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How do we identify those variants, or combinations of variants (patterns), that are of importance for the phenotype, given that the functionally relevant variants represent only a subset of the naturally occurring sequence variation?
This requires the development of powerful bioinformatic approaches that allow prediction of haplotypes from numerous variants, and the classification of haplotypes into functionally related categories, in order to identify those specific sequence variants, or combinations of variants, associated with the disease phenotype.
GenProfile focuses on the systematic analysis of DNA sequence variation in biomedically relevant genes in order to identify those variants, or combinations of variants (gene profiles), that determine an individual's genetic risk for disease, predict individually different drug response, and ultimately pave the way to a personalized medicine.
Having identified SNPs in the coding region in each ethnic group, we next sought to identify any association between individual TIRAP variants, or combinations of variants, with disease.
Probes would then be designed by tiling along the consensus sequence with probes designed to hybridise to all possible combinations of variants that had been previously observed.
The presence of epistatic interactions between genes has fundamental consequences for the course and outcome of evolution by natural selection [1] [4] and results in the emergence of co-adapted gene complexes, i.e. combinations of variants at different genes that give a selective advantage only when both are present in the same individual.
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They propose a measure to infer combinations of variant services that are, in fact, effective.
Common diseases may be attributed to combinations of variant alleles, but there are few model systems where the interactions among such variants can be studied in controlled genetic crosses.
These findings of similar ages of onset with similar mutations are consistent with a genotype phenotype correlation within combinations of variant R229Q plus pathogenic podocin mutations.
For Figure 2, we took the five best combinations of variant callers and aligners as determined by their sensitivity and false positive rate (FPR).
Although the numbers of total variant alleles were significantly associated with recurrence and cancer-specific survival (univariate analysis), no significant relationships were found between specific combinations of variant alleles and recurrence or cancer-specific survival (data not shown).
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