R package:blupADC-Feature 5

Overview

🙉 In the application of animal and plant breeding, the key step is the construction of kinship matrix. Package: blupADC provides cal_kinship function which can construct various type of kinship matrix directly, including additive kinship matrix(pedigree, genomic and single-step), and dominance kinship matrix(pedigree, genomic and single-step), and the inverse matrix of these kinship matrix.

In the construction of single-step kinship matrix, users can select metafounder algorithm. In terms of the construction of dominance kinship matrix, users can select difference coding manners for genomic dominance kinship matrix, gene dropping algorithm for pedigree dominance kinship matrix. In addition, cal_kinship can calculate several types of inbreeding coefficients(pedigree, genomic, and sigle-step) .

Example

😾 Pedigree kinship matrix

library(blupADC)
kinship_result=cal_kinship(
                input_pedigree=origin_pedigree,          #provided hapmap data object
                kinship_type=c("P_A"),      #type of  kinship matrix
                inbred_type=c("Pedigree"),      #type of inbreeding coefficients
                return_result=TRUE)               #return result              

Note: In the construction of pedigree and single-step relationship matrix, people need to provide genotype data.

🤡 Genomic kinship matrix

library(blupADC)
kinship_result=cal_kinship(
                input_data_hmp=data_hmp,          #provided hapmap data object
                kinship_type=c("G_A","G_D"),      #type of  kinship matrix
                dominance_type=c("genotypic"),    #type of dominance effect
                inbred_type=c("Homozygous"),      #type of inbreeding coefficients
                return_result=TRUE)               #return result              

Note: In the construction of genomic and single-step relationship matrix, people need to provide genotype data. These parameters are the same as in genotype_data_format_conversion function((see more details).

💨Single-step kinship matrix

library(blupADC)
kinship_result=cal_kinship(
                input_data_hmp=data_hmp,          #provided hapmap data object
                input_pedigree=origin_pedigree,
                kinship_type=c("H_A"),      #type of  kinship matrix
                inbred_type=c("H_diag"),      #type of inbreeding coefficients
                return_result=TRUE)               #return result              

Parameter

💪Basic

• 1：kinship_type

  Type of kinship matrix, character class. User can select multiple types simultaneously, including:

  • “G_A” : genomic additive kinship matrix
  • “G_Ainv” :inverse of genomic additive kinship matrix
  • “G_D” :genomic dominance kinship matrix
  • “G_Dinv” :inverse of genomic dominance kinship matrix
  • “P_A” :pedigree additive kinship matrix
  • “P_Ainv” :inverse of pedigree additive kinship matrix
  • “P_D” : pedigree dominance kinship matrix
  • “P_Dinv” :inverse of pedigree dominance kinship matrix
  • “H_A” :single-step additive kinship matrix
  • “H_Ainv” :inverse of single-step additive kinship matrix
  • “H_D” :single-step dominance kinship matrix
  • “H_Dinv” :inverse of single-step dominance kinship matrix

Note: In the construction of pedigree and single-step kinship matrix, user need to provide pedigree data. In the construction of genomic and single-step kinship matrix, user need to provide genotype data.

• 2：dominance_type

  Type of dominance effect in the construction of dominance kinship matrix , character class.

  • “genotypic” : coded by $0-2pq$,$1-2pq$, and $0-2pq$ for $AA$, $Aa$, and $aa$, respectively.
  • “classical” : coded by $-2q^2$, $2pq$ , and $-2p^2$ for $AA$, $Aa$, and $aa$, respectively.

More details about these two types dominance effects could be seen in this reference ：On the Additive and Dominant Variance and Covariance of Individuals Within the Genomic Selection Scope

• 3：inbred_type

  Type of inbreeding coefficients, character class.

  • “Homozygous” : proportion of homozygous sites

  • “G_Diag” : diagonal of genomic additive kinship matrix minus 1

  • “H_diag” :diagonal of single-step additive kinship matrix minus 1

  • “Pedigree” :diagonal of pedigree additive kinship matrix minus 1

• 4：input_pedigree

User-provided pedigree data ，data.frame or matrix class. (see more details about the format of pedigree data)

• 5：pedigree_rename

If pedigree need to be renamed, logical class. Default is TRUE.

• **6：IND_geno_rename **

If genotype individuals need to be renamed according to the renamed pedigree, logical class. Default is FALSE.

• 7：rename_to_origin

If renamed individuals should keep original name, logical class. Default is FALSE.

• 8：output_matrix_type

  Type of output kinship matrix type, character class. Default is “col_three”.

  • “col_all” : n*n format
  • “col_three” : 3 columns format. The first and the second column is the name of individuals, the third column is the value of kinship 。

• 9：output_matrix_path

File path of output kinship matrix, character class.

• 10：output_matrix_name

File name of output kinship matrix, character class.

🦶Advanced

• 11：cpu_cores

Number of cpu in calculating, numericcalss. Default is 1.

• 12：kinship_base

If take $p=q=0.5$ in the construction of kinship matrix, logical class. Default is FALSE.

• 13：kinship_trace

If take the trace of kinship matrix to scale kinship matrix, logical class. Default is FALSE.

• 14：kinship_metafounder

If take the metafounder algorithm to construct single-step kinship matrix, logical class. Default is FALSE.

• 15：SSBLUP_omega

The value of omega in the construction of single-step additive kinship matrix, numericcalss. Default is 0.05.

• 16：gene_dropping

If take the gene dropping algorithm to construct pedigree dominance kinship matrix, logical class. Default is FALSE.

• 17：gene_dropping_iteration

The number of iteration for gene dropping algorithm , numericcalss. Default is 1000.

• 18：memory_save

If take memory saved way to construct kinship matrix, logical class. Default is FALSE. (This way need consume more storage space)