Sentence examples for variants to explain from inspiring English sources

As mentioned in the genetic debate, it takes 300,000 gene variants to explain only 50% of something like height.

As expected, the worst in terms of potential discoveries is rare-variant low-penetrant model (C), which requires 3114 variants to explain a heritability of 0.4.

Assuming similar allele frequencies and effect sizes of the currently validated SNPs, complex phenotypes such as type-2 diabetes would need approximately 800 variants to explain its 40% heritability.

Although it is reassuring to note that most associations identified in genome-wide scanning efforts reflect biologically plausible mechanisms, even highly heritable traits such as height (H2[Height] ∼0.8) would require nearly 105 discrete variants to explain the phenotype based on current statistical modeling [52].

A number of different methods have been developed for the prioritization of causal variants to explain association signals.

These results should be of interest for future investigations to characterize causal variants to explain their functional role in the diversity of bovine stature.

However, we were unable to single out a unique variant to explain the correlation between low BLK gene expression and risk alleles of SNPs in B1 and B3 due to the strong LD.

In spite of the success of genome-wide association studies in finding many common variants associated with disease, these variants seem to explain only a small proportion of the estimated heritability.

Although there is imputation uncertainty in the 1000 Genomes dataset, this is greatest for low frequency (below 1%) variants, whereas to explain away our observed epistatic interactions we would most likely require variants of higher allele frequency.

These variants tend to explain only very small proportions of overall heritability, leaving many wondering where the missing heritability can be found.

"It is now clear that common risk variants fail to explain the vast majority of genetic heritability for any human disease," they wrote in an essay, arguing that many of the hundreds of GWAS findings to date "stem from factors other than a true association with disease risk".