RHD maternal–fetal genotype incompatibility and schizophrenia: extending the MFG test to include multiple siblings and birth order

The Quantitative-MFG Test: A Linear Mixed Effect Model to Detect Maternal-Offspring Gene Interactions

Maternal-offspring gene interactions, aka maternal-fetal genotype (MFG) incompatibilities, are neglected in complex diseases and quantitative trait studies. They are implicated in birth to adult onset diseases but there are limited ways to investigate their influence on quantitative traits. We present the quantitative-MFG (QMFG) test, a linear mixed model where maternal and offspring genotypes are fixed effects and residual correlations between family members are random effects. The QMFG handles families of any size, common or general scenarios of MFG incompatibility, and additional covariates. We develop likelihood ratio tests (LRTs) and rapid score tests and show they provide correct inference. In addition, the LRT's alternative model provides unbiased parameter estimates. We show that testing the association of SNPs by fitting a standard model, which only considers the offspring genotypes, has very low power or can lead to incorrect conclusions. We also show that offspring genetic effects are missed if the MFG modeling assumptions are too restrictive. With genome-wide association study data from the San Antonio Family Heart Study, we demonstrate that the QMFG score test is an effective and rapid screening tool. The QMFG test therefore has important potential to identify pathways of complex diseases for which the genetic etiology remains to be discovered.

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.

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.

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.

Serious obstetric complications interact with hypoxia-regulated/vascular-expression genes to influence schizophrenia risk

The etiology of schizophrenia is thought to include both epistasis and gene-environment interactions. We sought to test whether a set of schizophrenia candidate genes regulated by hypoxia or involved in vascular function in the brain (AKT1, BDNF, CAPON, CHRNA7, COMT, DTNBP1, GAD1, GRM3, NOTCH4, NRG1, PRODH, RGS4, TNF-alpha) interacted with serious obstetric complications to influence risk for schizophrenia. A family-based study of transmission disequilibrium was conducted in 116 trios. Twenty-nine probands had at least one serious obstetric complication (OC) using the McNeil-Sjostrom Scale, and many of the OCs reported were associated with the potential for fetal hypoxia. Analyses were conducted using conditional logistic regression and a likelihood ratio test (LRT) between nested models was performed to assess significance. Of the 13 genes examined, four (AKT1 (three SNPs), BDNF (two SNPs), DTNBP1 (one SNP) and GRM3 (one SNP)) showed significant evidence for gene-by-environment interaction (LRT P-values ranged from 0.011 to 0.037). Although our sample size was modest and the power to detect interactions was limited, we report significant evidence for genes involved in neurovascular function or regulated by hypoxia interacting with the presence of serious obstetric complications to increase risk for schizophrenia.

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 liability to schizophrenia in Oceanic Palau: a search in the affected and maternal generation

While liability to schizophrenia (Scz) is due to genetic and environmental factors, specific factors are largely unknown. We postulate a two-hit model for Scz, in which initial liability is generated during fetal brain development: this "hit" is precipitated by environmental stressors biologically interacting with maternal genetic vulnerability to the stress. Additional liability to Scz is generated by individual genetic vulnerability. To evaluate these putative levels of vulnerability, we search in the genome of both affected individuals and their mothers for variation that differs, statistically, from that in the general population. For parental analyses, mothers were treated as "affected," rather than their offspring, and the fathers were treated as "controls". We used a sample from the Palauan population: 175 individuals diagnosed with Scz, broadly defined; 87 mothers and 45 fathers of affected individuals. Pedigree and diagnostic data were available on 2,953 living and deceased subjects. DNA from 553 individuals was genotyped for short tandem repeats (STR) spaced approximately every 10 cM across the genome. We tested for association between affection status and STR alleles; such an approach was reasonable, despite the widely spaced markers, because this population has far-ranging linkage disequilibrium (LD). Results for the truly affected individuals were modest, whereas results from the maternal generation were promising. For a recessive model and a test for excess allele matching across mothers, significant findings occurred for D20S481, D10S1221, D6S1021, D13S317, and D18S976. Regions in which at least two adjacent markers produced substantial association statistics include 2p12-11.2, 2q24.1-32.1, 6q12-14.1, 10q23.2-24.21, 12q23.2-24.21 and 17q23.2-23.3.

Lack of association between Rh status, Rh immune globulin in pregnancy and autism

Though causes of autism are considered largely genetic, considerable concern remains that exposure to Rh immune globulin (RhIg), which until 2001 in the United States contained the preservative thimerosal, can cause autism. To determine whether mothers of children with autism are more likely to be Rh negative (Rh(-)) or to have received RhIg preserved with thimerosal, which is 49.6% ethyl mercury, we surveyed families of children with an autism spectrum disorder (ASD) ascertained through a University-based autism clinic considered free of ascertainment biases related to type of autism or severity. Between 2004 and 2006, 305 mothers of 321 children with an ASD agreed to participate in a telephone interview. Analysis of complete records including the blood group status and RhIg exposure of 214 families showed that Rh(-) status is no higher in mothers of children with autism than in the general population, exposure to antepartum RhIg, preserved with thimerosal is no higher for children with autism and pregnancies are no more likely to be Rh incompatible. This was also true for autism subgroups defined by behavioral phenotype, gender, IQ, regressive onset, head circumference, dysmorphology, birth status, essential, or complex phenotype. These findings support the consensus that exposure to ethylmercury in thimerosal is not the cause of the increased prevalence of autism. These data are important not only for parents in this country but also for the international health community where thimerosal continues to be used to preserve multi-dose vials which in turn makes vaccines affordable.

Allowing for missing data at highly polymorphic genes when testing for maternal, offspring and maternal-fetal genotype incompatibility effects

Genes can be associated with disease through an individual's inherited genotype, the maternal genotype or the interaction between these two. When the gene is highly polymorphic, it is more difficult to identify the gene's functional role than for less polymorphic loci, because different alleles at the locus may be associated with the disease through separate and joint effects from maternal and offspring genotypes. Family-based studies are used to test genetic associations because of their robustness to population stratification. However, parental genotype data are often missing, and omitting incompletely genotyped families is inefficient. Methods have been proposed to accommodate incomplete families in family-based association studies. They are not easily generalized to allow simultaneous examination of offspring allelic, maternal allelic and maternal-fetal genotype (MFG) incompatibility effects. Since many MFG incompatibility effects occur through matching between maternal and offspring's genotypes, we present an identity-by-state (IBS) framework to incorporate incomplete families in the MFG test when modeling genetic effects produced by a polymorphic gene. Using simulations, we examine the MFG test's performance with incomplete parental genotype data and an IBS framework. The MFG test using the IBS framework is immune to population stratification and efficiently uses information from incomplete families.

HLA-B maternal-fetal genotype matching increases risk of schizophrenia

Schizophrenia and human leukocyte antigen (HLA) matching between couples or between mothers and offspring have independently been associated with prenatal/obstetric complications, including preeclampsia and low birth weight. Here, we report the results of a family-based candidate-gene study that brings together these two disparate lines of research by assessing maternal-fetal genotype matching at HLA-A, -B, and -DRB1 as a risk factor of schizophrenia. We used a conditional-likelihood modeling approach with a sample of 274 families that had at least one offspring with schizophrenia or a related spectrum disorder. A statistically significant HLA-B maternal-fetal genotype-matching effect on schizophrenia was demonstrated for female offspring (P=.01; parameter estimate 1.7 [95% confidence interval 1.22-2.49]). Because the matching effect could be associated with pregnancy complications rather than with schizophrenia per se, these findings are consistent with the neurodevelopmental hypothesis of schizophrenia and with accumulating evidence that the prenatal period is involved in the origins of this disease. Our approach demonstrates how genetic markers can be used to characterize the biology of prenatal risk factors of schizophrenia.

The v-MFG test: investigating maternal, offspring and maternal-fetal genetic incompatibility effects on disease and viability

The MFG test is a family-based association test that detects genetic effects contributing to disease in offspring, including offspring allelic effects, maternal allelic effects and MFG incompatibility effects. Like many other family-based association tests, it assumes that the offspring survival and the offspring-parent genotypes are conditionally independent provided the offspring is affected. However, when the putative disease-increasing locus can affect another competing phenotype, for example, offspring viability, the conditional independence assumption fails and these tests could lead to incorrect conclusions regarding the role of the gene in disease. We propose the v-MFG test to adjust for the genetic effects on one phenotype, e.g., viability, when testing the effects of that locus on another phenotype, e.g., disease. Using genotype data from nuclear families containing parents and at least one affected offspring, the v-MFG test models the distribution of family genotypes conditional on offspring phenotypes. It simultaneously estimates genetic effects on two phenotypes, viability and disease. Simulations show that the v-MFG test produces accurate genetic effect estimates on disease as well as on viability under several different scenarios. It generates accurate type-I error rates and provides adequate power with moderate sample sizes to detect genetic effects on disease risk when viability is reduced. We demonstrate the v-MFG test with HLA-DRB1 data from study participants with rheumatoid arthritis (RA) and their parents, we show that the v-MFG test successfully detects an MFG incompatibility effect on RA while simultaneously adjusting for a possible viability loss.

Incorporating serotypes into family based association studies using the MFG test

Family based association tests are widely used to detect genetic effects. The focus of this paper is the maternal-fetal genotype (MFG) incompatibility test, a family based association test which can be used to detect genetic effects that contribute to disease, including alleles in the child that increase disease risk, maternal alleles that increase disease risk in the child, and maternal-fetal genotype incompatibilities. Consideration of incomplete data resulting from using serotypes could expand the power of the MFG test for detecting genetic effects. Serotypes may be all that are available in certain families, or preferred because of convenience or low cost, and thus a modification of the MFG test will allow optimal use of such data. The modified MFG likelihood can accommodate the incomplete data that result from using serotypes rather than the corresponding codominant genotypes. The modified MFG test was evaluated with serotypes and genotypes from families with members affected with schizophrenia. In addition, simulation studies were performed. Results of the data analyses and simulation studies showed that serotypes can be used to augment genotypes within a sample, to increase power to detect effects when the candidate gene produces serotypes.

Estimating haplotype relative risks in complex disease from unphased SNPs data in families using a likelihood adjusted for ascertainment

The understanding of complex diseases and insights to improve their medical management may be achieved through the deduction of how specific haplotypes may play a joint effect to change relative risk information. In this paper we describe an ascertainment adjusted likelihood-based method to estimate haplotype relative risks using pooled family data coming from association and/or linkage studies that were used to identify specific haplotypes. Haplotype-based analysis tends to require a large amount of parameters to capture all the information that leads to efficiency problems. An adaptation of the Stochastic Expectation Maximization algorithm is used for haplotypes inference from genotypic data and to reduce the number of nuisance parameters for risk estimation. Using different simulations, we show that this method provides unbiased relative risk estimates even in case of departure from Hardy-Weinberg equilibrium.

Maternal-fetal blood incompatibility and the risk of schizophrenia in offspring

OBJECTIVE

Predicated on a maternal immune response to paternally inherited foreign fetal blood antigens, we hypothesized that maternal-fetal blood incompatibility increases susceptibility to schizophrenia in the offspring. The relation between schizophrenia and maternal-fetal blood incompatibility, arising from the D antigen of the Rhesus (Rh) and the ABO blood group antigens, was examined in a cohort of live-births.

METHOD

The data were drawn from the Prenatal Determinants of Schizophrenia Study, a cohort of births occurring between 1959 and 1967 to women enrolled in a Kaiser Permanente Plan-Northern California Region (KP). Adult offspring belonging to the KP from 1981 to 1997 were followed for the incidence of schizophrenia spectrum disorder (SSD). Cox proportional hazards regression was the primary analytic technique.

RESULTS

Among second and later born offspring, the adjusted incidence rate ratio (RR(adj)) of SSD was 1.80 (95% CI=0.71-4.58) for the Rh incompatible offspring compared with the Rh compatible offspring; with the males exhibiting higher rate ratio (RR(adj)=2.37; 95% CI=0.82-6.86) than the females (RR(adj)=0.93 95% CI=0.12-7.01). Among all offspring, the RR(adj) for ABO incompatibility was lower and the elevated rate ratio was similarly limited to the males (RR(adj)=1.68; 95% CI=0.76-3.70). For Rh and/or ABO incompatibility, the RR(adj) was 1.57 (95% CI=0.87-2.82). A statistically significant result was detected only for the male offspring (RR(adj)=2.22; 95% CI=1.10-4.47).

CONCLUSION

Although the results should be interpreted with caution given the few events of SSD, the findings extend the line of evidence that maternal-fetal blood incompatibility is a risk factor for schizophrenia spectrum disorder; with the strongest evidence to date implicating that the susceptibility pertains only to male offspring.

An exact maternal-fetal genotype incompatibility (MFG) test

The maternal-fetal genotype incompatibility (MFG) test can be used for a variety of genetic applications concerning disease risk in offspring including testing for the presence of alleles that act directly through offspring genotypes (child allelic effects), alleles that act through maternal genotypes (maternal allelic effects), or maternal-fetal genotype incompatibilities. The log-linear version of the MFG model divides the genotype data into many cells, where each cell represents one of the possible mother, father, and child genotype combinations. Currently, tests of hypotheses about different allelic effects are accomplished by an asymptotic MFG test, but it is unknown if this is appropriate under conditions that produce small cell counts. In this report, we develop an exact MFG test that is based on the permutation distribution of cell counts. We determine by simulation the type I error and power of both the exact MFG test and the asymptotic MFG test for four different biologically relevant scenarios: a test of child allelic effects in the presence of maternal allelic effects, a test of maternal allelic effects in the presence of child allelic effects, and tests of maternal-fetal genotype incompatibility with and without child allelic effects. These simulations show that, in general, the exact test is slightly conservative whereas the asymptotic test is slightly anti-conservative. However, the asymptotic MFG test produces significantly inflated type I error rates under conditions with extreme null allele frequencies and sample sizes of 75, 100, and 150. Under these conditions, the exact test is clearly preferred over the asymptotic test. Under all other conditions that we tested, the user can safely choose either the exact test or the asymptotic test.

Exploiting gene-environment independence in family-based case-control studies: increased power for detecting associations, interactions and joint effects

Family-based case-control studies are popularly used to study the effect of genes and gene-environment interactions in the etiology of rare complex diseases. We consider methods for the analysis of such studies under the assumption that genetic susceptibility (G) and environmental exposures (E) are independently distributed of each other within families in the source population. Conditional logistic regression, the traditional method of analysis of the data, fails to exploit the independence assumption and hence can be inefficient. Alternatively, one can estimate the multiplicative interaction between G and E more efficiently using cases only, but the required population-based G-E independence assumption is very stringent. In this article, we propose a novel conditional likelihood framework for exploiting the within-family G-E independence assumption. This approach leads to a simple and yet highly efficient method of estimating interaction and various other risk parameters of scientific interest. Moreover, we show that the same paradigm also leads to a number of alternative and even more efficient methods for analysis of family-based case-control studies when parental genotype information is available on the case-control study participants. Based on these methods, we evaluate different family-based study designs by examining their relative efficiencies to each other and their efficiencies compared to a population-based case-control design of unrelated subjects. These comparisons reveal important design implications. Extensions of the methodologies for dealing with complex family studies are also discussed.