Predicting male fertility in dairy cattle using markers with large effect and functional annotation data
Juan Pablo Nani; Fernanda Marcondes de Rezende; Francisco Peñagaricano · 2019 · BMC Genomics
WASTE classifies this as Negative / Null Result Report · AI classification, approximate
The study found no significant effect — useful as a negative control or null benchmark for your own design.
Abstract
BACKGROUND: Fertility is among the most important economic traits in dairy cattle. Genomic prediction for cow fertility has received much attention in the last decade, while bull fertility has been largely overlooked. The goal of this study was to assess genomic prediction of dairy bull fertility using markers with large effect and functional annotation data. Sire conception rate (SCR) was used as a measure of service sire fertility. Dataset consisted of 11.5 k U.S. Holstein bulls with SCR records and about 300 k single nucleotide polymorphism (SNP) markers. The analyses included the use of bo
Abstract by Juan Pablo Nani; Fernanda Marcondes de Rezende; Francisco Peñagaricano, BMC Genomics (2019) — licensed CC BY 4.0.
About to run something similar?
Run an AI Precheck on your own design to catch failure modes like this one before you spend the time. Your first desk check is free.
Related failures
Power and Predictive Accuracy of Polygenic Risk Scores
Negative / Null Result ReportLocoregional Recurrence After Sentinel Lymph Node Dissection With or Without Axillary Dissection in Patients With Sentinel Lymph Node Metastases
Negative / Null Result ReportCritical aspects of using bacterial cell viability assays with the fluorophores SYTO9 and propidium iodide
Negative / Null Result ReportAlpelisib plus fulvestrant for PIK3CA-mutated, hormone receptor-positive, human epidermal growth factor receptor-2–negative advanced breast cancer: final overall survival results from SOLAR-1
Negative / Null Result ReportA randomized placebo-controlled trial of idebenone in Leber’s hereditary optic neuropathy
Negative / Null Result ReportThe CAFA challenge reports improved protein function prediction and new functional annotations for hundreds of genes through experimental screens
WASTE indexes this work — it does not host or republish it. Failure-type classification is automated and approximate.
Metadata source: OpenAlex · DOI 10.1186/s12864-019-5644-y