Evidence map›Paper›PMID 31878893›Full record

ArticleGenetics, selection, evolution : GSE2019

A multi-breed reference panel and additional rare variants maximize imputation accuracy in cattle.

Troy N Rowan, Jesse L Hoff, Tamar E Crum, Jeremy F Taylor, Robert D Schnabel, Jared E Decker

Open access · goldAbstract read
In one paragraph

Article in Genetics, selection, evolution : GSE, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
33citing papers in PubMed, 1 pooled it
9.5field-weighted citation impact, top 2% 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

33 citing papers in PubMed, 1 synthesis or guideline pooled it, 68 citations in OpenAlex.

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  10. Uncovering the architecture of selection in twoEvolutionary applications · 2024
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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

6 authors at 1 institution in 1 country.

Troy N RowanDivision of Animal Sciences, University of Missouri, Columbia, MO, 65211, USA.ORCID http://orcid.org/0000-0001-5347-7695
Jesse L HoffDivision of Animal Sciences, University of Missouri, Columbia, MO, 65211, USA.ORCID http://orcid.org/0000-0003-4885-1587
Tamar E CrumDivision of Animal Sciences, University of Missouri, Columbia, MO, 65211, USA.ORCID http://orcid.org/0000-0003-2326-0078
Jeremy F TaylorDivision of Animal Sciences, University of Missouri, Columbia, MO, 65211, USA.ORCID http://orcid.org/0000-0002-6997-7245
Robert D SchnabelDivision of Animal Sciences, University of Missouri, Columbia, MO, 65211, USA. schnabelr@missouri.edu.ORCID http://orcid.org/0000-0001-5018-7641
Jared E DeckerDivision of Animal Sciences, University of Missouri, Columbia, MO, 65211, USA. deckerje@missouri.edu.ORCID http://orcid.org/0000-0001-5685-7343
University of Missouri · US

Funding

National Institute of Food and Agriculture 2013-68004-20364National Institute of Food and Agriculture 2015-67015-23183National Institute of Food and Agriculture 2016-68004-24827
6 · The paper itself

Abstract

backgroundDuring the last decade, the use of common-variant array-based single nucleotide polymorphism (SNP) genotyping in the beef and dairy industries has produced an astounding amount of medium-to-low density genomic data. Although low-density assays work well in the context of genomic prediction, they are less useful for detecting and mapping causal variants and the effects of rare variants are not captured. The objective of this project was to maximize the accuracies of genotype imputation from medium- and low-density assays to the marker set obtained by combining two high-density research assays (~ 850,000 SNPs), the Illumina BovineHD and the GGP-F250 assays, which contains a large proportion of rare and potentially functional variants and for which the assay design is described here. This 850 K SNP set is useful for both imputation to sequence-level genotypes and direct downstream analysis.

resultsWe found that a large multi-breed composite imputation reference panel that includes 36,131 samples with either BovineHD and/or GGP-F250 genotypes significantly increased imputation accuracy compared with a within-breed reference panel, particularly at variants with low minor allele frequencies. Individual animal imputation accuracies were maximized when more genetically similar animals were represented in the composite reference panel, particularly with complete 850 K genotypes. The addition of rare variants from the GGP-F250 assay to our composite reference panel significantly increased the imputation accuracy of rare variants that are exclusively present on the BovineHD assay. In addition, we show that an assay marker density of 50 K SNPs balances cost and accuracy for imputation to 850 K.

conclusionsUsing high-density genotypes on all available individuals in a multi-breed reference panel maximized imputation accuracy for tested cattle populations. Admixed animals or those from breeds with a limited representation in the composite reference panel were still imputed at high accuracy, which is expected to further increase as the reference panel expands. We anticipate that the addition of rare variants from the GGP-F250 assay will increase the accuracy of imputation to sequence level.

Indexed as

BreedingPolymorphism, Single NucleotideAnimalsCattleGenomicsGenotypeGenotyping Techniques

Identifiers

PMID31878893
PMCPMC6933688
OpenAlexW2998614679

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

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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.