Non-heritable genetics of human disease: spotlight on post-zygotic genetic variation acquired during lifetime

Abstract

The heritability of most common, multifactorial diseases is rather modest and known genetic effects account for a small part of it. The remaining portion of disease aetiology has been conventionally ascribed to environmental effects, with an unknown part being stochastic. This review focuses on recent studies highlighting stochastic events of potentially great importance in human disease-the accumulation of post-zygotic structural aberrations with age in phenotypically normal humans. These findings are in agreement with a substantial mutational load predicted to occur during lifetime within the human soma. A major consequence of these results is that the genetic profile of a single tissue collected at one time point should be used with caution as a faithful portrait of other tissues from the same subject or the same tissue throughout life. Thus, the design of studies in human genetics interrogating a single sample per subject or applying lymphoblastoid cell lines may come into question. Sporadic disorders are common in medicine. We wish to stress the non-heritable genetic variation as a potentially important factor behind the development of sporadic diseases. Moreover, associations between post-zygotic mutations, clonal cell expansions and their relation to cancer predisposition are central in this context. Post-zygotic mutations are amenable to robust examination and are likely to explain a sizable part of non-heritable disease causality, which has routinely been thought of as synonymous with environmental factors. In view of the widespread accumulation of genetic aberrations with age and strong predictions of disease risk from such analyses, studies of post-zygotic mutations may be a fruitful approach for delineation of variants that are causative for common human disorders.

Figure 1

An illustration of the three main types of post-zygotic structural genetic aberrations, selected from Forsberg et al. Panels A, B and C display a deletion, a copy number neutral loss of heterozygosity (CNNLOH, also called acquired uniparental disomy, aUPD), and a gain, respectively. Each panel is composed of images from Illumina single nucleotide polymorphism (SNP) beadchips showing a selected aberrant chromosome, with the affected regions highlighted in pink. The results from Illumina SNP arrays consist of two data tracks: log R ratio (LRR) values of fluorescent intensities from array probes (upper part), and B allele frequency (BAF) values representing the fraction of fluorescent intensity at each SNP accounted for by the B allele (lower part). Normally, BAF values cluster around 0 (AA genotype), 0.5 (AB) or 1 (BB). On the right hand side, a schematic explanatory figure displaying the mosaic mixture of cells with aberrant and wild-type chromosomes is shown. Two hypothetical homologous chromosomes (labelled in green and white) with heterozygous genotypes for six SNPs are shown. Panel A shows data for chromosome 5 in a monozygotic (MZ) twin pair sampled at the age of 77 years. MZ twin TP25-1 has a normal profile, while its co-twin TP25-2 has a 32.5 Mb deletion on 5q in approximately 55% of nucleated blood cells. This deletion is uncovered using both LRR (downward shift) and BAF (heterozygous SNPs cluster away from 0.5) data from the Illumina SNP array. The Illumina profile contains a mixture of genotypes from aberrant cells (approximately 55%) and wild-type cells representing approximately 45% of nucleated blood cells. Panel B shows data for chromosome 10 in MZ twin pair sampled at the age of 77 years. Twin TP12-1 shows a normal profile. Using BAF values a 76.5 Mb large CNNLOH/aUPD was identified on 10q in co-twin TP12-2. Quantification of cells containing the CNNLOH/aUPD suggests that 34% of cells are affected. As this aberration does not change the copy number of the aberrant segment, LRR values are normal. However, the genotypes of SNPs within this segment are all homozygous in aberrant cells. Panel C shows data for chromosome 8 from subject ULSAM-298 using two samples collected at the ages of 71 and 88 years. The sample collected at 71 years shows a normal profile, while the sample taken at the age of 88 years shows a 70 Mb gain of chromosome 8 in approximately 30% of cells, visible with both LRR and BAF data from the Illumina SNP array.

Figure 2

The whole genome profiles in longitudinal analysis of 4 peripheral blood samples collected from subject ULSAM-697 at the ages of 71, 82, 88, and 90 years (panels A, B, C and D, respectively). This figure illustrates a clonal cell expansion containing a terminal CNNLOH/aUPD encompassing 103 Mb of the long arm of chromosome 4, with an increase and a decrease in the number of cells at different ages (data from ref. [1]). Each panel is composed of images from Illumina SNP beadchips showing the BAF-values, as CNNLOH/aUPD is not detectable using LRR data (see Fig. 1). The estimated percentage of cells displaying CNNLOH/aUPD on chromosome 4 is shown for each studied sample. This aberration was not detectable at the age of 71, reached approximately 58% at the ages of 82 and 88 years and decreased radically to approximately 30% of cells at the age of 90 years. This figure also displays the BAF-profiles for the whole genome from genotyping of sorted blood cells (CD19+ B lymphocytes, CD4+ T lymphocytes, and granulocytes) as well as skin fibroblasts collected at the age of 90 years (panels E, F, G and H, respectively). Sorting of blood cells at the age of 90 years showed that CD4+ T-cells and granulocytes were affected to a similar degree, as identified in DNA from unsorted blood at the same age. However, CD19+ B-cells were unexpectedly free from this aberration. Thus, both myeloid and lymphoid lineages were affected to a similar degree, with the notable exception of B-lymphocytes. Panels I and J show statistical analysis of data. Panel I shows comparisons of “BAF-value deviation from 0.5” for heterozygous probes only and within the aberrant region of 4q, derived from analysis displayed in panels A through D. Similar analysis is shown in panel J for data derived from panels E through H. The proportion of cells with the 4q aberration changes with time and between different types of cells. These changes are significantly different between all samplings (ANOVA p<0.001; Tukey's test for multiple comparisons).