Structural variation in the chicken genome identified by paired-end next-generation DNA sequencing of reduced representation libraries

<p>Abstract</p> <p>Background</p> <p>Variation within individual genomes ranges from single nucleotide polymorphisms (SNPs) to kilobase, and even megabase, sized structural variants (SVs), such as deletions, insertions, inversions, and more complex rearrangements. Although much is known about the extent of SVs in humans and mice, species in which they exert significant effects on phenotypes, very little is known about the extent of SVs in the 2.5-times smaller and less repetitive genome of the chicken.</p> <p>Results</p> <p>We identified hundreds of shared and divergent SVs in four commercial chicken lines relative to the reference chicken genome. The majority of SVs were found in intronic and intergenic regions, and we also found SVs in the coding regions. To identify the SVs, we combined high-throughput short read paired-end sequencing of genomic reduced representation libraries (RRLs) of pooled samples from 25 individuals and computational mapping of DNA sequences from a reference genome.</p> <p>Conclusion</p> <p>We provide a first glimpse of the high abundance of small structural genomic variations in the chicken. Extrapolating our results, we estimate that there are thousands of rearrangements in the chicken genome, the majority of which are located in non-coding regions. We observed that structural variation contributes to genetic differentiation among current domesticated chicken breeds and the Red Jungle Fowl. We expect that, because of their high abundance, SVs might explain phenotypic differences and play a role in the evolution of the chicken genome. Finally, our study exemplifies an efficient and cost-effective approach for identifying structural variation in sequenced genomes.</p

Dietary intake of micronutrients and the risk of developing bladder cancer: results from the Belgian case–control study on bladder cancer risk

OBJECTIVE: We aimed to investigate the effect of dietary intake of micronutrients that are metabolized and excreted via the urinary tract on bladder cancer risk. METHODS: A semi-quantitative 322 item food frequency questionnaire (FFQ) was used to collect dietary data from 200 bladder cancer cases and 386 control subjects participating in the Belgian case-control study on bladder cancer risk. Odds ratios (ORs) and 95% confidence intervals (CIs) were calculated using unconditional logistic regression adjusting for age, sex, smoking characteristics, occupational exposures, and energy intake. RESULTS: We observed a positive association between calcium intake and bladder cancer (OR: 1.77; 95% CI: 1.00-3.15; p-trend = 0.049) and increased odds, although not statistically significant, for highest tertile of phosphorus intake (OR: 1.82; 95% CI: 0.95-3.49; p-trend = 0.06). We identified possible modification of the effects of both calcium and phosphorus by level of magnesium intake. Increased odds of bladder cancer were also observed for participants with highest intake of phosphorus and lowest intake of vitamin D (OR: 4.25; 95% CI: 1.44-12.55) and among older participants with the highest intakes of calcium (OR: 1.90; 95% CI: 1.08-3.36) and phosphorus (OR: 2.02; 95% CI: 1.05-3.92). CONCLUSION: The positive associations we observed between bladder cancer and intake of calcium and phosphorus require confirmation by other studies. The balances between inter-related micronutrients also warrant further examination

Regional Management Units for Marine Turtles: A Novel Framework for Prioritizing Conservation and Research across Multiple Scales

Background: Resolving threats to widely distributed marine megafauna requires definition of the geographic distributions of both the threats as well as the population unit(s) of interest. In turn, because individual threats can operate on varying spatial scales, their impacts can affect different segments of a population of the same species. Therefore, integration of multiple tools and techniques - including site-based monitoring, genetic analyses, mark-recapture studies and telemetry - can facilitate robust definitions of population segments at multiple biological and spatial scales to address different management and research challenges. Methodology/Principal Findings: To address these issues for marine turtles, we collated all available studies on marine turtle biogeography, including nesting sites, population abundances and trends, population genetics, and satellite telemetry. We georeferenced this information to generate separate layers for nesting sites, genetic stocks, and core distributions of population segments of all marine turtle species. We then spatially integrated this information from fine-to coarse-spatial scales to develop nested envelope models, or Regional Management Units (RMUs), for marine turtles globally. Conclusions/Significance: The RMU framework is a solution to the challenge of how to organize marine turtles into units of protection above the level of nesting populations, but below the level of species, within regional entities that might be on independent evolutionary trajectories. Among many potential applications, RMUs provide a framework for identifying data gaps, assessing high diversity areas for multiple species and genetic stocks, and evaluating conservation status of marine turtles. Furthermore, RMUs allow for identification of geographic barriers to gene flow, and can provide valuable guidance to marine spatial planning initiatives that integrate spatial distributions of protected species and human activities. In addition, the RMU framework - including maps and supporting metadata - will be an iterative, user-driven tool made publicly available in an online application for comments, improvements, download and analysis

Identifying social clusters of endangered main Hawaiian Islands false killer whales

The presence of distinct social groups within an animal population can result in heterogeneity in many aspects of its life history and ecology. The ability to accurately assess social group membership increases with the number of times individuals are identified, but obtaining sufficient sightings of rarely encountered species can be difficult. Three social clusters were previously identified for the endangered population of false killer whales Pseudorca crassidens around the main Hawaiian Islands, using modularity among associations within a 12 yr photographic dataset with no restrictions on the number of times seen. In this study, we used photo-identification data over a 23 yr period to reassess the number and membership of social clusters, restricted to individuals seen on at least 5 different days. We compared the robustness of clustering assignments from 6 community detection algorithms using modularity and found that the 3 highest-ranking algorithms all identified the same number (4) and membership of social clusters. Spatial use of clusters varied among the islands, with 3 of the 4 clusters encountered regularly only off 1 or 2 of the 3 main island study areas. Comparison of genetic differentiation among social clusters revealed significant differentiation in nuclear DNA. Furthermore, all individuals in 2 of the clusters possess the same mitochondrial DNA haplotype, while in the other 2 clusters, approximately 40% of animals possess a second haplotype. This level of clustering and associated heterogeneity within the population may have implications for mark-recapture abundance estimation, as well as for mitigating exposure to anthropogenic activities, including interactions with fisheries.HŌ‘ULU‘ULU MANA‘O: Pili nā ‘ano like ‘ole o ka nohona a me ke kālaikaiaola o nā pū‘uo holoholona i ka loa‘a ‘ana o nā pū‘ulu kiko‘ī. Pi‘i a‘e ka hiki ke helu kūpono‘ia ka māhuahua ‘ana o nā heluna o ia mau pū‘ulu i ka helu ‘ana i nā wā e ‘ike ‘ia ai kēlā me kēia holoholona, ‘o ka lawa ‘ana na‘e o ka ‘ike ‘ana i nā lāhulu ‘ane halapohe kekahi ālaina. Hō‘ia ‘ia ‘ekolu pū‘ulu o ke koholā ‘ane halapohe, ‘o ka Pseudorca crassidens, a puni nā mokupuni nui ‘ewalu o Hawai‘i, ma ka ho‘owae‘anona ‘ana i ka pilina i loko o kekahi ‘ikepili ki‘a he ‘umikūmālua makahiki me ke kāohi ‘ole i ka nui o ka ‘ike ‘ia ‘ana. Ma kēia kilo ‘ana, ua ho‘ohana mākou i ka ‘ikepili ma o nā makahiki he iwakāluakūmākolu i mea e hō‘oia hou ai i ka heluna a me nā lālā o nā pū‘ulu launa i loko o kekahi pū‘uo holoholona, a pāpā ‘ia nā kālailaina i nā mea i ‘ike ‘ia ma ‘elima mau lā ‘oko‘a ma ka li‘ili‘i loa. Ho‘ohālikelike mākou i ke ‘ano me ka ikaika o kēia mau pū‘ulu launa ma ka ho‘ohana ‘ana i ka ho‘owae‘anona ‘ana ma ‘eono pū‘ulu ha‘ilula a ‘o ka mea i loa‘a, ‘o ia ho‘i ka ‘ike ‘ana, ma o nā ha‘ilula nui ‘ekolu, i ka heluna a me ka lālā ho‘okahi o nā pū‘ulu launa. Loli ka ho‘ohana ‘ana i ke koana o nā pū‘ulu ma waena o nā mokupuni, ‘ike ‘ia ‘ekolu pū‘ulu ma ho‘okahi a ‘elua paha mokupuni mai loko mai o nā mokupuni nui ‘ekolu e kālailai ‘ia ana. Ma ka ho‘ohālikelike ‘ana aku i nā hi‘ohi‘ona ōewe ‘oko‘a o nā pū‘ulu launa, ‘ike ‘ia ka ‘oko‘a ‘ano nui ma ka piko ōewe o nā pū‘ulu. A no laila, loa‘a i nā mea a pau o ia mau pū‘ulu ‘elua ke ōewe ho‘oilina ho‘okahi, a ma nā pū‘ulu ‘ē a‘e ‘elua, loa‘a he hi‘ohi‘ona ōewe ‘elua i nā holoholona he 40 pākēneka. Hiki nō paha i kēia ‘ano ho‘opū‘ulu ‘ana me kēia ‘ano wae‘anona ōewe ho‘opili ma kekahi pū‘uo ke pili i ke kuhi ‘ana i ka nui ma ka hopu kaha ‘ana, a i ke kāohi a ho‘ēmi ‘ana mai i nā hopena o nā hana kanaka, e la‘a ho‘i me ka hana ma ke kai lawai‘a