A human study of changes in gene expression

This 2015 international human study of genetic and epigenetic factors was the largest in its field:

“We perform a whole-blood gene expression meta-analysis in 14,983 individuals of European ancestry (including replication) and identify 1,497 genes that are differentially expressed with chronological age.

We further used the gene expression profiles to calculate the ‘transcriptomic age’ of an individual, and show that differences between transcriptomic age and chronological age are associated with biological features.”

Items of interest:

  • About 1,450 of the “1,497 genes that are differentially expressed” are newly identified;
  • The subjects’ mean age was 55.81 with a pooled standard deviation of 11.59;
  • The mean difference “between transcriptomic age and chronological age” was 7.84 years; and
  • Native American, Mexican American, and African American studies were used as replication cohorts.

It was refreshing to see the peer-review influence of numerous coauthors on the study. Papers that are written by only one or two researchers don’t often have frank limitation explanations such as:

“A potential limitation of our study is that we relied on a linear regression model to identify age-associated genes. A linear model assumes constant change over age, which may not be always correct in biological processes that stretch over several decades (adulthood). A recent study demonstrated that a quadratic regression model has a higher statistical fit to cross-sectional gene expression datasets over linear model.

A limitation of our study is that we used the Illumina Infinium Human Methylation 450K Bead Chip Array for measuring methylation levels: this array queries only 1.6% of all CpGs in the genome and the CpG selection is biased towards CpG islands.

In addition, we did not examine non-CpG methylated sites, which have recently been suggested to play a role in regulating gene expression as well.

Other techniques—whole-genome bisulfite sequencing and methylC-capture (MCC) sequencing, for example—have definite technical advantages (higher resolution and no CpG island selection bias), but these have currently not been applied to a large number of samples.”

http://www.nature.com/ncomms/2015/151022/ncomms9570/full/ncomms9570.html “The transcriptional landscape of age in human peripheral blood”

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