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Finn, C.

Publications and source records attributed to Finn, C..

2 recordsLinked to original sources

Temporal and spatial patterns of vertebrate extinctions during the Anthropocene

The human-induced annihilation of modern biodiversity is dragging the planet into a mass extinction that has already altered patterns of life globally. Among vertebrates, over 500 species have become extinct or possibly extinct in the last five centuries - an extinction rate that would have taken several millennia without human intervention. Vertebrate extinctions have often been quantified as cumulative counts that reveal sharp increases in losses over time. Here, we quantify global tetrapod extinctions since the 1400s using numbers of species losses across successive and independent time periods until present. Our results reveal that extinctions were low and fundamentally restricted to islands in pre-industrial times, experiencing a significant increase and spread over continental mainland following the onset of the industrial revolution. Recent amphibian extinctions alarmingly exceed the extinctions of all tetrapods, while extinctions of island birds account for a third of all extinctions. Finally, we quantified the relationship between human population growth (HPG, as a proxy for aggregate human effects on the environment) and extinctions between 1800-2000, to then predict that an estimated 838 tetrapod species will go extinct between 2030-2100 based on United Nations HPG projections. These findings further warn humanity about the need to sustainably control HPG and the destructive impacts of rapid environmental change on ecosystems worldwide.

ecology↗

Copy-number variation contributes 9% of pathogenicity in the inherited retinal degenerations

PurposeCurrent sequencing strategies can genetically solve 55-60% of inherited retinal degeneration (IRD) cases, despite recent progress in sequencing. This can partially be attributed to elusive pathogenic variants (PVs) in known IRD genes, including copy number variations (CNVs), which we believe are a major contributor to unsolved IRD cases.\n\nMethodsFive hundred IRD patients were analyzed with targeted next generation sequencing (NGS). The NGS data was used to detect CNVs with ExomeDepth and gCNV and the results were compared to CNV detection with a SNP-Array. Likely causal CNV predictions were validated by quantitative (q)PCR.\n\nResultsLikely disease-causing single nucleotide variants (SNVs) and small indels were found in 55.8% of subjects. PVs in USH2A (11.6%), RPGR (4%) and EYS (4%) were the most common. Likely causal CNVs were found in an additional 8.8% of patients. Of the three CNV detection methods, gCNV showed the highest accuracy. Approximately 30% of unsolved subjects had a single likely PV in a recessive IRD gene.\n\nConclusionsCNV detection using NGS-based algorithms is a reliable method that greatly increases the genetic diagnostic rate of IRDs. Experimentally validating CNVs helps estimate the rate at which IRDs might be solved by a CNV plus a more elusive variant.

genomics↗