Investigating the potential of single-cell DNA methylation data to detect allele-specific methylation and imprinting.

Allele-specific methylation (ASM) is an epigenetic modification whereby one parental allele becomes methylated and the other unmethylated at a specific locus. ASM is most often driven by the presence of nearby heterozygous variants that influence methylation, but also occurs somatically in the context of genomic imprinting. In this study, we investigate ASM using publicly available single-cell reduced representation bisulfite sequencing (scRRBS) data on 608 B cells sampled from six healthy B cell samples and 1,230 cells from 11 chronic lymphocytic leukemia (CLL) samples. We developed a likelihood-based criterion to test whether a CpG exhibited ASM, based on the distributions of methylated and unmethylated reads both within and across cells. Applying our likelihood ratio test, 65,998 CpG sites exhibited ASM in healthy B cell samples according to a Bonferroni criterion (p < 8.4 × 10-9), and 32,862 CpG sites exhibited ASM in CLL samples (p < 8.5 × 10-9). We also called ASM at the sample level. To evaluate the accuracy of our method, we called heterozygous variants from the scRRBS data, which enabled variant-based calls of ASM within each cell. Comparing sample-level ASM calls to the variant-based measures of ASM, we observed a positive predictive value of 76%-100% across samples. We observed high concordance of ASM across samples and an overrepresentation of ASM in previously reported imprinted genes and genes with imprinting binding motifs. Our study demonstrates that single-cell bisulfite sequencing is a potentially powerful tool to investigate ASM, especially as studies expand to increase the number of samples and cells sequenced.

Identifying latent genetic interactions in genome-wide association studies using multiple traits.

The "missing" heritability of complex traits may be partly explained by genetic variants interacting with other genes or environments that are difficult to specify, observe, and detect. We propose a new kernel-based method called Latent Interaction Testing (LIT) to screen for genetic interactions that leverages pleiotropy from multiple related traits without requiring the interacting variable to be specified or observed. Using simulated data, we demonstrate that LIT increases power to detect latent genetic interactions compared to univariate methods. We then apply LIT to obesity-related traits in the UK Biobank and detect variants with interactive effects near known obesity-related genes (URL: https://CRAN.R-project.org/package=lit ).

Genome-wide study of gene-by-sex interactions identifies risks for cleft palate.

Structural birth defects affect 3-4% of all live births and, depending on the type, tend to manifest in a sex-biased manner. Orofacial clefts (OFCs) are the most common craniofacial structural birth defects and are often divided into cleft lip with or without cleft palate (CL/P) and cleft palate only (CP). Previous studies have found sex-specific risks for CL/P, but these risks have yet to be evaluated in CP. CL/P is more common in males and CP is more frequently observed in females, so we hypothesized there would also be sex-specific differences for CP. Using a trio-based cohort, we performed sex-stratified genome-wide association studies (GWAS) based on proband sex followed by a genome-wide gene-by-sex (GxS) interaction testing. There were 13 loci significant for GxS interactions, with the top finding in LTBP1 (RR=3.37 [2.04 - 5.56], p=1.93x10 -6 ). LTBP1 plays a role in regulating TGF-B bioavailability, and knockdown in both mice and zebrafish lead to craniofacial anomalies. Further, there is evidence for differential expression of LTBP1 between males and females in both mice and humans. Therefore, we tested the association between the imputed genetically regulated gene expression of genes with significant GxS interactions and the CP phenotype. We found significant association for LTBP1 in cell cultured fibroblasts in female probands (p=0.0013) but not in males. Taken altogether, we show there are sex-specific risks for CP that are otherwise undetectable in a combined sex cohort, and LTBP1 is a candidate risk gene, particularly in females.