Importance sampling of word patterns in DNA and protein sequences

Family-Based Quantitative Trait Meta-Analysis Implicates Rare Noncoding Variants in DENND1A in Polycystic Ovary Syndrome

CONTEXT

Polycystic ovary syndrome (PCOS) is among the most common endocrine disorders of premenopausal women, affecting 5% to15% of this population depending on the diagnostic criteria applied. It is characterized by hyperandrogenism, ovulatory dysfunction, and polycystic ovarian morphology. PCOS is highly heritable, but only a small proportion of this heritability can be accounted for by the common genetic susceptibility variants identified to date.

OBJECTIVE

The objective of this study was to test whether rare genetic variants contribute to PCOS pathogenesis.

DESIGN, PATIENTS, AND METHODS

We performed whole-genome sequencing on DNA from 261 individuals from 62 families with one or more daughters with PCOS. We tested for associations of rare variants with PCOS and its concomitant hormonal traits using a quantitative trait meta-analysis.

RESULTS

We found rare variants in DENND1A (P = 5.31 × 10-5, adjusted P = 0.039) that were significantly associated with reproductive and metabolic traits in PCOS families.

CONCLUSIONS

Common variants in DENND1A have previously been associated with PCOS diagnosis in genome-wide association studies. Subsequent studies indicated that DENND1A is an important regulator of human ovarian androgen biosynthesis. Our findings provide additional evidence that DENND1A plays a central role in PCOS and suggest that rare noncoding variants contribute to disease pathogenesis.

Functional Genetic Variation in the Anti-Müllerian Hormone Pathway in Women With Polycystic Ovary Syndrome

CONTEXT

Polycystic ovary syndrome (PCOS) is a highly heritable, common endocrine disorder characterized by hyperandrogenism, irregular menses, and polycystic ovaries. PCOS is often accompanied by elevated levels of anti-Müllerian hormone (AMH). AMH inhibits follicle maturation. AMH also inhibits steroidogenesis through transcriptional repression of CYP17A1. We recently identified 16 rare PCOS-specific pathogenic variants in AMH.

OBJECTIVE

To test whether additional members of the AMH signaling pathway also contribute to the etiology of PCOS.

PARTICIPANTS/DESIGN

Targeted resequencing of coding and regulatory regions of AMH and its specific type 2 receptor, AMHR2, was performed on 608 women affected with PCOS and 142 reproductively normal control women. Prediction tools of deleteriousness and in silico evidence of epigenetic modification were used to prioritize variants for functional evaluation. Dual-luciferase reporter assays and splicing assays were used to measure the impact of genetic variants on function.

RESULTS

We identified 20 additional variants in/near AMH and AMHR2 with significantly reduced signaling activity in in vitro assays. Collectively, from our previous study and as reported herein, we have identified a total of 37 variants with impaired activity in/near AMH and AMHR2 in 41 women affected with PCOS, or 6.7% of our PCOS cohort. Furthermore, no functional variants were observed in the 142 phenotyped controls. The functional variants were significantly associated with PCOS in our cohort of 608 women with PCOS and 142 controls (P = 2.3 × 10-5) and very strongly associated with PCOS relative to a larger non-Finnish European (gnomAD) population-based control cohort (P < 1 × 10-9).

CONCLUSION

The AMH signaling cascade plays an important role in PCOS etiology.

Genetic variants influence on the placenta regulatory landscape

From genomic association studies, quantitative trait loci analysis, and epigenomic mapping, it is evident that significant efforts are necessary to define genetic-epigenetic interactions and understand their role in disease susceptibility and progression. For this reason, an analysis of the effects of genetic variation on gene expression and DNA methylation in human placentas at high resolution and whole-genome coverage will have multiple mechanistic and practical implications. By producing and analyzing DNA sequence variation (n = 303), DNA methylation (n = 303) and mRNA expression data (n = 80) from placentas from healthy women, we investigate the regulatory landscape of the human placenta and offer analytical approaches to integrate different types of genomic data and address some potential limitations of current platforms. We distinguish two profiles of interaction between expression and DNA methylation, revealing linear or bimodal effects, reflecting differences in genomic context, transcription factor recruitment, and possibly cell subpopulations. These findings help to clarify the interactions of genetic, epigenetic, and transcriptional regulatory mechanisms in normal human placentas. They also provide strong evidence for genotype-driven modifications of transcription and DNA methylation in normal placentas. In addition to these mechanistic implications, the data and analytical methods presented here will improve the interpretability of genome-wide and epigenome-wide association studies for human traits and diseases that involve placental functions.

atSNP: transcription factor binding affinity testing for regulatory SNP detection

MOTIVATION

Genome-wide association studies revealed that most disease-associated single nucleotide polymorphisms (SNPs) are located in regulatory regions within introns or in regions between genes. Regulatory SNPs (rSNPs) are such SNPs that affect gene regulation by changing transcription factor (TF) binding affinities to genomic sequences. Identifying potential rSNPs is crucial for understanding disease mechanisms. In silico methods that evaluate the impact of SNPs on TF binding affinities are not scalable for large-scale analysis.

RESULTS

We describe A: ffinity T: esting for regulatory SNP: s (atSNP), a computationally efficient R package for identifying rSNPs in silico. atSNP implements an importance sampling algorithm coupled with a first-order Markov model for the background nucleotide sequences to test the significance of affinity scores and SNP-driven changes in these scores. Application of atSNP with >20 K SNPs indicates that atSNP is the only available tool for such a large-scale task. atSNP provides user-friendly output in the form of both tables and composite logo plots for visualizing SNP-motif interactions. Evaluations of atSNP with known rSNP-TF interactions indicate that SNP is able to prioritize motifs for a given set of SNPs with high accuracy.

AVAILABILITY AND IMPLEMENTATION

https://github.com/keleslab/atSNP.

CONTACT

keles@stat.wisc.edu

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.