Trisomies Reorganize Human 3D Genome.

Trisomy is the presence of one extra copy of an entire chromosome or its part in a cell nucleus. In humans, autosomal trisomies are associated with severe developmental abnormalities leading to embryonic lethality, miscarriage or pronounced deviations of various organs and systems at birth. Trisomies are characterized by alterations in gene expression level, not exclusively on the trisomic chromosome, but throughout the genome. Here, we applied the high-throughput chromosome conformation capture technique (Hi-C) to study chromatin 3D structure in human chorion cells carrying either additional chromosome 13 (Patau syndrome) or chromosome 16 and in cultured fibroblasts with extra chromosome 18 (Edwards syndrome). The presence of extra chromosomes results in systematic changes of contact frequencies between small and large chromosomes. Analyzing the behavior of individual chromosomes, we found that a limited number of chromosomes change their contact patterns stochastically in trisomic cells and that it could be associated with lamina-associated domains (LAD) and gene content. For trisomy 13 and 18, but not for trisomy 16, the proportion of compacted loci on a chromosome is correlated with LAD content. We also found that regions of the genome that become more compact in trisomic cells are enriched in housekeeping genes, indicating a possible decrease in chromatin accessibility and transcription level of these genes. These results provide a framework for understanding the mechanisms of pan-genome transcription dysregulation in trisomies in the context of chromatin spatial organization.

Accurate fetal variant calling in the presence of maternal cell contamination.

High-throughput sequencing of fetal DNA is a promising and increasingly common method for the discovery of all (or all coding) genetic variants in the fetus, either as part of prenatal screening or diagnosis, or for genetic diagnosis of spontaneous abortions. In many cases, the fetal DNA (from chorionic villi, amniotic fluid, or abortive tissue) can be contaminated with maternal cells, resulting in the mixture of fetal and maternal DNA. This maternal cell contamination (MCC) undermines the assumption, made by traditional variant callers, that each allele in a heterozygous site is covered, on average, by 50% of the reads, and therefore can lead to erroneous genotype calls. We present a panel of methods for reducing the genotyping error in the presence of MCC. All methods start with the output of GATK HaplotypeCaller on the sequencing data for the (contaminated) fetal sample and both of its parents, and additionally rely on information about the MCC fraction (which itself is readily estimated from the high-throughput sequencing data). The first of these methods uses a Bayesian probabilistic model to correct the fetal genotype calls produced by MCC-unaware HaplotypeCaller. The other two methods "learn" the genotype-correction model from examples. We use simulated contaminated fetal data to train and test the models. Using the test sets, we show that all three methods lead to substantially improved accuracy when compared with the original MCC-unaware HaplotypeCaller calls. We then apply the best-performing method to three chorionic villus samples from spontaneously terminated pregnancies.