The phenotype concept

“Phenotype” is an extremely broad term
In this course, it connotes low-dimensional representation of observable characteristics of an organism
Representation can be numerical or categorical
- units of measurement should be recorded
- “codes” for categorical items should be clear

Example: ExperimentalData package on COPD

We cross-tabulate gender and disease status for individuals in a study of chronic obstructive pulmonary disease

library(COPDSexualDimorphism.data)
data(lgrc.expr.meta)
with(expr.meta, table(gender, diagmaj))

##           diagmaj
## gender     2-COPD/Emphysema 3-Control
##   1-Male                 93        26
##   2-Female               71        39

Continuous by categorical

Here’s a boxplot of pack-years distributions, stratified by gender and disease status. The stratum labels become clumsy.

gd = with(expr.meta, factor(paste(gender,diagmaj)))
expr.meta$gd = gd
library(ggplot2)

## Loading required package: methods

ggplot(expr.meta, aes(x=gd, y=pkyrs)) + geom_boxplot()

plot of chunk lkbx

#plot(pkyrs~gd, data=expr.meta)

Phenotype carefully and record faithfully

Validated questionnaires and protocols
Standardized terminology, units
Precise phenotypic characterization fosters more accurate mechanistic modeling
Caveat: molecular “basis” suggests causal directionality, but phenotype and environment can influence molecular state

Computing tools for inference on molecular mechanisms

“Molecular basis” is likewise a broad notion
Systematic terminologies exist to help clarify what is asserted in a given hypothesis or finding
At the boundaries of scientific knowledge, disagreement is common and terminologies diverge
Two examples:
- What is a gene?
- What is a gene’s function?

Gene: A concrete computational definition

ORMDL3 is a gene implicated in genome-wide association studies as a factor in risk of asthma
Here’s a view of its “structure” according to human reference build hg19 (use ph525x::modPlot)

library(ph525x)

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modPlot("ORMDL3", collapse=FALSE, useGeneSym=FALSE)

## Loading required package: Gviz
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## Loading required package: TxDb.Hsapiens.UCSC.hg19.knownGene

plot of chunk domo

This will change with new reference build GRCh38

Characterizing ORMDL3 functionality

library(Homo.sapiens)
orfunc = select(Homo.sapiens, key="ORMDL3", keytype="SYMBOL", 
   columns=c("GO", "TERM"))

## Warning in .generateExtraRows(tab, keys, jointype): 'select' resulted in
## 1:many mapping between keys and return rows

## Warning in .generateExtraRows(tab, keys, jointype): 'select' resulted in
## 1:many mapping between keys and return rows

orfunc[,c("ONTOLOGY", "TERM")]

##   ONTOLOGY                           TERM
## 1       MF                protein binding
## 2       CC          endoplasmic reticulum
## 3       CC endoplasmic reticulum membrane
## 4       BP     ceramide metabolic process
## 5       CC integral component of membrane
## 6       CC                  SPOTS complex

Gene Ontology standardizes terminology for biological processes, cellular components, and molecular functions

Summary

Phenotype characterization is challenging and frequently non-standard
Tokens available for data analysis in R are fairly simple and are used in ad hoc ways to characterize sample phenotype and condition
Reasoning about molecular processes underlying phenotype and disease states is intrinsically complex
Standardized vocabularies and models exist and are available in Bioconductor, but limitations must be admitted

Annotating phenotypes and molecular function