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.

Trends in overweight and obesity in Bergen, Norway, using data from routine child healthcare 2010-2022.

AIM: Trends in childhood overweight, obesity and severe obesity have been lacking in Norway. This study assessed pre-pandemic trends from 2010 to 2019 and evaluated differences in prevalence during the 2020-2022 pandemic years. METHODS: Routine height and weight measurements from child and school health centres were extracted retrospectively from children aged 2, 4, 6, 8 and 13 years. Overweight, obesity and severe obesity was classified according to the International Obesity Task Force cut-offs. Pre-pandemic trends were estimated using linear regression. The prevalence during the pandemic was compared to the 95% prediction interval of this model. RESULTS: We obtained 181 527 body mass index measurements on 78 024 children (51.0% boys). There was a decrease in the prevalence of overweight including obesity from 2010 to 2019 in boys and this was statistically significant at 4 and 13 years of age. We found no significant trends in girls during this period. During the pandemic, the prevalence of overweight including obesity exceeded the prediction intervals for boys aged 4, 6, and 8 years, and for 6-year-old girls. CONCLUSION: From 2010-2019, overweight including obesity plateaued in girls and decreased in boys but increased during the pandemic among prepubertal boys. Routine healthcare data is useful for estimating the prevalence of different weight status.

Cross-fitted instrument: A blueprint for one-sample Mendelian randomization.

Bias from weak instruments may undermine the ability to estimate causal effects in instrumental variable regression (IVR). We present here a new approach to handling weak instrument bias through the application of a new type of instrumental variable coined 'Cross-Fitted Instrument' (CFI). CFI splits the data at random and estimates the impact of the instrument on the exposure in each partition. These estimates are then used to perform an IVR on each partition. We adapt CFI to the Mendelian randomization (MR) setting and term this adaptation 'Cross-Fitting for Mendelian Randomization' (CFMR). We show that, even when using weak instruments, CFMR is, at worst, biased towards the null, which makes it a conservative one-sample MR approach. In particular, CFMR remains conservative even when the two samples used to perform the MR analysis completely overlap, whereas current state-of-the-art approaches (e.g., MR RAPS) display substantial bias in this setting. Another major advantage of CFMR lies in its use of all of the available data to select genetic instruments, which maximizes statistical power, as opposed to traditional two-sample MR where only part of the data is used to select the instrument. Consequently, CFMR is able to enhance statistical power in consortia-led meta-analyses by enabling a conservative one-sample MR to be performed in each cohort prior to a meta-analysis of the results across all the cohorts. In addition, CFMR enables a cross-ethnic MR analysis by accounting for ethnic heterogeneity, which is particularly important in meta-analyses where the participating cohorts may have different ethnicities. To our knowledge, none of the current MR approaches can account for such heterogeneity. Finally, CFMR enables the application of MR to exposures that are either rare or difficult to measure, which would normally preclude their analysis in the regular two-sample MR setting.

Retirement age and disability status as pathways to later-life cognitive impairment: Evidence from the Norwegian HUNT Study linked with Norwegian population registers.

BACKGROUND: Research shows that retirement age is associated with later-life cognition but has not sufficiently distinguished between retirement pathways. We examined how retirement age was associated with later-life dementia and mild cognitive impairment (MCI) for people who retired via the disability pathway (received a disability pension prior to old-age pension eligibility) and those who retired via the standard pathway. METHODS: The study sample comprised 7210 participants from the Norwegian Trøndelag Health Study (HUNT4 70+, 2017-2019) who had worked for at least one year in 1967-2019, worked until age 55+, and retired before HUNT4. Dementia and MCI were clinically assessed in HUNT4 70+ when participants were aged 69-85 years. Historical data on participants' retirement age and pathway were retrieved from population registers. We used multinomial regression to assess the dementia/MCI risk for women and men retiring via the disability pathway, or early (<67 years), on-time (age 67, old-age pension eligibility) or late (age 68+) via the standard pathway. RESULTS: In our study sample, 9.5% had dementia, 35.3% had MCI, and 28.1% retired via the disability pathway. The disability retirement group had an elevated risk of dementia compared to the on-time standard retirement group (relative risk ratio [RRR]: 1.64, 95% CI 1.14-2.37 for women, 1.70, 95% CI 1.17-2.48 for men). MCI risk was lower among men who retired late versus on-time (RRR, 0.76, 95% CI 0.61-0.95). CONCLUSION: Disability retirees should be monitored more closely, and preventive policies should be considered to minimize the dementia risk observed among this group of retirees.

Deep phenotyping of pubertal development in Norwegian children: the Bergen Growth Study 2.

BACKGROUND: The Bergen Growth Study 2 (BGS2) aims to characterise somatic and endocrine changes in healthy Norwegian children using a novel methodology. SUBJECTS AND METHODS: A cross-sectional sample of 1285 children aged 6-16 years was examined in 2016 using novel objective ultrasound assessments of breast developmental stages and testicular volume in addition to the traditional Tanner pubertal stages. Blood samples allowed for measurements of pubertal hormones, endocrine disruptive chemicals, and genetic analyses. RESULTS: Ultrasound staging of breast development in girls showed a high degree of agreement within and between observers, and ultrasound measurement of testicular volume in boys also showed small intra- and interobserver differences. The median age was 10.4 years for Tanner B2 (pubertal onset) and 12.7 years for menarche. Norwegian boys reached a pubertal testicular volume at a mean age of 11.7 years. Continuous reference curves for testicular volume and sex hormones were constructed using the LMS method. CONCLUSIONS: Ultrasound-based assessments of puberty provided novel references for breast developmental stages and enabled the measurement of testicular volume on a continuous scale. Endocrine z-scores allowed for an intuitive interpretation of changing hormonal levels during puberty on a quantitative scale, which, in turn, provides opportunities for further analysis of pubertal development using machine-learning approaches.

Evolution and dispersal of mitochondrial DNA haplogroup U5 in Northern Europe: insights from an unsupervised learning approach to phylogeography.

BACKGROUND: We combined an unsupervised learning methodology for analyzing mitogenome sequences with maximum likelihood (ML) phylogenetics to make detailed inferences about the evolution and diversification of mitochondrial DNA (mtDNA) haplogroup U5, which appears at high frequencies in northern Europe. METHODS: Haplogroup U5 mitogenome sequences were gathered from GenBank. The hierarchal Bayesian Analysis of Population Structure (hierBAPS) method was used to generate groups of sequences that were then projected onto a rooted maximum likelihood (ML) phylogenetic tree to visualize the pattern of clustering. The haplogroup statuses of the individual sequences were assessed using Haplogrep2. RESULTS: A total of 23 hierBAPS groups were identified, all of which corresponded to subclades defined in Phylotree, v.17. The hierBAPS groups projected onto the ML phylogeny accurately clustered all haplotypes belonging to a specific haplogroup in accordance with Haplogrep2. By incorporating the geographic source of each sequence and subclade age estimates into this framework, inferences about the diversification of U5 mtDNAs were made. Haplogroup U5 has been present in northern Europe since the Mesolithic, and spread in both eastern and western directions, undergoing significant diversification within Scandinavia. A review of historical and archeological evidence attests to some of the population interactions contributing to this pattern. CONCLUSIONS: The hierBAPS algorithm accurately grouped mitogenome sequences into subclades in a phylogenetically robust manner. This analysis provided new insights into the phylogeographic structure of haplogroup U5 diversity in northern Europe, revealing a detailed perspective on the diversity of subclades in this region and their distribution in Scandinavian populations.

Associations between epigenetic age acceleration and infertility.

STUDY QUESTION: Is the use of ART, a proxy for infertility, associated with epigenetic age acceleration? SUMMARY ANSWER: The epigenetic age acceleration measured by Dunedin Pace of Aging methylation (DunedinPoAm) differed significantly between non-ART and ART mothers. WHAT IS KNOWN ALREADY: Among mothers who used ART, epigenetic age acceleration may be associated with low oocyte yield and poor ovarian response. However, the difference in epigenetic age acceleration between non-ART and ART mothers (or even fathers) has not been examined. STUDY DESIGN, SIZE, DURATION: The Norwegian Mother, Father and Child Cohort Study (MoBa) recruited pregnant women and their partners across Norway at around 18 gestational weeks between 1999 and 2008. Approximately 95 000 mothers, 75 000 fathers and 114 000 children were included. Peripheral blood samples were taken from mothers and fathers at ultrasound appointments or from mothers at childbirth, and umbilical cord blood samples were collected from the newborns at birth. PARTICIPANTS/MATERIALS, SETTING, METHODS: Among the MoBa participants, we selected 1000 couples who conceived by coitus and 894 couples who conceived by IVF (n = 525) or ICSI (n = 369). We measured their DNA methylation (DNAm) levels using the Illumina MethylationEPIC array and calculated epigenetic age acceleration. A linear mixed model was used to examine the differences in five different epigenetic age accelerations between non-ART and ART parents. MAIN RESULTS AND THE ROLE OF CHANCE: We found a significant difference in the epigenetic age acceleration calculated by DunedinPoAm between IVF and non-ART mothers (0.021 years, P-value = 2.89E-06) after adjustment for potential confounders. Further, we detected elevated DunedinPoAm in mothers with tubal factor infertility (0.030 years, P-value = 1.34E-05), ovulation factor (0.023 years, P-value = 0.0018) and unexplained infertility (0.023 years, P-value = 1.39E-04) compared with non-ART mothers. No differences in epigenetic age accelerations between non-ART and ICSI fathers were found. DunedinPoAm also showed stronger associations with smoking, education and parity than the other four epigenetic age accelerations. LIMITATIONS, REASONS FOR CAUTION: We were not able to determine the directionality of the causal pathway between the epigenetic age accelerations and infertility. Since parents' peripheral blood samples were collected after conception, we cannot rule out the possibility that the epigenetic profile of ART mothers was influenced by the ART treatment. Hence, the results should be interpreted with caution, and our results might not be generalizable to non-pregnant women. WIDER IMPLICATIONS OF THE FINDINGS: A plausible biological mechanism behind the reported association is that IVF mothers could be closer to menopause than non-ART mothers. The pace of decline of the ovarian reserve that eventually leads to menopause varies between females yet, in general, accelerates after the age of 30, and some studies show an increased risk of infertility in females with low ovarian reserve. STUDY FUNDING/COMPETING INTEREST(S): This study was partly funded by the Research Council of Norway (Women's fertility, project no. 320656) and through its Centres of Excellence Funding Scheme (project no. 262700). M.C.M. has received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement number 947684). The authors declare no conflict of interest. TRIAL REGISTRATION NUMBER: N/A.

Detecting differentially methylated regions using a fast wavelet-based approach to functional association analysis.

BACKGROUND: We present here a computational shortcut to improve a powerful wavelet-based method by Shim and Stephens (Ann Appl Stat 9(2):665-686, 2015. https://doi.org/10.1214/14-AOAS776 ) called WaveQTL that was originally designed to identify DNase I hypersensitivity quantitative trait loci (dsQTL). RESULTS: WaveQTL relies on permutations to evaluate the significance of an association. We applied a recent method by Zhou and Guan (J Am Stat Assoc 113(523):1362-1371, 2017. https://doi.org/10.1080/01621459.2017.1328361 ) to boost computational speed, which involves calculating the distribution of Bayes factors and estimating the significance of an association by simulations rather than permutations. We called this simulation-based approach "fast functional wavelet" (FFW), and tested it on a publicly available DNA methylation (DNAm) dataset on colorectal cancer. The simulations confirmed a substantial gain in computational speed compared to the permutation-based approach in WaveQTL. Furthermore, we show that FFW controls the type I error satisfactorily and has good power for detecting differentially methylated regions. CONCLUSIONS: Our approach has broad utility and can be applied to detect associations between different types of functions and phenotypes. As more and more DNAm datasets are being made available through public repositories, an attractive application of FFW would be to re-analyze these data and identify associations that might have been missed by previous efforts. The full R package for FFW is freely available at GitHub https://github.com/william-denault/ffw .

Wavelet Screening: a novel approach to analyzing GWAS data.

BACKGROUND: Traditional methods for single-variant genome-wide association study (GWAS) incur a substantial multiple-testing burden because of the need to test for associations with a vast number of single-nucleotide polymorphisms (SNPs) simultaneously. Further, by ignoring more complex joint effects of nearby SNPs within a given region, these methods fail to consider the genomic context of an association with the outcome. RESULTS: To address these shortcomings, we present a more powerful method for GWAS, coined 'Wavelet Screening' (WS), that greatly reduces the number of tests to be performed. This is achieved through the use of a sliding-window approach based on wavelets to sequentially screen the entire genome for associations. Wavelets are oscillatory functions that are useful for analyzing the local frequency and time behavior of signals. The signals can then be divided into different scale components and analyzed separately. In the current setting, we consider a sequence of SNPs as a genetic signal, and for each screened region, we transform the genetic signal into the wavelet space. The null and alternative hypotheses are modeled using the posterior distribution of the wavelet coefficients. WS is enhanced by using additional information from the regression coefficients and by taking advantage of the pyramidal structure of wavelets. When faced with more complex genetic signals than single-SNP associations, we show via simulations that WS provides a substantial gain in power compared to both the traditional GWAS modeling and another popular regional association test called SNP-set (Sequence) Kernel Association Test (SKAT). To demonstrate feasibility, we applied WS to a large Norwegian cohort (N=8006) with genotypes and information available on gestational duration. CONCLUSIONS: WS is a powerful and versatile approach to analyzing whole-genome data and lends itself easily to investigating various omics data types. Given its broader focus on the genomic context of an association, WS may provide additional insight into trait etiology by revealing genes and loci that might have been missed by previous efforts.

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.

Matrilineal diversity and population history of Norwegians.

BACKGROUND: While well known for its Viking past, Norway's population history and the influences that have shaped its genetic diversity are less well understood. This is particularly true with respect to its demography, migration patterns, and dialectal regions, despite there being curated historical records for the past several centuries. In this study, we undertook an analysis of mitochondrial DNA (mtDNA) diversity within the country to elaborate this history from a matrilineal genetic perspective. METHODS: We aggregated 1174 partial modern Norwegian mtDNA sequences from the published literature and subjected them to detailed statistical and phylogenetic analysis by dialectal regions and localities. We further contextualized the matrilineal ancestry of modern Norwegians with data from Mesolithic, Iron Age, and historic period populations. RESULTS: Modern Norwegian mtDNAs fell into eight West Eurasian (N, HV, JT, I, U, K, X, W), five East Eurasian (A, F, G, N11, Z), and one African (L2) haplogroups. Pairwise analysis of molecular variance (AMOVA) estimates for all Norwegians indicated they were differentiated from each other at 1.68% (p < 0.001). Norwegians within the same dialectal region also showed genetic similarities to each other, although differences between subpopulations within dialectal regions were also observed. In addition, certain mtDNA lineages in modern Norwegians were also found among prehistoric and historic period populations, suggesting some level of genetic continuity over hundreds to many thousands of years. CONCLUSIONS: This analysis of mtDNA diversity provides a detailed picture of the genetic variation within Norway in light of its topography, settlement history, and historical migrations over the past several centuries.

Blood-based epigenetic estimators of chronological age in human adults using DNA methylation data from the Illumina MethylationEPIC array.

BACKGROUND: Epigenetic clocks have been recognized for their precise prediction of chronological age, age-related diseases, and all-cause mortality. Existing epigenetic clocks are based on CpGs from the Illumina HumanMethylation450 BeadChip (450 K) which has now been replaced by the latest platform, Illumina MethylationEPIC BeadChip (EPIC). Thus, it remains unclear to what extent EPIC contributes to increased precision and accuracy in the prediction of chronological age. RESULTS: We developed three blood-based epigenetic clocks for human adults using EPIC-based DNA methylation (DNAm) data from the Norwegian Mother, Father and Child Cohort Study (MoBa) and the Gene Expression Omnibus (GEO) public repository: 1) an Adult Blood-based EPIC Clock (ABEC) trained on DNAm data from MoBa (n = 1592, age-span: 19 to 59 years), 2) an extended ABEC (eABEC) trained on DNAm data from MoBa and GEO (n = 2227, age-span: 18 to 88 years), and 3) a common ABEC (cABEC) trained on the same training set as eABEC but restricted to CpGs common to 450 K and EPIC. Our clocks showed high precision (Pearson correlation between chronological and epigenetic age (r) > 0.94) in independent cohorts, including GSE111165 (n = 15), GSE115278 (n = 108), GSE132203 (n = 795), and the Epigenetics in Pregnancy (EPIPREG) study of the STORK Groruddalen Cohort (n = 470). This high precision is unlikely due to the use of EPIC, but rather due to the large sample size of the training set. CONCLUSIONS: Our ABECs predicted adults' chronological age precisely in independent cohorts. As EPIC is now the dominant platform for measuring DNAm, these clocks will be useful in further predictions of chronological age, age-related diseases, and mortality.

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.

A genome-wide association meta-analysis of diarrhoeal disease in young children identifies FUT2 locus and provides plausible biological pathways.

More than a million childhood diarrhoeal episodes occur worldwide each year, and in developed countries a considerable part of them are caused by viral infections. In this study, we aimed to search for genetic variants associated with diarrhoeal disease in young children by meta-analyzing genome-wide association studies, and to elucidate plausible biological mechanisms. The study was conducted in the context of the Early Genetics and Lifecourse Epidemiology (EAGLE) consortium. Data about diarrhoeal disease in two time windows (around 1 year of age and around 2 years of age) was obtained via parental questionnaires, doctor interviews or medical records. Standard quality control and statistical tests were applied to the 1000 Genomes imputed genotypic data. The meta-analysis (N = 5758) followed by replication (N = 3784) identified a genome-wide significant association between rs8111874 and diarrhoea at age 1 year. Conditional analysis suggested that the causal variant could be rs601338 (W154X) in the FUT2 gene. Children with the A allele, which results in a truncated FUT2 protein, had lower risk of diarrhoea. FUT2 participates in the production of histo-blood group antigens and has previously been implicated in the susceptibility to infections, including Rotavirus and Norovirus Gene-set enrichment analysis suggested pathways related to the histo-blood group antigen production, and the regulation of ion transport and blood pressure. Among others, the gastrointestinal tract, and the immune and neuro-secretory systems were detected as relevant organs. In summary, this genome-wide association meta-analysis suggests the implication of the FUT2 gene in diarrhoeal disease in young children from the general population.

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.

Effect of polygenic scores of telomere length alleles on telomere length in newborns and parents.

In adults, polygenic scores (PGSs) of telomere length (TL) alleles explain about 4.5% of the variance in TL, as measured by quantitative polymerase chain reaction (qPCR). Yet, these PGSs strongly infer a causal role of telomeres in aging-related diseases. To better understand the determinants of TL through the lifespan, it is essential to examine to what extent these PGSs explain TL in newborns. This study investigates the effect of PGSs on TL in both newborns and their parents, with TL measured by Southern blotting and expressed in base-pairs (bp). Additionally, the study explores the impact of PGSs related to transmitted or non-transmitted alleles on TL in newborns. For parents and newborns, the PGS effects on TL were 172 bp (p = 2.03 × 10-15) and 161 bp (p = 3.06 × 10-8), explaining 6.6% and 5.2% of the TL variance, respectively. The strongest PGS effect was shown for maternally transmitted alleles in newborn girls, amounting to 214 bp (p = 3.77 × 10-6) and explaining 7.8% of the TL variance. The PGS effect of non-transmitted alleles was 56 bp (p = 0.0593) and explained 0.6% of the TL variance. Our findings highlight the importance of TL genetics in understanding early-life determinants of TL. They point to the potential utility of PGSs composed of TL alleles in identifying susceptibility to aging-related diseases from birth and reveal the presence of sexual dimorphism in the effect of TL alleles on TL in newborns. Finally, we attribute the higher TL variance explained by PGSs in our study to TL measurement by Southern blotting.