Part 1 Discussion

The quality control of DNA methylation microarray data is extremely important in EWAS. On smaller scales, this process is relatively straightforward and many of the tools and methods have been developed with these small datasets in mind. As there is now a large amount of information that is available on public repositories, it is likely that larger analyses will be taking place and therefore it is useful to consider how signals on the 450K microarray will behave during this bigger analyses.

Table 5.1: Breakdown of all 103,128 CpGs identified to be filtered from analysis

Characteristic CpG Context Type I Type II

Bead Count<3, n >5%, >20% datasets

Island 212 89 Shore 56 70 Shelf 18 35 Open Sea 65 104 Detection P>0.05, n >1%, >20% datasets Island 397 618 Shore 531 1960 Shelf 225 2479 Open Sea 878 7593

< 5th percentile variation in > 50% datasets

Island 3383 5737

Shore 125 2069

Shelf 19 60

Open Sea 96 449

Intersected HW SNPs from GEO and Marmal-Aid

Island 2813 2509

Shore 651 3455

Shelf 222 1124

Open Sea 685 3545

Zhou et al. (2017)’s List + Cross Hybridised

Island 8892 8447 Shore 2711 10589 Shelf 930 4834 Open Sea 4544 17218 X, Y and SNP probes Island 2208 2204 Shore 481 2497 Shelf 97 1093 Open Sea 379 2754 Total Combined Island 16306 17672 Shore 4237 19430 Shelf 1425 8391 Open Sea 6287 29380 Total 28255 74873

will be of bad quality but is more of an addition to the existing understanding of how the 450K array performs in preparation for larger analyses. I had set out to describe the characteristics and test a few thresholds to identify a set of probes that perform and behave differently from the bulk of the 450K microarray. I do not recommend that these probes be used instead of quality control but this list can be used in place of quality control if performing such quality control is not possible such as obtaining a preprocessedβ matrix from GEO.

When using the default parameters as defined by the pfilter function (beadcount<3, n >5%; detection p>0.05, n >1%) to identify probes that fail 20% of the time I identify 15,124 probes to fail one or both of these two thresholds. The probes that fail these thresholds are fairly well distributed across all the chromosomes and divided between Type I and Type II probes evenly. This is with the exception of Type II probes that are located within intergenic regions of the genome. Additionally, this probeset is inclusive of nearly 90% of the CpGs located on the Y Chromosome. The pfilter function was designed more than 6 years ago and the default thresholds were chosen based on past experience with EWAS at the time and was tested on datasets of around 100-200 samples. These thresholds nonetheless appear to perform well on a majority of the datasets that are within GEO with a few exceptions. Alternative threshold such as detection p>0.01 or Lehne et al. (2015)’s suggestion of 10−16 _{yielded a non-significant difference in the}

number of probes detected, suggesting that any of these three thresholds are sufficient for use. Most of the probes on the 450K microarray appear to be fairly well represented with only 649 probes consistently having low representations.

I also try to characterise probes by how much they vary across thousands of samples and tissues. What is apparent and expected is that the Type I and Type II probes behave wildly differently. Type I probes are defined by a single peak around a SD of 0.02 while Type II probes have one peak at an SD of 0.03 and a wider peak at an SD of around 0.06. This suggested that using an arbitrary cutoff, e.g. <0.02) would

identify 11,938 probes which were shown to vary an extremely small amount. However, the treatment of invariable probes is not as clear cut as it is with the other probes on this list. In fact, I would recommend keeping these low variance probes for analysis. As EWAS are growing in size the advantages of having a large number of samples is that there is the a large enough power to identify very small effect sizes - even from probes with a SD of 0.01. While I have included these in the probe-list I would like to note that I do not recommend that these probes be removed when considering a large number of samples as it is possible to identify associations with them. Additionally, it is possible that these probes can vary wildly in certain diseases and tissues that were missed in the 91 datasets I decided to look at.

Lastly, I characterised probes that exhibited a typical SNP-like distribution. Removing probes that have signals that are subjected to genetic confounding is a well known and established technique already. And the probe-lists have already identified public SNPs that are within the probe sequences of the 450K microarray. I set out to identify an additional set of unknown SNPs (or low-frequency alleles) which were otherwise missed. The intersection of the detected probes between GEO and Marmal-aid dataset appeared to be well distributed between both CpG islands and probe designs. Out of the 51,000 SNPs that are listed in Zhou et al. (2017)’s list only 1607 of these were identified in my intersection of SNP-like probes.

The detection p-values are a measure of confidence that a signal is presented above the background signal. However, there are numerous ways to calculate this. The most popular way of computing the detection p-values will generate a different set of detection p-values according to the official method (Illumina’s GenomeStudio). As a result, the software that was used to read in the raw data will affect the detection p-values for each probe. Although this is a small matter, the detection p-values used in this analysis were derived from the detection p algorithm described in methylumi. If I were to go back and read all of the idat files using minfi I would have received a completely different set of results. Thus the application of pfilter (with the detection p thresholds) could have identified a completely different probe-set to the one presented here.

analysis. In the future, I would like to implement the extension I provide in this chapter to a number of EWAS. It is entirely possible to combine the results of this study with the multiple EWAS that were performed in Chapter 2. This would allow me to explore whether or not the application of probe-filtering does lead to improved results.

In this part of this chapter, I extend the widely used probe lists that researchers use extensively in EWAS with a new set of features that are more reflective of the overall quality of the probes in question. The intention behind this is to provide alternative methods of quality control where there is no opportunity to perform the routine quality control steps as defined by individual software packages.

Considering the 450K is approaching the end of its lifespan where the last few remaining datasets are being produced and submitted to online repositories the wealth of data provided by these 450K arrays will enable us to generate more assumptions about these DNA methylation microarrays behave. As the use of meta-analyses and large scale data analyses taken precedence the need for generalised, reproducible quality control will become increasingly more needed. This work describes the first steps towards achiev- ing a thoughtful starting point towards the quality control of large numbers of data.