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Table 4 RHM analysis conditioned on adding significant regions to the model sequentially

From: Detection of genomic regions underlying resistance to gastrointestinal parasites in Australian sheep

Scenario

\({\mathbf{GRM}}_{i}\) (SE)

\({\mathbf{GRM}}_{c}\) (SE)

Logl

Logl null

LRT

R1

0.004 (0.002)

0.19 (0.02)

− 10667

− 10671

8

R1 + R2

0.006 (0.003)

0.18 (0.02)

− 10666

− 10672

12

R1 + R2 + R3

0.008 (0.003)

0.18 (0.02)

− 10664

− 10672

16

R1 + R2 + R3 + R4

0.015 (0.005)

0.17 (0.02)

− 10656

− 10674

36

R1 + R2 + R3 + R4 + R5

0.024 (0.007)

0.16 (0.02)

− 10649

− 10674

50

R1 + R2 + R3 + R4 + R5 + R6

0.030 (0.008)

0.16 (0.02)

− 10644

− 10675

62

  1. R1: between 107 and 108 Mbp on OAR2; R2: between 110 and 113 Mbp on OAR2; R3: between 117 and 118 Mbp on OAR2; R4: between 34 and 39 Mbp on OAR6, R5: between 17 and 18 Mbp on OAR18; and R6: between 40 and 41 Mbp on OAR24
  2. \({\mathbf{GRM}}_{i}\): variance due to regions defined in each scenario and estimated with a GRM constructed from SNPs in these regions
  3. \({\mathbf{GRM}}_{c}\): is the complementary GRM containing all SNPs from the 600 k excluding the SNPs fitted in \({\mathbf{GRM}}_{i}\)
  4. Logl: log likelihood for the tested model which includes both \({\mathbf{GRM}}_{i}\) and \({\mathbf{GRM}}_{c}\)
  5. Logl null: log likelihood for the null model which includes only \({\mathbf{GRM}}_{c}\)
  6. SE: standard error