The size of dogs -- from Chihuahua to Great Dane -- is controlled by a single gene!
NYTimes: Scientists have just discovered which gene fragment controls the size of dogs, which have the greatest size range of any mammal — no other species produces adults with 100-fold differences, like that between a two-pound chihuahua and a 200-pound Newfoundland.
In a study to be published tomorrow in the journal Science, researchers analyzed 3,241 purebred dogs from 143 breeds. Genetically, the yapper arguing with your ankle is almost identical to the drooling behemoth bred to hunt bears, except for a tiny bit of DNA that suppresses the “insulin-like growth factor 1” gene.
Dog breeders have unwittingly been selecting for it since the last Ice Age. Dogs emerged from the wolf about 15,000 years ago, and as far back as 10,000 years ago, domesticated dogs as big as mastiffs and as small as Jack Russell terriers were trotting the earth.
Bonus! From Gene Expression, the following figure is taken from this PLoS Genetics paper (excerpt from abstract below). See related earlier post here.
Neighbor-Joining Trees Depicting the Genetic Relationships of 1,040 Individuals from 51 World Populations Collected by the CEPH-HGDP
(A) Individuals are color coded according to which of five major geographic regions of the globe they are collected from.
(B) Individuals are color coded according to which of the 51 populations they are associated with (1: Biaka Pygmy, 2: San, 3: Mbuti Pygmy, 4: Druze; 5: Bedouin, 6: Mozabite, 7: Palestinian, 8: Kalash, 9: Pima, 10: Columbian, 11: Karitiana, 12: Surui, 13: New Guinea, 14: Yakut).
Generalized Analysis of Molecular Variance: ... As in the original AMOVA strategy, the proposed approach, termed generalized AMOVA (GAMOVA), requires a genetic similarity matrix constructed from the allelic profiles of individuals under study and/or allele frequency summaries of the populations from which the individuals have been sampled. The proposed strategy can be used to either estimate the fraction of genetic variation explained by grouping factors such as country of origin, race, or ethnicity, or to quantify the strength of the relationship of the observed genetic background variation to quantitative measures collected on the subjects, such as blood pressure levels or anthropometric measures. Since the formulation of our test statistic is rooted in multivariate linear models, sets of variables can be related to genetic background in multiple regression-like contexts. GAMOVA can also be used to complement graphical representations of genetic diversity such as tree diagrams (dendrograms) or heatmaps. We examine features, advantages, and power of the proposed procedure and showcase its flexibility by using it to analyze a wide variety of published data sets, including data from the Human Genome Diversity Project, classical anthropometry data collected by Howells, and the International HapMap Project.