Genetic variants associated with mosaic Y chromosome loss highlight cell cycle genes and overlap with cancer susceptibility.
Daniel J WrightFelix R DayNicola D KerrisonFlorian ZinkAlexia CardonaPatrick SulemDeborah J ThompsonSvanhvit SigurjonsdottirDaniel F GudbjartssonAgnar HelgasonJ Ross ChapmanSteve P JacksonClaudia LangenbergNicholas J WarehamRobert A ScottUnnur ThorsteindottirKen K OngKari StefanssonJohn R B PerryPublished in: Nature genetics (2017)
The Y chromosome is frequently lost in hematopoietic cells, which represents the most common somatic alteration in men. However, the mechanisms that regulate mosaic loss of chromosome Y (mLOY), and its clinical relevance, are unknown. We used genotype-array-intensity data and sequence reads from 85,542 men to identify 19 genomic regions (P < 5 × 10-8) that are associated with mLOY. Cumulatively, these loci also predicted X chromosome loss in women (n = 96,123; P = 4 × 10-6). Additional epigenome-wide methylation analyses using whole blood highlighted 36 differentially methylated sites associated with mLOY. The genes identified converge on aspects of cell proliferation and cell cycle regulation, including DNA synthesis (NPAT), DNA damage response (ATM), mitosis (PMF1, CENPN and MAD1L1) and apoptosis (TP53). We highlight the shared genetic architecture between mLOY and cancer susceptibility, in addition to inferring a causal effect of smoking on mLOY. Collectively, our results demonstrate that genotype-array-intensity data enables a measure of cell cycle efficiency at population scale and identifies genes implicated in aneuploidy, genome instability and cancer susceptibility.
Keyphrases
- cell cycle
- genome wide
- cell proliferation
- copy number
- dna methylation
- papillary thyroid
- dna damage response
- squamous cell
- cell cycle arrest
- induced apoptosis
- electronic health record
- endoplasmic reticulum stress
- pi k akt
- high throughput
- cell death
- high resolution
- lymph node metastasis
- middle aged
- high intensity
- bone marrow
- big data
- type diabetes
- pregnant women
- machine learning
- genome wide analysis
- polycystic ovary syndrome
- cell free
- skeletal muscle
- transcription factor
- cervical cancer screening