Parental Genotype Data Research Articles

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.

Accurate Sequencing and Haplotyping from 10 Cells Using Long Fragment Read (LFR) Technology.

In this chapter, we describe how Long Fragment Read (LFR) technology can be applied to samples consisting of very few cells (5-20) to enable complete genome sequencing and haplotyping with a very low false positive error rate. LFR is a method for processing DNA or cells prior to sequencing on any second-generation DNA sequencing platform (e.g., MGI's DNBSEQ, Illumina sequencers, etc.). First, the LFR process incorporates a low-bias whole genome amplification step allowing accurate sequencing from very low DNA inputs (as low as 32 picograms, the mass contained within 5 diploid human cells). In addition, LFR enables the haplotyping of nearly all genomic variations with N50 contig lengths up to ~1Mb. Furthermore, if data from this method are analyzed with parental genotype data, it is possible to generate phased variants in uninterrupted contigs spanning entire chromosomes. Importantly, the barcoding process utilized in this method allows for the detection and correction of most amplification, sequencing, and mapping errors, yielding false positive error rates as low as 10-9. Finally, the cost of this method is modest and enables extremely high-quality whole genome sequence and haplotype data from as few as 5 cells. We know of few other methods that can achieve this.

393. InterPregGen: The discovery and validation of genetic variants associated with pre-eclampsia in mothers and babies

30 years of molecular genetic studies of common disorders have demonstrated the need for large, well-powered studies to detect genetic variants with individually small effects on disease susceptibility; pre-eclampsia is no exception. InterPregGen is a collaboration between research groups from Europe and Central Asia seeking to identify DNA sequence variants predisposing to pre-eclampsia. The primary approach is meta-analysis of genome-wide association screening (GWAS) data from over 9000 affected mothers, 6500 offspring, and 370,000 control subjects. Variants with evidence of association with pre-eclampsia are followed-up by targeted genotyping in independent sample collections. In offspring affected by pre-eclampsia there is a highly significant association with a common variant located close to the FLT1 gene, which encodes Fms-like tyrosine kinase 1 and its soluble isoform sFlt-1 (P = 5.38 × 10 - 11 ) . The strongest association was in pregnancies with onset of pre-eclampsia after 34 weeks gestation. Investigation of the maternal genome in pre-eclampsia has highlighted associations with multiple DNA variants which also have confirmed associations with essential hypertension in non-pregnant individuals. In a further 2800 women with non-proteinuric gestational hypertension there is a similar profile of susceptibility variants shared with essential hypertension and pre-eclampsia. Typical of many common complex disorders, there is no single ‘pre-eclampsia gene’. Current parental or fetal genotype data do not enable prediction of preeclampsia in individual pregnancies. However, the results highlight specific biological pathways responsible for pre-eclampsia’s aetiology. Larger studies are essential to identify additional variants and other pathways affecting pre-eclampsia. The genetic findings justify targeted investigation of FLT1 regulation in the placenta, and indicate that raised sFlt-1 has a causal role in pre-eclampsia, and is not just a consequence of the disease. Maternal genotyping results confirm a pathophysiological overlap between essential hypertension, gestational hypertension and pre-eclampsia, and emphasise the imperative for long-term cardiovascular monitoring of women with a history of hypertension in pregnancy.

An efficient study design to test parent-of-origin effects in family trios.

Increasing evidence has shown that genes may cause prenatal, neonatal, and pediatric diseases depending on their parental origins. Statistical models that incorporate parent-of-origin effects (POEs) can improve the power of detecting disease-associated genes and help explain the missing heritability of diseases. In many studies, children have been sequenced for genome-wide association testing. But it may become unaffordable to sequence their parents and evaluate POEs. Motivated by the reality, we proposed a budget-friendly study design of sequencing children and only genotyping their parents through single nucleotide polymorphism array. We developed a powerful likelihood-based method, which takes into account both sequence reads and linkage disequilibrium to infer the parental origins of children's alleles and estimate their POEs on the outcome. We evaluated the performance of our proposed method and compared it with an existing method using only genotypes, through extensive simulations. Our method showed higher power than the genotype-based method. When either the mean read depth or the pair-end length was reasonably large, our method achieved ideal power. When single parents' genotypes were unavailable or parental genotypes at the testing locus were not typed, both methods lost power compared with when complete data were available; but the power loss from our method was smaller than the genotype-based method. We also extended our method to accommodate mixed genotype, low-, and high-coverage sequence data from children and their parents. At presence of sequence errors, low-coverage parental sequence data may lead to lower power than parental genotype data.

Salvage of fetal karyotype information from SNP array data obtained from products of conception with maternal cell contamination.

Maternal cell contamination (MCC) is known to increase the risk of misdiagnosis in prenatal diagnosis as well as in diagnostic tests for the products of conception (POC) from miscarriages. We aimed to develop a data correction method to salvage fetal karyotype information from single-nucleotide polymorphism (SNP) array data for POC with MCC when parental genotype data are available. We obtained SNP array data from mixed genomic DNAs of a mother-child pair and used the dataset to assess the accuracy of data correction. We subsequently applied our method to miscarriage specimens with MCC. We adopted a linear interpolation model as a data correction method and implemented the method in an R package, snpsal. We successfully determined the fetal karyotypes of two miscarriage specimens that were previously undiagnosed due to MCC to be normal in one case and trisomy 16 in the other case using snpsal. The R package, snpsal, developed in this study facilitates rapid and accurate estimation of the fetal karyotype from SNP array data for POC with MCC. © 2017 John Wiley & Sons, Ltd.

Flexible and robust methods for rare-variant testing of quantitative traits in trios and nuclear families.

Most rare-variant association tests for complex traits are applicable only to population-based or case-control resequencing studies. There are fewer rare-variant association tests for family-based resequencing studies, which is unfortunate because pedigrees possess many attractive characteristics for such analyses. Family-based studies can be more powerful than their population-based counterparts due to increased genetic load and further enable the implementation of rare-variant association tests that, by design, are robust to confounding due to population stratification. With this in mind, we propose a rare-variant association test for quantitative traits in families; this test integrates the QTDT approach of Abecasis et al. [Abecasis etal., ] into the kernel-based SNP association test KMFAM of Schifano et al. [Schifano etal., ]. The resulting within-family test enjoys the many benefits of the kernel framework for rare-variant association testing, including rapid evaluation of P-values and preservation of power when a region harbors rare causal variation that acts in different directions on phenotype. Additionally, by design, this within-family test is robust to confounding due to population stratification. Although within-family association tests are generally less powerful than their counterparts that use all genetic information, we show that we can recover much of this power (although still ensuring robustness to population stratification) using a straightforward screening procedure. Our method accommodates covariates and allows for missing parental genotype data, and we have written software implementing the approach in R for public use.

CAPL: a novel association test using case‐control and family data and accounting for population stratification

The recent successes of GWAS based on large sample sizes motivate combining independent datasets to obtain larger sample sizes and thereby increase statistical power. Analysis methods that can accommodate different study designs, such as family-based and case-control designs, are of general interest. However, population stratification can cause spurious association for population-based association analyses. For family-based association analysis that infers missing parental genotypes based on the allele frequencies estimated in the entire sample, the parental mating-type probabilities may not be correctly estimated in the presence of population stratification. Therefore, any approach to combining family and case-control data should also properly account for population stratification. Although several methods have been proposed to accommodate family-based and case-control data, all have restrictions. Most of them require sampling a homogeneous population, which may not be a reasonable assumption for data from a large consortium. One of the methods, FamCC, can account for population stratification and uses nuclear families with arbitrary number of siblings but requires parental genotype data, which are often unavailable for late-onset diseases. We extended the family-based test, Association in the Presence of Linkage (APL), to combine family and case-control data (CAPL). CAPL can accommodate case-control data and families with multiple affected siblings and missing parents in the presence of population stratification. We used simulations to demonstrate that CAPL is a valid test either in a homogeneous population or in the presence of population stratification. We also showed that CAPL can have more power than other methods that combine family and case-control data.

A Bayesian approach to genetic association studies with family-based designs

For genome-wide association studies with family-based designs, we propose a Bayesian approach. We show that standard transmission disequilibrium test and family-based association test statistics can naturally be implemented in a Bayesian framework, allowing flexible specification of the likelihood and prior odds. We construct a Bayes factor conditional on the offspring phenotype and parental genotype data and then use the data we conditioned on to inform the prior odds for each marker. In the construction of the prior odds, the evidence for association for each single marker is obtained at the population-level by estimating its genetic effect size by fitting the conditional mean model. Since such genetic effect size estimates are statistically independent of the effect size estimation within the families, the actual data set can inform the construction of the prior odds without any statistical penalty. In contrast to Bayesian approaches that have recently been proposed for genome-wide association studies, our approach does not require assumptions about the genetic effect size; this makes the proposed method entirely data-driven. The power of the approach was assessed through simulation. We then applied the approach to a genome-wide association scan to search for associations between single nucleotide polymorphisms and body mass index in the Childhood Asthma Management Program data.

A Family-Based Likelihood Ratio Test for General Pedigree Structures That Allows for Genotyping Error and Missing Data

The purpose of this work is the development of a family-based association test that allows for random genotyping errors and missing data and makes use of information on affected and unaffected pedigree members. We derive the conditional likelihood functions of the general nuclear family for the following scenarios: complete parental genotype data and no genotyping errors; only one genotyped parent and no genotyping errors; no parental genotype data and no genotyping errors; and no parental genotype data with genotyping errors. We find maximum likelihood estimates of the marker locus parameters, including the penetrances and population genotype frequencies under the null hypothesis that all penetrance values are equal and under the alternative hypothesis. We then compute the likelihood ratio test. We perform simulations to assess the adequacy of the central chi-square distribution approximation when the null hypothesis is true. We also perform simulations to compare the power of the TDT and this likelihood-based method. Finally, we apply our method to 23 SNPs genotyped in nuclear families from a recently published study of idiopathic scoliosis (IS). Our simulations suggest that this likelihood ratio test statistic follows a central chi-square distribution with 1 degree of freedom under the null hypothesis, even in the presence of missing data and genotyping errors. The power comparison shows that this likelihood ratio test is more powerful than the original TDT for the simulations considered. For the IS data, the marker rs7843033 shows the most significant evidence for our method (p = 0.0003), which is consistent with a previous report, which found rs7843033 to be the 2nd most significant TDTae p value among a set of 23 SNPs.

European Mathematical Genetics Meeting, Heidelberg, Germany, 12th–13th April 2007

European Mathematical Genetics Meeting, Heidelberg, Germany, 12^th–13^th April 2007

Relative Contribution of Genetic and Nongenetic Modifiers to Intestinal Obstruction in Cystic Fibrosis

Neonatal intestinal obstruction (meconium ileus [MI]) occurs in 15% of patients with cystic fibrosis (CF). Our aim was to determine the relative contribution of genetic and nongenetic modifiers to the development of this major complication of CF. A total of 65 monozygous twin pairs, 23 dizygous twin/triplet sets, and 349 sets of siblings with CF were analyzed for MI status, significant covariates, and genome-wide linkage. Specific mutations in the CF transmembrane conductance regulator (CFTR), the gene responsible for CF, correlated with MI, indicating a role for CFTR genotype. Monozygous twins showed substantially greater concordance for MI than dizygous twins and siblings (P = 1 x 10(-5)), showing that modifier genes independent of CFTR contribute substantially to this trait. Regression analysis revealed that MI was correlated with distal intestinal obstruction syndrome (P = 8 x 10(-4)). Unlike MI, concordance analysis indicated that the risk for development of distal intestinal obstruction syndrome in CF patients is caused primarily by nongenetic factors. Regions of suggestive linkage (logarithm of the odds of linkage >2.0) for modifier genes that cause MI (chromosomes 4q35.1, 8p23.1, and 11q25) or protect from MI (chromosomes 20p11.22 and 21q22.3) were identified by genome-wide analyses. These analyses did not support the existence of a major modifier gene on chromosome 19 in a region previously linked to MI. The CFTR gene along with 2 or more modifier genes are the major determinants of intestinal obstruction in newborn CF patients, whereas intestinal obstruction in older CF patients is caused primarily by nongenetic factors.

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.

Precision and type I error rate in the presence of genotype errors and missing parental data: a comparison between the original transmission disequilibrium test (TDT) and TDTae statistics

BackgroundTwo factors impacting robustness of the original transmission disequilibrium test (TDT) are: i) missing parental genotypes and ii) undetected genotype errors. While it is known that independently these factors can inflate false-positive rates for the original TDT, no study has considered either the joint impact of these factors on false-positive rates or the precision score of TDT statistics regarding these factors. By precision score, we mean the absolute difference between disease gene position and the position of markers whose TDT statistic exceeds some threshold.MethodsWe apply our transmission disequilibrium test allowing for errors (TDTae) and the original TDT to phenotype and modified single-nucleotide polymorphism genotype simulation data from Genetic Analysis Workshop. We modify genotype data by randomly introducing genotype errors and removing a percentage of parental genotype data. We compute empirical distributions of each statistic's precision score for a chromosome harboring a simulated disease locus. We also consider inflation in type I error by studying markers on a chromosome harboring no disease locus.ResultsThe TDTae shows median precision scores of approximately 13 cM, 2 cM, 0 cM, and 0 cM at the 5%, 1%, 0.1%, and 0.01% significance levels, respectively. By contrast, the original TDT shows median precision scores of approximately 23 cM, 21 cM, 15 cM, and 7 cM at the corresponding significance levels, respectively. For null chromosomes, the original TDT falsely rejects the null hypothesis for 28.8%, 14.8%, 5.4%, and 1.7% at the 5%, 1%, 0.1% and 0.01%, significance levels, respectively, while TDTae maintains the correct false-positive rate.ConclusionBecause missing parental genotypes and undetected genotype errors are unknown to the investigator, but are expected to be increasingly prevalent in multilocus datasets, we strongly recommend TDTae methods as a standard procedure, particularly where stricter significance levels are required.

A method for identifying genes related to a quantitative trait, incorporating multiple siblings and missing parents

When studying either qualitative or quantitative traits, tests of association in the presence of linkage are necessary for fine-mapping. In a previous report, we suggested a polytomous logistic approach to testing linkage and association between a di-allelic marker and a quantitative trait locus, using genotyped triads, consisting of an individual whose quantitative trait has been measured and his or her two parents. Here we extend that approach to incorporate marker information from entire nuclear families. By computing a weighted score function instead of a maximum likelihood test, we allow for both an unspecified correlation structure between siblings and "informative" family size. Both this approach and our original approach allow for population admixture by conditioning on parental genotypes. The proposed method allows for missing parental genotype data through a multiple imputation procedure. We use simulations based on a population with admixture to compare our method to a popular non-parametric family-based association test (FBAT), testing the null of no association in the presence of linkage.

Continuous Covariates in Genetic Association Studies of Case-Parent Triads: Gene and Gene-Environment Interaction Effects, Population Stratification, and Power Analysis

We propose a multinomial logistic regression method which permits estimation and likelihood ratio tests for allele effects, their interactions with continuous covariates, and assessment of the degree of population stratification in genetic association studies of case-parent triads. Our approach overcomes the constraint imposed by the categorical nature of explanatory variables in the log-linear model. We also demonstrate that the multinomial logistic method can yield efficient inference in the presence of missing parental genotype data via the use of the Expectation-Maximization (EM) algorithm. We performed simulations to compare the multinomial logistic model with the case-pseudosibling conditional logistic model approach, both of which permit the incorporation of continuous covariates. Simulation results indicate that the multinomial logistic model and the conditional logistic model lead to similar estimates in large samples. A simulation-based method of sample size estimation is also used to show that the two models are approximately equivalent in sample size requirements. When parental genotype data are missing, either completely at random or dependent on covariates, the use of the EM algorithm gives multinomial logistic model greater power. Since the multinomial logistic model offers the possibility of assessing the degree of population stratification in the sample and can also provide efficient inference in the presence of missing parental genotypes, the proposed model has an important application in epidemiological family-based association studies.

Ignoring Linkage Disequilibrium among Tightly Linked Markers Induces False-Positive Evidence of Linkage for Affected Sib Pair Analysis

Most multipoint linkage programs assume linkage equilibrium among the markers being studied. The assumption is appropriate for the study of sparsely spaced markers with intermarker distances exceeding a few centimorgans, because linkage equilibrium is expected over these intervals for almost all populations. However, with recent advancements in high-throughput genotyping technology, much denser markers are available, and linkage disequilibrium (LD) may exist among the markers. Applying linkage analyses that assume linkage equilibrium to dense markers may lead to bias. Here, we demonstrated that, when some or all of the parental genotypes are missing, assuming linkage equilibrium among tightly linked markers where strong LD exists can cause apparent oversharing of multipoint identity by descent (IBD) between sib pairs and false-positive evidence for multipoint model-free linkage analysis of affected sib pair data. LD can also mimic linkage between a disease locus and multiple tightly linked markers, thus causing false-positive evidence of linkage using parametric models, particularly when heterogeneity LOD score approaches are applied. Bias can be eliminated by inclusion of parental genotype data and can be reduced when additional unaffected siblings are included in the analysis.

Method for using complete and incomplete trios to identify genes related to a quantitative trait.

A number of tests for linkage and association with qualitative traits have been developed, with the most well-known being the transmission/disequilibrium test (TDT). For quantitative traits, varying extensions of the TDT have been suggested. The quantitative trait approach we propose is based on extending the log-linear model for case-parent trio data (Weinberg et al. [1998] Am. J. Hum. Genet. 62:969-978). Like the log-linear approach for qualitative traits, our proposed polytomous logistic approach for quantitative traits allows for population admixture by conditioning on parental genotypes. Compared to other methods, simulations demonstrate good power and robustness of the proposed test under various scenarios of the genotype effect, distribution of the quantitative trait, and population stratification. In addition, missing parental genotype data can be accommodated through an expectation-maximization (EM) algorithm approach. The EM approach allows recovery of most of the lost power due to incomplete trios.

Family-based tests for associating haplotypes with general phenotype data: application to asthma genetics.

We provide a general purpose family-based testing strategy for associating disease phenotypes with haplotypes when phase may be ambiguous and parental genotype data may be missing. These tests for linkage and association can be used in candidate gene studies with tightly linked markers. Our proposed weighted conditional approach extends the method described in Rabinowitz and Laird to multiple markers. It is attractive because it provides haplotype tests for family-based studies that are efficient and robust to population admixture, phenotype distribution specification, and ascertainment based on phenotypes. It can handle missing parental genotypes and/or missing phase in both offspring and parents. It yields either haplotype-specific (univariate) tests or multi-haplotype (global) tests. This extension has been implemented in the freely available software haplotype FBAT. We used the haplotype FBAT program to test for associations between asthma phenotypes and single nucleotide polymorphisms (SNPs) in the beta-2 adrenergic receptor gene. Whereas no single SNP showed significant association with asthma diagnosis or bronchodilator responsiveness (quantitative trait), a haplotype-based global test found a highly significant association with asthma diagnosis (P value <0.00005) and the measure of bronchodilator responsiveness (P value =0.016).

Prediction of offspring fitness based on parental genetic diversity in endangered salmonid populations

By simulating all possible offspring genotypic combinations based on pair‐wise matings in three vulnerable or endangered populations of salmonids (two Atlantic salmonSalmo salarand one Arctic charrSalvelinus alpinus), family level estimates of potential offspring genetic diversity were calculated using several commonly used genetic diversity estimators (,andHEST). By using this approach, the genetic variability of offspring could be predicted accurately based solely on parental genotype data. In addition, significant associations between offspring genetic variability and fitness‐related traits were observed in the two Atlantic salmon populations and these associations were consistent between fitness measures and either estimated or observed genetic diversity. This indicates that associations with offspring fitness measures can be predicted without genetic analysis of the offspring themselves.

Power evaluations for family-based tests of association with incomplete parental genotypes.

While a variety of methods have been developed to deal with incomplete parental genotype information in family-based association tests, sampling design issues with incomplete parental genotype data still have not received much attention. In this article, we present simulation studies with four genetic models and various sampling designs and evaluate power in family-based association studies. Efficiency depends heavily on disease prevalence. With rare diseases, sampling affecteds and their parents is preferred, and three sibs will be required to have close power if parents are unavailable. With more common diseases, sampling affecteds and two sibs will generally be more efficient than trios. When parents are unavailable, siblings need not be phenotyped if the disease is rare, but a loss of power will result with common diseases. Finally, for a class of complex traits where other genetic and environmental factors also cause phenotypic correlation among siblings, little loss of efficiency occurs to rare disease, but substantial loss of efficiency occurs to common disease.