• Journal of the Korean Child Neurology Society
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• v.25 no.3
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• pp.200-203
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• 2017

Spinocerebellar ataxias (SCAs) are autosomal dominant neurodegenerative disorders which disrupt the afferent and efferent pathways of the cerebellum that cause cerebellar ataxia. Spectrin beta non-erythrocytic 2 (SPTBN2) gene encodes the ${\beta}-III$ spectrin protein with high expression in Purkinje cells that is involved in excitatory glutamate signaling through stabilization of the glutamate transporter, and its mutation is known to cause spinocerebellar ataxia type 5. Three years and 5 months old boy with delayed development showed leukodystrophy and cerebellar atrophy in brain magnetic resonance imaging (MRI). Diagnostic exome sequencing revealed that the patient has heterozygous mutation in SPTBN2 (p.Glu1251Gln) which is a causative genetic mutation for spinocerebellar ataxia type 5. With the patient's clinical findings, it seems reasonable to conclude that p.Glu1251Gln mutation of SPTBN2 gene caused spinocerebellar ataxia type 5 in this patient.

• Asian-Australasian Journal of Animal Sciences
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• v.29 no.1
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• pp.36-42
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• 2016

Milk-related traits (milk yield, fat and protein) have been crucial to selection of Holstein. It is essential to find the current selection trends of Holstein. Despite this, uncovering the current trends of selection have been ignored in previous studies. We suggest a new formula to detect the current selection trends based on single nucleotide polymorphisms (SNP). This suggestion is based on the best linear unbiased prediction (BLUP) and the Fisher's fundamental theorem of natural selection both of which are trait-dependent. Fisher's theorem links the additive genetic variance to the selection coefficient. For Holstein milk production traits, we estimated the additive genetic variance using SNP effect from BLUP and selection coefficients based on genetic variance to search highly selective SNPs. Through these processes, we identified significantly selective SNPs. The number of genes containing highly selective SNPs with p-value <0.01 (nearly top 1% SNPs) in all traits and p-value <0.001 (nearly top 0.1%) in any traits was 14. They are phosphodiesterase 4B (PDE4B), serine/threonine kinase 40 (STK40), collagen, type XI, alpha 1 (COL11A1), ephrin-A1 (EFNA1), netrin 4 (NTN4), neuron specific gene family member 1 (NSG1), estrogen receptor 1 (ESR1), neurexin 3 (NRXN3), spectrin, beta, non-erythrocytic 1 (SPTBN1), ADP-ribosylation factor interacting protein 1 (ARFIP1), mutL homolog 1 (MLH1), transmembrane channel-like 7 (TMC7), carboxypeptidase X, member 2 (CPXM2) and ADAM metallopeptidase domain 12 (ADAM12). These genes may be important for future artificial selection trends. Also, we found that the SNP effect predicted from BLUP was the key factor to determine the expected current selection coefficient of SNP. Under Hardy-Weinberg equilibrium of SNP markers in current generation, the selection coefficient is equivalent to $2^*SNP$ effect.