A Meta-analysis Study of Association Between FecB Polymorphism and Litter Size in Sheep

Litter size is perhaps the main commercial trait since it has an observable effect on benet in the sheep industry. Fecundity genes, play a vital role in expression of litter size. One of the most popular of these genes is the Booroola gene (FecB). In many past researches there was a dependency between the BMPR1B gene polymorphism and litter size of sheep. In the current study, a meta-analysis directed by coalescing outcoming of 9902 cases of 26 published research wherein various breeds of sheep to assess the inuence of the FecB gene on litter size utilizing additive, recessive, dominant, and co-dominant genetic models. The random effects model was used for data analysis according to the Cochran Q test and I 2 quantity. Under additive (SMD = 0.528), dominant (SMD = 0.468) and recessive (SMD = 0.250) models, the signicant effect (P<0.01) of FecB genotypes has been identied. Furthermore, under the co-dominant (SMD = -0.050, P = 0.3332) model, the association between FecB genotypes and litter size trait had not been detected. A growth in litter size by about 0.47 lambs (Dominant model) was associated with the rst copy of the FecB gene and 0.25 lambs (Recessive model) with the second copy of this gene. Consequences of the current study support the idea that BMPR1B fundamentally inuenced litter size and subsequently it may be utilized for Marker-assisted selection programmers for improved genetic merit of reproductive futures and furthermore insert this gene by crossbreeding in low prolic breeds may improve reproductive characteristics.


Introduction
Sheep farming is incompetent and unpro table due to low e ciency. One of the best ways to improve this situation is the improvement of the fecundity and fertility of sheep via selective practices (Li et al., 2021). One of the fundamental reproductive traits in sheep is litter size that is affected by many unknown factors. Because of low heritability of this trait, traditional breeding strategies have been useless to increase it rapidly (Mahdavi et al., 2014; Wang et al., 2015). Consequently, researchers hold sought to seek a number of variants of candidate genes act on litter size that should stand helpful for marker-assisted selection (MAS) and molecular genetics techniques. Findings demonstrated that a range of different genes, commonly termed fecundity (Fec) genes, play an important roleon litter size. One of the most popular of these genes is Bone morphogenetic protein receptor type IB (BMPR1B) which is famous as activin-like Kinase 6 or FecB or Booroola gene (Wilson et al. 2001, Chu et al. 2011, Plakkot et al. 2020).
Booroola gene is the member of the transforming growth factor (TGF) β super-family, which is mapped at the FecB locus existing among the Osteopontin (OPN) and epidermal growth factor (EGF) genes on chromosome 6 including a coding sequence of 1509 bp, component of ten exons as code for 502 amino acids (Singh et al., 2020). A transition of adenine to guanine at the nucleotide site 746, result in the substitution of glutamine amino acid to arginine amino acid at location 249 (Q249R) for mutant types (Mulsant et al., 2001;Potki et al., 2020). This mutation in Fec B allele is associated with the additive effect on ovulation rate and litter size in Booroola Merino sheep (Souza et  A meta-analysis of the in uences of the FecB gene polymorphism on sheep litter size (Chong et al., 2019) utilized just Chinese sheep breeds, and contrasted only FecB genotypes to evaluate appropriately the relationship between litter size and FecB polymorphism. Therefore, the current study intended to do a meta-analysis of relationships of litter size and FecB polymorphism in sheep with four distinct genetic models including the additive, dominant, co-dominant and recessive.

Materials And Methods
Searching strategy and screening criteria for literature review Criteria from the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) checklist were used to pick quali ed documents for the meta-analysis (Moher et al., 2009).
In order to locate all related research studies in distinct languages, a systematic literature search was performed for electronic databases and publications (PubMed, Science Direct, Springer, Web of Science, Google Scholar, Wiley Online Library, CNKI, Magiran and sid). The following search terms were combined to identify appropriate studies: 'BMPR1B','BMPR-1B', ' Bone morphogenetic protein receptor 1B', 'ALK6', 'activin receptor-like kinase 6', 'fecundity' ,'FecB', 'Booroola', 'Gene polymorphism', 'association', 'sheep', ' reproductive traits', 'litter size' and 'proli cacy', As well, to classify eligible studies that may not have been found in the searching processes, reference lists of the documents were thoroughly investigated.

Criteria of eligibility and method of selection
In the present meta-analysis study, a research has been incorporated if it only matched all of the following criteria: (1) supplied allele frequencies and various genotype frequencies of the corresponding litter sizes, (2) stated sample size for each genotype of FecB, (3) assessing the association between FecB gene polymorphism and sheep litter size, (4) For each genotype, litter size least square means identi ed and (5) standard error/standard deviation for least square means recorded has been given. The studies have been ruled out whether they're: (i) in summary form; (ii) duplicate studies; (iii) inadequate information; or (iv) review paper.

Data collection process
To extract data le, a standard data extraction form was utilized, counting the rst author name, sample size, country study, year of publication breed, frequency of genotype, least square mean and standard deviation/standard error of each genotype. When the standard deviation was not published it was determined using the following function: where, N is a sample size and SE is the standard error of mean for genotypes. Based on the Cochrane Methodology (Higgins et al., 2019), pooled the least square means and standard deviations were determined as shown below: In which, N1 and N2 are the sample sizes, the least square mean is M1 and M2, and the standard deviations recorded for the rst and second classes are SD1 and SD2, respectively.

Study selection
In order to provide an overview of the literature survey and research selection at different stages of the assessment process, a PRISMA ow chart was followed (Fig. 1). Surveys of the database and screening of the reference list resulted in 127 and 24 theoretically related research, respectively.
Among them, 23 papers were discarded as duplicates after the rst assessment. In addition, 39 papers were in the form of a summary; and thus excluded. Of the remaining 89 papers, 63 papers were denied in total for the reasons stated: (1) The association between FecB gene polymorphisms and the litter size trait was not investigated. (2) Su cient data such as genotype frequencies, standard deviation and appropriate mutation were not provided; and (3) Concentrated on other traits, not litter size. Finally, 26 articles with 9902 sheep were chosen to be used in the meta-analysis. There were inquiries of more than one sheep breed in six papers thus each breed was considered as a separate research. Heterogeneity assessment between studies

Analysis of sensitivity and assessment of publication bias
Evidence of data distribution asymmetry was shown by visual inspection of funnel plots in additive and recessive models (Fig. 2). This was veri ed by a substantial Egger intercept test (Table 4). Also, in contrast, in the dominant and codominant genetic models, intercept of Egger's test (Table 4) and funnel plots (Fig. 2) showed no signi cant and no evidence of publication bias, respectively. The trim-and-ll method was used (Duval and Tweedie, 2000) to adjust the estimates for possible publication bias in additive and recessive models. For the additive model, nine missing studies were assigned through the trim and ll technique and the mean effect size was corrected from SMD=0.767 to SMD=0.528. Furthermore, 10 missing studies for recessive model were imputed and modi ed mean effect size from SMD=0.373 to SMD=0.250.
Meta-analysis of the relationship of the FecB polymorphism and litter size of sheep  (iv) For this meta-analysis, 9902 sheep records have been used, which is higher than the numbers used with 5089 by Chong et al. Such data may be conclusive proof that the latest meta-analysis has much greater predictive strength and provides more accurate results.
In addition, the current meta-analysis has several strengths: (i) all studies published in different language in the meta-analysis literature have been used; (ii) sensitivity analysis has been carried out by eliminating one study at a time to assess the consistency of the outcomes obtained;(iii) To accurately investigate the relationship among the FecB polymorphism and sheep litter size, four distinct genetic models containing additive, dominant, co-dominant and recessive models were used; and (iv) In order to perform the meta-analysis, have pooled a big dataset with 9902 records, which may result in more reliable results than small sample sizes.
The current meta-analysis study, however, may have some restrictions: (i) moderate to high research heterogeneity was observed under the utilized genetic models; (ii) The sample sizes of several studies involved were not su ciently large; (iii) Only the in uence of genetic factors on sheep litter size has been investigated, though litter size is a complex trait. Precautions should be taken for these considerations when interpreting the ndings of this meta-analysis.
In conclusion, consequences of the current review support the idea that BMPR-IB fundamentally in uenced litter size and was related to litter size in sheep and subsequently it may be utilized adequately for marker-assisted selection programmers for improving genetic merit of reproductive traits and furthermore introgression of this gene by crossbreeding in low proli c breeds can improve reproductive performance in sheep breeds.      Figure 1 The PRISMA ow chart displaying incorporation and rejection rules Figure 2 Funnel plot showing the relationship between the observed effect size (Standardized mean differences; solid circles) and its standard error for different genetic models in a meta-analysis