Genetic Association of Mutation at Agouti Locus with Adrenal X Zone Morphology in BALB/c Mice

Abstract Recombinant inbred strain and interspecific backcross mice were used to create a molecular genetic linkage map of the distal portion of mouse chromosome 2. The orientation and distance of the Ada, Emv-13, Emv-15, Hck-1, Il-1a, Pck-1, Psp, Src-1 and Svp-1 loci from the beta 2-microglobulin locus and the agouti locus were established. Our mapping results have provided the identification of molecular markers both proximal and distal to the agouti locus. The recombinants obtained provide valuable resources for determining the direction of chromosome walking experiments designed to clone sequences at the agouti locus. Comparisons between the mouse and human genome maps suggest that the human homolog of the agouti locus resides on human chromosome 20q. Three loci not present on mouse chromosome 2 were also identified and were provisionally named Psp-2, Hck-2 and Hck-3. The Psp-2 locus maps to mouse chromosome 14. The Hck-2 locus maps near the centromere of mouse chromosome 4 and may identify the Lyn locus. The Hck-3 locus maps near the distal end of mouse chromosome 4 and may identify the Lck locus.

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Exploring the Genetic Association of the ABAT Gene with Alzheimer’s Disease

Molecular Neurobiology ◽

10.1007/s12035-020-02271-z ◽

2021 ◽

Author(s):

Quanzhen Zheng ◽

Rui Bi ◽

Min Xu ◽

Deng-Feng Zhang ◽

Li-Wen Tan ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Genetic Association

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“Guilt by association” is not competitive with genetic association for identifying autism risk genes

Scientific Reports ◽

10.1038/s41598-021-95321-y ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Margot Gunning ◽

Paul Pavlidis

Keyword(s):

Machine Learning ◽

Genetic Association ◽

Gene Networks ◽

Rare Variants ◽

Association Studies ◽

Genetic Disorders ◽

Autism Spectrum ◽

Biological Data ◽

Disease Genes ◽

Risk Genes

AbstractDiscovering genes involved in complex human genetic disorders is a major challenge. Many have suggested that machine learning (ML) algorithms using gene networks can be used to supplement traditional genetic association-based approaches to predict or prioritize disease genes. However, questions have been raised about the utility of ML methods for this type of task due to biases within the data, and poor real-world performance. Using autism spectrum disorder (ASD) as a test case, we sought to investigate the question: can machine learning aid in the discovery of disease genes? We collected 13 published ASD gene prioritization studies and evaluated their performance using known and novel high-confidence ASD genes. We also investigated their biases towards generic gene annotations, like number of association publications. We found that ML methods which do not incorporate genetics information have limited utility for prioritization of ASD risk genes. These studies perform at a comparable level to generic measures of likelihood for the involvement of genes in any condition, and do not out-perform genetic association studies. Future efforts to discover disease genes should be focused on developing and validating statistical models for genetic association, specifically for association between rare variants and disease, rather than developing complex machine learning methods using complex heterogeneous biological data with unknown reliability.

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