Open Access Asian-Australas J Anim Sci Vol. 31, No. 5:628-635 May 2018 https://doi.org/10.5713/ajas.17.0028 pISSN 1011-2367 eISSN 1976-5517
Effect of single nucleotide polymorphism on the total number of piglets born per parity of three different pig breeds Kyoung-Tag Do1,a, Soon-Woo Jung2,a, Kyung-Do Park3,*, and Chong-Sam Na3,*
*C orresponding Authors: Kyung-Do Park Tel: +82-63-270-5934, Fax: +82-63-270-5936, E-mail:
[email protected] Chong-Sam Na Tel: +82-63-270-2607, Fax: +82-63-270-2614, E-mail:
[email protected] Department of Animal Biotechnology, Jeju National University, Jeju 63243, Korea Hamyang-guncheong, Hamyang 50031, Korea 3 Department of Animal Biotechnology, Chonbuk National University, Jeonju 54896, Korea 1
2
a
These authors contributed equally to this work.
ORCID Kyung-Do Park https://orcid.org/0000-0002-1945-6708 Chong-Sam Na https://orcid.org/0000-0002-8979-5633 Submitted Jan 12, 2017; Revised Mar 29, 2017; Accepted Jul 28, 2017
Objective: To determine the effects of genomic breeding values (GBV) and single nucleotide polymorphisms (SNP) on the total number of piglets born (TNB) in 3 pig breeds (Berkshire, Landrace, and Yorkshire). Methods: After collecting genomic information (Porcine SNP BeadChip) and phenotypic TNB records for each breed, the effects of GBV and SNP were estimated by using single step best linear unbiased prediction (ssBLUP) method. Results: The heritability estimates for TNB in Berkshire, Landrace, and Yorkshire breeds were 0.078, 0.107, and 0.121, respectively. The breeding value estimates for TNB in Berkshire, Landrace, and Yorkshire breeds were in the range of –1.34 to 1.47 heads, –1.79 to 1.87 heads, and –2.60 to 2.94 heads, respectively. Of sows having records for TNB, the reliability of breed ing value for individuals with SNP information was higher than that for individuals without SNP information. Distributions of the SNP effects on TNB did not follow gamma distribution. Most SNP effects were near zero. Only a few SNPs had large effects. The numbers of SNPs with absolute value of more than 4 standard deviations in Berkshire, Landrace, and Yorkshire breeds were 11, 8, and 19, respectively. There was no SNP with absolute value of more than 5 standard deviations in Berkshire or Landrace. However, in Yorkshire, four SNPs (ASGA 0089457, ASGA0103374, ALGA0111816, and ALGA0098882) had absolute values of more than 5 standard deviations. Conclusion: There was no common SNP with large effect among breeds. This might be due to the large genetic composition differences and the small size of reference population. For the precise evaluation of genetic performance of individuals using a genomic selection method, it may be necessary to establish the appropriate size of reference population. Keywords: Berkshire; Genomic Breeding Value; Landrace; Single Nucleotide Polymorphism; Total Number of Piglets Born; Yorkshire
INTRODUCTION Since the deoxyribonucleic acid (DNA) structure was identified in 1950s, exploration techno logy for genetic variation of organisms has been developed rapidly due to rapid development of molecular biology technology with many genome projects to identify genome-wide base pair sequence. Due to the development of DNA chip technology using microarray which enables exhaustive analysis of several hundreds to millions of single nucleotide polymor phism (SNP) markers through selective hybridization on solid surface based on by genotype, many genes can be identified in a short period. Currently, the imputation of genotypes using higher density chips from low-density chips is being undertaken. Meuwissen et al [1] and Van Eenennaam et al [2] have proposed the genomic selection method using genome-wide high-density SNP markers for the first time. Genetic performance of individuals can be predicted by genomic selection through marker
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Do et al (2018) Asian-Australas J Anim Sci 31:628-635
Table 1. Description of single nucleotide polymorphism (SNP) dataset mapping with dense interval. This is more accurate than con ventional breeding value estimation method. Especially, it is Breed Description highly accurate for breeding value estimation of young animals Berkshire Landrace Yorkshire without phenotypic data, thus enabling juvenile selection [1]. Total No. of animals 1,903 1,041 1,054 Gengler et al [3] have proposed an algorithm to predict ge No. of animals with missing over 10% 32 3 19 nomic information of animals without phenotypic data. No. of selected animals 1,871 1,038 1,035 VanRaden [4] has suggested an algorithm to calculate ge No. of sows with record 546 836 898 nomic relationship coefficient matrix and estimate genomic No. of common markers on autosome 42,276 48,245 51,984 breeding value (GBV). Also, Misztal et al [5] have reported an No. of selected (useful) markers 31,354 36,392 40,783 algorithm that combines the conventional pedigree informa tion with genomic information. Recently, Liu et al [6] have (7,325 records from 1,923 heads) breeds. The total number of developed an SNP Single-step genomic model as a method pigs was 3,600 Berkshires, 1,952 Landraces, and 2,424 York to estimate SNP effects directly from the analysis model. shires. The average TNB for Berkshire, Landrace, and Yorkshire In this experiment, genomic information and phenotypic breeds were 8.58, 11.92, and 12.66 heads, respectively. data on the total number of piglets born were collected from 5 Berkshire, Landrace, and Yorkshire breeds. Their GBVs were 5 Statistical model estimated and the accuracies of these estimated breeding values = Xb+Za+Wp+e 110 Estimation of genomic breeding values: For fixed yeffect, parity were compared. In addition, SNP effects on total number of y = Xb+Za+Wp+e 110 and farrowing year-month-week included and the fol piglets by pig breed were investigated. yy==Xb+Za+Wp+e 110 y =were Xb+Za+Wp+e 110 Xb+Za+Wp+e 110 y = Xb+Za+Wp+e 110 born (TNB) 111 y = Xb+Za+Wp+e 110 lowing analysis model was used: y = Xb+Za+Wp+e 110
5 Where, y = n×1 vector o 112 Where, y = Xb+Za+Wp+e 111 y= n×1 vector bof=observation, b= p×1effect, vectoraof=fixed effect, of a 112 111112 Where, yy==n×1 vector of vector fixed vector Where, y of =genetic vector of aobservation, b= 112 vector Where, n×1 vector of=observation, observation, b =p×1 p×1 vector ofn×1 fixed effect, =q×1 q×1 vector 112 Where, y = n×1 vector of observation, b = p×1 vector of fixed effect, a = q×1 of additive 112 yof Xb+Za+Wp+e 110 random effect, = q×1 vectoo 113 of SNP data and quality control 110 Where, y = n×1 vector observation, b = p×1 vector fixed effect, a =pq×1 vector 112y = Xb+Za+Wp+e Where, yy = ofofobservation, b environmental = p×1 vector ofrandom fixed effect, 112 Where, y113 = n×1 vector of observation, bq×1 =vector p×1 vector ofpermanent fixed effect, a= =p×1 q×1 vector ofof additive genea 112 by Illumina random effect, pn×1 =permanent vector of effect, Where, =of n×1 vector observation, b= vector Using porcine SNP60 (v1, pv2) manufactured com 111 random effect, pq×1 q×1 vector environmental random eeof ==permanent n×1 random effect, peffect, q×1effect, vector 113 random effect, p= = q×1 vector of permanent environmental random effect, n×1vector vectoreo 113of fixed Where, y113 = n×1 vector observation, = p×1 vector effect, a = vector of additive genetic randomof effect, = yq×1 vector of113 permanent environmental random effect, e = n×1 vector of residual =bXb+Za+Wp+e 110 X(n×p), Z(n×q), and W(n×q 114 random effect, p fixed = q×1effect, vector ofq×1 permanent environmental random effect, e = n×1 vector 113 111 high a= vector of additive genetic random effect, pany and genomic profiler for porcine (GGP random effect, p = q×1 vector permanent environmental random effect, evector = n×1 of residual effe 113 density random effect, pand = vector permanent environmental random effect, 113 Where, y =q×1 n×1 vector of of observation, bincidence = p×1 of vector fixedcorresponding effect, a = q×1 vect 112 of X(n×p), Z(n×q), W(n×q) were known matrix to 114 X(n×p), Z(n×q), and W(n×q) were known incidence matrix corresponding to b,b,a, and p,p,inci resp 114 p = q×1 vector of permanent environmental random effect, e X(n×p), Z(n×q), and W(n×q) were known 114 X(n×p), Z(n×q), and W(n×q) were known incidence matrix corresponding to a, and res 114 Porcine genotyping BeadChip manufactured by Gene X(n×p), Z(n×q), and W(n×q) were114 known incidence matrix corresponding to b, a, and p, respectively. 114p =HD) random effect, q×1 vector of permanent environmental random effect, e = n×1 vector of residual effect, 111 Mixed model equation w 115 X(n×p), Z(n×q), and W(n×q) were known incidence matrix corresponding to b, a, and p, res Where, = n×1and vector of observation, b p==p×1 vector of fixed environmental effect, q×1 vector 112 random effect, q×1 matrix vector ofcorresponding permanent random effect, of e =additiv n×1 vec X(n×p), yZ(n×q), were known incidence to b,aand a,=matrix and p, respectively. =113 n×1 vector of residual effect, W(n×q) Seek company, genomic information for114 3,998 breeding pigs X(n×p), Z(n×q), and W(n×q) knownZ(n×q), incidence corresponding t 114 W(n×q) Mixed model waswere asX(n×p), follows: 115 Mixed model equation was asasequation follows: 115 Mixed model equation was as follows: 115 Mixed model equation was follows: 115 X(n×p), Z(n×q), and W(n×q) were known incidence matrix corresponding to b, a, and p, respectively. Mixed model equation was as follows: 115 Where,113 y = n×1random vector effect, of (1,041 observation, bwere = p×1 vector of follows: fixed effect, a corresponding =known q×1 vector genetic X(n×p), Z(n×q), and W(n×q) were incidenceofmatrix b, a, and p, 114 known matrix a,n×1 and p, to of 116 was collected for112 Berkshire (1,903 heads), Landrace Mixed equation wasincidence as p = model q×1 vector environmental random effect,toadditive eb,=corresponding vector residu Mixed model equation wasof aspermanent follows: 115115 Mixed model equation was as follows: 115 116 respectively. heads), and Yorkshire (1,054 heads) breeds.116 Mixed model equation was as follows: 115 116 116 Mixed model 116 equation was ′ ′ random effect, p =X(n×p), q×1 vector of permanent effect, e =corresponding n×1 vector oftoresidual 113as follows: 𝑍𝑍 𝑋𝑋 116 environmental Mixed modelrandom equation was as follows: Z(n×q), and W(n×q) were known incidence matrix b, 𝑋𝑋a, andeffect, p, 𝑋𝑋 respectively 114 116 For quality control, SNPs on sex chromosome, SNPs with ′ ′ ′ 116 ′ ′ ′ 116 𝑋𝑋 𝑍𝑍 𝑍𝑍 + 𝛼𝛼 𝐻𝐻 −1 𝑍𝑍 ̂ 𝑦𝑦 𝑋𝑋 117 [ ′ 𝑋𝑋 𝑋𝑋 𝑍𝑍 𝑋𝑋 𝑊𝑊 𝑋𝑋 ′ ′ ′ ′ 𝑏𝑏 ′ ′ ′ ′ ′ ′ ′ ′ 𝑋𝑋𝑋𝑋𝑍𝑍 𝑋𝑋corresponding 𝑊𝑊 𝑋𝑋𝑋𝑋𝑋𝑋 out location information on markers with more 𝑦𝑦 𝑋𝑋𝑦𝑦 𝑋𝑋 and p, respectively. 𝑋𝑋 𝑍𝑍 ′ ′ 𝑋𝑋 𝑊𝑊 ′ 1 𝑏𝑏̂ 𝑋𝑋𝑦𝑦𝑋𝑋 ′ 𝑍𝑍 incidence 𝑊𝑊 −1𝑋𝑋 ′𝑏𝑏̂𝑦𝑦𝑏𝑏̂ to𝑋𝑋 ′ ̂ ′ 𝑋𝑋 𝑋𝑋 𝑋𝑋chromosome, 𝑍𝑍 Z(n×q), 𝑋𝑋and 𝑊𝑊W(n×q) 𝑋𝑋 ′114 ′ a, X(n×p), were b, ′ 𝑋𝑋 ′𝑋𝑋 𝑏𝑏 ′𝑋𝑋known ′ 𝑍𝑍 ′ 𝑋𝑋matrix ′𝑏𝑏 ̂ 𝑊𝑊 𝑋𝑋 ̂ 𝑊𝑊 𝑊𝑊 𝑍𝑍 𝑋𝑋 follows: 𝑍𝑍 ′𝑍𝑍 𝑋𝑋 +′ 𝑊𝑊 𝛼𝛼 𝐻𝐻 𝑍𝑍 −1 𝑋𝑋′ 𝑋𝑋 ′ ′ 𝑋𝑋was ] 𝑋𝑋 117 [𝑍𝑍𝐻𝐻 ′′ ′𝑦𝑦 Mixed′model equation 115 ′̂ | =𝑋𝑋 ′[𝑦𝑦 𝑍𝑍 ′𝑦𝑦 −1 ′𝑋𝑋 ′𝑊𝑊 ′ 117 ̂|𝑎𝑎 𝑊𝑊 𝑦𝑦 ′ −1 ′ 1 𝑋𝑋 ′ 𝑍𝑍] ]|′𝑎𝑎 ′ ′ 𝑦𝑦 ] [𝑍𝑍[𝑋𝑋𝑍𝑍′𝑊𝑊 𝑋𝑋] ] ]𝑏𝑏𝑍𝑍 𝑍𝑍 + 𝛼𝛼1𝑋𝑋𝐻𝐻′ 𝑦𝑦 𝑍𝑍 ′ 𝑊𝑊 𝑋𝑋 +𝛼𝛼𝛼𝛼 𝐻𝐻as 𝑍𝑍𝑋𝑋 𝑊𝑊 𝑍𝑍𝑊𝑊 ] |𝑎𝑎̂ | = |̂𝑏𝑏𝑎𝑎̂|]|== 1 1𝑋𝑋 than 𝑍𝑍 ′ 𝑍𝑍 +markers 𝛼𝛼1 𝐻𝐻 −1 without 𝑍𝑍117 𝑊𝑊polymorphism ] [|𝑎𝑎𝑍𝑍̂ |𝑋𝑋[=[[𝑍𝑍𝑍𝑍𝑍𝑍 11710% of missing [ 𝑍𝑍 ′ 𝑋𝑋 rate, [𝑍𝑍′𝑍𝑍𝑋𝑋 𝑍𝑍 +𝑍𝑍𝑍𝑍 𝛼𝛼𝑍𝑍 𝐻𝐻+−1 ′1𝑍𝑍 𝑋𝑋𝑍𝑍 ′ 𝑊𝑊𝑋𝑋𝑍𝑍′′𝑋𝑋𝑋𝑋𝑍𝑍]𝑍𝑍 ′ 𝑦𝑦 |′𝑍𝑍𝑎𝑎̂𝑊𝑊 117 = [𝛼𝛼 𝑍𝑍]𝐻𝐻|𝑦𝑦 ′−1 ′′|𝑍𝑍117 ′ 𝑋𝑋 𝑏𝑏̂= −1𝑊𝑊 ′+ ′ 𝑊𝑊 𝑦𝑦 𝑍𝑍 + 𝐻𝐻 𝑍𝑍 𝑦𝑦 ] 117 | = [ 𝑊𝑊 𝑋𝑋 𝑊𝑊 𝑍𝑍 𝑊𝑊 𝛼𝛼 𝐼𝐼 ′ ′ ′ 𝑍𝑍 ′ 𝛼𝛼 𝑝𝑝̂ 𝑍𝑍 + 𝑍𝑍 𝑊𝑊 𝑎𝑎 ̂ 𝑍𝑍 𝑦𝑦 ′ ] | ] 117 [ | [ 𝑎𝑎 ̂ 1 ′ ′ ′ ′ ′ ′ ′ ′ 1 2 ′ ′ ′ ′ ′ ′ ′ ′ ′ ′ −1 ′ 𝑊𝑊 𝑋𝑋 𝑊𝑊 𝑍𝑍 𝑊𝑊 𝑊𝑊 + 𝛼𝛼 𝐼𝐼 ′ 𝑝𝑝̂ 𝑊𝑊 𝑦𝑦 𝑋𝑋 𝑦𝑦𝑊𝑊equation 𝑋𝑋 𝑊𝑊 𝑍𝑍 𝑊𝑊 𝑊𝑊 + 𝛼𝛼 𝐼𝐼 𝑊𝑊 𝑋𝑋 𝑊𝑊 𝑍𝑍 𝑊𝑊 𝑊𝑊 + 𝛼𝛼 𝐼𝐼 𝑝𝑝̂ 𝑋𝑋 𝑋𝑋 (homo𝑋𝑋or𝑍𝑍hetero 𝑝𝑝̂ 𝑊𝑊 𝑦𝑦 2 𝑊𝑊 𝑋𝑋 𝑊𝑊 𝑍𝑍 𝑊𝑊 𝑊𝑊 + 𝛼𝛼 𝐼𝐼 Mixed model was as follows: 𝑊𝑊115 𝑋𝑋 𝑋𝑋 𝑊𝑊 𝑊𝑊 𝑍𝑍 𝑏𝑏̂ 116 𝑊𝑊 + 𝛼𝛼with 𝐼𝐼 𝑝𝑝̂ 𝑊𝑊 𝑦𝑦 ′ ′ ′ 𝑝𝑝̂ 2 𝑊𝑊 𝑦𝑦 2 genotype markers), markers less than ′ 𝑋𝑋 𝑍𝑍 𝑍𝑍 + 𝛼𝛼 𝐻𝐻 𝑍𝑍 𝑊𝑊 𝑍𝑍 𝑍𝑍 𝑦𝑦 ] ̂| = 2 1𝛼𝛼 𝑊𝑊 𝑊𝑊 𝑍𝑍 𝑊𝑊 𝑊𝑊𝑊𝑊 +′ 𝛼𝛼 𝐼𝐼 ]𝑝𝑝̂|𝑎𝑎 𝑊𝑊 [𝑦𝑦 𝑊𝑊 ′117 𝑋𝑋 𝑊𝑊 ′ [𝑍𝑍 ′ 𝑊𝑊2𝑋𝑋′ 𝑊𝑊 + 𝑦𝑦2118 −1 ′ 2 𝐼𝐼 𝑝𝑝̂ ′ ′ ′ ′ 𝑍𝑍 + 𝛼𝛼 𝐻𝐻 𝑍𝑍 𝑊𝑊 𝑍𝑍 𝑦𝑦 ] | ] [ 𝑍𝑍 ′ 𝑋𝑋 1%𝑍𝑍 ′of | = [ 𝑎𝑎 ̂ 1 𝑊𝑊 𝑋𝑋 𝑊𝑊 𝑍𝑍 𝑊𝑊 𝑊𝑊 + 𝛼𝛼 𝐼𝐼 𝑝𝑝̂ 𝑊𝑊 𝑦𝑦 minor allele frequency, and markers with more than 2 118 ′ 118 118 118 ′ 𝑊𝑊 ′ 𝑋𝑋 23.93 𝑊𝑊 ′ 𝑍𝑍 –6) of 116 𝑊𝑊 ′ 𝑊𝑊 + 𝛼𝛼2 𝐼𝐼 𝑝𝑝̂ disequilibrium 𝑊𝑊118 𝑦𝑦 118 ′chi-square ′ 118(p
