ArticleJDS communications2026
Genotyping errors can affect indirect predictions of young selection candidates: A simulation study.
Article in JDS communications, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study aimed to evaluate the effect of genotyping errors on indirect predictions (IP). For that, we simulated a dairy cattle population with 5 replicates, using QMSim. Benchmark GEBV for young animals (n = 11,250) were computed using a full single-step genomic BLUP (generations 11-15). Three SNP error rates (5%, 10%, and 20%) were applied to the genotypes of selection candidates (generation 15). A reduced analysis with generations 11 to 14 (44,500 animals) was conducted to obtain SNP solutions for computing IP in young animals using either the correct genotype files or files with genotyping errors. The average IP values decreased as the error rate increased. The correlation between the benchmark GEBV and the IP obtained with the correct genotype was 0.98 ± 0.01. As expected, this correlation decreased with increasing genotyping errors, reaching its lowest value (0.93) at 20% errors. The regression coefficient of GEBV on IP increased (i.e., more underdispersed breeding values) as the error rate increased: b
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.