Abstract
BACKGROUND: The majority of structural variation in genomes is caused by insertions of transposable elements (TEs). In mammalian genomes, the main TE fraction is made up of autonomous and non-autonomous non-LTR retrotransposons commonly known as LINEs and SINEs (Long and Short Interspersed Nuclear Elements). Here we present one of the first population-level analysis of TE insertions in a non-model organism, the giraffe. Giraffes are ruminant artiodactyls, one of the few mammalian groups with genomes that are colonized by putatively active LINEs of two different clades of non-LTR retrotransposons, namely the LINE1 and RTE/BovB LINEs as well as their associated SINEs. We analyzed TE insertions of both types, and their associated SINEs in three giraffe genome assemblies, as well as across a population level sampling of 48 individuals covering all extant giraffe species. RESULTS: The comparative genome screen identified 139,525 recent LINE1 and RTE insertions in the sampled giraffe population. The analysis revealed a drastically reduced RTE activity in giraffes, whereas LINE1 is still actively propagating in the genomes of extant (sub)-species. In concert with the extremely low activity of the giraffe RTE, we also found that RTE-dependent SINEs, namely Bov-tA and Bov-A2, have been virtually immobile in the last 2 million years. Despite the high current activity of the giraffe LINE1, we did not find evidence for the presence of currently active LINE1-dependent SINEs. TE insertion heterozygosity rates differ among the different (sub)-species, likely due to divergent population histories. CONCLUSIONS: The horizontally transferred RTE/BovB and its derived SINEs appear to be close to inactivation and subsequent extinction in the genomes of extant giraffe species. This is the first time that the decline of a TE family has been meticulously analyzed from a population genetics perspective. Our study shows how detailed information about past and present TE activity can be obtained by analyzing large-scale population-level genomic data sets.