Detecting genotype combinations that increase risk for disease: maternal-fetal genotype incompatibility test

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.

A gene-based association test of interactions for maternal-fetal genotypes identifies genes associated with nonsyndromic congenital heart defects

The risk of congenital heart defects (CHDs) may be influenced by maternal genes, fetal genes, and their interactions. Existing methods commonly test the effects of maternal and fetal variants one-at-a-time and may have reduced statistical power to detect genetic variants with low minor allele frequencies. In this article, we propose a gene-based association test of interactions for maternal-fetal genotypes (GATI-MFG) using a case-mother and control-mother design. GATI-MFG can integrate the effects of multiple variants within a gene or genomic region and evaluate the joint effect of maternal and fetal genotypes while allowing for their interactions. In simulation studies, GATI-MFG had improved statistical power over alternative methods, such as the single-variant test and functional data analysis (FDA) under various disease scenarios. We further applied GATI-MFG to a two-phase genome-wide association study of CHDs for the testing of both common variants and rare variants using 947 CHD case mother-infant pairs and 1306 control mother-infant pairs from the National Birth Defects Prevention Study (NBDPS). After Bonferroni adjustment for 23,035 genes, two genes on chromosome 17, TMEM107 (p = 1.64e-06) and CTC1 (p = 2.0e-06), were identified for significant association with CHD in common variants analysis. Gene TMEM107 regulates ciliogenesis and ciliary protein composition and was found to be associated with heterotaxy. Gene CTC1 plays an essential role in protecting telomeres from degradation, which was suggested to be associated with cardiogenesis. Overall, GATI-MFG outperformed the single-variant test and FDA in the simulations, and the results of application to NBDPS samples are consistent with existing literature supporting the association of TMEM107 and CTC1 with CHDs.

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-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.

Innovative approach to identify multigenomic and environmental interactions associated with birth defects in family-based hybrid designs

Genes, including those with transgenerational effects, work in concert with behavioral, environmental, and social factors via complex biological networks to determine human health. Understanding complex relationships between causal factors underlying human health is an essential step towards deciphering biological mechanisms. We propose a new analytical framework to investigate the interactions between maternal and offspring genetic variants or their surrogate single nucleotide polymorphisms (SNPs) and environmental factors using family-based hybrid study design. The proposed approach can analyze diverse genetic and environmental factors and accommodate samples from a variety of family units, including case/control-parental triads, and case/control-parental dyads, while minimizing potential bias introduced by population admixture. Comprehensive simulations demonstrated that our innovative approach outperformed the log-linear approach, the best available method for case-control family data. The proposed approach had greater statistical power and was capable to unbiasedly estimate the maternal and child genetic effects and the effects of environmental factors, while controlling the Type I error rate against population stratification. Using our newly developed approach, we analyzed the associations between maternal and fetal SNPs and obstructive and conotruncal heart defects, with adjustment for demographic and lifestyle factors and dietary supplements. Fourteen and 11 fetal SNPs were associated with obstructive and conotruncal heart defects, respectively. Twenty-seven and 17 maternal SNPs were associated with obstructive and conotruncal heart defects, respectively. In addition, maternal body mass index was a significant risk factor for obstructive defects. The proposed approach is a powerful tool for interrogating the etiological mechanism underlying complex traits.

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.

Investigation of Maternal Effects, Maternal-Fetal Interactions, and Parent-of-Origin Effects (Imprinting) for Candidate Genes Positioned on Chromosome 18q21, in Probands with Schizophrenia and their First-Degree Relatives

OBJECTIVE

A popular design for the investigation of such effects, including effects of parent-of-origin (imprinting), maternal genotype, and maternal-fetal genotype interactions, is to collect deoxyribonucleic acid (DNA) from affected offspring and their mothers and to compare with an appropriate control sample. We investigate the effects of estimation of maternal, imprinting and interaction effects using multimodal modeling using parents and their offspring with schizophrenia in Korean population.

METHODS

We have recruited 27 probands (with schizophrenia) with their parents and siblings whenever possible. We analyzed 20 SNPs of 7 neuronal genes in chromosome 18. We used EMIM analysis program for the estimation of maternal, imprinting and interaction effects using multimodal modeling.

RESULTS

Of analyzed 20 single nucleotide polymorphisms (SNPs), significant SNP (rs 2276186) was suggested in EMIM analysis for child genetics effects (p=0.0225438044) and child genetic effects allowing for maternal genetic effects (p=0.0209453210) with very stringent multiple comparison Bonferroni correction.

CONCLUSION

Our results are the pilot study for epigenetic study in mental disorder and help to understanding and use of EMIM statistical genetics analysis program with many limitations including small pedigree numbers.

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.

Electronic supplementary material

The online version of this article (10.1186/s12859-019-2727-3) contains supplementary material, which is available to authorized users.

Human Birth Weight and Reproductive Immunology: Testing for Interactions between Maternal and Offspring KIR and HLA-C Genes

BACKGROUND/AIMS

Maternal and offspring cell contact at the site of placentation presents a plausible setting for maternal-fetal genotype (MFG) interactions affecting fetal growth. We test hypotheses regarding killer cell immunoglobulin-like receptor (KIR) and HLA-C MFG effects on human birth weight by extending the quantitative MFG (QMFG) test.

METHODS

Until recently, association testing for MFG interactions had limited applications. To improve the ability to test for these interactions, we developed the extended QMFG test, a linear mixed-effect model that can use multi-locus genotype data from families.

RESULTS

We demonstrate the extended QMFG test's statistical properties. We also show that if an offspring-only model is fit when MFG effects exist, associations can be missed or misattributed. Furthermore, imprecisely modeling the effects of both KIR and HLA-C could result in a failure to replicate if these loci's allele frequencies differ among populations. To further illustrate the extended QMFG test's advantages, we apply the extended QMFG test to a UK cohort study and the Norwegian Mother and Child Cohort (MoBa) study.

CONCLUSION

We find a significant KIR-HLA-C interaction effect on birth weight. More generally, the QMFG test can detect genetic associations that may be missed by standard genome-wide association studies for quantitative traits.

Analysis of Case-Parent Trios Using a Loglinear Model with Adjustment for Transmission Ratio Distortion

Transmission of the two parental alleles to offspring deviating from the Mendelian ratio is termed Transmission Ratio Distortion (TRD), occurs throughout gametic and embryonic development. TRD has been well-studied in animals, but remains largely unknown in humans. The Transmission Disequilibrium Test (TDT) was first proposed to test for association and linkage in case-trios (affected offspring and parents); adjusting for TRD using control-trios was recommended. However, the TDT does not provide risk parameter estimates for different genetic models. A loglinear model was later proposed to provide child and maternal relative risk (RR) estimates of disease, assuming Mendelian transmission. Results from our simulation study showed that case-trios RR estimates using this model are biased in the presence of TRD; power and Type 1 error are compromised. We propose an extended loglinear model adjusting for TRD. Under this extended model, RR estimates, power and Type 1 error are correctly restored. We applied this model to an intrauterine growth restriction dataset, and showed consistent results with a previous approach that adjusted for TRD using control-trios. Our findings suggested the need to adjust for TRD in avoiding spurious results. Documenting TRD in the population is therefore essential for the correct interpretation of genetic association studies.

A powerful nonparametric statistical framework for family-based association analyses

Family-based study design is commonly used in genetic research. It has many ideal features, including being robust to population stratification (PS). With the advance of high-throughput technologies and ever-decreasing genotyping cost, it has become common for family studies to examine a large number of variants for their associations with disease phenotypes. The yield from the analysis of these family-based genetic data can be enhanced by adopting computationally efficient and powerful statistical methods. We propose a general framework of a family-based U-statistic, referred to as family-U, for family-based association studies. Unlike existing parametric-based methods, the proposed method makes no assumption of the underlying disease models and can be applied to various phenotypes (e.g., binary and quantitative phenotypes) and pedigree structures (e.g., nuclear families and extended pedigrees). By using only within-family information, it can offer robust protection against PS. In the absence of PS, it can also utilize additional information (i.e., between-family information) for power improvement. Through simulations, we demonstrated that family-U attained higher power over a commonly used method, family-based association tests, under various disease scenarios. We further illustrated the new method with an application to large-scale family data from the Framingham Heart Study. By utilizing additional information (i.e., between-family information), family-U confirmed a previous association of CHRNA5 with nicotine dependence.

Placental genome and maternal-placental genetic interactions: a genome-wide and candidate gene association study of placental abruption

While available evidence supports the role of genetics in the pathogenesis of placental abruption (PA), PA-related placental genome variations and maternal-placental genetic interactions have not been investigated. Maternal blood and placental samples collected from participants in the Peruvian Abruptio Placentae Epidemiology study were genotyped using Illumina's Cardio-Metabochip platform. We examined 118,782 genome-wide SNPs and 333 SNPs in 32 candidate genes from mitochondrial biogenesis and oxidative phosphorylation pathways in placental DNA from 280 PA cases and 244 controls. We assessed maternal-placental interactions in the candidate gene SNPS and two imprinted regions (IGF2/H19 and C19MC). Univariate and penalized logistic regression models were fit to estimate odds ratios. We examined the combined effect of multiple SNPs on PA risk using weighted genetic risk scores (WGRS) with repeated ten-fold cross-validations. A multinomial model was used to investigate maternal-placental genetic interactions. In placental genome-wide and candidate gene analyses, no SNP was significant after false discovery rate correction. The top genome-wide association study (GWAS) hits were rs544201, rs1484464 (CTNNA2), rs4149570 (TNFRSF1A) and rs13055470 (ZNRF3) (p-values: 1.11e-05 to 3.54e-05). The top 200 SNPs of the GWAS overrepresented genes involved in cell cycle, growth and proliferation. The top candidate gene hits were rs16949118 (COX10) and rs7609948 (THRB) (p-values: 6.00e-03 and 8.19e-03). Participants in the highest quartile of WGRS based on cross-validations using SNPs selected from the GWAS and candidate gene analyses had a 8.40-fold (95% CI: 5.8-12.56) and a 4.46-fold (95% CI: 2.94-6.72) higher odds of PA compared to participants in the lowest quartile. We found maternal-placental genetic interactions on PA risk for two SNPs in PPARG (chr3:12313450 and chr3:12412978) and maternal imprinting effects for multiple SNPs in the C19MC and IGF2/H19 regions. Variations in the placental genome and interactions between maternal-placental genetic variations may contribute to PA risk. Larger studies may help advance our understanding of PA pathogenesis.

Disentangling pooled triad genotypes for association studies

Association studies that genotype affected offspring and their parents (triads) offer robustness to genetic population structure while enabling assessments of maternal effects, parent-of-origin effects, and gene-by-environment interaction. We propose case-parents designs that use pooled DNA specimens to make economical use of limited available specimens. One can markedly reduce the number of genotyping assays required by randomly partitioning the case-parent triads into pooling sets of h triads each and creating three pools from every pooling set, one pool each for mothers, fathers, and offspring. Maximum-likelihood estimation of relative risk parameters proceeds via log-linear modeling using the expectation-maximization algorithm. The approach can assess offspring and maternal genetic effects and accommodate genotyping errors and missing genotypes. We compare the power of our proposed analysis for testing offspring and maternal genetic effects to that based on a difference approach and that of the gold standard based on individual genotypes, under a range of allele frequencies, missing parent proportions, and genotyping error rates. Power calculations show that the pooling strategies cause only modest reductions in power if genotyping errors are low, while reducing genotyping costs and conserving limited specimens.

Entropy-based selection for maternal-fetal genotype incompatibility with application to preterm prelabor rupture of membranes

Background

Maternal-fetal genotype incompatibility (MFGI) is increasingly reported to influence human diseases, especially pregnancy-related complications. In practice, it is challenging to identify the ideal incompatibility model for analysis, since the true MFGI mechanism is generally unknown. The underlying MFGI mechanism for different genetic variants can vary, and to use a single incompatibility model for all circumstances would cause power loss in testing MFGI.

Results

In this article, we propose a practical 2-step procedure that incorporates a model selection strategy based on an entropy measurement to select the most appropriate MFGI model represented by data and test the significance of the MFGI effect using the chosen model within the generalized linear regression framework.

Conclusions

Our simulation studies show that the proposed two-step procedure controls the type I error rate and increase the testing power under various scenarios. In a real data application, our analysis reveals genes having an MFGI effect, which may not be detected with a non-model selection counterpart.

Review of statistical methodologies for the detection of parent-of-origin effects in family trio genome-wide association data with binary disease traits

The detection of parent-of-origin effects aims to identify whether the functionality of alleles, and in turn associated phenotypic traits, depends on the parental origin of the alleles. Different parent-of-origin effects have been identified through a variety of mechanisms and a number of statistical methodologies for their detection have been proposed, in particular for genome-wide association studies (GWAS). GWAS have had limited success in explaining the heritability of many complex disorders and traits, but successful identification of parent-of-origin effects using trio (mother, father and offspring) GWAS may help shed light on this missing heritability. However, it is important to choose the most appropriate parent-of-origin test or methodology, given knowledge of the phenotype, amount of available data and the type of parent-of-origin effect(s) being considered. This review brings together the parent-of-origin detection methodologies available, comparing them in terms of power and type I error for a number of different simulated data scenarios, and finally offering guidance as to the most appropriate choice for the different scenarios.

Detecting maternal-fetal genotype interactions associated with conotruncal heart defects: a haplotype-based analysis with penalized logistic regression

Nonsyndromic congenital heart defects (CHDs) develop during embryogenesis as a result of a complex interplay between environmental exposures, genetics, and epigenetic causes. Genetic factors associated with CHDs may be attributed to either independent effects of maternal or fetal genes, or the intergenerational interactions between maternal and fetal genes. Detecting gene-by-gene interactions underlying complex diseases is a major challenge in genetic research. Detecting maternal-fetal genotype (MFG) interactions and differentiating them from the maternal/fetal main effects has presented additional statistical challenges due to correlations between maternal and fetal genomes. Traditionally, genetic variants are tested separately for maternal/fetal main effects and MFG interactions on a single-locus basis. We conducted a haplotype-based analysis with a penalized logistic regression framework to dissect the genetic effect associated with the development of nonsyndromic conotruncal heart defects (CTD). Our method allows simultaneous model selection and effect estimation, providing a unified framework to differentiate maternal/fetal main effect from the MFG interaction effect. In addition, the method is able to test multiple highly linked SNPs simultaneously with a configuration of haplotypes, which reduces the data dimensionality and the burden of multiple testing. By analyzing a dataset from the National Birth Defects Prevention Study (NBDPS), we identified seven genes (GSTA1, SOD2, MTRR, AHCYL2, GCLC, GSTM3, and RFC1) associated with the development of CTDs. Our findings suggest that MFG interactions between haplotypes in three of seven genes, GCLC, GSTM3, and RFC1, are associated with nonsyndromic conotruncal heart defects.

A genome-wide survey of transgenerational genetic effects in autism

Effects of parental genotype or parent-offspring genetic interaction are well established in model organisms for a variety of traits. However, these transgenerational genetic models are rarely studied in humans. We have utilized an autism case-control study with 735 mother-child pairs to perform genome-wide screening for maternal genetic effects and maternal-offspring genetic interaction. We used simple models of single locus parent-child interaction and identified suggestive results (P<10⁻⁴) that cannot be explained by main effects, but no genome-wide significant signals. Some of these maternal and maternal-child associations were in or adjacent to autism candidate genes including: PCDH9, FOXP1, GABRB3, NRXN1, RELN, MACROD2, FHIT, RORA, CNTN4, CNTNAP2, FAM135B, LAMA1, NFIA, NLGN4X, RAPGEF4, and SDK1. We attempted validation of potential autism association under maternal-specific models using maternal-paternal comparison in family-based GWAS datasets. Our results suggest that further study of parental genetic effects and parent-child interaction in autism is warranted.

Non-random mating, parent-of-origin, and maternal-fetal incompatibility effects in schizophrenia

Although the association of common genetic variation in the extended MHC region with schizophrenia is the most significant yet discovered, the MHC region is one of the more complex regions of the human genome, with unusually high gene density and long-range linkage disequilibrium. The statistical test on which the MHC association is based is a relatively simple, additive model which uses logistic regression of SNP genotypes to predict case-control status. However, it is plausible that more complex models underlie this association. Using a well-characterized sample of trios, we evaluated more complex models by looking for evidence for: (a) non-random mating for HLA alleles, schizophrenia risk profiles, and ancestry; (b) parent-of-origin effects for HLA alleles; and (c) maternal-fetal genotype incompatibility in the HLA. We found no evidence for non-random mating in the parents of individuals with schizophrenia in terms of MHC genotypes or schizophrenia risk profile scores. However, there was evidence of non-random mating that appeared mostly to be driven by ancestry. We did not detect over-transmission of HLA alleles to affected offspring via the general TDT test (without regard to parent of origin) or preferential transmission via paternal or maternal inheritance. We evaluated the hypothesis that maternal-fetal HLA incompatibility may increase risk for schizophrenia using eight classical HLA loci. The most significant alleles were in HLA-B, HLA-C, HLA-DQB1, and HLA-DRB1 but none was significant after accounting for multiple comparisons. We did not find evidence to support more complex models of gene action, but statistical power may have been limiting.

Combining family and twin data in association studies to estimate the noninherited maternal antigens effect

It is hypothesized that certain alleles can have a protective effect not only when inherited by the offspring but also as noninherited maternal antigens (NIMA). To estimate the NIMA effect, large samples of families are needed. When large samples are not available, we propose a combined approach to estimate the NIMA effect from ascertained nuclear families and twin pairs. We develop a likelihood-based approach allowing for several ascertainment schemes, to accommodate for the outcome-dependent sampling scheme, and a family-specific random term, to take into account the correlation between family members. We estimate the parameters using maximum likelihood based on the combined joint likelihood (CJL) approach. Simulations show that the CJL is more efficient for estimating the NIMA odds ratios as compared to a families-only approach. To illustrate our approach, we used data from a family and a twin study from the United Kingdom on rheumatoid arthritis, and confirmed the protective NIMA effect, with an odds ratio of 0.477 (95% CI 0.264-0.864).

Analysis of genetic interactions involving maternal and offspring genotypes at different Loci: power simulation and application to testicular cancer

The analyses of genetic interaction between maternal and offspring genotypes are usually conducted considering a single locus. Here, we propose testing maternal × offspring (M×O) and maternal × maternal (M×M) genotype interactions involving two unlinked loci. We reformulate the log-linear approach of analyzing cases and their parents (family trios) to accommodate two loci, fit fuller models to avoid confounding in a first analysis step and propose that the model be reduced to the most prominent effects in a second step. We conduct extensive simulations to assess the validity and power of this approach under various model assumptions. We show that the approach is valid and has good power to detect M×O and M×M interactions. For example, the power to detect a dominant interaction relative risk of 1.5 (both M×O and M×M) is 70% with 300 trios and approaches 100% with 1,000 trios. Unlike the main effects, M×O and M×M interactions are conditionally independent of mating types, and consequently, their power is not affected by missing paternal genotypes. When applied to single-locus M×O interaction, our method is as powerful as other existing methods. Applying the method to testicular cancer, we found a nominally significant M×M interaction between single nucleotide polymorphisms from C-Kit Ligand (KITLG) and Sex Hormone Binding Globulin (SHBG) using 210 families (relative risk 2.2, P = 0.03). This finding supports a role of maternal hormones in offspring testicular cancer and warrants confirmation in a larger dataset.

PREMIM and EMIM: tools for estimation of maternal, imprinting and interaction effects using multinomial modelling

Background

Here we present two new computer tools, PREMIM and EMIM, for the estimation of parental and child genetic effects, based on genotype data from a variety of different child-parent configurations. PREMIM allows the extraction of child-parent genotype data from standard-format pedigree data files, while EMIM uses the extracted genotype data to perform subsequent statistical analysis. The use of genotype data from the parents as well as from the child in question allows the estimation of complex genetic effects such as maternal genotype effects, maternal-foetal interactions and parent-of-origin (imprinting) effects. These effects are estimated by EMIM, incorporating chosen assumptions such as Hardy-Weinberg equilibrium or exchangeability of parental matings as required.

Results

In application to simulated data, we show that the inference provided by EMIM is essentially equivalent to that provided by alternative (competing) software packages such as MENDEL and LEM. However, PREMIM and EMIM (used in combination) considerably outperform MENDEL and LEM in terms of speed and ease of execution.

Conclusions

Together, EMIM and PREMIM provide easy-to-use command-line tools for the analysis of pedigree data, giving unbiased estimates of parental and child genotype relative risks.

Detection of intergenerational genetic effects with application to HLA-B matching as a risk factor for schizophrenia

BACKGROUND AND METHODS

Association studies using unrelated individuals cannot detect intergenerational genetic effects contributing to disease. To detect these effects, we improve the extended maternal-fetal genotype (EMFG) incompatibility test to estimate any combination of maternal effects, offspring effects, and their interactions at polymorphic loci or multiple SNPs, using any size pedigrees. We explore the advantages of using extended pedigrees rather than nuclear families. We apply our methods to schizophrenia pedigrees to investigate whether the previously associated mother-daughter HLA-B matching is a genuine risk or the result of bias.

RESULTS

Simulations demonstrate that using the EMFG test with extended pedigrees increases power and precision, while partitioning extended pedigrees into nuclear families can underestimate intergenerational effects. Application to actual data demonstrates that mother-daughter HLA-B matching remains a schizophrenia risk factor. Furthermore, ascertainment and mate selection biases cannot by themselves explain the observed HLA-B matching and schizophrenia association.

CONCLUSIONS

Our results demonstrate the power of the EMFG test to examine intergenerational genetic effects, highlight the importance of pedigree rather than case/control or case-mother/control-mother designs, illustrate that pedigrees provide a means to examine alternative, non-causal mechanisms, and they strongly support the hypothesis that HLA-B matching is causally involved in the etiology of schizophrenia in females.

Dissecting prenatal, postnatal, and inherited effects: ART and design

With the failure of common variants alone to explain the bulk of trait heritability, it becomes more important to understand the contribution of maternally inherited effects, prenatal effects, and postnatal environmental effects. These effects can be disentangled by studying families containing children conceived by assisted reproductive technologies (ART). We propose and develop a model that is an extension of the variance component model commonly used in pedigree analysis. Our model is flexible enough to allow any number of family members and degrees of relationship; thus, researchers can use both small and extended families simultaneously. Simulations demonstrate that our method has appropriate statistical properties and is robust to model misspecification and accurate in the presence of missing data. Most importantly, our method is able to disentangle maternally inherited effects from prenatal effects, which are confounded in traditional family studies. Our analyses also provide guidance to researchers designing studies that will use ART families to clarify genetic and environmental factors underlying traits.

Investigation of maternal effects, maternal-fetal interactions and parent-of-origin effects (imprinting), using mothers and their offspring

Many complex genetic effects, including epigenetic effects, may be expected to operate via mechanisms in the inter-uterine environment. A popular design for the investigation of such effects, including effects of parent-of-origin (imprinting), maternal genotype, and maternal-fetal genotype interactions, is to collect DNA from affected offspring and their mothers (case/mother duos) and to compare with an appropriate control sample. An alternative design uses data from cases and both parents (case/parent trios) but does not require controls. In this study, we describe a novel implementation of a multinomial modeling approach that allows the estimation of such genetic effects using either case/mother duos or case/parent trios. We investigate the performance of our approach using computer simulations and explore the sample sizes and data structures required to provide high power for detection of effects and accurate estimation of the relative risks conferred. Through the incorporation of additional assumptions (such as Hardy-Weinberg equilibrium, random mating and known allele frequencies) and/or the incorporation of additional types of control sample (such as unrelated controls, controls and their mothers, or both parents of controls), we show that the (relative risk) parameters of interest are identifiable and well estimated. Nevertheless, parameter interpretation can be complex, as we illustrate by demonstrating the mathematical equivalence between various different parameterizations. Our approach scales up easily to allow the analysis of large-scale genome-wide association data, provided both mothers and affected offspring have been genotyped at all variants of interest. Genet. Epidemiol. 35:19–45, 2011. © 2010 Wiley-Liss, Inc.

Using imputed genotypes for relative risk estimation in case-parent studies

Meta-analyses of genome-wide association studies are often based on imputed single nucleotide polymorphism (SNP) data, because component studies were genotyped using different platforms. One would like to include case-parent triad studies along with case-control studies in such meta-analyses. However, there are no published methods for estimating relative risks from imputed data for case-parent triad studies. The authors propose a method for estimating the relative risk for a variant SNP allele based on a log-additive model. Their simulations first confirm that the proposed method performs well with genotyped SNP data. As an empirical test of the method's behavior with imputed SNPs, the authors then apply it to chromosome 22 data from the Mexico City Childhood Asthma Study (1998-2003). For chromosome 22, the authors had data on 7,293 SNPs that were both genotyped and imputed using the software MACH, which relies on linkage disequilibrium with nearby SNPs. Correlation between estimated relative risks based on the actual genotypes and those based on the imputed genotypes was remarkably high (r(2) = 0.95), validating this method of relative risk estimation for the case-parent study design. This method should be useful to investigators who wish to conduct meta-analyses using imputed SNP data from both case-parent triad and case-control studies.

Modeling maternal-offspring gene-gene interactions: the extended-MFG test

Maternal-fetal genotype (MFG) incompatibility is an interaction between the genes of a mother and offspring at a particular locus that adversely affects the developing fetus, thereby increasing susceptibility to disease. Statistical methods for examining MFG incompatibility as a disease risk factor have been developed for nuclear families. Because families collected as part of a study can be large and complex, containing multiple generations and marriage loops, we create the Extended-MFG (EMFG) Test, a model-based likelihood approach, to allow for arbitrary family structures. We modify the MFG test by replacing the nuclear-family based "mating type" approach with Ott's representation of a pedigree likelihood and calculating MFG incompatibility along with the Mendelian transmission probability. In order to allow for extension to arbitrary family structures, we make a slightly more stringent assumption of random mating with respect to the locus of interest. Simulations show that the EMFG test has appropriate type-I error rate, power, and precise parameter estimation when random mating holds. Our simulations and real data example illustrate that the chief advantages of the EMFG test over the earlier nuclear family version of the MFG test are improved accuracy of parameter estimation and power gains in the presence of missing genotypes.

Detection of fetomaternal genotype associations in early-onset disorders: evaluation of different methods and their application to childhood leukemia

Several designs and analytical approaches have been proposed to dissect offspring from maternal genetic contributions to early-onset diseases. However, lack of parental controls halts the direct verification of the assumption of mating symmetry (MS) required to assess maternally-mediated effects. In this study, we used simulations to investigate the performance of existing methods under mating asymmetry (MA) when parents of controls are missing. Our results show that the log-linear, likelihood-based framework using a case-triad/case-control hybrid design provides valid tests for maternal genetic effects even under MA. Using this approach, we examined fetomaternal associations between 29 SNPs in 12 cell-cycle genes and childhood pre-B acute lymphoblastic leukemia (ALL). We identified putative fetomaternal effects at loci CDKN2A rs36228834 (P = .017) and CDKN2B rs36229158 (P = .022) that modulate the risk of childhood ALL. These data further corroborate the importance of the mother's genotype on the susceptibility to early-onset diseases.

Maternal-zygotic epistasis and the evolution of genetic diseases

Many birth defects and genetic diseases are expressed in individuals that do not carry the disease causing alleles. Genetic diseases observed in offspring can be caused by gene expression in mothers and by interactions between gene expression in mothers and offspring. It is not clear whether the underlying pattern of gene expression (maternal versus offspring) affects the incidence of genetic disease. Here we develop a 2-locus population genetic model with epistatic interactions between a maternal gene and a zygotic gene to address this question. We show that maternal effect genes that affect disease susceptibility in offspring persist longer and at higher frequencies in a population than offspring genes with the same effects. We find that specific forms of maternal-zygotic epistasis can maintain disease causing alleles at high frequencies over a range of plausible values. Our findings suggest that the strength and form of epistasis and the underlying pattern of gene expression may greatly influence the prevalence of human genetic diseases.

Evidence for maternal-fetal genotype incompatibility as a risk factor for schizophrenia

Prenatal/obstetric complications are implicated in schizophrenia susceptibility. Some complications may arise from maternal-fetal genotype incompatibility, a term used to describe maternal-fetal genotype combinations that produce an adverse prenatal environment. A review of maternal-fetal genotype incompatibility studies suggests that schizophrenia susceptibility is increased by maternal-fetal genotype combinations at the RHD and HLA-B loci. Maternal-fetal genotype combinations at these loci are hypothesized to have an effect on the maternal immune system during pregnancy which can affect fetal neurodevelopment and increase schizophrenia susceptibility. This article reviews maternal-fetal genotype incompatibility studies and schizophrenia and discusses the hypothesized biological role of these ‘‘incompatibility genes”. It concludes that research is needed to further elucidate the role of RHD and HLA-B maternal-fetal genotype incompatibility in schizophrenia and to identify other genes that produce an adverse prenatal environment through a maternal-fetal genotype incompatibility mechanism. Efforts to develop more sophisticated study designs and data analysis techniques for modeling maternal-fetal genotype incompatibility effects are warranted.

Association of combined maternal-fetal TNF-alpha gene G308A genotypes with preterm delivery: a gene-gene interaction study

Preterm delivery (PTD) is a complicated perinatal adverse event. We were interested in association of G308A polymorphism in tumor necrosis factor-α (TNF-α) gene with PTD; so we conducted a genetic epidemiology study in Anqing City, Anhui Province, China. Case families and control families were all collected between July 1999 and June 2002. To control potential population stratification as we could, all eligible subjects were ethnic Han Chinese. 250 case families and 247 control families were included in data analysis. A hybrid design which combines case-parent triads and control parents was employed, to test maternal-fetal genotype (MFG) incompatibility. The method is based on a log-linear modeling approach. In summary, we found that when the mother's or child's genotype was G/A, there was a reduced risk of PTD; however when the mother's or child's genotype was genotype A/A, there was a relatively higher risk of PTD. Combined maternal-fetal genotype GA/GA showed the most reduced risk of PTD. Comparison of the LRTs showed that the model with maternal-fetal genotype effects fits significantly better than the model with only maternal and fetal genotype main effects (log-likelihood = −719.4, P = .023, significant at 0.05 level). That means that the combined maternal-fetal genotype incompatibility was significantly associated with PTD. The model with maternal-fetal genotype effects can be considered a gene-gene interaction model. We claim that both maternal effects and fetal effects should be considered together while investigating genetic factors of certain perinatal diseases.

Detection of imprinting and heterogeneous maternal effects on high blood pressure using Framingham Heart Study data

Both imprinting and maternal effects could lead to parent-of-origin patterns in complex traits of human disorders. Statistical methods that differentiate these two effects and identify them simultaneously by using family-based data from retrospective studies are available. The usual data structures include case-parents triads and nuclear families with multiple affected siblings. We develop a likelihood-based method to detect imprinting and maternal effects simultaneously using data from prospective studies. The proposed method utilizes both affected and unaffected siblings in nuclear families by modeling familial genotypes and offspring's disease status jointly. Maternal effect is usually modeled as a fixed effect under the assumption that maternal variant allele(s) has (have) identical effect on any offspring. However, recent studies report that different people may carry different amounts of substances encoded by the mother's variant allele(s) (called maternal microchimerism), which could result in heterogeneity of maternal effects. The proposed method incorporates the heterogeneity of maternal effects by adding a random component to the logit of the penetrance. Our method was applied to the Framingham Heart Study data in two steps to detect single-nucleotide polymorphisms (SNPs) that may be associated with high blood pressure. In the first step, SNPs that affect susceptibility of high blood pressure through minor allele, genomic imprinting, or maternal effects were identified by using the proposed model without the random effect component. In the second step, we fitted the mixed effect model to the identified SNPs that have significant maternal effect to detect heterogeneity of the maternal effects.

Likelihood ratio tests for maternal and fetal genetic effects on obstetric complications

It is well recognized that both maternal and fetal genes could contribute to susceptibility for obstetric complications. Logistic regression models are usually adopted to model the separate or joint action of maternal and fetal loci with case-control data. The standard likelihood ratio tests (LRTs) can be used to test the significance of appropriate odds ratio parameters. This method, although simple to implement, fails to exploit a unique feature of genetic epidemiology studies of obstetric complications. Specifically, it does not take into consideration the correlation between the maternal and offspring genomes. We propose novel LRT that take advantage of this information by incorporating the fact that half of a child's genome is inherited from the mother. Our methods have substantially improved power for detecting marginal, main, and interactive maternal and fetal genotype effects, as evidenced by results from extensive simulation studies. We demonstrate our new methods by applying them to the analysis of data from a pilot study of preeclampsia.

Testing Hardy-Weinberg equilibrium using mother-child case-control samples

Genetic association studies of obstetric complications may genotype case and control mothers, or their respective newborns, or both case-control mothers and their children. The relatively high prevalence of many obstetric complications and the availability of both maternal and offspring's genotype data have provided motivation to study new methods for testing for deviations from Hardy-Weinberg equilibrium (HWE). We propose four novel test statistics, each of which uses a different type of data as follows: (1) a test using maternal case-control genotype data, (2) a test using offspring genotype data, (3) a combination of the first and second tests, and (4) a test based on the joint classification of case-control maternal-child genotype data. The selection of case and control mothers (and thus their children) is accounted for by weighting both maternal and child contributions to the test statistics with sampling probabilities. Our tests thus do not require that the phenotype be rare as is the case for HWE tests using only controls, and are particularly suitable for genetic association studies of relatively common complications such as premature birth. The third and fourth tests described above utilize both maternal and child genotype data and appropriately account for the correlation between maternal and child genotypes. On the basis of extensive simulation studies to compare the type-I error and power for proposed tests, we recommend the third combined test statistic for routine use in the analysis of case-control studies of mother-child pairs.

Evidence that TGFA influences risk to cleft lip with/without cleft palate through unconventional genetic mechanisms

This study examined the association between markers in transforming growth factor alpha (TGFA) and isolated, non-syndromic cleft lip with/without palate (CL/P) using a case-parent trio design, considering parent-of-origin effects. We also tested for gene-environmental interaction with common maternal exposures, and for gene-gene interaction using markers in TGFA and another recognized causal gene, IRF6. CL/P case-parent trios from four populations (76 from Maryland, 146 from Taiwan, 35 from Singapore, and 40 from Korea) were genotyped for 17 single nucleotide polymorphisms (SNPs) in TGFA. The transmission disequilibrium test was used to test individual SNPs, and the parent-of-origin likelihood ratio test (PO-LRT) was used to assess parent-of-origin effects. We also screened for possible gene-environment interaction using PBAT, and tested for gene-gene interaction using conditional logistic regression models. When all trios were combined, four SNPs showed significant excess maternal transmission, two of which gave significant PO-LRT values [rs3821261: P = 0.004 and OR(imprinting) = 4.17; and rs3771475: P = 0.027 and OR(imprinting) = 2.44]. Haplotype analysis of these two SNPS also supported excess maternal transmission. We saw intriguing but suggestive evidence of G x E interaction for several SNPs in TGFA when either individual SNPs or haplotypes of adjacent SNPs were considered. Thus, TGFA appears to influence risk of CL/P through unconventional means with an apparent parent-of-origin effect (excess maternal transmission) and possible interaction with maternal exposures.

Differential parental transmission of markers in BCL3 among Korean cleft case-parent trios

OBJECTIVES

Isolated cleft lip with or without cleft palate (CL/P) is among the most common human birth defects, with a prevalence of approximately 1 in 700 live births. The B-Cell Leukemia/lymphoma 3 (BCL3) gene has been suggested as a candidate gene for CL/P based on association and linkage studies in some populations. This study tests for an association between markers in BCL3 and isolated, non-syndromic CL/P using a case-parent trio design, while considering parent-of-origin effects.

METHODS

Forty case-parent trios were genotyped for two single nucleotide polymorphisms (SNPs) in the BCL3 gene. We performed a transmission disequilibrium test (TDT) on individual SNPs, and the FAMHAP package was used to estimate haplotype frequencies and to test for excess transmission of multi-SNP haplotypes.

RESULTS

The odds ratio for transmission of the minor allele, OR (transmission), was significant for SNP rs8100239 (OR=3.50, p=0.004) and rs2965169 (OR=2.08, p=0.027) when parent-of-origin was not considered. Parent-specific TDT revealed that SNP rs8100239 showed excess maternal transmission. Analysis of haplotypes of rs2965169 and rs8100239 also suggested excess maternal transmission.

CONCLUSIONS

BCL3 appears to influence risk of CL/P through a parent-of-origin effect with excess maternal transmission.

A hybrid design: case-parent triads supplemented by control-mother dyads

Hybrid designs arose from an effort to combine the benefits of family-based and population-based study designs. A recently proposed hybrid approach augments case-parent triads with population-based control-parent triads, genotyping everyone except the control offspring. Including parents of controls substantially improves statistical efficiency for testing and estimating both offspring and maternal genetic relative risk parameters relative to using case-parent triads alone. Moreover, it allows testing of required assumptions. Nevertheless, control fathers can be hard to recruit, whereas control offspring and their mothers may be readily available. Consequently, we propose an alternative hybrid design where offspring-mother pairs, instead of parents, serve as population-based controls. We compare the power of our proposed method with several competitors and show that it performs well in various scenarios, though it is slightly less powerful than the hybrid design that uses control parents. We describe approaches for checking whether population stratification will bias inferences that use controls and whether the mating-symmetry assumption holds. Surprisingly, if mating symmetry is violated, even though mating-type parameters cannot be directly estimated using control-mother dyads alone, and maternal effects cannot be estimated using case-parent triads alone, combining both sources of data allows estimation of all the parameters. This hybrid design can also be used to study environmental influences on disease risk and gene-by-environment interactions.

Estimation and testing of gene-environment interactions in family-based association studies

Gene-environment interactions are of interest in genetic association studies for several reasons. First, the power to detect genetic effects may be substantially decreased if those effects differ according to environmental exposure and if no account is taken of this interaction with environmental exposure in the analysis. Second, such interactions may indicate a phenomenon of genuine biological interest (whereby a particular genetic effect operates only in the presence of an environmental trigger, or vice versa), understanding of which can lead us to a greater understanding of possible mechanisms and pathways in disease progression. Here I discuss the testing and estimation of gene-environment interactions via the case/pseudocontrol and related approaches. As originally proposed, the case/pseudocontrol approach applies to case/parents trios with no missing genotype data. I discuss some recent extensions that allow larger pedigree structures with some missing genotype data and present computer simulations to compare the performance of several competing approaches.

Investigating parent of origin effects in studies of type 2 diabetes and obesity

The role of parent-of-origin effects (POE) in the etiology of complex diseases such as type 2 diabetes (T2DM) and obesity is currently of intense interest, but still largely unclear. POE are transmittable genetic effects whereby the expression of the phenotype in the offspring depends upon whether the transmission originated from the mother or father. In mammals, POE can be caused by genetic imprinting, intrauterine effects, or maternally inherited mitochondrial genes. In this paper, we describe the different mechanisms underlying POE, characterize known examples of POE in rare forms of diabetes, and review the evidence from linkage and association studies for POE in T2DM and obesity. Finally, we summarize some of the new and established statistical and experimental approaches commonly used to detect POE. Through this paper, we hope emphasizes the potentially significant importance of POE in the etiology of T2DM and obesity.

Differential parental transmission of markers in RUNX2 among cleft case-parent trios from four populations

Isolated cleft lip with or without cleft palate (CL/P) is among the most common human birth defects, with a prevalence around 1 in 700 live births. The Runt-related transcription factor 2 (RUNX2) gene has been suggested as a candidate gene for CL/P based largely on mouse models; however, no human studies have focused on RUNX2 as a risk factor for CL/P. This study examines the association between markers in RUNX2 and isolated, nonsyndromic CL/P using a case-parent trio design, while considering parent-of-origin effects. Case-parent trios from four populations (77 from Maryland, 146 from Taiwan, 35 from Singapore, and 40 from Korea) were genotyped for 24 single nucleotide polymorphisms (SNPs) in the RUNX2 gene. We performed the transmission disequilibrium test on individual SNPs. Parent-of-origin effects were assessed using the transmission asymmetry test and the parent-of-origin likelihood ratio test (PO-LRT). When all trios were combined, the transmission asymmetry test revealed a block of 11 SNPs showing excess maternal transmission significant at the P<0.01 level, plus one SNP (rs1934328) showing excess paternal transmission (P=0.002). For the 11 SNPs showing excess maternal transmission, odds ratios of being transmitted to the case from the mother ranged between 3.00 and 4.00. The parent-of-origin likelihood ratio tests for equality of maternal and paternal transmission were significant for three individual SNPs (rs910586, rs2819861, and rs1934328). Thus, RUNX2 appears to influence risk of CL/P through a parent-of-origin effect with excess maternal transmission.

Excess maternal transmission of markers in TCOF1 among cleft palate case-parent trios from three populations

Isolated cleft palate is among the most common human birth defects. The TCOF1 gene has been suggested as a candidate gene for cleft palate based on animal models. This study tests for association between markers in TCOF1 and isolated, nonsyndromic cleft palate using a case-parent trio design considering parent-of-origin effects. Case-parent trios from three populations (comprising a total of 81 case-parent trios) were genotyped for single nucleotide polymorphisms (SNPs) in the TCOF1 gene. We used the transmission disequilibrium test and the transmission asymmetry test on individual SNPs. When all trios were combined, the odds ratio for transmission of the minor allele, OR(transmission), was significant for SNP rs15251 (OR = 2.88, P = 0.007), as well as rs2255796 and rs2569062 (OR = 2.08, P = 0.03; OR = 2.43, P = 0.041; respectively) when parent of origin was not considered. The transmission asymmetry test also revealed one SNP (rs15251) showing excess maternal transmission significant at the P = 0.005 level (OR = 6.50). Parent-of-origin effects were assessed using the parent-of-origin likelihood ratio test on both SNPs and haplotypes. While the parent-of-origin likelihood ratio test was only marginally significant for this SNP (P = 0.136), analysis of haplotypes of rs2255796 and rs15251 suggested excess maternal transmission. Therefore, these data suggest TCOF1 may influence risk of cleft palate through a parent-of-origin effect.

Effect of Rhesus D incompatibility on schizophrenia depends on offspring sex

Rhesus D incompatibility increases risk for schizophrenia, with some evidence that risk is limited to male offspring. The purpose of this study is to determine whether risk for schizophrenia due to Rhesus D incompatibility differs by offspring sex using a nuclear family-based candidate gene approach and a meta-analysis approach. The genetic study is based on a sample of 277 nuclear families with RHD genotype data on at least one parent and at least one child diagnosed with schizophrenia or related disorder. Meta-analysis inclusion criteria were (1) well-defined sample of schizophrenia patients with majority born before 1970, (2) Rhesus D incompatibility phenotype or genotype data available on mother and offspring, and by offspring sex. Two of ten studies, plus the current genetic study sample, fulfilled these criteria, for a total of 358 affected males and 226 affected females. The genetic study found that schizophrenia risk for incompatible males was significantly greater than for compatible offspring (p=0.03), while risk for incompatible and compatible females was not significantly different (p=.32). Relative risks for incompatible males and females were not significantly different from each other. Meta-analysis using a larger number of affected males and females supports their difference. Taken together, these results provide further support that risk of schizophrenia due to Rhesus D incompatibility is limited to incompatible males, although a weak female incompatibility effect cannot be excluded. Sex differences during fetal neurodevelopment should be investigated to fully elucidate the etiology of schizophrenia.

Analytical approaches to detect maternal/fetal genotype incompatibilities that increase risk of pre-eclampsia

BACKGROUND

In utero interactions between incompatible maternal and fetal genotypes are a potential mechanism for the onset or progression of pregnancy related diseases such as pre-eclampsia (PE). However, the optimal analytical approach and study design for evaluating incompatible maternal/offspring genotype combinations is unclear.

METHODS

Using simulation, we estimated the type I error and power of incompatible maternal/offspring genotype models for two analytical approaches: logistic regression used with case-control mother/offspring pairs and the log-linear regression used with case-parent triads. We evaluated a real dataset consisting of maternal/offspring pairs with and without PE for incompatibility effects using the optimal analysis based on the results of the simulation study.

RESULTS

We identified a single coding scheme for the incompatibility effect that was equally or more powerful than all of the alternative analysis models evaluated, regardless of the true underlying model for the incompatibility effect. In addition, the log-linear regression was more powerful than the logistic regression when the heritability was low, and more robust to adjustment for maternal or fetal effects. For the PE data, this analysis revealed three genes, lymphotoxin alpha (LTA), von Willebrand factor (VWF), and alpha 2 chain of type IV collagen (COL4A2) with possible incompatibility effects.

CONCLUSION

The incompatibility model should be evaluated for complications of pregnancy, such as PE, where the genotypes of two individuals may contribute to the presence of disease.

Using the maternal-fetal genotype incompatibility test to assess non-inherited maternal HLA-DRB1 antigen coding alleles as rheumatoid arthritis risk factors

Non-inherited maternal antigens encoded by specific HLA-DRB1 alleles (NIMA) have been implicated as a rheumatoid arthritis (RA) risk factor. Using genotype data from North American Rheumatoid Arthritis Consortium study participants and the maternal-fetal genotype incompatibility (MFG) test, we find evidence for offspring allelic effects but no evidence for NIMA as a RA risk factor. We discuss possible reasons why our result conflicts with several previous studies (including one of our own) that used RA patients from northern Europe.

Genetic epidemiologic studies of preterm birth: guidelines for research

Over the last decade, it has become increasingly apparent that the cause of preterm birth is multifactorial, involving both genetic and environmental factors. With the development of new technologies capable of probing the genome, exciting possibilities now present themselves to gain new insight into the mechanisms leading to preterm birth. This review aims to develop research guidelines for the conduct of genetic epidemiology studies of preterm birth with the expectation that this will ultimately facilitate the comparison of data sets between study cohorts, both nationally and internationally. Specifically, the 4 areas addressed in this review includes: (1) phenotypic criteria, (2) study design, (3) considerations in the selection of control populations, and (4) candidate gene selection. This article is the product of discussions initiated by the authors at the 3rd International Workshop on Biomarkers and Preterm Birth held at the University of California, Los Angeles, Los Angeles, CA, in March 2005.

The use of high-dimensional biology (genomics, transcriptomics, proteomics, and metabolomics) to understand the preterm parturition syndrome

High-dimensional biology (HDB) refers to the simultaneous study of the genetic variants (DNA variation), transcription (messenger RNA [mRNA]), peptides and proteins, and metabolites of an organ, tissue, or an organism in health and disease. The fundamental premise is that the evolutionary complexity of biological systems renders them difficult to comprehensively understand using only a reductionist approach. Such complexity can become tractable with the use of "omics" research. This term refers to the study of entities in aggregate. The current nomenclature of "omics" sciences includes genomics for DNA variants, transcriptomics for mRNA, proteomics for proteins, and metabolomics for intermediate products of metabolism. Another discipline relevant to medicine is pharmacogenomics. The two major advances that have made HDB possible are technological breakthroughs that allow simultaneous examination of thousands of genes, transcripts, and proteins, etc., with high-throughput techniques and analytical tools to extract information. What is conventionally considered hypothesis-driven research and discovery-driven research (through "omic" methodologies) are complementary and synergistic. Here we review data which have been derived from: 1) genomics to examine predisposing factors for preterm birth; 2) transcriptomics to determine changes in mRNA in reproductive tissues associated with preterm labour and preterm prelabour rupture of membranes; 3) proteomics to identify differentially expressed proteins in amniotic fluid of women with preterm labour; and 4) metabolomics to identify the metabolic footprints of women with preterm labour likely to deliver preterm and those who will deliver at term. The complementary nature of discovery science and HDB is emphasised.