The SYNBREED chicken diversity panel: a global resource to assess chicken diversity at high genomic resolution

Malomane, Dorcus Kholofelo; Simianer, Henner; Weigend, Annett; Reimer, Christian; Schmitt, Armin O.; Weigend, Steffen

doi:10.1186/s12864-019-5727-9

Cited by 85 publications

(123 citation statements)

References 42 publications

(64 reference statements)

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“…Firstly, a chicken diversity panel containing 1,810 chicken of 82 breeds including Asian types, European types, wild types, commercial broilers and layers (Weigend et al . 2014; Malomane et al . 2019).…”

Section: Methodsmentioning

confidence: 99%

Improving imputation quality in BEAGLE for crop and livestock data

Pook¹,

Mayer

Geibel³

et al. 2019

Preprint

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Imputation is one of the key steps in the preprocessing and quality control protocol of any genetic study. Most imputation algorithms were originally developed for the use in human genetics and thus are optimized for a high level of genetic diversity. Different versions of BEAGLE were evaluated on genetic datasets of doubled haploids of two European maize landraces, a commercial breeding line and a diversity panel in chicken, respectively, with different levels of genetic diversity and structure which can be taken into account in BEAGLE by parameter tuning. Especially for phasing BEAGLE 5.0 outperformed the newest version (5.1) which in turn also lead to improved imputation. Earlier versions were far more dependent on the adaption of parameters in all our tests. For all versions, the parameter ne (effective population size) had a major effect on the error rate for imputation of ungenotyped markers, reducing error rates by up to 98.5%. Further improvement was obtained by tuning of the parameters affecting the structure of the haplotype cluster that is used to initialize the underlying Hidden Markov Model of BEAGLE. The number of markers with extremely high error rates for the maize datasets were more than halved by the use of a flint reference genome (F7, PE0075 etc.) instead of the commonly used B73. On average, error rates for imputation of ungenotyped markers were reduced by 8.5% by excluding genetically distant individuals from the reference panel for the chicken diversity panel. To optimize imputation accuracy one has to find a balance between representing as much of the genetic diversity as possible while avoiding the introduction of noise by including genetically distant individuals. KEYWORDSimputation BEAGLE reference panel reference genome 1 23 special tools for both cases have been developed. As fully homozy-24 gous lines are commonly present in crops, the software TASSEL 25 (Bradbury et al. 2007) was developed to work well on this data 26 structure (Swarts et al. 2014). Since pedigrees in animal breeding 27 1 INVESTIGATIONS can be much denser than in human populations (both w.r.t. depth 28 and family size), tools like FImpute (Sargolzaei et al. 2014) and 29 AlphaImpute (Hickey et al. 2011) have been developed to fully 30 utilize this information. 31In the imputation process all those methods use the fact that physi-32 cally close markers are likely inherited together, resulting in non-33 random associations of alleles. These methods thereby rely on the 34 knowledge of the physical position or at least the order of markers 35 for modeling linkage and thus the resulting linkage disequilibrium 36 (LD). In contrast, the software LinkImpute (Money et al. 2015) ac-37 counts for LD between pairs of markers and not their physical 38 positions. This can be particularly relevant for species in which no 39 reference sequence is available or whose genomes are known for a 40 high amount of translocations and inversions. 41 In contrast to other methods using a HMM, the Markov chain in 42 BEAGLE is not initialized by ...

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Section: Methodsmentioning

confidence: 99%

Improving imputation quality in BEAGLE for crop and livestock data

Pook¹,

Mayer

Geibel³

et al. 2019

Preprint

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“…This is the case for Asian (red symbols) and European (green symbols) type breeds. As it is shown in Fig 1 and as well highlighted in [7], the Asian and European chickens sampled from the German fancy breeders (denoted with prefix DE_) have highly reduced genetic diversity as well as higher genetic distance to the wild chickens ( G .gallus ) than their respective local breeds. However, when considering the sampling areas, the genetic diversity may correlates to the geographic distances to the G. gallus within the Asian breed categories but not in the European breeds.…”

Section: Resultsmentioning

confidence: 89%

“…Furthermore, if geographic distance was a better predictor for the loss of genetic diversity and increased differentiation of breeds to the wild populations, then the African and South American breeds might be expected to have highly reduced genetic diversity due to geographic distances. They also would be expected to have high genetic distances to the wild populations as well as to the rest of the Asian populations; in fact, both expectations are not fulfilled, and some of the African populations were found to be clustered with the wild type breeds [7].…”

Section: Resultsmentioning

confidence: 99%

“…One of the expectations from such expansion processes is the increase of genetic distances (increased differentiation) of the outward populations to the original ancestors, and the loss of genetic diversity within such populations due to genetic drift and subsequent serial founder effects [4–6]. In Malomane et al [7] we studied the overall genetic diversity between and within the chicken breeds. In the current study we aimed at investigating if the observed genetic diversity in the chicken breeds is a result of their genetic expansion from the chicken wild populations following the concepts behind the theory of genetic isolation by distance [8–10] and the model of expansion from a single location such as the ‘Out of Africa’ migration model [4].…”

Section: Introductionmentioning

confidence: 99%

“…Consequences of selection are often measured by non-neutral genetic variation as it is assumed that non-neutral regions with functional fitness effects in the genome evolve differently to the neutral genome. In this study we used the global collection of chicken breeds [7] to investigate the pattern of the overall genetic diversity moving outwards the centers of chicken domestication, given all events taking place in the genome. Furthermore, we investigate if different functional regions of the genome present similar patterns as the overall genome.…”

Section: Introductionmentioning

confidence: 99%

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Genetic diversity in global chicken breeds as a function of genetic distance to the wild populations

Malomane

Weigend

Schmitt

et al. 2020

Preprint

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26Migration of populations from their founder population is expected to cause a reduction in 27 genetic diversity and facilitates population differentiation between the populations and their 28 founder population as predicted by the theory of genetic isolation by distance. Consistent with 29 that, a model of expansion from a single founder predicts that patterns of genetic diversity in 30 populations can be well explained by their geographic expansion from the founders, which is 31 correlated to the genetic differentiation. To investigate this in the chicken, we have estimated the 32 relationship between the genetic diversity in 172 domesticated chicken populations and their 33 genetic distances to wild populations. We have found a strong inverse relationship whereby 34 87.5% of the variation in the overall genetic diversity of domesticated chicken can be explained 35 by the genetic distance to the wild populations. We also investigated if different types of SNPs 36 and genes present similar patterns of genetic diversity as the overall genome. Among different 37 SNP classes, the non-synonymous ones were the most deviating from the overall genome. 38However, the genetic distances to wild populations still explained more variation in domesticated 39 chicken diversity in all SNP classes ranging from 81.7 to 88.7%. The genetic diversity seemed to 40 change at a faster rate within the chicken in genes that are associated with transmembrane 41 transport, protein transport and protein metabolic processes, and lipid metabolic processes. In 42 general, such genes are flexible to be manipulated according to the population needs. On the 43 other hand, genes which the genetic diversity hardly changes despite the genetic distance to the 44 wild populations are associated with major functions e.g. brain development. Therefore, changes 45 in the genes may be detrimental to the chickens. These results contribute to the knowledge of 46 different evolutionary patterns of different functional genomic regions in the chicken.47 48Author summary 49 The chicken was first domesticated about 6000 B.C. in Asia from the jungle fowl. Following 50 domestication, chickens were taken to different parts of the world mainly by humans. 51Evolutionary forces such as selection and genetic drift have shaped diversification within the 52 chicken species. In addition, new breeds or strains have been developed from crossbreeding 53 programs facilitated by man. These events, together with other breeding practices, have led to 54 genomic alterations causing genetic differentiation between the domesticated chickens and their 55 ancestral/wild population as well as manipulation of the genetic diversity within the 56 domesticated chickens. We investigated the relationship between 172 domesticated chicken 57 populations from different selection, breeding and management backgrounds and their genetic 58 distance to the wild type chickens. We found that the genetic diversity within the populations 59 decreases with the increasing genetic distances to the wil...

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Recovering High‐Quality Host Genomes from Gut Metagenomic Data through Genotype Imputation

2022

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Metagenomic datasets of host‐associated microbial communities often contain host DNA that is usually discarded because the amount of data is too low for accurate host genetic analyses. However, genotype imputation can be employed to reconstruct host genotypes if a reference panel is available. Here, the performance of a two‐step strategy is tested to impute genotypes from four types of reference panels built using different strategies to low‐depth host genome data (≈2× coverage) recovered from intestinal samples of two chicken genetic lines. First, imputation accuracy is evaluated in 12 samples for which both low‐ and high‐depth sequencing data are available, obtaining high imputation accuracies for all tested panels (>0.90). Second, the impact of reference panel choice in population genetics statistics on 100 chickens is assessed, all four panels yielding comparable results. In light of the observations, the feasibility and application of the applied imputation strategy are discussed for different species with regard to the host DNA proportion, genomic diversity, and availability of a reference panel. This method enables leveraging insofar discarded host DNA to get insights into the genetic structure of host populations, and in doing so, facilitates the implementation of hologenomic approaches that jointly analyze host and microbial genomic data.

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The SYNBREED chicken diversity panel: a global resource to assess chicken diversity at high genomic resolution

Cited by 85 publications

References 42 publications

Improving imputation quality in BEAGLE for crop and livestock data

Improving imputation quality in BEAGLE for crop and livestock data

Genetic diversity in global chicken breeds as a function of genetic distance to the wild populations

Recovering High‐Quality Host Genomes from Gut Metagenomic Data through Genotype Imputation

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