Fluxus Engineering's Network median joining network for John 2

Fluxus Engineering's Network median joining network for John 2. The data is from the IGNTP transcriptions, automatically collated using CollateX using the Needleman-Wunsch algorithm. Drawn with Graphviz' SFDP algorithm

Mesquite consensus tree of John 1

Consensus tree for Maximum Parsimony trees for John chapter 1 created using Mesquite. The data is from the IGNTP transcriptions, automatically collated using CollateX using the Needleman-Wunsch algorithm

Textual flow diagram for John 1:8/16 from the ECM of John, as of 31 October 2017.

CBGM Textual flow diagram for John 1:8/16 from an interim stage in the ECM of John, as of 31 October 201

MrBayes consensus tree for John 2:1 (automatic collation)

MrBayes consensus tree for John 2:1. The data is from the IGNTP transcriptions, automatically collated using CollateX using the Needleman-Wunsch algorithm

SplitsTree Neighbour-Joining tree for John (automatic collation)

Neighbour-Joining tree for John's Gospel created using SplitsTree. The data is from the IGNTP transcriptions, automatically collated using CollateX using the Needleman-Wunsch algorithm

MrBayes consensus tree for IGNTP data of John 18

All 1,663 extant witnesses to John 18 were manually collated by the International Greek New Testament Project (IGNTP), and stored in a database. This image represents the consensus tree from running MrBayes on this data as part of my PhD studies

Median Joining Network for ECM John as of 31 October 2017

Median Joining Network for ECM John as of 31 October 2017 (excluding witnesses <85% extant) Created using Network from http://www.fluxus-engineering.com/sharenet.htm (See Bandelt, H. J., P. Forster, and A. Rohl. ‘Median-Joining Networks for Inferring Intraspecific Phylogenies’. Molecular Biology and Evolution 16, no. 1 (1 January 1999): 37–48. https://doi.org/10.1093/oxfordjournals.molbev.a026036.

SplitsTree Neighbour-Joining tree for John chapter 1 (automatic collation)

Neighbour-Joining tree for John chapter 1 created using SplitsTree. The data is from the IGNTP transcriptions, automatically collated using CollateX using the Needleman-Wunsch algorithm

Global Stemma for ECM John as of 31 October 2017

This CBGM Global Stemma represents an interim stage in the ECM of John, as of 31 October 2017

Pathway-Wide Association Study Implicates Multiple Sterol Transport and Metabolism Genes in HDL Cholesterol Regulation

Pathway-based association methods have been proposed to be an effective approach in identifying disease genes, when single-marker association tests do not have sufficient power. The analysis of quantitative traits may be benefited from these approaches, by sampling from two extreme tails of the distribution. Here we tested a pathway association approach on a small genome-wide association study (GWAS) on 653 subjects with extremely high high-density lipoprotein cholesterol (HDL-C) levels and 784 subjects with low HDL-C levels. We identified 102 genes in the sterol transport and metabolism pathways that collectively associate with HDL-C levels, and replicated these association signals in an independent GWAS. Interestingly, the pathways include 18 genes implicated in previous GWAS on lipid traits, suggesting that genuine HDL-C genes are highly enriched in these pathways. Additionally, multiple biologically relevant loci in the pathways were not detected by previous GWAS, including genes implicated in previous candidate gene association studies (such as LEPR, APOA2, HDLBP, SOAT2), genes that cause Mendelian forms of lipid disorders (such as DHCR24), and genes expressing dyslipidemia phenotypes in knockout mice (such as SOAT1, PON1). Our study suggests that sampling from two extreme tails of a quantitative trait and examining genetic pathways may yield biological insights from smaller samples than are generally required using single-marker analysis in large-scale GWAS. Our results also implicate that functionally related genes work together to regulate complex quantitative traits, and that future large-scale studies may benefit from pathway-association approaches to identify novel pathways regulating HDL-C levels