RESULTS: A significant nonparametric linkage (NPL) score was detected in family 100. Other suggestive NPL and logarithm of the odds (LOD) scores were attained from families 50, 58, 99 and 100 under autosomal recessive mode. Heterogeneity LOD (HLOD) score ≥ 1 was determined for all families, confirming genetic heterogeneity of the population and indicating that a proportion of families might be linked to each other. Several candidate genes in linkage intervals were determined; LPHN2 at 1p31, SATB2 at 2q33.1-q35, PVRL3 at 3q13.3, COL21A1 at 6p12.1, FOXP2 at 7q22.3-q33, FOXG1 and HECTD1 at 14q12 and TOX3 at 16q12.1.
CONCLUSIONS: We have identified several novel and known candidate genes for nonsyndromic cleft lip and/or palate through genome-wide linkage analysis. Further analysis of the involvement of these genes in the condition will shed light on the disease mechanism. Comprehensive genetic testing of the candidate genes is warranted.
RESULTS: The kinship coefficient between individuals in this family ranged from 0.35 to 0.62. S/F and O/DM had the highest genomic heritability, whereas F/B and O/P had the lowest. The accuracies using 135 SSRs were low, with accuracies of the traits around 0.20. The average accuracy of machine learning methods was 0.24, as compared to 0.20 achieved by other methods. The trait with the highest mean accuracy was F/B (0.28), while the lowest were both M/F and O/P (0.18). By using whole genomic SNPs, the accuracies for all traits, especially for O/DM (0.43), S/F (0.39) and M/F (0.30) were improved. The average accuracy of machine learning methods was 0.32, compared to 0.31 achieved by other methods.
CONCLUSION: Due to high genomic resolution, the use of whole-genome SNPs improved the efficiency of GS dramatically for oil palm and is recommended for dura breeding programs. Machine learning slightly outperformed other methods, but required parameters optimization for GS implementation.