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A comparison of 100 human genes using an alu element-based instability model.

ABSTRACT: The human retrotransposon with the highest copy number is the Alu element. The human genome contains over one million Alu elements that collectively account for over ten percent of our DNA. Full-length Alu elements are randomly distributed throughout the genome in both forward and reverse orientations. However, full-length widely spaced Alu pairs having two Alus in the same (direct) orientation are statistically more prevalent than Alu pairs having two Alus in the opposite (inverted) orientation. The cause of this phenomenon is unknown. It has been hypothesized that this imbalance is the consequence of anomalous inverted Alu pair interactions. One proposed mechanism suggests that inverted Alu pairs can ectopically interact, exposing both ends of each Alu element making up the pair to a potential double-strand break, or "hit". This hypothesized "two-hit" (two double-strand breaks) potential per Alu element was used to develop a model for comparing the relative instabilities of human genes. The model incorporates both 1) the two-hit double-strand break potential of Alu elements and 2) the probability of exon-damaging deletions extending from these double-strand breaks. This model was used to compare the relative instabilities of 50 deletion-prone cancer genes and 50 randomly selected genes from the human genome. The output of the Alu element-based genomic instability model developed here is shown to coincide with the observed instability of deletion-prone cancer genes. The 50 cancer genes are collectively estimated to be 58% more unstable than the randomly chosen genes using this model. Seven of the deletion-prone cancer genes, ATM, BRCA1, FANCA, FANCD2, MSH2, NCOR1 and PBRM1, were among the most unstable 10% of the 100 genes analyzed. This algorithm may lay the foundation for comparing genetic risks posed by structural variations that are unique to specific individuals, families and people groups.

SUBMITTER: Cook GW

PROVIDER: S-EPMC3670932 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Long and short interspersed elements (LINEs and SINEs) are retroelements that make up almost half of the human genome. L1 and Alu represent the most prolific human LINE and SINE families, respectively. Only a few Alu elements are able to retropose, and the factors determining their retroposition capacity are poorly understood. The data presented in this paper indicate that the length of Alu "A-tails" is one of the principal factors in determining the retropositional capability of an Alu element. The A stretches of the Alu subfamilies analyzed, both old (Alu S and J) and young (Ya5), had a Poisson distribution of A-tail lengths with a mean size of 21 and 26, respectively. In contrast, the A-tails of very recent Alu insertions (disease causing) were all between 40 and 97 bp in length. The L1 elements analyzed displayed a similar tendency, in which the "disease"-associated elements have much longer A-tails (mean of 77) than do the elements even from the young Ta subfamily (mean of 41). Analysis of the draft sequence of the human genome showed that only about 1000 of the over one million Alu elements have tails of 40 or more adenosine residues in length. The presence of these long A stretches shows a strong bias toward the actively amplifying subfamilies, consistent with their playing a major role in the amplification process. Evaluation of the 19 Alu elements retrieved from the draft sequence of the human genome that are identical to the Alu Ya5a2 insert in the NF1 gene showed that only five have tails with 40 or more adenosine residues. Sequence analysis of the loci with the Alu elements containing the longest A-tails (7 of the 19) from the genomes of the NF1 patient and the father revealed that there are at least two loci with A-tails long enough to serve as source elements within our model. Analysis of the A-tail lengths of 12 Ya5a2 elements in diverse human population groups showed substantial variability in both the Alu A-tail length and sequence homogeneity. On the basis of these observations, a model is presented for the role of A-tail length in determining which Alu elements are active.

Project description:BACKGROUND:Identifying cancer driver genes (CDG) is a crucial step in cancer genomic toward the advancement of precision medicine. However, driver gene discovery is a very challenging task because we are not only dealing with huge amount of data; but we are also faced with the complexity of the disease including the heterogeneity of background somatic mutation rate in each cancer patient. It is generally accepted that CDG harbor variants conferring growth advantage in the malignant cell and they are positively selected, which are critical to cancer development; whereas, non-driver genes harbor random mutations with no functional consequence on cancer. Based on this fact, function prediction based approaches for identifying CDG have been proposed to interrogate the distribution of functional predictions among mutations in cancer genomes (eLS 1-16, 2016). Assuming most of the observed mutations are passenger mutations and given the quantitative predictions for the functional impact of the mutations, genes enriched of functional or deleterious mutations are more likely to be drivers. The promises of these methods have been continually refined and can therefore be applied to increase accuracy in detecting new candidate CDGs. However, current function prediction based approaches only focus on coding mutations and lack a systematic way to pick the best mutation deleteriousness prediction algorithms for usage. RESULTS:In this study, we propose a new function prediction based approach to discover CDGs through a gene-based permutation approach. Our method not only covers both coding and non-coding regions of the genes; but it also accounts for the heterogeneous mutational context in cohort of cancer patients. The permutation model was implemented independently using seven popular deleteriousness prediction scores covering splicing regions (SPIDEX), coding regions (MetaLR, and VEST3) and pan-genome (CADD, DANN, Fathmm-MKL coding and Fathmm-MKL noncoding). We applied this new approach to somatic single nucleotide variants (SNVs) from whole-genome sequences of 119 breast and 24 lung cancer patients and compared the seven deleteriousness prediction scores for their performance in this study. CONCLUSION:The new function prediction based approach not only predicted known cancer genes listed in the Cancer Gene Census (CGC), but also new candidate CDGs that are worth further investigation. The results showed the advantage of utilizing pan-genome deleteriousness prediction scores in function prediction based methods. Although VEST3 score, a deleteriousness prediction score for missense mutations, has the best performance in breast cancer, it was topped by CADD and Fathmm-MKL coding, two pan-genome deleteriousness prediction scores, in lung cancer.

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A comparison of 100 human genes using an alu element-based instability model.

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