Increased Power for Detection of Parent-of-Origin Effects via the Use of Haplotype Estimation

Efficient inference of parent-of-origin effect using case-control mother-child genotype data

Parent-of-origin effect plays an important role in mammal development and disorder. Case-control mother-child pair genotype data can be used to detect parent-of-origin effect and is often convenient to collect in practice. Most existing methods for assessing parent-of-origin effect do not incorporate any covariates, which may be required to control for confounding factors. We propose to model the parent-of-origin effect through a logistic regression model, with predictors including maternal and child genotypes, parental origins, and covariates. The parental origins may not be fully inferred from genotypes of a target genetic marker, so we propose to use genotypes of markers tightly linked to the target marker to increase inference efficiency. A robust statistical inference procedure is developed based on a modified profile log-likelihood in a retrospective way. A computationally feasible expectation-maximization algorithm is devised to estimate all unknown parameters involved in the modified profile log-likelihood. This algorithm differs from the conventional expectation-maximization algorithm in the sense that it is based on a modified instead of the original profile log-likelihood function. The convergence of the algorithm is established under some mild regularity conditions. This expectation-maximization algorithm also allows convenient handling of missing child genotypes. Large sample properties, including weak consistency, asymptotic normality, and asymptotic efficiency, are established for the proposed estimator under some mild regularity conditions. Finite sample properties are evaluated through extensive simulation studies and the application to a real dataset.

POIROT: a powerful test for parent-of-origin effects in unrelated samples leveraging multiple phenotypes

MOTIVATION

There is widespread interest in identifying genetic variants that exhibit parent-of-origin effects (POEs) wherein the effect of an allele on phenotype expression depends on its parental origin. POEs can arise from different phenomena including genomic imprinting and have been documented for many complex traits. Traditional tests for POEs require family data to determine parental origins of transmitted alleles. As most genome-wide association studies (GWAS) sample unrelated individuals (where allelic parental origin is unknown), the study of POEs in such datasets requires sophisticated statistical methods that exploit genetic patterns we anticipate observing when POEs exist. We propose a method to improve discovery of POE variants in large-scale GWAS samples that leverages potential pleiotropy among multiple correlated traits often collected in such studies. Our method compares the phenotypic covariance matrix of heterozygotes to homozygotes based on a Robust Omnibus Test. We refer to our method as the Parent of Origin Inference using Robust Omnibus Test (POIROT) of multiple quantitative traits.

RESULTS

Through simulation studies, we compared POIROT to a competing univariate variance-based method which considers separate analysis of each phenotype. We observed POIROT to be well-calibrated with improved power to detect POEs compared to univariate methods. POIROT is robust to non-normality of phenotypes and can adjust for population stratification and other confounders. Finally, we applied POIROT to GWAS data from the UK Biobank using BMI and two cholesterol phenotypes. We identified 338 genome-wide significant loci for follow-up investigation.

AVAILABILITY AND IMPLEMENTATION

The code for this method is available at https://github.com/staylorhead/POIROT-POE.

Maternal age and the prevalence of congenital heart defects in Europe, 1995-2015: A register-based study

BACKGROUND

Evidence on the direction and strength of association between maternal age and the prevalence of congenital heart defects (CHD) in different age group categories is conflicting. Some studies have illustrated different trends with an increase in prevalence in younger and older age groups while other studies have reported a linear relationship. Given the increase in maternal age over recent years, it is important to study the CHD prevalence by maternal age.

OBJECTIVES

To examine the association between maternal age and the prevalence of CHD in Europe between 1995 and 2015 using population-based data from 24 registries belonging to the European Surveillance of Congenital Anomalies (EUROCAT) network.

METHODS

Associations over time of all nonsyndromic CHD according to maternal age category and for three CHD severity groupings (severity group I: very severe; severity group II: severe; severity group III: less severe) were examined using Bayesian multilevel Poisson regression modeling. Further subgroup analyses were undertaken within four maternal age-bands: ≤24, 25-29, 30-34 and 35-44 years. Descriptive summaries are also presented.

RESULTS

There were 51,608 nonsyndromic CHD cases in Europe over the 20-year study period. Total prevalence for all CHD combined was increased for younger mothers (≤24 years) and for mothers 35-44 years of age when compared with mothers aged 25-29 years (reference group) (IRR: 1.05, 95% CI: 1.02, 1.07). The total prevalence was increased for severity group I (very severe) only for younger mothers compared to those aged 25-29 years (IRR: 1.14, 95% CI: 1.04, 1.23). We found an increased prevalence of the following CHD subtypes: double outlet right ventricle (IRR:1.33, 95% CI: 1.09, 1.60), hypoplastic left heart syndrome (IRR: 1.18, 95% CI: 1.05, 1.32), hypoplastic right heart syndrome (IRR: 1.41, 95% CI: 1.05, 1.84), atrioventricular septal defect (IRR: 1.15, 95% CI: 1.01, 1.32), coarctation of aorta (IRR: 1.15, 95% CI: 1.03, 1.28) and atrial septal defect (IRR: 1.08, 95% CI: 1.02, 1.13). For older mothers (35-44 years) compared to the reference category, we observed an increased risk in the prevalence for severity group II (IRR: 1.09, 95% CI: 1.03, 1.14), severity group III (IRR: 1.05, 95% CI: 1.01, 1.08) and an increased prevalence of the CHD subtypes: Pulmonary valve stenosis (IRR: 1.22, 95% CI: 1.09, 1.34), ASD (IRR: 1.07, 95% CI: 1.02, 1.13), CoA (IRR: 1.18, 95% CI: 1.06, 1.32) and Tetralogy of Fallot (IRR: 1.14, 95% CI: 1.01, 1.28). Finally, for all age categories compared to the reference category, different associations of ASD and an increased prevalence of CoA was also observed.

CONCLUSIONS

Based on data for cases of CHD from 24 European population-based registries, evidence of a positive association between maternal age and the total prevalence of CHD for younger (≤24 years old) and older (35-44 years old) mothers was observed. The results suggest that young maternal age (≤24 years old) is a factor associated with severe CHD phenotypes while a positive association between advanced maternal age (35-44 years old) and mild CHD phenotypes was observed.

Maternal-fetal genetic interactions, imprinting, and risk of placental abruption

RESULTS

Abruption cases were more likely to experience preeclampsia, have shorter gestational age, and deliver infants with lower birthweight compared with controls. Models with MFGI effects provided improved fit than models with only maternal and fetal genotype main effects for SNP rs12530904 (p-value = 1.2e-04) in calcium/calmodulin-dependent protein kinase [CaM kinase] II beta (CAMK2B), and, SNP rs73136795 (p-value = 1.9e-04) in peroxisome proliferator-activated receptor-gamma (PPARG), both MB genes. We identified 320 SNPs in 45 maternally-imprinted genes (including potassium voltage-gated channel subfamily Q member 1 [KCNQ1], neurotrimin [NTM], and, ATPase phospholipid transporting 10 A [ATP10A]) associated with abruption. Top hits included rs2012323 (p-value = 1.6E-16) and rs12221520 (p-value1.3e-13) in KCNQ1, rs8036892 (p-value = 9.3E-17) and rs188497582 in ATP10A, rs12589854 (p-value = 2.9E-11) and rs80203467 (p-value = 4.6e-11) in maternally expressed 8, small nucleolar RNA host (MEG8), and rs138281088 in solute carrier family 22 member 2 (SLC22A2) (p-value = 6.8e-9).

CONCLUSIONS

We identified novel PA-related maternal-fetal MB gene interactions and imprinting effects that highlight the role of the fetus in PA risk development. Findings can inform mechanistic investigations to understand the pathogenesis of PA.

Covariate adjusted inference of parent-of-origin effects using case-control mother-child paired multilocus genotype data

It is of great interest to identify parent-of-origin effects (POEs) since POEs play an important role in many human heritable disorders and human early life growth and development. POE is sometimes referred to as imprinting effect in the literature. Compared with the standard logistic regression analyses, retrospective likelihood-based statistical methods are more powerful in identifying POEs when data are collected from related individuals retrospectively. However, none of existing retrospective-based methods can appropriately incorporate covariates that should be adjusted for if they are confounding factors. In this paper, a novel semiparametric statistical method, M-HAP, is developed to detect POEs by fully exploring available information from multilocus genotypes of case-control mother-child pairs and covariates. Some large sample properties are established for M-HAP. Finite sample properties of M-HAP are illustrated by extensive simulation studies and real data applications to the Jerusalem Perinatal Study and the Danish National Birth Cohort study, which confirm the desired superiority of M-HAP over some existing methods. M-HAP has been implemented in the updated R package CCMO.

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.

Incorporating information from markers in LD with test locus for detecting imprinting and maternal effects

Numerous statistical methods have been developed to explore genomic imprinting and maternal effects by identifying parent-of-origin patterns in complex human diseases. However, because most of these methods only use available locus-specific genotype data, it is sometimes impossible for them to infer the distribution of parental origin of a variant allele, especially when some genotypes are missing. In this article, we propose a two-step approach, LIMEhap, to improve upon a recent partial likelihood inference method. In the first step, the distribution of the missing genotypes is inferred through the construction of haplotypes by using information from nearby loci. In the second step, a partial likelihood method is applied to the inferred data. To substantiate the validity of the proposed procedures, we simulated data in a genomic region of gene GPX1. The results show that, by borrowing genetic information from nearby loci, the power of the proposed method can be close to that with complete genotype data at the locus of interest. Since the inference on the genotype distribution is made under the assumption of Hardy-Weinberg Equilibrium (HWE), we further studied the robustness of LIMEhap to violation of HWE. Finally, we demonstrate the utility of LIMEhap by applying it to an autism dataset.

Language deficits in specific language impairment, attention deficit/hyperactivity disorder, and autism spectrum disorder: An analysis of polygenic risk

Language is one of the cognitive domains often impaired across many neurodevelopmental disorders. While for some disorders the linguistic deficit is the primary impairment (e.g., specific language impairment, SLI), for others it may accompany broader behavioral problems (e.g., autism). The precise nature of this phenotypic overlap has been the subject of debate. Moreover, several studies have found genetic overlaps across neurodevelopmental disorders. This raises the question of whether these genetic overlaps may correlate with phenotypic overlaps and, if so, in what manner. Here, we apply a genome‐wide approach to the study of the linguistic deficit in SLI, autism spectrum disorder (ASD), and attention deficit/hyperactivity disorder (ADHD). Using a discovery genome‐wide association study of SLI, we generate polygenic risk scores (PRS) in an independent sample which includes children with language impairment, SLI, ASD or ADHD and age‐matched controls and perform regression analyses across groups. The SLI‐trained PRS significantly predicted risk in the SLI case–control group (adjusted R² = 6.24%; P = 0.024) but not in the ASD or ADHD case‐control groups (adjusted R² = 0.0004%, 0.01%; P = 0.984, 0.889, respectively) nor for height, used as a negative control (R² = 0.2%; P = 0.452). Additionally, there was a significant difference in the normalized PRS between children with SLI and children with ASD (common language effect size = 0.66; P = 0.044). Our study suggests no additive common‐variant genetic overlap between SLI and ASD and ADHD. This is discussed in the context of phenotypic studies of SLI and related disorders. Autism Res 2020, 13: 369–381. © 2019 The Authors. Autism Research published by International Society for Autism Research published by Wiley Periodicals, Inc.

Lay Summary

Language deficits are characteristic of specific language impairment (SLI), but may also be found in other neurodevelopmental disorders, such as autism spectrum disorder (ASD) and attention deficit/hyperactivity disorder (ADHD). Many studies examined the overlaps and differences across the language deficits in these disorders, but few studies have examined the genetic aspect thereof. In this study, we use a genome‐wide approach to evaluate whether common genetic variants increasing risk of SLI may also be associated with ASD and ADHD in the same manner. Our results suggest that this is not the case, and we discuss this finding in the context of theories concerning the etiologies of these disorders.

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.

Effects of ploidy level and haplotype on variation of photosynthetic traits: Novel evidence from two Fragaria species

To reveal the effects of ploidy level and haplotype on photosynthetic traits, we chose 175 genotypes of wild strawberries belonging to two haplotypes at two types of ploidy levels (diploidy and tetraploidy) and measured photosynthetic traits. Our results revealed that ploidy significantly affected the characteristics of light-response curves, CO2-response curves, and leaf gas exchange parameters, except intercellular CO2 concentration (Ci). Tetraploid species had a lower light saturation point (LSP) and CO2 saturation point (CSP), higher light compensation point (LCP), dark respiration (Rd), and CO2 compensation point (CCP) than diploid species. Furthermore, tetraploid species have lower photosynthetic capacity than diploid species, including net photosynthetic rate (Pn), stomatal conductivity (Gs), and transpiration rate (Tr). In addition, haplotype had a significant effect on LSP, CSP, Tr, and Ci as well as a significant interactive effect between ploidy and haplotype on the maximal photosynethic rate of the light-response curve and Rd. Most of the variance existed within haplotypes among individuals. These results suggest that polyploidization was the main driver for the evolution of photosynthesis with increasing ploidy level (i.e. from diploidy to tetraploidy in Fragaria species), while the origin of a chromosome could also affect the photosynthetic traits and the polyploidization effect on photosynthetic traits.

A genome-wide investigation into parent-of-origin effects in autism spectrum disorder identifies previously associated genes including SHANK3

Autism spectrum disorder (ASD) is known to be a heritable neurodevelopmental disorder affecting more than 1% of the population but in the majority of ASD cases, the genetic cause has not been identified. Parent-of-origin effects have been highlighted as an important mechanism in the pathology of neurodevelopmental disorders such as Prader-Willi and Angelman syndrome, with individuals with these syndromes often exhibiting ASD symptoms. Consequently, systematic investigation of these effects in ASD is clearly an important line of investigation in elucidating the underlying genetic mechanisms. Using estimation of maternal, imprinting and interaction effects using multinomial modelling (EMIM), we simultaneously investigated imprinting, maternal genetic effects and associations in the Autism Genome Project and Simons Simplex Consortium genome-wide association data sets. To avoid using the overly stringent genome-wide association study significance level, we used a Bayesian threshold that takes into account the sample size, allele frequency and any available prior knowledge. Between the two data sets, we identified a total of 18 imprinting effects and 68 maternal genetic effects that met this Bayesian threshold criteria, but none met the threshold in both data sets. We identified imprinting and maternal genetic effects for regions that have previously shown evidence for parent-of-origin effects in ASD. Together with these findings, we have identified maternal genetic effects not previously identified in ASD at a locus in SHANK3 on chromosome 22 and a locus in WBSCR17 on chromosome 7 (associated with Williams syndrome). Both genes have previously been associated with ASD.

Further evidence for a parent-of-origin effect at the NOP9 locus on language-related phenotypes

BACKGROUND

Specific language impairment (SLI) is a common neurodevelopmental disorder, observed in 5-10 % of children. Family and twin studies suggest a strong genetic component, but relatively few candidate genes have been reported to date. A recent genome-wide association study (GWAS) described the first statistically significant association specifically for a SLI cohort between a missense variant (rs4280164) in the NOP9 gene and language-related phenotypes under a parent-of-origin model. Replications of these findings are particularly challenging because the availability of parental DNA is required.

METHODS

We used two independent family-based cohorts characterised with reading- and language-related traits: a longitudinal cohort (n = 106 informative families) including children with language and reading difficulties and a nuclear family cohort (n = 264 families) selected for dyslexia.

RESULTS

We observed association with language-related measures when modelling for parent-of-origin effects at the NOP9 locus in both cohorts: minimum P = 0.001 for phonological awareness with a paternal effect in the first cohort and minimum P = 0.0004 for irregular word reading with a maternal effect in the second cohort. Allelic and parental trends were not consistent when compared to the original study.

CONCLUSIONS

A parent-of-origin effect at this locus was detected in both cohorts, albeit with different trends. These findings contribute in interpreting the original GWAS report and support further investigations of the NOP9 locus and its role in language-related traits. A systematic evaluation of parent-of-origin effects in genetic association studies has the potential to reveal novel mechanisms underlying complex traits.