%###CI###%Weight###P

Total###Dairy###6###97,7**###0,146###-0,442 0,734###40,84###0,626###98,8**###-0,012###-0,683###0,659###44,50###0,972###98.6** -0,256###-0,815 0,304###45,52###0,370

Milk###Other###8###97,8**###-0,834###-1,705 0,038###59,16###0,061###94,9**###0,260###-0,272###0,792###55,50###0,338###97.7**###0,282###-0,402 0,966###54,48###0,420

Yield###Overall###14###97,9**###-0,245###-0,722 0,232###100,00###0,313###97,6**###0,101###-0,290###0,492###100,00###0,613###98.1** -0,075###-0,464 0,315###100,00###0,708

Fat###Dairy###9###95,9**###-0,244###-0,945 0,457###40,36###0,495###91,0**###-0,235###-0,509###0,038###48,37###0,092###88.8** -0,22###-0,438 -0,003###48,06###0,047

Content###Other###11###68,8**###0,101###-0,105 0,307###59,64###0,336###89,9**###0,075###-0,262###0,411###51,63###0,664###90.4**###0,553###0,249 0,857###51,94###0,000

###Overall###20###91,1**###0,029###-0,252 0,311###100###0,838###89,9**###-0,063###-0,257###0,130###100,00###0,521###90.0**###0,141###-0,032 0,314###100,00###0,109

Protein###Dairy###8###95,9**###-0,244###-0,945 0,457###40,36###0,495###88,3**###-0,005###-0,327###0,317###44,06###0,977###90.4**###0,015###-0,298 0,327###43,73###0,926

Content###Other###11###68,8**###0,101###-0,105 0,307###59,64###0,336###92,1**###0,245###-0,120###0,609###55,94###0,188###95.0**###0,100###-0,299 0,499###56,27###0,622

###Overall###19###91,1**###0,029###-0,252 0,311###100,00###0,838###90,5**###0,134###-0,104###0,372###100,00###0,269###93.5**###0,068###-0,182 0,317###100,00###0,594

Lactose###Dairy###2###99,0**###3,192###-2,913 9,297###17,15###0,305###98,6**###1,457###-1,465###4,379###17,35###0,328###0,0###-0,099###-0,334 0,136###17,44###0,411

Content###Other###9###95,9**###-0,348###-0,990 0,294###82,85###0,288###93,2**###-0,269###-0,699###0,162###82,65###0,221###96.1** -0,373###-0,875 0,128###82,56###0,145

###Overall###11###96,8**###0,197###-0,490 0,883###100,00###0,574###94,9**###-0,026###-0,480###0,428###100,00###0,911###95.3** -0,328###-0,745 0,089###100,00###0,124

Casein###Dairy###4###76,3**###0,27###-0,132 0,673###83,84###0,188###27,6###-0,076###-0,224###0,073###85,74###0,318###0,0###-0,266###-0,328 -0,204###83,50###0,000

Content###Other###1###0,0###-6,612###-7,974 -5,25###16,16###0,000###0,0###-3,816###-4,795 -2,836###14,26###0,000###0,0###2,932###2,183 3,682###16,50###0,000

###Overall###5###96,5**###-0,84###-1,876 0,196###100,00###0,112###93,5**###-0,645###-1,227 -0,062###100,00###0,030###94.3**###0,311###-0,255 0,878###100,00###0,281

Table 3. The results of the Meta-analysis regarding the association between [beta]lg genotypes and certain yield traits; SMD values and certain statistical results.

###AA versus AB###AA versus BB###AB versus BB

Traits###Type###n###I2###SMD###95%###CI###%Weight###P###I2###SMD###95%###CI###%Weight###P###I2###SMD###95%###CI###%Weight###P

Total###Dairy###6###19,2###1,419###0,214###2,624###36,66###0,021###82,3**###0,183###-0,257###0,623###35,560###0,415###96,8**###-1,07###-1,983###-0,157###33,08###0,022

Milk###Other###8###98,8**###-0,109###-0,198 -0,021###63,34###0,016###95,8**###-0,078###-0,607###0,452###64,440###0,774###97,3**###0,072###-0,484###0,628###66,92###0,800

Yield###Overall###14###97,2**###-0,051###-0,434###0,332###100,00###0,795###94,8**###0,07###-0,304###0,444###100,00###0,714###97,4**###-0,254###-0,718###0,209###100,00###0,282

Fat###Dairy###9###83,0**###0,033###-0,247###0,313###44,87###0,818###90,6**###-0,081###-0,677###0,515###40,73###0,790###94,0**###-0,06###-0,674###0,553###41,02###0,847

Content###Other###12###92,3**###0,245###-0,152###0,642###55,13###0,226###74,7**###-0,003###-0,293###0,288###59,27###0,986###90,4**###0,21###-0,180###0,601###58,98###0,291

###Overall###21###89,7**###0,159###-0,083###0,401###100,00###0,197###84,3**###-0,004###-0,280###0,271###100,00###0,975###91,9**###0,12###-0,194###0,434###100,00###0,452

Protein###Dairy###9###91,8**###-0,321###-0,621 -0,021###48,70###0,036###84,2**###-0,031###-0,348###0,287###48,73###0,850###81,0**###0,21###-0,036###0,456###47,17###0,095

Content###Other###11###85,4**###0,288###-0,014###0,591###51,30###0,062###91,2**###-0,242###-0,784###0,300###51,27###0,381###93,1**###-0,748###-1,250###-0,247###52,83###0,003

###Overall###20###88,7**###-0,006###-0,200###0,188###100,00###0,952###90,0**###-0,07###-0,358###0,218###100,00###0,635###91,8**###-0,18###-0,448###0,088###100,00###0,188

Lactose###Dairy###2###97,9**###1,097###-1,160###3,354###17,96###0,341###98,2**###3,002###-2,814###8,818###17,14###0,312###98,4**###2,713###-2,424###7,850###17,16###0,301

Content###Other###10###93,1**###-0,382###-0,846###0,083###82,04###0,107###89,6**###-0,362###-0,898###0,173###82,86###0,185###95,8**###-0,039###-0,716###0,638###82,84###0,911

###Overall###12###94,2**###-0,142###-0,598###0,314###100,00###0,542###92,8**###0,028###-0,568###0,625###100,00###0,926###96,2**###0,381###-0,295###1,058###100,00###0,269

Casein###Dairy###4###0,0###-0,151###-0,219 -0,084###88,09###0,000###73,2*###0,167###-0,263###0,597###95,92###0,447###78,9**###0,33###-0,115###0,775###84,19###0,146

Content###Other###1###0,0###-2,109###-2,942 -1,276###11,91###0,000###0,0###-12,55###-16,520###-8,584###4,08###0,000###0,0###-6,279###-7,702###-4,856###15,81###0,000

###Overall###5###82,1**###-0,429###-0,804 -0,055###100###0,025###92,3**###-0,361###-1,230###0,508###100,00###0,415###96,1**###-0,721###-1,743###0,301###100,00###0,167

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Date:Oct 31, 2020
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