Population analysis of retrotransposons in giraffe genomes supports RTE decline and widespread LINE1 activity in Giraffidae

Abstract

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.