Genome analysis of Yucatan miniature pigs to assess their potential as biomedical model animals

Kwon, Deok-Ho; Lee, Young-Sup; Shin, Donghyun; Won, Kyeong-Hye; Song, Ki Duk

doi:10.5713/ajas.18.0170

Cited by 6 publications

(4 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Most likely polymorphisms in these genes are the consequence of breed adaptation to environmental and human demands. For Black Slavonian pig, we identified important genes associated with steroid receptor activity, such as CYP-40 on porcine chromosome SSC8 (Ratajczak et al, 2015); meat-to-fat ratio in pigs, DEAF1 on SSC2 (Falker-Gieske et al, 2019); growth traits in cattle, KSR2 on SSC14 (Puig-Oliveras et al, 2014); animal organ and system development in pigs, SEZ6L on SSC14 (Kwon et al, 2019); hematological parameters in pigs, RHOBTB1 on SSC 14 (Bovo et al, 2019); female reproduction in mice, CDK1 on SSC 14 (Adhikari et al, 2016); salivary secretion in pigs, KCNMA1 on SSC14 (Li et al, 2013); milk fat percentage in buffaloes, KCTD8 on SSC8 (de Camargo et al, 2015); back fat thickness in pigs, RIMS4 on SSC17 (Lee and Shin, 2018); carcass length in pigs, SPTLC2 on SSC7 (Falker-Gieske et al, 2019); muscle fiber types in pigs, MYO18B on SSC14 (Ropka-Molik et al, 2018); and fatty acid profiles in cattle, RAPGEF2 on SSC8 (Cesar et al, 2014).…”

Section: Identification Of Adaptive Signatures Of Selectionmentioning

confidence: 99%

Conservation Genomic Analysis of the Croatian Indigenous Black Slavonian and Turopolje Pig Breeds

et al. 2020

View full text Add to dashboard Cite

Section: Identification Of Adaptive Signatures Of Selectionmentioning

confidence: 99%

Conservation Genomic Analysis of the Croatian Indigenous Black Slavonian and Turopolje Pig Breeds

et al. 2020

View full text Add to dashboard Cite

“…This SNP has a high CADD score (=11.02) and is conserved across vertebrates (vertebrate PhyloP = 0.916 and vertebrate PhastCons = 0.747). Interestingly, the region in which it is located also harbors signature of selection in Yucatan miniature pigs ( Kim et al 2015 ; Kwon et al 2019 ). TBC1D32 plays a role in cilia assembly ( Ko et al 2010 ) and may be involved in ciliopathic congenital abnormalities, including midline cleft, microcephaly, and microphthalmia (Adly et al 2014).…”

Section: Resultsmentioning

confidence: 99%

Detecting Selection in Multiple Populations by Modeling Ancestral Admixture Components

Cheng

Stern

Racimo

et al. 2021

Molecular Biology and Evolution

View full text Add to dashboard Cite

One of the most powerful and commonly used approaches for detecting local adaptation in the genome is the identification of extreme allele frequency differences between populations. In this paper, we present a new maximum likelihood method for finding regions under positive selection. It is based on a Gaussian approximation to allele frequency changes and it incorporates admixture between populations. The method can analyze multiple populations simultaneously and retains power to detect selection signatures specific to ancestry components that are not representative of any extant populations. Using simulated data, we compare our method to related approaches, and show that it is orders of magnitude faster than the state-of-the-art, while retaining similar or higher power for most simulation scenarios. We also apply it to human genomic data and identify loci with extreme genetic differentiation between major geographic groups. Many of the genes identified are previously known selected loci relating to hair pigmentation and morphology, skin and eye pigmentation. We also identify new candidate regions, including various selected loci in the Native American component of admixed Mexican-Americans. These involve diverse biological functions, like immunity, fat distribution, food intake, vision and hair development.

show abstract

“…Interestingly, the region in which it is located also harbors signature of selection in Yucatan miniature pigs (Kim et al, 2015;Kwon et al, 2018). TBC1D32 plays a role in cilia assembly (Ko et al, 2010) and may be involved in ciliopathic congenital abnormalities, including midline cleft, microcephaly, and microphthalmia (Adly et al, 2014).…”

Section: Native American Ancestry Scanmentioning

confidence: 99%

Ohana: detecting selection in multiple populations by modelling ancestral admixture components

Cheng

Racimo

Nielsen

2019

Preprint

View full text Add to dashboard Cite

One of the most powerful and commonly used methods for detecting local adaptation in the genome is the identification of extreme allele frequency differences between populations. In this paper, we present a new maximum likelihood method for finding regions under positive selection. The method is based on a Gaussian approximation to allele frequency changes and it incorporates admixture between populations.The method can analyze multiple populations simultaneously and retains power to detect selection signatures specific to ancestry components that are not representative of any extant populations. We evaluate the method using simulated data and compare it to related methods based on summary statistics.We also apply it to human genomic data and identify loci with extreme genetic differentiation between major geographic groups. Many of the genes identified are previously known selected loci relating to hair pigmentation and morphology, skin and eye pigmentation. We also identify new candidate regions, including various selected loci in the Native American component of admixed Mexican-Americans.These involve diverse biological functions, like immunity, fat distribution, food intake, vision and hair development.

show abstract

Genome analysis of Yucatan miniature pigs to assess their potential as biomedical model animals

Cited by 6 publications

References 27 publications

Conservation Genomic Analysis of the Croatian Indigenous Black Slavonian and Turopolje Pig Breeds

Conservation Genomic Analysis of the Croatian Indigenous Black Slavonian and Turopolje Pig Breeds

Detecting Selection in Multiple Populations by Modeling Ancestral Admixture Components

Ohana: detecting selection in multiple populations by modelling ancestral admixture components

Contact Info

Product

Resources

About