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Table 6 Top contributing genes to the linear prediction of feed efficiency

From: Investigation of muscle transcriptomes using gradient boosting machine learning identifies molecular predictors of feed efficiency in growing pigs

RFI

FCR

FCRe

24 VIP1

R2 = 0.73

R2 = 0.76

R2 = 0.75

 

Subset2

 Gene

P value

Gene

P value

Gene

P value

 FKBP5

< 0.001

FKBP5

< 0.001

FKBP5

< 0.001

 SERINC3

0.02

MUM1

0.03

MUM1

0.04

 IGF2

0.03

AKAP12

0.03

AKAP12

0.03

 CSRNP3

0.03

FYN

0.03

PHKB

0.08

 EZR

0.09

TMED3

0.08

SOCS6

0.07

 RPL16

0.08

PHKB

0.08

FYN

0.08

  

TFG

0.02

TFG

0.02

  

SOCS6

0.07

TMED3

0.09

  

ILR4

0.10

ILR4

0.10

  

FRAS1

0.12

FRAS1

0.12

R2 = 0.58

R2 = 0.73

R2 = 0.71

  1. 1A total of 24 target genes was used in a linear model for prediction of residual feed intake (RFI), feed-conversion ratio (FCR) and energy-based feed conversion ratio (FCRe)
  2. 2Stepwise selection was also used to retain the most significant variables in regression models for feed efficiency traits. Associated P-value for the entry of each variable (mRNA level of the gene) in the best model was indicated. All variables with P < 0.15 were considered
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BMC Genomics

ISSN: 1471-2164

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