Sentence examples for many complex phenotypes from inspiring English sources

In humans, genetic interactions are involved in many complex phenotypes and they contribute to most genetic disorders, but the organization of the underlying networks is largely unknown [2], [3].

Genome-wide association studies (GWAS) have been successfully applied in humans for the study of many complex phenotypes.

There has been a growing suspicion, particularly since the 1980s, that - along with classical genetics - epigenetics is required to explain many complex phenotypes associated with disease [ 11, 12].

However, little study to date has been done to investigate the potential effect of gene-gene interactions, or epistasis, which is thought to be a critical piece in the genetic architecture of many complex phenotypes [ 8- 10].

The findings of Rztetsky et al suggest that many complex phenotypes (non-Mendelian disorders including both physical and psychiatric disorders) are probably rooted in genetic variation that is significantly shared by multiple disease phenotypes.

The lack of strong genetic association with EPQ-N score might imply that neuroticism is controlled by many loci each of relatively small effect, as is the case for many other complex phenotypes.

As with many complex clinical phenotypes, the identification of heritable factors associated with schizophrenia (SZ) remains a challenge [1].

The first generation of plant genome-scale metabolic reconstructions have proven surprisingly functional and robust as well as capable of predicting many observed complex phenotypes.

Moreover, genome-wide association studies (GWAS) are useful and powerful for the identification of the genetic variations that underlie many important and complex phenotypes such as disease resistance.

This tremendous task requires the development of new approaches to link the rapidly growing dataset of gene disease associations with the many complex and overlapping phenotypes of human disease.

The solution to this problem requires the development of new approaches to link the rapidly growing dataset of gene disease associations with the many complex and overlapping phenotypes of human disease.