SNP profile for quantitative trait nucleotide in populations with small effective size and its impact on mapping and genomic predictions

In animal populations, increasing the SNP density by incorporating sequence information only marginally increases prediction accuracies. To find out why, we used statistical models and simulations to investigate the profile or distribution of SNP around Quantitative Trait Nucleotides (QTN) in populations with small effective population size (Ne). A QTN profile created by averaging SNP solutions around each QTN was similar to the shape of expected pairwise linkage disequilibrium (PLD) based on Ne and genetic distance between SNP, with a distinct peak for the QTN. Populations with smaller Ne showed lower but wider QTN profiles; however, adding more genotyped individuals with phenotypes dragged the profile closer to the QTN; the QTN profile was higher and narrower for populations with larger compared to smaller Ne. Assuming the PLD curve for the QTN profile, 80% of the additive genetic variance explained by each QTN is contained in 8 "Stam" segments (one segment = 1/4Ne Morgans), corresponding to 1.6 Mb in cattle, and 5 Mb in pigs and broiler chickens. With such large segments, identifying QTN is difficult even if all of them are in the data and the assumed genetic architecture is simplistic. Additional complexity in QTN detection arises from confounding of QTN profiles with signals due to relationships, overlapping profiles with closely-spaced QTN, and spurious signals due to imputation errors. However, small Ne allows for accurate prediction with large data even without QTN identification because QTN are accounted for by QTN profiles if SNP density is sufficient to saturate the segments.