Thorough Statistical Analyses of Breast Cancer Co-Methylation Patterns

Background: Breast cancer is one of the most commonly diagnosed cancers. It is associated with DNA methylation, an epigenetic event with a methyl group added to a cytosine paired with a guanine, i.e., a CG site. The methylation levels of different genes in a genome are correlated in certain ways that affect gene functions. This correlation pattern is known as co-methylation. It is still not clear how different genes co-methylate in the whole genome of breast cancer samples. Previous studies are conducted using relatively small datasets (Illumina 27K data). In this study, we analyze much larger datasets (Illumina 450K data). Results: Our key findings are summarized below. First, normal samples have more highly correlated, or co-methylated, CG pairs than tumor samples. Both tumor and normal have more than 93% of positive co-methylation, but normal samples have significantly more negatively correlated CG sites than tumor samples (6.6% vs. 2.8%). Second, both tumor and normal samples have about 94% of co-methylated CG pairs on different chromosomes, but normal samples have 470 million more CG pairs. Highly co-methylated pairs on the same chromosome tend to be close to each other. Third, a small proportion of CG sites’ co-methylation patterns change dramatically from normal to tumor. The percentage of differentially methylated (DM) sites among them is larger than the overall DM rate. Fourth, certain CG sites are highly correlated with many CG sites; the top 100 of such super-connector CG sites in tumor and normal samples have no overlaps. Fifth, both highly changing sites and super-connector sites’ locations are significantly different from the genome-wide CG sites’ locations. Sixth, chromosome X co-methylation patterns are very different from other chromosomes. Finally, the network analyses of genes associated with several sets of co-methylated CG sites identified above show that tumor and normal samples have different patterns. Conclusions: Our findings will provide researchers with a new understanding of co-methylation patterns in breast cancer. Our ability to thoroughly analyze co-methylation of large datasets will allow researchers to study relationships and associations between different genes in breast cancer.

Parent-of-origin effects in the UK Biobank

Identical genetic variations can have different phenotypic effects depending on their parent of origin (PofO). Yet, studies focussing on PofO effects have been largely limited in terms of sample size due to the need of parental genomes or known genealogies. Here, we used a novel probabilistic approach to infer PofO of individual alleles in the UK Biobank that does not require parental genomes nor prior knowledge of genealogy. Our model uses Identity-By-Descent (IBD) sharing with second- and third-degree relatives to assign alleles to parental groups and leverages chromosome X data in males to distinguish maternal from paternal groups. When combined with robust haplotype inference and haploid imputation, this allowed us to infer the PofO at 5.4 million variants genome-wide for 26,393 UK Biobank individuals. We used this large dataset to systematically screen 59 biomarkers and 38 anthropomorphic phenotypes for PofO effects and discovered 101 significant associations, demonstrating that this type of effects is widespread. Notably, we retrieved well known PofO effects, such as the MEG3/DLK1 locus on platelet count, and we discovered many new ones often at loci outside currently known imprinted regions and previously thought to harbour additive associations, implying that the underlying molecular mechanisms may be more complex than expected.

Parent-of-origin effects in the UK Biobank

Identical genetic variations can have different phenotypic effects depending on their parent of origin (PofO). Yet, studies focussing on PofO effects have been largely limited in terms of sample size due to the need of parental genomes or known genealogies. Here, we used a novel probabilistic approach to infer PofO of individual alleles in the UK Biobank that does not require parental genomes nor prior knowledge of genealogy. Our model uses Identity-By-Descent (IBD) sharing with second- and third-degree relatives to assign alleles to parental groups and leverages chromosome X data in males to distinguish maternal from paternal groups. When combined with robust haplotype inference and haploid imputation, this allowed us to infer the PofO at 5.4 million variants genome-wide for 26,393 UK Biobank individuals. We used this large dataset to systematically screen 59 biomarkers and 38 anthropomorphic phenotypes for PofO effects and discovered 101 significant associations, demonstrating that this type of effects contributes to the genetics of complex traits. Notably, we retrieved well known PofO effects, such as the MEG3/DLK1 locus on platelet count, and we discovered many new ones at loci often unsuspected of being imprinted and, in some cases, previously thought to harbour additive associations.

Correlation scan: identifying genomic regions that affect genetic correlations applied to fertility traits

Although the genetic correlation between complex traits have been estimated for more than a century, only recently we have started to map and understand the precise localization of the genomic region(s) that underpin these correlations. Reproductive traits are often genetically correlated, and yet we don't fully understand the complexities, synergism, or trade-offs between male and female fertility. In this study, we used reproductive traits in two cattle populations to develop a novel framework termed correlation scan. This framework was used to identify regions associated with the genetic correlations between male and female fertility traits across the bovine genome. The traits used were age at first corpus luteum (AGECL) and serum levels of insulin growth hormone (IGF1 measured in bulls, IGF1b, or cows, IGF1c). The methodology developed herein used correlations of 500-SNP (single nucleotide polymorphism) effects in a 100-SNPs sliding window in each chromosome to identify regions in the genome that either drive (i.e., SNP effects on the same direction) or antagonize (i.e., SNP effects in the opposite direction) the genetic correlations between traits. We used a permutation test to confirm which regions of the genome harboured significant correlations. Hence, this framework can also identify neutral genomic regions with no effect on the pairwise trait studied. About 40% of the total genomic regions were identified as driving and antagonizing genetic correlations between male and female fertility traits in the two population. These regions confirmed the polygenic nature of the traits being studied and pointed to genes of interest. Quantitative trait loci (QTL) and functional enrichment analysis revealed that many significant windows co-located with known QTLs related to milk production and fertility traits, especially puberty. In general, the enriched reproductive QTLs driving the genetic correlations between male and female fertility are the same for both cattle populations, while the antagonizing regions were population specific. Moreover, most of the antagonizing regions were mapped to the chromosome X. These results suggest regions of the chromosome X for further investigation into the trade-offs between male and female fertility. Although the methodology was applied to cattle phenotypes, using high-density SNP genotypes, the general framework developed can be applied to any species or traits, and it can easily accommodate genome sequence data.

Kallmann Syndrome and X-linked Ichthyosis Caused by Translocation Between Chromosomes X and Y: A Case Report

Background: Xp22.3 region is characterized by low frequency of interspersed repeats and low GC content. Several clinically important genes including ANOS1 (KAL1) reside in this region. This gene was first identified due to translocation between chromosomes X and Y in a patient with Kallmann syndrome. Case Presentation: A 20 year old male presented with complaints of delayed secondary sexual characteristics, impaired sense of smell, and poor scholastic performance. On examination, he had short stature (151 cm; <3rd centile). His sexual maturity corresponded to Tanner stage 3. Stretched penile length was 3.6 cm (<3rd centile). Right testis was undescended with low left testicular volume (12 ml). There was mild ichthyosis over abdomen and back. He had hyposmia, hoarse voice, and synkinesia. Investigations were suggestive of hypogonadotrophic hypogonadism. Karyotype revealed an extra chromosomal material on p arm of chromosome X (46,Xp+,Y). On cytogenetic microarray, deletion of 8.3 Mb on Xp22.33 region and duplication of 12.8 Mb on Yq11.22 region were identified. The breakpoint on X chromosome resulted in deletion of exons 7-14 of ANOS1 gene and complete STS, NLGN4X, ARSL (ARSE), SHOX, and VCX genes. Conclusion: Patients diagnosed with Kallmann syndrome should receive careful clinical evaluation to detect presence of a contiguous gene syndrome.

X-treme loss of sequence diversity linked to neo-X chromosomes in filarial nematodes

The sequence diversity of natural and laboratory populations of Brugia pahangi and Brugia malayi was assessed with Illumina resequencing followed by mapping to identify single nucleotide variants and insertions/deletions. In natural and laboratory Brugia populations, there is a lack of sequence diversity on chromosome X relative to the autosomes (πX/πA = 0.2), which is lower than the expected πX/πA = 0.75). A reduction in diversity is also observed in other filarial nematodes with neo-X chromosome fusions in the genera Onchocerca and Wuchereria, but not those without neo-X chromosome fusions in the genera Loa and Dirofilaria. In the species with neo-X chromosome fusions, chromosome X is abnormally large, containing a third of the genetic material such that a sizable portion of the genome is lacking sequence diversity. Such profound differences in genetic diversity can be consequential, having been associated with drug resistance and adaptability, with the potential to affect filarial eradication.

Thorough Statistical Analyses of Breast Cancer Co-methylation Patterns

Background: Breast cancer is one of the most commonly diagnosed cancers. It is associated with DNA methylation, an epigenetic event with a methyl group added to a cytosine paired with a guanine, i.e., a CG site. The methylation levels of different genes in a genome are correlated in certain ways that affect gene functions. This correlation pattern is known as co-methylation. It is still not clear how different genes co-methylate in the whole genome of breast cancer samples. Previous studies are conducted using relatively small datasets (Illumina 27K data). In this study, we analyze much larger datasets (Illumina 450K data). Results: Our key findings are summarized below. First, normal samples have more highly correlated, or co-methylated, CG pairs than tumor samples. Both tumor and normal have more than 93% of positive co-methylation, but normal samples have significantly more negatively correlated CG sites than tumor samples (6.6% vs. 2.8%). Second, both tumor and normal samples have about 94% of co-methylated CG pairs on different chromosomes, but normal samples have 470 million more CG pairs. Highly co-methylated pairs on the same chromosome tend to be close to each other. Third, a small proportion of CG sites’ co-methylation patterns change dramatically from normal to tumor. The percentage of differentially methylated (DM) sites among them is larger than the overall DM rate. Fourth, certain CG sites are highly correlated with many CG sites; the top 100 of such super-connector CG sites in tumor and normal samples have no overlaps. Fifth, both highly changing sites and super-connector sites’ locations are significantly different from the genome-wide CG sites’ locations. Sixth, chromosome X co-methylation patterns are very different from other chromosomes. Finally, the network analyses of genes associated with several sets of co-methylated CG sites identified above show that tumor and normal samples have different patterns. Conclusions: Our findings will provide researchers with a new understanding of co-methylation patterns in breast cancer. Our ability to thoroughly analyze co-methylation of large datasets will allow researchers to study relationships and associations between different genes in breast cancer.

SCReadCounts: estimation of cell-level SNVs expression from scRNA-seq data

Background Recent studies have demonstrated the utility of scRNA-seq SNVs to distinguish tumor from normal cells, characterize intra-tumoral heterogeneity, and define mutation-associated expression signatures. In addition to cancer studies, SNVs from single cells have been useful in studies of transcriptional burst kinetics, allelic expression, chromosome X inactivation, ploidy estimations, and haplotype inference. Results To aid these types of studies, we have developed a tool, SCReadCounts, for cell-level tabulation of the sequencing read counts bearing SNV reference and variant alleles from barcoded scRNA-seq alignments. Provided genomic loci and expected alleles, SCReadCounts generates cell-SNV matrices with the absolute variant- and reference-harboring read counts, as well as cell-SNV matrices of expressed Variant Allele Fraction (VAFRNA) suitable for a variety of downstream applications. We demonstrate three different SCReadCounts applications on 59,884 cells from seven neuroblastoma samples: (1) estimation of cell-level expression of known somatic mutations and RNA-editing sites, (2) estimation of cell- level allele expression of biallelic SNVs, and (3) a discovery mode assessment of the reference and each of the three alternative nucleotides at genomic positions of interest that does not require prior SNV information. For the later, we applied SCReadCounts on the coding regions of KRAS, where it identified known and novel somatic mutations in a low-to-moderate proportion of cells. The SCReadCounts read counts module is benchmarked against the analogous modules of GATK and Samtools. SCReadCounts is freely available (https://github.com/HorvathLab/NGS) as 64-bit self-contained binary distributions for Linux and MacOS, in addition to Python source. Conclusions SCReadCounts supplies a fast and efficient solution for estimation of cell-level SNV expression from scRNA-seq data. SCReadCounts enables distinguishing cells with monoallelic reference expression from those with no gene expression and is applicable to assess SNVs present in only a small proportion of the cells, such as somatic mutations in cancer.

Synovial Sarcoma of the Extremities: A Literature Review

Synovial sarcoma (SS) is a rare and highly malignant tumor and a type of soft tissue sarcoma (STS), for which survival has not improved significantly in recent years. Synovial sarcomas occur mostly in adolescents and young adults (15–35 years old), usually affecting the deep soft tissues near the large joints of the extremities, with males being at a slightly higher risk. Despite its name, synovial sarcoma is neither related to the synovial tissues that are a part of the joints, i.e., the synovium, nor does it express synovial markers; however, the periarticular synovial sarcomas can spread as a secondary tumor to the joint capsule. SS was initially described as a biphasic neoplasm comprising of both epithelial and uniform spindle cell components. Synovial sarcoma is characterized by the presence of the pathognomonic t (X; 18) (p11.2; q11.2) translocation, involving a fusion of the SS18 (formerly SYT) gene on chromosome 18 to one of the synovial sarcoma X (SSX) genes on chromosome X (usually SSX1 or SSX2), which is seen in more than 90% of SSs and results in the formation of SS18-SSX fusion oncogenes.

Thorough statistical analyses of breast cancer co-methylation patterns

Background Breast cancer is one of the most commonly diagnosed cancers. It is associated with DNA methylation, an epigenetic event with a methyl group added to a cytosine paired with a guanine, i.e., a CG site. The methylation levels of different genes in a genome are correlated in certain ways that affect gene functions. This correlation pattern is known as co-methylation. It is still not clear how different genes co-methylate in the whole genome of breast cancer samples. Previous studies are conducted using relatively small datasets (Illumina 27K data). In this study, we analyze much larger datasets (Illumina 450K data). Results Our key findings are summarized below. First, normal samples have more highly correlated, or co-methylated, CG pairs than tumor samples. Both tumor and normal have more than 93% of positive co-methylation, but normal samples have significantly more negatively correlated CG sites than tumor samples (6.6% vs. 2.8%). Second, both tumor and normal samples have about 94% of co-methylated CG pairs on different chromosomes, but normal samples have 470 million more CG pairs. Highly co-methylated pairs on the same chromosome tend to be close to each other. Third, a small proportion of CG sites’ co-methylation patterns change dramatically from normal to tumor. The percentage of differentially methylated (DM) sites among them is larger than the overall DM rate. Fourth, certain CG sites are highly correlated with many CG sites; the top 100 of such super-connector CG sites in tumor and normal samples have no overlaps. Fifth, both highly changing sites and super-connector sites’ locations are significantly different from the genome-wide CG sites’ locations. Sixth, chromosome X co-methylation patterns are very different from other chromosomes. Finally, the network analyses of genes associated with several sets of co-methylated CG sites identified above show that tumor and normal samples have different patterns. Conclusions Our findings will provide researchers with a new understanding of co-methylation patterns in breast cancer. Our ability to thoroughly analyze co-methylation of large datasets will allow researchers to study relationships and associations between different genes in breast cancer.