A molecular basis for classic blond hair color in Europeans

Guenther, Catherine; Tasic, Bosiljka; Luo, Liqun; Bedell, Mary A.; Kingsley, David M.

doi:10.1038/ng.2991

Cited by 160 publications

(146 citation statements)

References 45 publications

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“…We have seen that cis-regulatory alleles of Agouti have been repeatedly mapped in several populations and species of Peromyscus deer mice as well as in humans. The stickleback KITLG cis-regulatory changes were mirrored by other cis-regulatory variants driving both skin and hair color variation in human populations (Miller et al 2007;Guenther et al 2014). Finally, the WntA locus was mapped as a hotspot of wing pattern evolution in five Heliconius species as well as in a clade distant by about 65MY (Gallant et al 2014).…”

Section: How When and Why Ligand Genes Are Likely Drivers Of Pattermentioning

confidence: 97%

“…While the KIT receptor has been identified in a total of 17 color-related gephes, it is only linked to domesticated alleles in the cattle, pig, horse, donkey, domesticated fox, and domestic cat (see Advanced Search "Gene name and synonyms" ¼ "KIT" at www.gephebase.org for a complete list). In contrast, cis-regulatory alleles of KITLG have been shown to underlie natural pigment variation not only in stickleback fishes but also in humans (Miller et al 2007;Guenther et al 2014). An Ala193Asp mutation in KITLG has also been shown to cause piebald coat color phenotypes in cattle breeds (Seitz et al 1999;Qanbari et al 2014).…”

Section: à5mentioning

confidence: 99%

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Morphological Evolution Repeatedly Caused by Mutations in Signaling Ligand Genes

Martin

Courtier‐Orgogozo

2017

Diversity and Evolution of Butterfly Wing Patterns

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What types of genetic changes underlie evolution? Secreted signaling molecules (syn. ligands) can induce cells to switch states and thus largely contribute to the emergence of complex forms in multicellular organisms. It has been proposed that morphological evolution should preferentially involve changes in developmental toolkit genes such as signaling pathway components or transcription factors. However, this hypothesis has never been formally confronted to the bulk of accumulated experimental evidence. Here we examine the importance of ligandcoding genes for morphological evolution in animals. We use Gephebase (http:// www.gephebase.org), a database of genotype-phenotype relationships for evolutionary changes, and survey the genetic studies that mapped signaling genes as causative loci of morphological variation. To date, 19 signaling genes represent 20% of the cases where an animal morphological change has been mapped to a gene (80/391). This includes the signaling gene Agouti, which harbors multiple cis-regulatory alleles linked to color variation in vertebrates, contrasting with the effects of coding variation in its target, the melanocortin receptor MC1R. In sticklebacks, genetic mapping approaches have identified 4 signaling genes out of 14 loci associated with lake adaptations. Finally, in butterflies, a total of 18 allelic variants of the WntA Wnt-family ligand cause color pattern adaptations related to wing mimicry, both within and between species. We discuss possible hypotheses explaining these cases of natural replication (genetic parallelism) and conclude that signaling ligand loci are an important source of sequence variation underlying morphological change in nature.

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Section: How When and Why Ligand Genes Are Likely Drivers Of Pattermentioning

confidence: 97%

Section: à5mentioning

confidence: 99%

Morphological Evolution Repeatedly Caused by Mutations in Signaling Ligand Genes

Martin

Courtier‐Orgogozo

2017

Diversity and Evolution of Butterfly Wing Patterns

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“…Variants that change the amino acid sequence of proteins are more likely to affect phenotype than random sites in the genome, and this is used in the Bayes RC method described above. However, evidence is mounting that the majority of mutations that give rise to variation in complex traits reside in regulatory elements that alter gene expression [30][31][32] (reviewed by Pai et al [33]). …”

Section: Discussionmentioning

confidence: 99%

Genetics of complex traits: prediction of phenotype, identification of causal polymorphisms and genetic architecture

Goddard

Kemper

MacLeod³

et al. 2016

Proc. R. Soc. B.

150

119

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Complex or quantitative traits are important in medicine, agriculture and evolution, yet, until recently, few of the polymorphisms that cause variation in these traits were known. Genome-wide association studies (GWAS), based on the ability to assay thousands of single nucleotide polymorphisms (SNPs), have revolutionized our understanding of the genetics of complex traits. We advocate the analysis of GWAS data by a statistical method that fits all SNP effects simultaneously, assuming that these effects are drawn from a prior distribution. We illustrate how this method can be used to predict future phenotypes, to map and identify the causal mutations, and to study the genetic architecture of complex traits. The genetic architecture of complex traits is even more complex than previously thought: in almost every trait studied there are thousands of polymorphisms that explain genetic variation. Methods of predicting future phenotypes, collectively known as genomic selection or genomic prediction, have been widely adopted in livestock and crop breeding, leading to increased rates of genetic improvement.

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“…Enhancers, in concert with other cis-active elements such as promoters, silencers (Brand et al, 1985) and boundary elements/insulators (Kellum and Schedl, 1991) (Figure 4), play a central role in normal and pathological processes -consistent with the notion that the majority of disease-associated human single-nucleotidepolymorphisms (SNPs) are located in the non-coding part of the genome (Rada-Iglesias, 2014). Enhancers can even be responsible for curious effects such as determining the blond hair color of northern Europeans (Guenther et al, 2014). Undoubtedly we know a lot -but how much don't we know?…”

Section: Epiloguementioning

confidence: 99%

Enhancers, enhancers – from their discovery to today’s universe of transcription enhancers

Schaffner

2015

Biological Chemistry

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Transcriptional enhancers are short (200-1500 base pairs) DNA segments that are able to dramatically boost transcription from the promoter of a target gene. Originally discovered in simian virus 40 (SV40), a small DNA virus, transcription enhancers were soon also found in immunoglobulin genes and other cellular genes as key determinants of cell-type-specific gene expression. Enhancers can exert their effect over long distances of thousands, even hundreds of thousands of base pairs, either from upstream, downstream, or from within a transcription unit. The number of enhancers in eukaryotic genomes correlates with the complexity of the organism; a typical mammalian gene is likely controlled by several enhancers to fine-tune its expression at different developmental stages, in different cell types and in response to different signaling cues. Here, I provide a personal account of how enhancers were discovered more than 30 years ago, and also address the amazing development of the field since then.

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A molecular basis for classic blond hair color in Europeans

Cited by 160 publications

References 45 publications

Morphological Evolution Repeatedly Caused by Mutations in Signaling Ligand Genes

Morphological Evolution Repeatedly Caused by Mutations in Signaling Ligand Genes

Genetics of complex traits: prediction of phenotype, identification of causal polymorphisms and genetic architecture

Enhancers, enhancers – from their discovery to today’s universe of transcription enhancers

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