Longitudinal changes in the frequency of mosaic chromosome Y loss in peripheral blood cells of aging men varies profoundly between individuals

Marcus Danielsson, Jonatan Halvardson, Hanna Davies, Behrooz Torabi Moghadam, Jonas Mattisson, Edyta Rychlicka-Buniowska, Janusz Jaszczyński, Julia Heintz, Lars Lannfelt, Vilmantas Giedraitis, Martin Ingelsson, Jan P Dumanski, Lars A Forsberg, Marcus Danielsson, Jonatan Halvardson, Hanna Davies, Behrooz Torabi Moghadam, Jonas Mattisson, Edyta Rychlicka-Buniowska, Janusz Jaszczyński, Julia Heintz, Lars Lannfelt, Vilmantas Giedraitis, Martin Ingelsson, Jan P Dumanski, Lars A Forsberg

Abstract

Mosaic loss of chromosome Y (LOY) is the most common somatic genetic aberration and is associated with increased risk for all-cause mortality, various forms of cancer and Alzheimer's disease, as well as other common human diseases. By tracking LOY frequencies in subjects from which blood samples have been serially collected up to five times during up to 22 years, we observed a pronounced intra-individual variation of changes in the frequency of LOY within individual men over time. We observed that in some individuals the frequency of LOY in blood clearly progressed over time and that in other men, the frequency was constant or showed other types of longitudinal development. The predominant method used for estimating LOY is calculation of the median Log R Ratio of probes located in the male specific part of chromosome Y (mLRRY) from intensity data generated by SNP-arrays, which is difficult to interpret due to its logarithmic and inversed scale. We present here a formula to transform mLRRY-values to percentage of LOY that is a more comprehensible unit. The formula was derived using measurements of LOY from matched samples analysed using SNP-array, whole genome sequencing and a new AMELX/AMELY-based assay for droplet digital PCR. The methods described could be applied for analyses of the vast amount of SNP-array data already generated in the scientific community, allowing further discoveries of LOY associated diseases and outcomes.

Conflict of interest statement

JPD and LAF are cofounders and shareholders in Cray Innovation AB. All other authors declare no competing interest.

Figures

Fig. 1
Fig. 1
Results from longitudinal analyses of LOY mosaicism in whole blood DNA from 276 aging individuals from the ULSAM cohort. Subjects were sampled serially 2–5 times over a period of up to 22.2 years. Every point represents a measurement of LOY in a subject at one time point with the level of LOY estimated from SNP-array data at the Y-axes and age of sampling on the X-axes. The dataset before (a) and after transformation (b) of the mLRRY-values using the equation: LOY (%) = 100 × (1−22mLRRY). Grey lines connect the LOY measurements from the same individual at different time points. The dotted black line in b indicates a level of LOY mosaicism at which at least 30% of the nucleated blood cells are without a Y chromosome. This threshold was used to identify subjects displaying a high level of LOY at any time point during the study and the longitudinal changes in the frequency of LOY in this subset are displayed in c and d. To visualise changes in LOY frequency over time, points and lines were colour-coded to connect multiple measurements from the same individual. c Displays the subjects showing a clear progression in the frequency of LOY over time and d shows non-progressing individuals with miscellaneous types of longitudinal change
Fig. 2
Fig. 2
Illustration of the non-linear estimation of LOY mosaicism by mLRRY calculated from SNP-array data by comparisons with LOY estimates generated from the same set of samples using independent technologies. a, b show the comparisons between mLRRY calculated from SNP-array data with the corresponding LOY estimates generated from the pairwise studied samples using whole genome sequencing (WGS) and droplet digital PCR (ddPCR), respectively. c displays a high concordance between estimates of LOY in samples pairwise studied with WGS and ddPCR. A linear regression line with Pearson’s coefficient of determination (R2) is shown for each comparison
Fig. 3
Fig. 3
Transformation of mLRRY-values linearises LOY estimates from SNP-array data using the equation: LOY (%) = 100 × (1−22mLRRY). a displays estimations of the level of LOY from 26 pairwise studied samples using SNP-array and whole genome sequencing (WGS). The Y-axis show the predicted LOY (%) from SNP-array data using the above formula and the X-axis display the measured level of LOY using WGS. A linear regression line with Pearson’s coefficient of determination (R2) is shown. b shows a corresponding comparison between estimations of LOY from 121 pairwise studied samples using SNP-array and droplet digital PCR (ddPCR)
Fig. 4
Fig. 4
Comparison of the performance of two formulas developed for prediction of percentage of LOY from mLRRY-values, i.e. Danielsson’s formula presented here and the recently published Veitia’s formula. In a, the estimates of mosaicism from each formula are compared in a range of theoretically possible mLRRY-values representing varying degree of LOY. The values of mLRRY plotted on the Y-axis were used to predict the percentages of LOY plotted on the X-axis. In male subjects without LOY, mLRRY-values are close to zero and lower values indicate increasing level of LOY mosaicism. The mLRRY calculated from female samples typically range −3 to −4. The horizontal grey line at 100% represents an extreme level of mosaicism (Y loss in all cells) and thus indicates a maximum theoretical limit of predicted LOY mosaicism in men. b shows a similar comparison using authentic data generated from 121 samples studied with both SNP-array and ddPCR. The Y-axis shows the predicted percentage of LOY from SNP-array data using each formula and the level of mosaicism measured by ddPCR in corresponding samples are plotted on the X-axis. Grey lines indicate theoretical upper limits of LOY estimations. To illustrate the overestimation of LOY mosaicism generated by the Veitia’s formula, a black line connects the predictions from each formula in the sample with the highest level of LOY mosaicism

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