Evidence map›Paper›PMID 26087655›Full record

ArticleGenetics, selection, evolution : GSE2015

Association analysis for udder health based on SNP-panel and sequence data in Danish Holsteins.

Xiaoping Wu, Mogens S Lund, Goutam Sahana, Bernt Guldbrandtsen, Dongxiao Sun, Qin Zhang, Guosheng Su

Open access · goldAbstract readComparative Study
In one paragraph

Article in Genetics, selection, evolution : GSE, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
23citing papers in PubMed, 2 pooled it
4.2field-weighted citation impact, top 6% of its field
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

23 citing papers in PubMed, 2 syntheses or guidelines pooled it, 59 citations in OpenAlex.

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  20. Genetic susceptibility to infectious diseases: Current status and future perspectives from genome-wide approaches.Infection, genetics and evolution : journal of molecular epidemiology and evolutionary genetics in infectious diseases · 2018
    Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors at 2 institutions in 2 countries.

Xiaoping WuCenter for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Aarhus University, DK-8830, Tjele, Denmark. xpwu594419341@gmail.com.
Mogens S LundCenter for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Aarhus University, DK-8830, Tjele, Denmark. mogens.lund@mbg.au.dk.
Goutam SahanaCenter for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Aarhus University, DK-8830, Tjele, Denmark. goutam.sahana@mbg.au.dk.
Bernt GuldbrandtsenCenter for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Aarhus University, DK-8830, Tjele, Denmark. bernt.guldbrandtsen@mbg.au.dk.
Dongxiao SunKey Laboratory of Animal Genetics and Breeding of Ministry of Agriculture, National Engineering Laboratory for Animal Breeding, College of Animal Science and Technology, China Agricultural University, Beijing, 100193, China. sundx@cau.edu.cn.
Qin ZhangKey Laboratory of Animal Genetics and Breeding of Ministry of Agriculture, National Engineering Laboratory for Animal Breeding, College of Animal Science and Technology, China Agricultural University, Beijing, 100193, China. qzhang@cau.edu.cn.
Guosheng SuCenter for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Aarhus University, DK-8830, Tjele, Denmark. guosheng.su@mbg.au.dk.
Aarhus University · DKChina Agricultural University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe sensitivity of genome-wide association studies for the detection of quantitative trait loci (QTL) depends on the density of markers examined and the statistical models used. This study compares the performance of three marker densities to refine six previously detected QTL regions for mastitis traits: 54 k markers of a medium-density SNP (single nucleotide polymorphism) chip (MD), imputed 777 k markers of a high-density SNP chip (HD), and imputed whole-genome sequencing data (SEQ). Each dataset contained data for 4496 Danish Holstein cattle. Comparisons were performed using a linear mixed model (LM) and a Bayesian variable selection model (BVS).

resultsAfter quality control, 587, 7825, and 78 856 SNPs in the six targeted regions remained for MD, HD, and SEQ data, respectively. In general, the association patterns between SNPs and traits were similar for the three marker densities when tested using the same statistical model. With the LM model, 120 (MD), 967 (HD), and 7209 (SEQ) SNPs were significantly associated with mastitis, whereas with the BVS model, 43 (MD), 131 (HD), and 1052 (SEQ) significant SNPs (Bayes factor > 3.2) were observed. A total of 26 (MD), 75 (HD), and 465 (SEQ) significant SNPs were identified by both models. In addition, one, 16, and 33 QTL peaks for MD, HD, and SEQ data were detected according to the QTL intensity profile of SNP bins by post-analysis of the BVS model.

conclusionsThe power to detect significant associations increased with increasing marker density. The BVS model resulted in clearer boundaries between linked QTL than the LM model. Using SEQ data, the six targeted regions were refined to 33 candidate QTL regions for udder health. The comparison between these candidate QTL regions and known genes suggested that NPFFR2, SLC4A4, DCK, LIFR, and EDN3 may be considered as candidate genes for mastitis susceptibility.

Indexed as

Polymorphism, Single NucleotideQuantitative Trait LociAnimalsBayes TheoremCattleFemaleGenome-Wide Association StudyLinear ModelsMastitis, BovineSequence Analysis, DNA

Identifiers

PMID26087655
PMCPMC4472403
OpenAlexW1499545161

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.