A simulation study on the performance of differentiation‐based methods to detect selected loci using linked neutral markers

Vilas, Ana Fernández; Pérez‐Figueroa, Andrés; Caballero, Armando

doi:10.1111/j.1420-9101.2012.02526.x

Cited by 57 publications

(70 citation statements)

References 54 publications

(149 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Using simulations, Vilas et al [36] recommended caution regarding the extent of false positive selection signatures which could be in fact false positive results. They detected apparent selection signatures on a large proportion of the simulated chromosomes for which actually no QTL had been simulated.…”

Section: Discussionmentioning

confidence: 99%

Detection of selection signatures in dairy and beef cattle using high-density genomic information

et al. 2015

View full text Add to dashboard Cite

BackgroundArtificial selection for economically important traits in cattle is expected to have left distinctive selection signatures on the genome. Access to high-density genotypes facilitates the accurate identification of genomic regions that have undergone positive selection. These findings help to better elucidate the mechanisms of selection and to identify candidate genes of interest to breeding programs.ResultsInformation on 705 243 autosomal single nucleotide polymorphisms (SNPs) in 3122 dairy and beef male animals from seven cattle breeds (Angus, Belgian Blue, Charolais, Hereford, Holstein-Friesian, Limousin and Simmental) were used to detect selection signatures by applying two complementary methods, integrated haplotype score (iHS) and global fixation index (FST). To control for false positive results, we used false discovery rate (FDR) adjustment to calculate adjusted iHS within each breed and the genome-wide significance level was about 0.003. Using the iHS method, 83, 92, 91, 101, 85, 101 and 86 significant genomic regions were detected for Angus, Belgian Blue, Charolais, Hereford, Holstein-Friesian, Limousin and Simmental cattle, respectively. None of these regions was common to all seven breeds. Using the FST approach, 704 individual SNPs were detected across breeds. Annotation of the regions of the genome that showed selection signatures revealed several interesting candidate genes i.e. DGAT1, ABCG2, MSTN, CAPN3, FABP3, CHCHD7, PLAG1, JAZF1, PRKG2, ACTC1, TBC1D1, GHR, BMP2, TSG1, LYN, KIT and MC1R that play a role in milk production, reproduction, body size, muscle formation or coat color. Fifty-seven common candidate genes were found by both the iHS and global FST methods across the seven breeds. Moreover, many novel genomic regions and genes were detected within the regions that showed selection signatures; for some candidate genes, signatures of positive selection exist in the human genome. Multilevel bioinformatic analyses of the detected candidate genes suggested that the PPAR pathway may have been subjected to positive selection.ConclusionsThis study provides a high-resolution bovine genomic map of positive selection signatures that are either specific to one breed or common to a subset of the seven breeds analyzed. Our results will contribute to the detection of functional candidate genes that have undergone positive selection in future studies.Electronic supplementary materialThe online version of this article (doi:10.1186/s12711-015-0127-3) contains supplementary material, which is available to authorized users.

show abstract

Section: Discussionmentioning

confidence: 99%

Detection of selection signatures in dairy and beef cattle using high-density genomic information

et al. 2015

View full text Add to dashboard Cite

show abstract

“…In turbot, partial colocalization was observed between candidate genes for growth and previously published QTLs, whereas high colocalization was observed for candidate genes associated with immune response and previously published QTLs for resistance to disease or parasites (Figueras et al., 2016). Simulations of traits determined by 1 to 10 QTL suggest that outlier “SNP”‐like loci physically linked to QTL are more likely to show colocalization with the nearest QTL than randomly chosen “SNP” loci but that false positives are common (Vilas et al., 2012). …”

Section: Discussionmentioning

confidence: 99%

“…In this study, Arlequin 3.5 and BayeScan 2.1 were chosen for the outlier analysis to be a good combination to reduce type I (false positive) and type II (false negative) error rates, which have been evaluated using simulation (Lotterhos & Whitlock, 2014; Narum & Hess, 2011; Vilas, Perez‐Figueroa, & Caballero, 2012). FDIST2 typically detects more outliers than does BayeScan in empirical datasets of a few thousand SNPs (Tsumura et al., 2014).…”

Section: Methodsmentioning

confidence: 99%

A genome scan for selection signatures comparing farmed Atlantic salmon with two wild populations: Testing colocalization among outlier markers, candidate genes, and quantitative trait loci for production traits

Liu

Ang

Elliott

et al. 2016

Evolutionary Applications

View full text Add to dashboard Cite

Comparative genome scans can be used to identify chromosome regions, but not traits, that are putatively under selection. Identification of targeted traits may be more likely in recently domesticated populations under strong artificial selection for increased production. We used a North American Atlantic salmon 6K SNP dataset to locate genome regions of an aquaculture strain (Saint John River) that were highly diverged from that of its putative wild founder population (Tobique River). First, admixed individuals with partial European ancestry were detected using STRUCTURE and removed from the dataset. Outlier loci were then identified as those showing extreme differentiation between the aquaculture population and the founder population. All Arlequin methods identified an overlapping subset of 17 outlier loci, three of which were also identified by BayeScan. Many outlier loci were near candidate genes and some were near published quantitative trait loci (QTLs) for growth, appetite, maturity, or disease resistance. Parallel comparisons using a wild, nonfounder population (Stewiacke River) yielded only one overlapping outlier locus as well as a known maturity QTL. We conclude that genome scans comparing a recently domesticated strain with its wild founder population can facilitate identification of candidate genes for traits known to have been under strong artificial selection.

show abstract

“…Nevertheless, Fst is a popular measure of differentiation widely used to infer selection, although see Vilas et al (2012) for some caveats on its use, and so we compared the results obtained with either Fst or h 2 G . Following the same logic as for h 2 G , we ranked pathways according to their Fst, averaged across SNPs in that pathway (Supplementary Table 5).…”

Section: Heritability Of Domestication M Pérez-enciso Et Almentioning

confidence: 99%

The ‘heritability’ of domestication and its functional partitioning in the pig

et al. 2016

View full text Add to dashboard Cite

We propose to estimate the proportion of variance explained by regression on genome-wide markers (or genomic heritability) when wild/domestic status is considered the phenotype of interest. This approach differs from the standard Fst in that it can accommodate genetic similarity between individuals in a general form. We apply this strategy to complete genome data from 47 wild and domestic pigs from Asia and Europe. When we partitioned the total genomic variance into components associated to subsets of single nucleotide polymorphisms (SNPs) defined in terms of their annotation, we found that potentially deleterious non-synonymous mutations (9566 SNPs) explained as much genetic variance as the whole set of 25 million SNPs. This suggests that domestication may have affected protein sequence to a larger extent than regulatory or other kinds of mutations. A pathway-guided analysis revealed ovarian steroidogenesis and leptin signaling as highly relevant in domestication. The genomic regression approach proposed in this study revealed molecular processes not apparent through typical differentiation statistics. We propose that at least some of these processes are likely new discoveries because domestication is a dynamic process of genetic selection, which may not be completely characterized by a static metric like Fst. Nevertheless, and despite some particularly influential mutation types or pathways, our analyses tend to rule out a simplistic genetic basis for the domestication process: neither a single pathway nor a unique set of SNPs can explain the process as a whole.

show abstract

A simulation study on the performance of differentiation‐based methods to detect selected loci using linked neutral markers

Cited by 57 publications

References 54 publications

Detection of selection signatures in dairy and beef cattle using high-density genomic information

Detection of selection signatures in dairy and beef cattle using high-density genomic information

A genome scan for selection signatures comparing farmed Atlantic salmon with two wild populations: Testing colocalization among outlier markers, candidate genes, and quantitative trait loci for production traits

The ‘heritability’ of domestication and its functional partitioning in the pig

Contact Info

Product

Resources

About