Breuer, R. | bioRxiv Science

bioRxiv2019

Meningeal Worm Infection in Central Iowa Goat Herds II: Individual Cases and Treatment Using a Camelid Therapeutic Protocol

Summary and ImplicationsMeningeal worm (Paralaphostrongylus tenuis) infection, also known as cerebrospinal nematodiasis, is a common parasitic infection in New World Camelids in the United States. There is also a considerable risk for this disease in the Boer goat population. Despite the rapidly increasing size of the national goat herd, there are no treatment protocols reported in the literature for goats with this disease. This study describes a successful clinical approach and treatment of 3 Boer goat cases with therapy previously reported for use in New World Camelids. The clinical presentation, diagnosis, and long-term outcome of P. tenuis infections in these goats presented to ISU Food Animal and Camelid Hospital (FACH) is reported here within. Practitioners should be aware that clinical presentation and diagnosis are similar for goats as reported for camelids with cerebrospinal nematodiasis. Additionally, the described treatment protocols for camelids appear to demonstrate a comparative efficacy in goats.

pharmacology and toxicology↗

bioRxiv2017

Genotype-phenotype association mining in bipolar disorder: market research meets complex genetics

Disentangling the etiology of common, complex diseases is a major challenge in genetic research. For bipolar disorder (BD), several genome-wide association studies (GWAS) have been performed. Similar to other complex disorders, major breakthroughs in explaining the high heritability of BD through GWAS have remained elusive. To overcome this dilemma, genetic research into BD, has embraced a variety of strategies such as the formation of large consortia to increase sample size and sequencing approaches. Here we advocate a complementary approach making use of already existing GWAS data: applying a data mining procedure to identify yet undetected genotype-phenotype relationships. We adapted association rule mining, a data mining technique traditionally used in retail market research, to identify frequent and characteristic genotype patterns showing strong associations to phenotype clusters. We applied this strategy to three independent GWAS datasets from 2,835 phenotypically characterized patients with BD. In a discovery step, 20,882 candidate association rules were extracted. Two of these - one associated with eating disorder and the other with anxiety - remained significant in an independent dataset after robust correction for multiple testing, showing considerable effect sizes (odds ratio ~ 3.4 and 3.0, respectively). Our approach may help detect novel specific genotype-phenotype relationships in BD typically not explored by analyses like GWAS. While we adapted the data mining tool within the context of BD gene discovery, it may facilitate identifying highly specific genotype-phenotype relationships in subsets of genome-wide data sets of other complex phenotype with similar epidemiological properties and challenges to gene discovery efforts.

genomics↗