Missing Parental Genotypes Research Articles

Child-Driven Parenting: Differential Early Childhood Investment by Offspring Genotype

Abstract A growing literature points to children’s influence on parents’ behavior, including parental investments in children. Further, previous research has shown differential parental response by socioeconomic status to children’s birth weight, cognitive ability, and school outcomes—all early life predictors of later socioeconomic success. This study considers an even earlier, more exogenous predictor of parental investments: offspring genotype. Specifically, we analyze (1) whether children’s genetic propensity toward educational success affects parenting during early childhood and (2) whether parenting in response to children’s genetic propensity toward educational success is socially stratified. Using data from the Avon Longitudinal Survey of Parents and Children (N = 6,247), we construct polygenic indexes (PGIs) for educational attainment (EA) and regress cognitively stimulating parenting behavior during early childhood on these PGIs. We apply Mendelian imputation to construct the missing parental genotype. This approach allows us to control for both parents’ PGIs for EA and thereby achieve a natural experiment: Conditional on parental genotype, the offspring genotype is randomly assigned. In this way, we eliminate the possibility that child’s genotype may be proxying unmeasured parent characteristics. Results differ by parenting behavior: (1) parents’ singing to the child is not affected by the child’s EA PGI, (2) parents play more with children with higher EA PGIs, and (3) non-college-educated parents read more to children with higher education PGIs, while college-educated parents respond less to children’s EA PGI.

Mendelian imputation of parental genotypes improves estimates of direct genetic effects

Effects estimated by genome-wide association studies (GWASs) include effects of alleles in an individual on that individual (direct genetic effects), indirect genetic effects (for example, effects of alleles in parents on offspring through the environment) and bias from confounding. Within-family genetic variation is random, enabling unbiased estimation of direct genetic effects when parents are genotyped. However, parental genotypes are often missing. We introduce a method that imputes missing parental genotypes and estimates direct genetic effects. Our method, implemented in the software package snipar (single-nucleotide imputation of parents), gives more precise estimates of direct genetic effects than existing approaches. Using 39,614 individuals from the UK Biobank with at least one genotyped sibling/parent, we estimate the correlation between direct genetic effects and effects from standard GWASs for nine phenotypes, including educational attainment (r = 0.739, standard error (s.e.) = 0.086) and cognitive ability (r = 0.490, s.e. = 0.086). Our results demonstrate substantial confounding bias in standard GWASs for some phenotypes.

Modeling Parent-Specific Genetic Nurture in Families with Missing Parental Genotypes: Application to Birthweight and BMI.

Disaggregation and estimation of genetic effects from offspring and parents has long been of interest to statistical geneticists. Recently, technical and methodological advances have made the genome-wide and loci-specific estimation of direct offspring and parental genetic nurture effects more possible. However, unbiased estimation using these methods requires datasets where both parents and at least one child have been genotyped, which are relatively scarce. Our group has recently developed a method and accompanying software (IMPISH; Hwang et al. in PLoS Genet 16:e1009154, 2020) which is able to impute missing parental genotypes from observed data on sibships and estimate their effects on an offspring phenotype conditional on the effects of genetic transmission. However, this method is unable to disentangle maternal and paternal effects, which may differ in magnitude and direction. Here, we introduce an extension to the original IMPISH routine which takes advantage of all available nuclear families to impute parent-specific missing genotypes and obtain asymptotically unbiased estimates of genetic effects on offspring phenotypes. We apply this this method to data from related individuals in the UK Biobank, showing concordance with previous estimates of maternal genetic effects on offspring birthweight. We also conduct the first GWAS jointly estimating offspring-, maternal-, and paternal-specific genetic effects on body-mass index.

Rare variant association analysis in case-parents studies by allowing for missing parental genotypes

BackgroundThe development of next-generation sequencing technologies has facilitated the identification of rare variants. Family-based design is commonly used to effectively control for population admixture and substructure, which is more prominent for rare variants. Case-parents studies, as typical strategies in family-based design, are widely used in rare variant-disease association analysis. Current methods in case-parents studies are based on complete case-parents data; however, parental genotypes may be missing in case-parents trios, and removing these data may lead to a loss in statistical power. The present study focuses on testing for rare variant-disease association in case-parents study by allowing for missing parental genotypes.ResultsIn this report, we extended the collapsing method for rare variant association analysis in case-parents studies to allow for missing parental genotypes, and investigated the performance of two methods by using the difference of genotypes between affected offspring and their corresponding “complements” in case-parent trios and TDT framework. Using simulations, we showed that, compared with the methods just only using complete case-parents data, the proposed strategy allowing for missing parental genotypes, or even adding unrelated affected individuals, can greatly improve the statistical power and meanwhile is not affected by population stratification.ConclusionsWe conclude that adding case-parents data with missing parental genotypes to complete case-parents data set can greatly improve the power of our strategy for rare variant-disease association.

Polyunphased: an extension to polytomous outcomes of the Unphased package for family-based genetic association analysis.

Polytomous phenotypes arise when a disease has multiple subtypes or when two dichotomous phenotypes are analyzed simultaneously. Few software programs offer the option to analyze such phenotypes in family studies, and none implements conditional polytomous logistic regression for within-family analysis robust to population stratification. We introduce Polyunphased, an extension to polytomous phenotypes of the Unphased package, a flexible software tool for genetic association analysis in nuclear families. Like Unphased, Polyunphased is written in C++ and runs from the command line or from a Java graphical user interface. Most Unphased options remain available in Polyunphased, including those handling missing parental genotypes while preserving robustness to population stratification, and the modelling options. Simulation studies confirmed the expected statistical behaviour of the maximum likelihood estimates of the association parameters of the conditional logistic regression model when the corresponding association parameters in the parental term of the likelihood function are set to 0, but revealed convergence problems when estimating these parental association parameters separately. The former approach is thus recommended with polytomous phenotypes.

Single Marker Family-Based Association Analysis Conditional on Parental Information.

Family-based designs have been commonly used in association studies. Different family structures such as extended pedigrees and nuclear families, including parent-offspring triads and families with multiple affected siblings (multiplex families), can be ascertained for family-based association analysis. Flexible association tests that can accommodate different family structures have been proposed. The pedigree disequilibrium test (PDT) (Martin et al., Am J Hum Genet 67:146-154, 2000) can use full genotype information from general (possibly extended) pedigrees with one or multiple affected siblings but requires parental genotypes or genotypes of unaffected siblings. On the other hand, the association in the presence of linkage (APL) test (Martin et al., Am J Hum Genet 73:1016-1026, 2003) is restricted to nuclear families with one or more affected siblings but can infer missing parental genotypes properly by accounting for identity-by-descent (IBD) parameters. Both the PDT and APL test are powerful association tests in the presence of linkage and can be used as complementary tools for association analysis. This chapter introduces these two tests and compares their properties. Recommendations and notes for performing the tests in practice are provided.

Learning about the X from our parents.

The X chromosome is generally understudied in association studies, in part because the analyst has had limited methodological options. For nuclear-family-based association studies, most current methods extend the transmission disequilibrium test (TDT) to the X chromosome. We present a new method to study association in case-parent triads: the parent-informed likelihood ratio test for the X chromosome (PIX-LRT). Our method enables estimation of relative risks and takes advantage of parental genotype information and the sex of the affected offspring to increase statistical power to detect an effect. Under a parental exchangeability assumption for the X, if case-parent triads are complete, the parents of affected offspring provide an independent replication sample for estimates based on transmission distortion to their affected offspring. For each offspring sex we combine the parent-level and the offspring-level information to form a likelihood ratio test statistic; we then combine the two to form a combined test statistic. Our method can estimate relative risks under different modes of inheritance or a more general co-dominant model. In triads with missing parental genotypes, the method accounts for missingness with the Expectation-Maximization algorithm. We calculate non-centrality parameters to assess the power gain and robustness of our method compared to alternative methods. We apply PIX-LRT to publically available data from an international consortium of genotyped families affected by the birth defect oral cleft and find a strong, internally-replicated signal for a SNP marker related to cleft lip with or without cleft palate.

Powerful tests for association on quantitative trait loci incorporating imprinting effects

Genomic imprinting is an important epigenetic factor in complex traits study, and there has recently been considerable interest in association study for quantitative traits by incorporating imprinting. However, these methods need the assumptions of Hardy-Weinberg equilibrium or only use information from families with one child. In this paper, by taking imprinting into account and making no assumption about the distribution of the quantitative traits, we propose two novel classes of Q-C-TDTI(c) and Q-C-MAX(c) family-based association tests for quantitative traits. The tests flexibly accommodate family data with missing parental genotype and with multiple siblings. Q-C-TDTI(c) is derived from a two-stage analysis, where in the first stage Q-C-PAT(c) is applied to test for imprinting effects and in the second stage we select the most appropriate statistic among three transmission disequilibrium tests for association according to the finding from Q-C-PAT(c). Another proposed Q-C-MAX(c) approach takes the maximum of the three statistics. Compared with the existing alternative methods, the simulation results demonstrate that the two proposed tests are robust to population stratification and have better performance for testing association under various scenarios. Further, the powerful and versatile Q-C-TDTI(c) test is applied to analyze Framingham Heart Study data.

A powerful parent-of-origin effects test for qualitative traits incorporating control children in nuclear families

Genomic imprinting is an important epigenetic phenomenon in studying complex traits and has generally been examined by detecting parent-of-origin effects of alleles. The parental-asymmetry test (PAT) based on nuclear families with both parents and its extensions to deal with missing parental genotypes is simple and powerful for such a task. However, these methods only use case (affected) children in nuclear families and thus do not make full use of information on control (unaffected) children, if available, in these families. In this article, we propose a novel parent-of-origin effects test C-PATu (the combined test of PATu and 1-PATu) by using both the control and case children in nuclear families with one or both parents. C-PATu is essentially a weighted framework, in which the test based on all the control children and their parents and that based on all the case children and their parents are weighted according to the population disease prevalence. Simulation results demonstrate that the proposed tests control the size well under no parent-of-origin effects and using additional information from control children improves the power of the tests under the imprinting alternative. Application of C-PATu to a Framingham Heart Study data set further shows the feasibility in practical application of the test.

The (TAAAA)npolymorphism of sex hormone-binding globulin gene is not associated with testicular maldescent

The aim of this family-based study was to investigate the potential association/genetic linkage of the (TAAAA)n polymorphism of sex hormone-binding globulin gene proximal promoter with testicular maldescent (TMD). Genomic DNA was extracted from the peripheral blood of 487 subjects (174 index families): (i) 180 children with all phenotypes of TMD, (ii) 307 parents (156 mothers and 151 fathers). Conventional polymerase chain reaction amplification products were electrophoresed on 10% nondenaturating polyacrylamide gel and visualised by silver staining. After excluding ambiguous parental-child trios and most cases of index families with missing parental genotypes, 429 individuals were left for analysis: 138 completely typed nuclear families (five included a second affected child) and five child-parent couples (one parent missing). Eight fathers presented history of TMD, that is, a total of 156 cases with TMD were analysed. Alleles were analysed with the affected family-based control method and logistic regression-based extension of the transmission disequilibrium test for multiallelic loci. (ΤΑΑΑΑ)n polymorphism analysis revealed six alleles based on repeat numbers (n=5-10). No association/genetic linkage between the (TAAAA)n polymorphism and TMD was detected. Other factors should be investigated to potentially explain the genetic predisposition that seems to exist in at least a subgroup of these patients.

ParentChecker: a computer program for automated inference of missing parental genotype calls and linkage phase correction

BackgroundAccurate genetic maps are the cornerstones of genetic discovery, but their construction can be hampered by missing parental genotype information. Inference of parental haplotypes and correction of phase errors can be done manually on a one by one basis with the aide of current software tools, but this is tedious and time consuming for the high marker density datasets currently being generated for many crop species. Tools that help automate the process of inferring parental genotypes can greatly speed the process of map building. We developed a software tool that infers and outputs missing parental genotype information based on observed patterns of segregation in mapping populations. When phases are correctly inferred, they can be fed back to the mapping software to quickly improve marker order and placement on genetic maps.ResultsParentChecker is a user-friendly tool that uses the segregation patterns of progeny to infer missing genotype information of parental lines that have been used to construct a mapping population. It can also be used to automate correction of linkage phase errors in genotypic data that are in ABH format.ConclusionParentChecker efficiently improves genetic mapping datasets for cases where parental information is incomplete by automating the process of inferring missing genotypes of inbred mapping populations and can also be used to correct linkage phase errors in ABH formatted datasets.

The Transmission Disequilibrium/Heterogeneity Test with Parental-Genotype Reconstruction for Refined Genetic Mapping of Complex Diseases

In linkage analysis for mapping genetic diseases, the transmission/disequilibrium test (TDT) uses the linkage disequilibrium (LD) between some marker and trait loci for precise genetic mapping while avoiding confounding due to population stratification. The sib-TDT (S-TDT) and combined-TDT (C-TDT) proposed by Spielman and Ewens can combine data from families with and without parental marker genotypes (PMGs). For some families with missing PMG, the reconstruction-combined TDT (RC-TDT) proposed by Knapp may be used to reconstruct missing parental genotypes from the genotypes of their offspring to increase power and to correct for potential bias. In this paper, we propose a further extension of the RC-TDT, called the reconstruction-combined transmission disequilibrium/heterogeneity (RC-TDH) test, to take into account the identical-by-descent (IBD) sharing information in addition to the LD information. It can effectively utilize families with missing or incomplete parental genetic marker information. An application of this proposed method to Genetic Analysis Workshop 14 (GAW14) data sets and extensive simulation studies suggest that this approach may further increase statistical power which is particularly valuable when LD is unknown and/or when some or all PMGs are not available.

Genetic association of the AKT1 gene with schizophrenia in a British population

A number of studies have reported a genetic association of the AKT1 gene with schizophrenia, although some have failed to replicate the AKT1 association. This study was undertaken to further explore the AKT1 association with more single nucleotide polymorphisms in a British sample. A total of 221 families, consisting of 148 fathers, 204 mothers and 222 offspring affected with schizophrenia, were recruited for genetic analysis. Analysis for allelic and haplotypic associations was performed with the UNPHASED program, using likelihood-based association analysis for nuclear families with missing parental genotype data. Allelic association was detected at rs1130214 (chi(2)=6.28, P=0.012) and at rs11847866 (chi(2)=4.64, P=0.031), although the remaining single nucleotide polymorphisms did not show allelic association with schizophrenia. The global P value of overall associations was 0.059 after 10000 permutations. Assessment using the Haploview program revealed rs1130214, rs2494746 and rs11847866 in the same linkage disequilibrium block and haplotype analysis showed disease association for the rs1130214-rs2494746-rs11847866 haplotypes (chi(2)=10.18, d.f.=4, P=0.037), of which the T-G-A haplotype was excessively transmitted (chi(2)=6.93, uncorrected P=0.008) and this haplotypic association survived Bonferroni correction (P=0.04). The present results provide further evidence to support the AKT1 association with schizophrenia.

Ordered‐subset analysis (OSA) for family‐based association mapping of complex traits

Association analysis provides a powerful tool for complex disease gene mapping. However, in the presence of genetic heterogeneity, the power for association analysis can be low since only a fraction of the collected families may carry a specific disease susceptibility allele. Ordered-subset analysis (OSA) is a linkage test that can be powerful in the presence of genetic heterogeneity. OSA uses trait-related covariates to identify a subset of families that provide the most evidence for linkage. A similar strategy applied to genetic association analysis would likely result in increased power to detect association. Association in the presence of linkage (APL) is a family-based association test (FBAT) for nuclear families with multiple affected siblings that properly infers missing parental genotypes when linkage is present. We propose here APL-OSA, which applies the OSA method to the APL statistic to identify a subset of families that provide the most evidence for association. A permutation procedure is used to approximate the distribution of the APL-OSA statistic under the null hypothesis that there is no relationship between the family-specific covariate and the family-specific evidence for allelic association. We performed a comprehensive simulation study to verify that APL-OSA has the correct type I error rate under the null hypothesis. This simulation study also showed that APL-OSA can increase power relative to other commonly used association tests (APL, FBAT and FBAT with covariate adjustment) in the presence of genetic heterogeneity. Finally, we applied APL-OSA to a family study of age-related macular degeneration, where cigarette smoking was used as a covariate.

Variation in the miRNA-433 Binding Site of FGF20 Confers Risk for Parkinson Disease by Overexpression of α-Synuclein

Parkinson disease (PD) is a common neurodegenerative disorder caused by environmental and genetic factors. We have previously shown linkage of PD to chromosome 8p. Subsequently, fibroblast growth factor 20 (FGF20) at 8p21.3-22 was identified as a risk factor in several association studies. To identify the risk-conferring polymorphism in FGF20, we performed genetic and functional analysis of single-nucleotide polymorphisms within the gene. In a sample of 729 nuclear families with 1089 affected and 1165 unaffected individuals, the strongest evidence of association came from rs12720208 in the 3' untranslated region of FGF20. We show in several functional assays that the risk allele for rs12720208 disrupts a binding site for microRNA-433, increasing translation of FGF20 in vitro and in vivo. In a cell-based system and in PD brains, this increase in translation of FGF20 is correlated with increased alpha-synuclein expression, which has previously been shown to cause PD through both overexpression and point mutations. We suggest a novel mechanism of action for PD risk in which the modulation of the susceptibility gene's translation by common variations interfere with the regulation mechanisms of microRNA. We propose this is likely to be a common mechanism of genetic modulation of individual susceptibility to complex disease.

Maternal and fetal variation in genes of cholesterol metabolism is associated with preterm delivery

To examine the contribution of variants in fetal and maternal cholesterol metabolism genes in preterm delivery (PTD). A total of 40 single-nucleotide polymorphisms (SNPs) in 16 genes related to cholesterol metabolism were examined for 414 preterm infants (gestational ages 22 to 36 weeks; comprising 305 singletons and 109 twins) and at least 1 parent. Fetal effects were assessed using the transmission disequilibrium test (TDT) for each SNP, followed by a log linear model-based approach to utilize families with missing parental genotypes for those SNPs showing significance under TDT. Genetic variant effects were examined for a role in PTD, gestational age and birth weight. Maternal effects were estimated using a log linear model-based approach. Among singleton gestations, suggestive association (P<0.01 without adjusting for multiple comparisons) was found between birth weight and fetal DHCR7 gene/SNP combinations (rs1630498, P=0.002 and rs2002064, P=0.003). Among all gestations, suggestive associations were found between PTD and fetal HMGCR (rs2303152, P=0.002) and APOA1 (rs 5070, P=0.004). The result for HMGCR was further supported by the log linear model-based test in the single births (P=0.007) and in all births (P=0.006). New associations (APOE and ABCA1) were observed when birth weight was normalized for gestational age suggesting independent effects of variants on birth weight separate from effects on PTD. Testing for maternally mediated genetic effects has identified suggestive association between ABCA1 (rs4149313, P=0.004) and decreased gestational age. Variants in maternal and fetal genes for cholesterol metabolism were associated with PTD and decreased birth weight or gestational age in this study. Genetic markers may serve as one mechanism to identify high-risk mothers and fetuses for targeted nutritional treatment and/or prevention of low birth weight or PTD.

X-APL: An Improved Family-Based Test of Association in the Presence of Linkage for the X Chromosome

Family-based association methods have been developed primarily for autosomal markers. The X-linked sibling transmission/disequilibrium test (XS-TDT) and the reconstruction-combined TDT for X-chromosome markers (XRC-TDT) are the first association-based methods for testing markers on the X chromosome in family data sets. These are valid tests of association in family triads or discordant sib pairs but are not theoretically valid in multiplex families when linkage is present. Recently, XPDT and XMCPDT, modified versions of the pedigree disequilibrium test (PDT), were proposed. Like the PDT, XPDT compares genotype transmissions from parents to affected offspring or genotypes of discordant siblings; however, the XPDT can have low power if there are many missing parental genotypes. XMCPDT uses a Monte Carlo sampling approach to infer missing parental genotypes on the basis of true or estimated population allele frequencies. Although the XMCPDT was shown to be more powerful than the XPDT, variability in the statistic due to the use of an estimate of allele frequency is not properly accounted for. Here, we present a novel family-based test of association, X-APL, a modification of the test for association in the presence of linkage (APL) test. Like the APL, X-APL can use singleton or multiplex families and properly infers missing parental genotypes in linkage regions by considering identity-by-descent parameters for affected siblings. Sampling variability of parameter estimates is accounted for through a bootstrap procedure. X-APL can test individual marker loci or X-chromosome haplotypes. To allow for different penetrances in males and females, separate sex-specific tests are provided. Using simulated data, we demonstrated validity and showed that the X-APL is more powerful than alternative tests. To show its utility and to discuss interpretation in real-data analysis, we also applied the X-APL to candidate-gene data in a sample of families with Parkinson disease.

Effect of linkage disequilibrium between markers in linkage and association analyses

Contributions to Group 17 of the Genetic Analysis Workshop 15 considered dense markers in linkage disequilibrium (LD) in the context of either linkage or association analysis. Three contributions reported on methods for modeling LD or selecting a subset of markers in linkage equilibrium to perform linkage analysis. When all markers were used without modeling LD, inflated evidence for linkage was observed when parental genotypes were missing. All methods for handling LD led to some decreased linkage evidence. Two groups performed a genome-wide association scan using either mixed models to account for known or unknown relatedness between individuals, trend tests or combination statistics. All methods failed to detect four of the eight simulated loci because of low LD in some regions. Three groups performed association analysis using simulated dense markers on chromosome 6, where a simulated HLA-DRB1 locus played a major role in disease susceptibility along with two additional loci of smaller effect. The overall conditional genotype method correctly identified both additional loci while a novel transmission disequilibrium test-statistic to combine studies with non-overlapping markers identified one HLA locus after stratifying on the parental HLA-DRB1 genotypes; LD mapping using the Malécot model mapped two loci in this region, even when using greatly reduced marker density. While LD between markers appears to be a nuisance that may cause spurious linkage results with missing parental genotypes in linkage analysis, association analysis thrives on LD, and disease genes fail to be detected in regions of low LD.

Power calculations for likelihood ratio tests for offspring genotype risks, maternal effects, and parent-of-origin (POO) effects in the presence of missing parental genotypes when unaffected siblings are available.

Genotype-based likelihood-ratio tests (LRT) of association that examine maternal and parent-of-origin effects have been previously developed in the framework of log-linear and conditional logistic regression models. In the situation where parental genotypes are missing, the expectation-maximization (EM) algorithm has been incorporated in the log-linear approach to allow incomplete triads to contribute to the LRT. We present an extension to this model which we call the Combined_LRT that incorporates additional information from the genotypes of unaffected siblings to improve assignment of incompletely typed families to mating type categories, thereby improving inference of missing parental data. Using simulations involving a realistic array of family structures, we demonstrate the validity of the Combined_LRT under the null hypothesis of no association and provide power comparisons under varying levels of missing data and using sibling genotype data. We demonstrate the improved power of the Combined_LRT compared with the family-based association test (FBAT), another widely used association test. Lastly, we apply the Combined_LRT to a candidate gene analysis in Autism families, some of which have missing parental genotypes. We conclude that the proposed log-linear model will be an important tool for future candidate gene studies, for many complex diseases where unaffected siblings can often be ascertained and where epigenetic factors such as imprinting may play a role in disease etiology.

Allele frequency misspecification: effect on power and Type I error of model-dependent linkage analysis of quantitative traits under random ascertainment

BackgroundStudies of model-based linkage analysis show that trait or marker model misspecification leads to decreasing power or increasing Type I error rate. An increase in Type I error rate is seen when marker related parameters (e.g., allele frequencies) are misspecified and ascertainment is through the trait, but lod-score methods are expected to be robust when ascertainment is random (as is often the case in linkage studies of quantitative traits). In previous studies, the power of lod-score linkage analysis using the "correct" generating model for the trait was found to increase when the marker allele frequencies were misspecified and parental data were missing. An investigation of Type I error rates, conducted in the absence of parental genotype data and with misspecification of marker allele frequencies, showed that an inflation in Type I error rate was the cause of at least part of this apparent increased power. To investigate whether the observed inflation in Type I error rate in model-based LOD score linkage was due to sampling variation, the trait model was estimated from each sample using REGCHUNT, an automated segregation analysis program used to fit models by maximum likelihood using many different sets of initial parameter estimates.ResultsThe Type I error rates observed using the trait models generated by REGCHUNT were usually closer to the nominal levels than those obtained when assuming the generating trait model.ConclusionThis suggests that the observed inflation of Type I error upon misspecification of marker allele frequencies is at least partially due to sampling variation. Thus, with missing parental genotype data, lod-score linkage is not as robust to misspecification of marker allele frequencies as has been commonly thought.