Growth Hormone Receptor and PCSK9 Define a New Paradigm in the Initiation and Development of Chronic Kidney Disease as Revealed by Exome Sequencing on Illumina Platform
Bibliographic record
Abstract
Chronic kidney disease is a combination of many vascular diseases involving mutations of many genes. Hypertension diabetes and atherosclerosis are the most common causes of kidney disease, with hypertension causing just over a quarter of all cases of kidney failure and diabetes causing one-third of them. Other much less common conditions that can cause CKD include inflammation, infections, genetic factors, or longstanding blockage to the urinary system (such as enlarged prostate or kidney stones). In many cases, the causes remained unknown, albeit the manifestation of the diseases with clear phenotypes and biochemical profiles. Heredity and genetic determinants play major roles in the initiation, development, and establishment of CKD. Kidney disease phenotypes can be dissected into many underlying causing candidates’ genes and many molecular genetics approaches are striving to lift the veil on this nagging disease. Recent studies using genetic testing have demonstrated that Mendelian etiologies account for approximately 20% of cases of kidney disease of unknown etiology. CKD is known to be plagued with many genes mutations like mutation in Autosomal Dominant Polycystic Kidney Disease (ADPKD) and mutations in MYH9 and APOL1 genes, COL4A3, COL4A4, and COL4A5 genes playing important roles in the CKD picture. Genetic testing has modernized and revolutionized many areas of medical practices and diagnosis of many diseases and the field of nephrology is not an exception. The advance in Next-generation Sequencing, including whole exome sequencing has proven to be a powerful tool in personalized medicine and for potential noninvasive decryption for biomarkers in kidney disease thereby paving the way for better diagnostic purposes. In this regard, we run whole exome sequencing on whole blood genomic DNA from CKD patients. Bioinformatics analysis led us to uncover a total of more than 3000 single nucleotide polymorphisms (SNPs). To sort out these flurries of targeted SNPs, we undertook filtration using an R-algorithm in combination with the diseases association Clinvar database. This approach led us to 12 combined diagnostic missense variants scattered on different chromosomes. Combined missense reduction after FDR filtration with a Cellrate of 0.75 generated two missense variants located on PCSK9 and GHR genes on chromosomes 1 and 5 and lastly, reduction variants after Filtration by spliced region bring us to a single SNP located on the PCSK9 gene.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".