Statistical methods to detect mother-father genetic interaction effects on risk of infertility: A genome-wide approach.

Infertility is a heterogeneous phenotype, and for many couples, the causes of fertility problems remain unknown. One understudied hypothesis is that allelic interactions between the genotypes of the two parents may influence the risk of infertility. Our aim was, therefore, to investigate how allelic interactions can be modeled using parental genotype data linked to 15,789 pregnancies selected from the Norwegian Mother, Father, and Child Cohort Study. The newborns in 1304 of these pregnancies were conceived using assisted reproductive technologies (ART), and the remainder were conceived naturally. Treating the use of ART as a proxy for infertility, different parameterizations were implemented in a genome-wide screen for interaction effects between maternal and paternal alleles at the same locus. Some of the models were more similar in the way they were parameterized, and some produced similar results when implemented on a genome-wide scale. The results showed near-significant interaction effects in genes relevant to the phenotype under study, such as Dynein axonemal heavy chain 17 (DNAH17) with a recognized role in male infertility. More generally, the interaction models presented here are readily adaptable to the study of other phenotypes in which maternal and paternal allelic interactions are likely to be involved.

The X-factor in ART: does the use of assisted reproductive technologies influence DNA methylation on the X chromosome?

BACKGROUND: Assisted reproductive technologies (ART) may perturb DNA methylation (DNAm) in early embryonic development. Although a handful of epigenome-wide association studies of ART have been published, none have investigated CpGs on the X chromosome. To bridge this knowledge gap, we leveraged one of the largest collections of mother-father-newborn trios of ART and non-ART (natural) conceptions to date to investigate sex-specific DNAm differences on the X chromosome. The discovery cohort consisted of 982 ART and 963 non-ART trios from the Norwegian Mother, Father, and Child Cohort Study (MoBa). To verify our results from the MoBa cohort, we used an external cohort of 149 ART and 58 non-ART neonates from the Australian 'Clinical review of the Health of adults conceived following Assisted Reproductive Technologies' (CHART) study. The Illumina EPIC array was used to measure DNAm in both datasets. In the MoBa cohort, we performed a set of X-chromosome-wide association studies ('XWASs' hereafter) to search for sex-specific DNAm differences between ART and non-ART newborns. We tested several models to investigate the influence of various confounders, including parental DNAm. We also searched for differentially methylated regions (DMRs) and regions of co-methylation flanking the most significant CpGs. Additionally, we ran an analogous model to our main model on the external CHART dataset. RESULTS: In the MoBa cohort, we found more differentially methylated CpGs and DMRs in girls than boys. Most of the associations persisted after controlling for parental DNAm and other confounders. Many of the significant CpGs and DMRs were in gene-promoter regions, and several of the genes linked to these CpGs are expressed in tissues relevant for both ART and sex (testis, placenta, and fallopian tube). We found no support for parental DNAm-dependent features as an explanation for the observed associations in the newborns. The most significant CpG in the boys-only analysis was in UBE2DNL, which is expressed in testes but with unknown function. The most significant CpGs in the girls-only analysis were in EIF2S3 and AMOT. These three loci also displayed differential DNAm in the CHART cohort. CONCLUSIONS: Genes that co-localized with the significant CpGs and DMRs associated with ART are implicated in several key biological processes (e.g., neurodevelopment) and disorders (e.g., intellectual disability and autism). These connections are particularly compelling in light of previous findings indicating that neurodevelopmental outcomes differ in ART-conceived children compared to those naturally conceived.

A fast wavelet-based functional association analysis replicates several susceptibility loci for birth weight in a Norwegian population.

BACKGROUND: Birth weight (BW) is one of the most widely studied anthropometric traits in humans because of its role in various adult-onset diseases. The number of loci associated with BW has increased dramatically since the advent of whole-genome screening approaches such as genome-wide association studies (GWASes) and meta-analyses of GWASes (GWAMAs). To further contribute to elucidating the genetic architecture of BW, we analyzed a genotyped Norwegian dataset with information on child's BW (N=9,063) using a slightly modified version of a wavelet-based method by Shim and Stephens (2015) called WaveQTL. RESULTS: WaveQTL uses wavelet regression for regional testing and offers a more flexible functional modeling framework compared to conventional GWAS methods. To further improve WaveQTL, we added a novel feature termed "zooming strategy" to enhance the detection of associations in typically small regions. The modified WaveQTL replicated five out of the 133 loci previously identified by the largest GWAMA of BW to date by Warrington et al. (2019), even though our sample size was 26 times smaller than that study and 18 times smaller than the second largest GWAMA of BW by Horikoshi et al. (2016). In addition, the modified WaveQTL performed better in regions of high LD between SNPs. CONCLUSIONS: This study is the first adaptation of the original WaveQTL method to the analysis of genome-wide genotypic data. Our results highlight the utility of the modified WaveQTL as a complementary tool for identifying loci that might escape detection by conventional genome-wide screening methods due to power issues. An attractive application of the modified WaveQTL would be to select traits from various public GWAS repositories to investigate whether they might benefit from a second analysis.

Design efficiency in genetic association studies.

Selecting the best design for genetic association studies requires careful deliberation; different study designs can be used to scan for different genetic effects, and each design has its own set of strengths and limitations. A variety of family and unrelated control configurations are amenable to genetic association analyses, including the case-control design, case-parent triads, and case-parent triads in combination with unrelated controls or control-parent triads. Ultimately, the goal is to choose the design that achieves the highest statistical power using the lowest cost. For given parameter values and genotyped individuals, designs can be compared directly by computing the power. However, a more informative and general design comparison can be achieved by studying the relative efficiency, defined as the ratio of variances of two different parameter estimators, corresponding to two separate designs. Using log-linear modeling, we derive the relative efficiency from the asymptotic variance of the parameter estimators and relate it to the concept of Pitman efficiency. The relative efficiency takes into account the fact that different designs impose different costs relative to the number of genotyped individuals. We show that while optimal efficiency for analyses of regular autosomal effects is achieved using the standard case-control design, the case-parent triad design without unrelated controls is efficient when searching for parent-of-origin effects. Due to the potential loss of efficiency, maternal genes should generally not be adjusted for in an initial genome-wide association study scan of offspring genes but instead checked post hoc. The relative efficiency calculations are implemented in our R package Haplin.

Haplin power analysis: a software module for power and sample size calculations in genetic association analyses of family triads and unrelated controls.

BACKGROUND: Log-linear and multinomial modeling offer a flexible framework for genetic association analyses of offspring (child), parent-of-origin and maternal effects, based on genotype data from a variety of child-parent configurations. Although the calculation of statistical power or sample size is an important first step in the planning of any scientific study, there is currently a lack of software for genetic power calculations in family-based study designs. Here, we address this shortcoming through new implementations of power calculations in the R package Haplin, which is a flexible and robust software for genetic epidemiological analyses. Power calculations in Haplin can be performed analytically using the asymptotic variance-covariance structure of the parameter estimator, or else by a straightforward simulation approach. Haplin performs power calculations for child, parent-of-origin and maternal effects, as well as for gene-environment interactions. The power can be calculated for both single SNPs and haplotypes, either autosomal or X-linked. Moreover, Haplin enables power calculations for different child-parent configurations, including (but not limited to) case-parent triads, case-mother dyads, and case-parent triads in combination with unrelated control-parent triads. RESULTS: We compared the asymptotic power approximations to the power of analysis attained with Haplin. For external validation, the results were further compared to the power of analysis attained by the EMIM software using data simulations from Haplin. Consistency observed between Haplin and EMIM across various genetic scenarios confirms the computational accuracy of the inference methods used in both programs. The results also demonstrate that power calculations in Haplin are applicable to genetic association studies using either log-linear or multinomial modeling approaches. CONCLUSIONS: Haplin provides a robust and reliable framework for power calculations in genetic association analyses for a wide range of genetic effects and etiologic scenarios, based on genotype data from a variety of child-parent configurations.

Parent-of-origin-environment interactions in case-parent triads with or without independent controls.

With case-parent triad data, one can frequently deduce parent of origin of the child's alleles. This allows a parent-of-origin (PoO) effect to be estimated as the ratio of relative risks associated with the alleles inherited from the mother and the father, respectively. A possible cause of PoO effects is DNA methylation, leading to genomic imprinting. Because environmental exposures may influence methylation patterns, gene-environment interaction studies should be extended to allow for interactions between PoO effects and environmental exposures (i.e., PoOxE). One should thus search for loci where the environmental exposure modifies the PoO effect. We have developed an extensive framework to analyze PoOxE effects in genome-wide association studies (GWAS), based on complete or incomplete case-parent triads with or without independent control triads. The interaction approach is based on analyzing triads in each exposure stratum using maximum likelihood estimation in a log-linear model. Interactions are then tested applying a Wald-based posttest of parameters across strata. Our framework includes a complete setup for power calculations. We have implemented the models in the R software package Haplin. To illustrate our PoOxE test, we applied the new methodology to top hits from our previous GWAS, assessing whether smoking during the periconceptional period modifies PoO effects on cleft palate only.

When the Label Matters: Adsorption of Labeled and Unlabeled Proteins on Charged Surfaces.

Fluorescent labels are often attached to proteins to monitor binding and adsorption processes. Docking simulations for native hen egg white lysozyme (HEWL) and HEWL labeled with fluorescein isothiocyanate show that these adsorb differently on charged surfaces. Attachment of even a small label can significantly change the interaction properties of a protein. Thus, the results of experiments with fluorescently labeled proteins should be interpreted by modeling the structures and computing the interaction properties of both labeled and unlabeled species.

SDA 7: A modular and parallel implementation of the simulation of diffusional association software.

The simulation of diffusional association (SDA) Brownian dynamics software package has been widely used in the study of biomacromolecular association. Initially developed to calculate bimolecular protein-protein association rate constants, it has since been extended to study electron transfer rates, to predict the structures of biomacromolecular complexes, to investigate the adsorption of proteins to inorganic surfaces, and to simulate the dynamics of large systems containing many biomacromolecular solutes, allowing the study of concentration-dependent effects. These extensions have led to a number of divergent versions of the software. In this article, we report the development of the latest version of the software (SDA 7). This release was developed to consolidate the existing codes into a single framework, while improving the parallelization of the code to better exploit modern multicore shared memory computer architectures. It is built using a modular object-oriented programming scheme, to allow for easy maintenance and extension of the software, and includes new features, such as adding flexible solute representations. We discuss a number of application examples, which describe some of the methods available in the release, and provide benchmarking data to demonstrate the parallel performance.

Comparing aminoglycoside binding sites in bacterial ribosomal RNA and aminoglycoside modifying enzymes.

Aminoglycoside antibiotics are used against severe bacterial infections. They bind to the bacterial ribosomal RNA and interfere with the translation process. However, bacteria produce aminoglycoside modifying enzymes (AME) to resist aminoglycoside actions. AMEs form a variable group and yet they specifically recognize and efficiently bind aminoglycosides, which are also diverse in terms of total net charge and the number of pseudo-sugar rings. Here, we present the results of 25 molecular dynamics simulations of three AME representatives and aminoglycoside ribosomal RNA binding site, unliganded and complexed with an aminoglycoside, kanamycin A. A comparison of the aminoglycoside binding sites in these different receptors revealed that the enzymes efficiently mimic the nucleic acid environment of the ribosomal RNA binding cleft. Although internal dynamics of AMEs and their interaction patterns with aminoglycosides differ, the energetical analysis showed that the most favorable sites are virtually the same in the enzymes and RNA. The most copied interactions were of electrostatic nature, but stacking was also replicated in one AME:kanamycin complex. In addition, we found that some water-mediated interactions were very stable in the simulations of the complexes. We show that our simulations reproduce well findings from NMR or X-ray structural studies, as well as results from directed mutagenesis. The outcomes of our analyses provide new insight into aminoglycoside resistance mechanism that is related to the enzymatic modification of these drugs.

Stability selection enhances feature selection and enables accurate prediction of gestational age using only five DNA methylation sites.

BACKGROUND: DNA methylation (DNAm) is robustly associated with chronological age in children and adults, and gestational age (GA) in newborns. This property has enabled the development of several epigenetic clocks that can accurately predict chronological age and GA. However, the lack of overlap in predictive CpGs across different epigenetic clocks remains elusive. Our main aim was therefore to identify and characterize CpGs that are stably predictive of GA. RESULTS: We applied a statistical approach called 'stability selection' to DNAm data from 2138 newborns in the Norwegian Mother, Father, and Child Cohort study. Stability selection combines subsampling with variable selection to restrict the number of false discoveries in the set of selected variables. Twenty-four CpGs were identified as being stably predictive of GA. Intriguingly, only up to 10% of the CpGs in previous GA clocks were found to be stably selected. Based on these results, we used generalized additive model regression to develop a new GA clock consisting of only five CpGs, which showed a similar predictive performance as previous GA clocks (R2 = 0.674, median absolute deviation = 4.4 days). These CpGs were in or near genes and regulatory regions involved in immune responses, metabolism, and developmental processes. Furthermore, accounting for nonlinear associations improved prediction performance in preterm newborns. CONCLUSION: We present a methodological framework for feature selection that is broadly applicable to any trait that can be predicted from DNAm data. We demonstrate its utility by identifying CpGs that are highly predictive of GA and present a new and highly performant GA clock based on only five CpGs that is more amenable to a clinical setting.

Does maternal genetic liability to folate deficiency influence the risk of antiseizure medication-associated language impairment and autistic traits in children of women with epilepsy?

BACKGROUND: Prenatal exposure to antiseizure medication (ASM) may lead to low plasma folate concentrations and is associated with impaired neurodevelopment. OBJECTIVES: To examine whether maternal genetic liability to folate deficiency interacts with ASM-associated risk of language impairment and autistic traits in children of women with epilepsy. METHODS: We included children of women with and without epilepsy and with available genetic data enrolled in the Norwegian Mother, Father, and Child Cohort Study. Information on ASM use, folic acid supplement use and dose, dietary folate intake, child autistic traits, and child language impairment was obtained from parent-reported questionnaires. Using logistic regression, we examined the interaction between prenatal ASM exposure and maternal genetic liability to folate deficiency expressed as polygenic risk score of low folate concentrations or maternal rs1801133 genotype (CC or CT/TT) on risk of language impairment or autistic traits. RESULTS: We included 96 children of women with ASM-treated epilepsy, 131 children of women with ASM-untreated epilepsy, and 37,249 children of women without epilepsy. The polygenic risk score of low folate concentrations did not interact with the ASM-associated risk of language impairment or autistic traits in ASM-exposed children of women with epilepsy compared with ASM-unexposed children aged 1.5-8 y. ASM-exposed children had increased risk of adverse neurodevelopment regardless of maternal rs1801133 genotype {adjusted odds ratio [aOR] for language impairment aged 8 y was 2.88 [95% confidence interval (CI): 1.00, 8.26] if CC and aOR 2.88 [95% CI: 1.10, 7.53] if CT/TT genotypes}. In children of women without epilepsy aged 3 y, those with maternal rs1801133 CT/TT compared with CC genotype had increased risk of language impairment (aOR: 1.18; 95% CI: 1.05, 1.34). CONCLUSIONS: In this cohort of pregnant women reporting widespread use of folic acid supplements, maternal genetic liability to folate deficiency did not significantly influence the ASM-associated risk of impaired neurodevelopment.

Beta2-adrenoreceptor agonists and long-term risk of Parkinson's disease.

INTRODUCTION: There is limited information on how the association between Parkinson's disease and the use of beta2-adrenoreceptor (ß2AR) agonists varies among groups of short-, long-, and ultra-long-acting ß2AR agonists (SABA, LABA and ultraLABA). METHODS: In this prospective study of the Norwegian population, we estimated the incidence of Parkinson's disease according to exposure to ß2AR agonists as a time-dependent variable by means of Cox regression. We adjusted for educational level, comorbidity and performed a sensitivity analysis excluding individuals with chronic obstructive pulmonary disease (COPD), all factors associated with smoking. Anticholinergics and corticosteroids as drugs with the same indication were analyzed for comparison. RESULTS: In the follow-up period from 2005 to 2019, 15,807 incident Parkinson's cases were identified. After adjustments for sex, education and age as the timescale, SABA (Hazard ratio (HR) = 0.84; 95%CI: 0.79, 0.89; p < 0.001), LABA (HR = 0.85; 95%CI: 0.81, 0.90; p < 0.001) and ultraLABA (HR = 0.6; 95%CI: 0.49, 0.73; p < 0.001) were all associated with a lower risk of Parkinson's disease. After exclusion of COPD patients, corticosteroids and anticholinergics were no longer inversely associated, whereas ß2AR agonists remained associated. CONCLUSION: Of drugs with the same indication of use, only ß2AR agonists remained inversely associated with PD risk after all adjustments, with ultraLABA displaying the overall strongest association. Although the precision of the estimate is limited by the modest number of exposed PD cases without COPD, the association is intriguing and suggest that longer-acting, more lipophilic, and thus likely more brain-penetrant ß2AR agonists could be prioritized for further studies.

Association Between Use of Any of the Drugs Prescribed in Norway and the Subsequent Risk of Parkinson Disease: A Drug-wide Association Study.

BACKGROUND AND OBJECTIVES: The incidence rate of Parkinson disease (PD) has been increasing rapidly during the past years. Yet, no treatments exist to prevent or slow the progression of the disease. Moreover, we are unable to detect early disease stages during which intervention with disease-modifying therapies is most likely to succeed. The objective of this study was to perform an agnostic drug-wide association study estimating the association between the use of any of the drugs prescribed in Norway and the subsequent risk of PD. METHODS: This registry-based cohort study use data from the entire Norwegian population between 2004 and 2019 linked to the Norwegian Prescription Registry, with more than 600 million individual prescriptions. Drug classes were screened according to Anatomical Therapeutic Chemical codes at level 2, corresponding to therapeutic subgroups. We used Cox regression models to estimate hazard ratios (HRs) and 95% CIs for the associations between drug classes and PD risk. All p values were corrected for multiple testing using the false discovery rate. In addition, we conducted sensitivity analyses of exposure definition as well as time-lag and dose-response analyses. RESULTS: The study population comprised 3,223,672 individuals, 15,849 of whom developed PD during the follow-up. We identified 31 drug classes that were statistically significantly associated with PD risk in Norway during the follow-up. Drugs acting on the renin-angiotensin system (HR 0.92, 95% CI 0.89-0.95), corticosteroids for systemic use (0.88, 95% CI 0.84-0.93), and vaccines (0.89, 95% CI 0.82-0.96) were associated with a decreased risk of PD even up to 10 years before PD onset. Drug classes used to treat symptoms related to prodromal signs of PD, such as constipation, urological issues, and depression, were associated with an increased risk of subsequent diagnosis of PD with HRs of 1.6 (95% CI 1.49-1.73), 1.48 (1.42-1.53), and 1.94 (1.87-2.01), respectively. DISCUSSION: This drug-wide study identified 31 drug classes that were associated with the PD risk change. It reveals the links of renin-angiotensin system medications, vaccines, and corticosteroids with PD risk and suggests that monitoring drug usage using pharmacoepidemiology may allow identifying individuals with prodromal PD.

Nucleated red blood cells explain most of the association between DNA methylation and gestational age.

Determining if specific cell type(s) are responsible for an association between DNA methylation (DNAm) and a given phenotype is important for understanding the biological mechanisms underlying the association. Our EWAS of gestational age (GA) in 953 newborns from the Norwegian MoBa study identified 13,660 CpGs significantly associated with GA (pBonferroni<0.05) after adjustment for cell type composition. When the CellDMC algorithm was applied to explore cell-type specific effects, 2,330 CpGs were significantly associated with GA, mostly in nucleated red blood cells [nRBCs; n = 2,030 (87%)]. Similar patterns were found in another dataset based on a different array and when applying an alternative algorithm to CellDMC called Tensor Composition Analysis (TCA). Our findings point to nRBCs as the main cell type driving the DNAm-GA association, implicating an epigenetic signature of erythropoiesis as a likely mechanism. They also explain the poor correlation observed between epigenetic age clocks for newborns and those for adults.

Understanding the origins of bacterial resistance to aminoglycosides through molecular dynamics mutational study of the ribosomal A-site.

Paromomycin is an aminoglycosidic antibiotic that targets the RNA of the bacterial small ribosomal subunit. It binds in the A-site, which is one of the three tRNA binding sites, and affects translational fidelity by stabilizing two adenines (A1492 and A1493) in the flipped-out state. Experiments have shown that various mutations in the A-site result in bacterial resistance to aminoglycosides. In this study, we performed multiple molecular dynamics simulations of the mutated A-site RNA fragment in explicit solvent to analyze changes in the physicochemical features of the A-site that were introduced by substitutions of specific bases. The simulations were conducted for free RNA and in complex with paromomycin. We found that the specific mutations affect the shape and dynamics of the binding cleft as well as significantly alter its electrostatic properties. The most pronounced changes were observed in the U1406C∶U1495A mutant, where important hydrogen bonds between the RNA and paromomycin were disrupted. The present study aims to clarify the underlying physicochemical mechanisms of bacterial resistance to aminoglycosides due to target mutations.

Health effects on leaders and co-workers of an art-based leadership development program.

BACKGROUND: There are very few evaluations of the effectiveness of leadership development programs. The purpose of the study was to examine whether an art-based leadership program may have a more beneficial effect than a conventional one on leaders' and their corresponding subordinates' mental and biological stress. METHODS: Participating leaders were randomized to 2 year-long leadership programs, 1 art-based and 1 conventional, with follow-up of the leaders and their subordinates at 12 and 18 months. The art-based program built on an experimental theatre form, a collage of literary text and music, followed by writing and discussions focused on existential and ethical problems. RESULTS: After 18 months a pattern was clearly visible with advantage for the art-based group. In the art group (leaders and their subordinates together as well as for subordinates only) compared to the conventional group, there was a significant improvement of mental health, covert coping and performance-based self-esteem as well as significantly less winter/fall deterioration in the serum concentration of the regenerative/anabolic hormone dehydroepiandrosterone-sulfate. CONCLUSIONS: Our findings indicate a more beneficial long-term health effect of the art-based intervention compared to a conventional approach. Positive results for both standardized questionnaires and biological parameters strengthened the findings. The study provides a rationale for further evaluation of the effectiveness of this alternative educational approach.

Wavelet Screening identifies regions highly enriched for differentially methylated loci for orofacial clefts.

DNA methylation is the most widely studied epigenetic mark in humans and plays an essential role in normal biological processes as well as in disease development. More focus has recently been placed on understanding functional aspects of methylation, prompting the development of methods to investigate the relationship between heterogeneity in methylation patterns and disease risk. However, most of these methods are limited in that they use simplified models that may rely on arbitrarily chosen parameters, they can only detect differentially methylated regions (DMRs) one at a time, or they are computationally intensive. To address these shortcomings, we present a wavelet-based method called 'Wavelet Screening' (WS) that can perform an epigenome-wide association study (EWAS) of thousands of individuals on a single CPU in only a matter of hours. By detecting multiple DMRs located near each other, WS identifies more complex patterns that can differentiate between different methylation profiles. We performed an extensive set of simulations to demonstrate the robustness and high power of WS, before applying it to a previously published EWAS dataset of orofacial clefts (OFCs). WS identified 82 associated regions containing several known genes and loci for OFCs, while other findings are novel and warrant replication in other OFCs cohorts.

Gene-methylation interactions: discovering region-wise DNA methylation levels that modify SNP-associated disease risk.

BACKGROUND: Current technology allows rapid assessment of DNA sequences and methylation levels at a single-site resolution for hundreds of thousands of sites in the human genome, in thousands of individuals simultaneously. This has led to an increase in epigenome-wide association studies (EWAS) of complex traits, particularly those that are poorly explained by previous genome-wide association studies (GWAS). However, the genome and epigenome are intertwined, e.g., DNA methylation is known to affect gene expression through, for example, genomic imprinting. There is thus a need to go beyond single-omics data analyses and develop interaction models that allow a meaningful combination of information from EWAS and GWAS. RESULTS: We present two new methods for genetic association analyses that treat offspring DNA methylation levels as environmental exposure. Our approach searches for statistical interactions between SNP alleles and DNA methylation (G ×Me) and between parent-of-origin effects and DNA methylation (PoO ×Me), using case-parent triads or dyads. We use summarized methylation levels over nearby genomic region to ease biological interpretation. The methods were tested on a dataset of parent-offspring dyads, with EWAS data on the offspring. Our results showed that methylation levels around a SNP can significantly alter the estimated relative risk. Moreover, we show how a control dataset can identify false positives. CONCLUSIONS: The new methods, G ×Me and PoO ×Me, integrate DNA methylation in the assessment of genetic relative risks and thus enable a more comprehensive biological interpretation of genome-wide scans. Moreover, our strategy of condensing DNA methylation levels within regions helps overcome specific disadvantages of using sparse chip-based measurements. The methods are implemented in the freely available R package Haplin ( https://cran.r-project.org/package=Haplin ), enabling fast scans of multi-omics datasets.

From Genotype to Phenotype: Through Chromatin.

Advances in sequencing technologies have enabled the exploration of the genetic basis for several clinical disorders by allowing identification of causal mutations in rare genetic diseases. Sequencing technology has also facilitated genome-wide association studies to gather single nucleotide polymorphisms in common diseases including cancer and diabetes. Sequencing has therefore become common in the clinic for both prognostics and diagnostics. The success in follow-up steps, i.e., mapping mutations to causal genes and therapeutic targets to further the development of novel therapies, has nevertheless been very limited. This is because most mutations associated with diseases lie in inter-genic regions including the so-called regulatory genome. Additionally, no genetic causes are apparent for many diseases including neurodegenerative disorders. A complementary approach is therefore gaining interest, namely to focus on epigenetic control of the disease to generate more complete functional genomic maps. To this end, several recent studies have generated large-scale epigenetic datasets in a disease context to form a link between genotype and phenotype. We focus DNA methylation and important histone marks, where recent advances have been made thanks to technology improvements, cost effectiveness, and large meta-scale epigenome consortia efforts. We summarize recent studies unravelling the mechanistic understanding of epigenetic processes in disease development and progression. Moreover, we show how methodology advancements enable causal relationships to be established, and we pinpoint the most important issues to be addressed by future research.

A genome-wide scan of cleft lip triads identifies parent-of-origin interaction effects between ANK3 and maternal smoking, and between ARHGEF10 and alcohol consumption.

Background: Although both genetic and environmental factors have been reported to influence the risk of isolated cleft lip with or without cleft palate (CL/P), the exact mechanisms behind CL/P are still largely unaccounted for. We recently developed new methods to identify parent-of-origin (PoO) interactions with environmental exposures (PoOxE) and applied them to families with children born with isolated cleft palate only. Here, we used the same genome-wide association study (GWAS) dataset and methodology to screen for PoOxE effects in the larger sample of CL/P triads. Methods: Genotypes from 1594 complete triads and 314 dyads (1908 nuclear families in total) with CL/P were available for the current analyses. Of these families, 1024 were Asian, 825 were European and 59 had other ancestries. After quality control, 341,191 SNPs remained from the original 569,244. The exposures were maternal cigarette smoking, use of alcohol, and use of vitamin supplements in the periconceptional period. The methodology applied in the analyses is implemented in the R-package Haplin. Results: Among Europeans, there was evidence of a PoOxSmoke effect for ANK3 with three SNPs (rs3793861, q=0.20, p=2.6e-6; rs7087489, q=0.20, p=3.1e-6; rs4310561, q=0.67, p=4.0e-5) and a PoOxAlcohol effect for ARHGEF10 with two SNPs (rs2294035, q=0.32, p=2.9e-6; rs4876274, q=0.76, p=1.3e-5). Conclusion: Our results indicate that the detected PoOxE effects have a plausible biological basis, and thus warrant replication in other independent cleft samples. Our demonstration of the feasibility of identifying complex interactions between relevant environmental exposures and PoO effects offers new avenues for future research aimed at unravelling  the complex etiology of cleft lip defects.