Parental Genotype Information Research Articles

SmartPhase: Accurate and fast phasing of heterozygous variant pairs for genetic diagnosis of rare diseases.

There is an increasing need to use genome and transcriptome sequencing to genetically diagnose patients suffering from suspected monogenic rare diseases. The proper detection of compound heterozygous variant combinations as disease-causing candidates is a challenge in diagnostic workflows as haplotype information is lost by currently used next-generation sequencing technologies. Consequently, computational tools are required to phase, or resolve the haplotype of, the high number of heterozygous variants in the exome or genome of each patient. Here we present SmartPhase, a phasing tool designed to efficiently reduce the set of potential compound heterozygous variant pairs in genetic diagnoses pipelines. The phasing algorithm of SmartPhase creates haplotypes using both parental genotype information and reads generated by DNA or RNA sequencing and is thus well suited to resolve the phase of rare variants. To inform the user about the reliability of a phasing prediction, it computes a confidence score which is essential to select error-free predictions. It incorporates existing haplotype information and applies logical rules to determine variants that can be excluded as causing a recessive, monogenic disease. SmartPhase can phase either all possible variant pairs in predefined genetic loci or preselected variant pairs of interest, thus keeping the focus on clinically relevant results. We compared SmartPhase to WhatsHap, one of the leading comparable phasing tools, using simulated data and a real clinical cohort of 921 patients. On both data sets, SmartPhase generated error-free predictions using our derived confidence score threshold. It outperformed WhatsHap with regard to the percentage of resolved pairs when parental genotype information is available. On the cohort data, SmartPhase enabled on average the exclusion of approximately 22% of the input variant pairs in each singleton patient and 44% in each trio patient. SmartPhase is implemented as an open-source Java tool and freely available at http://ibis.helmholtz-muenchen.de/smartphase/.

Genotype imputation from various low-density SNP panels and its impact on accuracy of genomic breeding values in pigs

The uptake of genomic selection (GS) by the swine industry is still limited by the costs of genotyping. A feasible alternative to overcome this challenge is to genotype animals using an affordable low-density (LD) single nucleotide polymorphism (SNP) chip panel followed by accurate imputation to a high-density panel. Therefore, the main objective of this study was to screen incremental densities of LD panels in order to systematically identify one that balances the tradeoffs among imputation accuracy, prediction accuracy of genomic estimated breeding values (GEBVs), and genotype density (directly associated with genotyping costs). Genotypes using the Illumina Porcine60K BeadChip were available for 1378 Duroc (DU), 2361 Landrace (LA) and 3192 Yorkshire (YO) pigs. In addition, pseudo-phenotypes (de-regressed estimated breeding values) for five economically important traits were provided for the analysis. The reference population for genotyping imputation consisted of 931 DU, 1631 LA and 2103 YO animals and the remainder individuals were included in the validation population of each breed. A LD panel of 3000 evenly spaced SNPs (LD3K) yielded high imputation accuracy rates: 93.78% (DU), 97.07% (LA) and 97.00% (YO) and high correlations (>0.97) between the predicted GEBVs using the actual 60 K SNP genotypes and the imputed 60 K SNP genotypes for all traits and breeds. The imputation accuracy was influenced by the reference population size as well as the amount of parental genotype information available in the reference population. However, parental genotype information became less important when the LD panel had at least 3000 SNPs. The correlation of the GEBVs directly increased with an increase in imputation accuracy. When genotype information for both parents was available, a panel of 300 SNPs (imputed to 60 K) yielded GEBV predictions highly correlated (⩾0.90) with genomic predictions obtained based on the true 60 K panel, for all traits and breeds. For a small reference population size with no parents on reference population, it is recommended the use of a panel at least as dense as the LD3K and, when there are two parents in the reference population, a panel as small as the LD300 might be a feasible option. These findings are of great importance for the development of LD panels for swine in order to reduce genotyping costs, increase the uptake of GS and, therefore, optimize the profitability of the swine industry.

Development of a comprehensive noninvasive prenatal test.

Our aim was to develop and apply a comprehensive noninvasive prenatal test (NIPT) by using high-coverage targeted next-generation sequencing to estimate fetal fraction, determine fetal sex, and detect trisomy and monogenic disease without parental genotype information. We analyzed 45 pregnancies, 40 mock samples, and eight mother-child pairs to generate 35 simulated datasets. Fetal fraction (FF) was estimated based on analysis of the single nucleotide polymorphism (SNP) allele fraction distribution. A Z-score was calculated for trisomy of chromosome 21 (T21), and fetal sex detection. Monogenic disease detection was performed through variant analysis. Model validation was performed using the simulated datasets. The novel model to estimate FF was robust and accurate (r2= 0.994, p-value < 2.2e-16). For samples with FF > 0.04, T21 detection had 100% sensitivity (95% CI: 63.06 to 100%) and 98.53% specificity (95% CI: 92.08 to 99.96%). Fetal sex was determined with 100% accuracy. We later performed a proof of concept for monogenic disease diagnosis of 5/7 skeletal dysplasia cases. In conclusion, it is feasible to perform a comprehensive NIPT by using only data from high coverage targeted sequencing, which, in addition to detecting trisomies, also make it possible to identify pathogenic variants of the candidate genes for monogenic diseases.

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.

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.

Weighted variance FBAT: a powerful method for including covariates in FBAT analyses

The family-based association test (FBAT), an extension of transmission/disequilibrium test, capitalizes on linkage disequilibrium to assess the association of genetic markers and traits in nuclear families. It does not permit a formal inclusion of covariates, although an offset under the FBAT -o option allows for an overall trait-intercept adjustment. The PBAT software provides additional features and permits the inclusion of covariates in the FBAT test statistic, but does not account for the parental genotype information when the traits are adjusted for the covariates. We propose the weighted variance FBAT (WVF) method to generate trait values adjusted for both parental genotypes and covariate values. WVF is expected to be more powerful, because the variance is minimized considering both of these factors simultaneously using a weighted Gauss-Newton algorithm. Two simulated parent/child trio data sets, both with a covariate and the second with a gene by covariate interaction, were simulated to compare WVF power with FBAT and PBAT for a quantitative trait. WVF is most powerful when levels of significance are greater and covariates have a larger influence, indicating WVF may be especially effective when multiple comparisons are an important consideration, such as with whole genome association studies. WVF will also improve the cost of an association study when environmental covariates are considered. A SAS program (www.SAS.com) for generating WVF residuals that can be input to the current versions of the FBAT (www.biostat.harvard.edu/fbat/fbat.html) and PBAT (www.biostat.harvard.edu/clange/default.htm) software is provided.

Which strategy is better for linkage analysis: single-nucleotide polymorphisms or microsatellites? Evaluation by identity-by-state – identity-by-descent transformation affected sib-pair method on GAW14 data

The central issue for Genetic Analysis Workshop 14 (GAW14) is the question, which is the better strategy for linkage analysis, the use of single-nucleotide polymorphisms (SNPs) or microsatellite markers? To answer this question we analyzed the simulated data using Duffy's SIB-PAIR program, which can incorporate parental genotypes, and our identity-by-state – identity-by-descent (IBS-IBD) transformation method of affected sib-pair linkage analysis which uses the matrix transformation between IBS and IBD. The advantages of our method are as follows: the assumption of Hardy-Weinberg equilibrium is not necessary; the parental genotype information maybe all unknown; both IBS and its related IBD transformation can be used in the linkage analysis; the determinant of the IBS-IBD transformation matrix provides a quantitative measure of the quality of the marker in linkage analysis. With the originally distributed simulated data, we found that 1) for microsatellite markers there are virtually no differences in types I and II error rates when parental genotypes were or were not used; 2) on average, a microsatellite marker has more power than a SNP marker does in linkage detection; 3) if parental genotype information is used, SNP markers show lower type I error rates than microsatellite markers; and 4) if parental genotypes are not available, SNP markers show considerable variation in type I error rates for different methods.

Expectation Maximization Algorithm Based Haplotype Relative Risk (EM-HRR): Test of Linkage Disequilibrium Using Incomplete Case-Parents Trios

The Haplotype Relative Risk (HRR) was first proposed [Falk et al., Ann Hum Genet 1987] to test for Linkage Disequilibrium (LD) between a marker and a putative disease locus using case-parent trios. Spurious association does not appear in such family-based studies under population admixture. In this paper, we extend the HRR to accommodate incomplete trios via the Expectation-Maximization (EM) algorithm [Dempster et al., J R Stat Soc Ser B, 1977]. In addition to triads and dyads (parent-offspring pair), the EM-HRR easily incorporates individuals with no parental genotype information available, which is excluded from the one parent Transmission/Disequilibrium Test (1-TDT) [Sun et al., Am J Epidemiol 1999]. Due to the data structure of EM-HRR, transmitted alleles are always available regardless of the number of missing parental genotypes. As a result of having a larger sample size, computer simulations reveal that the EM-HRR is more powerful in detecting LD than the 1-TDT in a population under Hardy-Weinberg Equilibirum (HWE). If admixture is not extreme, the EM-HRR remains more powerful. When a large degree of admixture exists, the EM-HRR performs better the 1-TDT when the association is strong, though not as well when the association is weak. We illustrate the proposed method with an application to the Framingham Heart Study.

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.

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.

Evaluation of linkage disequilibrium between chromosome 22q11 single nucleotide polymorphisms in a large outbred population.

To assess the utility of linkage disequilibrium (LD) as a tool for fine-mapping disease genes in non-isolated populations, we have assessed the linkage disequilibrium strength among a series of single nucleotide polymorphisms (SNPs) in an approximate 1 Mb region of human chromosome 22q11. Nineteen random SNPs were discovered and tested across this region with an average spacing of 57 kb (range=1.4-289 kb). These 19 SNPs were genotyped in a population consisting of 444 unrelated pedigrees that were largely collected in the U.S. and U.K. Haplotypes for all pedigrees were derived from pedigree data and over 1,400 haplotypes from unrelated individuals were evaluated for linkage disequilibrium between marker alleles. In addition, linkage disequilibrium between marker alleles was also evaluated using estimated haplotypes without genealogical information (i.e., without parental genotype information). Every marker pair combination was tested for a total of 171 tests and 2x2 contingency tables were constructed to measure LD strength. In general the haplotypes derived from pedigree data provided a more conservative estimate of LD strength. Using genealogical information for estimates of D', 59% (10/17) of marker pairs less than 50 kb apart had D' values >0.30. Finally, we observed a 60 kb region with non-significant LD, which could reflect increased recombination in this region.

Testing Quantitative Traits for Association and Linkage in the Presence or Absence of Parental Data

Zhu and Elston developed a transmission disequilibrium test for quantitative traits by defining a linear transformation to condition out founder information. The method tests the null hypothesis of no linkage or association and can be applied to general pedigree structures. However, this method requires both genotype and phenotype parental information, which may be difficult to obtain. In this paper, we describe parametric and non-parametric methods to relax this requirement when only nuclear families are sampled. We show that neither method is affected by population stratification in the absence of linkage. The statistical power and validity of the tests are investigated by simulation. A simple simulation method to calculate the power of the nonparametric method is also discussed. In practice, the data may have some families with parental phenotype and genotype information available and some without. We briefly discuss how all the data may be analyzed jointly.

A genome-wide autosomal screen for schizophrenia susceptibility loci in 71 families with affected siblings: support for loci on chromosome 10p and 6.

Evidence from epidemiological studies and segregation analysis suggests oligo- or polygenic inheritance in schizophrenia. Since model independent methods are thought to be most appropriate for linkage analysis in complex disorders, we performed a genome-wide autosomal screen in 71 families from Germany and Israel containing 86 independent affected sib-pairs with parental genotype information for statistical analysis strictly identity by descent. We genotyped 305 individuals with 463 markers at an average distance of approximately 10 cM genome-wide, and 1-2 cM in candidate regions (5q, 6p, q, 8p, 10p, 18p, 22q). The highest multipoint LOD scores (ASPEX) were obtained on 6p (D6S260, LOD = 2.0; D6S274, LOD = 2.2, MHC region, LOD = 2.15) and on 10p (D10S1714, LOD = 2.1), followed by 5q (D5S2066, LOD = 1.36), 6q (D6S271, LOD = 1.12; D6S1613, LOD = 1.11), 1q (D1S2675, LOD = 1.04), and 18p (broad disease model: D18S1116, LOD = 1.0). One hundred and thirty-three additional family members were available for some of the families (extended families) and were genotyped for these regions. GENEHUNTER produced a maximum NPL of 3.3 (P = 0.001) for the MHC region and NPL of 3.13 (P = 0.0015) for the region on 10p. There is support for these regions by independent groups. In genome-wide TDT analysis (sTDT, implemented in ASPEX), no marker passed the significance level of 0.0001 given by multiple testing, but nominal significance values for D10S211 (P = 0.03) and for GOLF (P = 0.0032) support further the linkage results on 10p and 18p. Our survey of 22 chromosomes identified candidate regions which should be useful to screen for schizophrenia susceptibility genes.

Testing Mode of Inheritance of a Candidate Mutation at a Quantitative Trait Locus

Here is presented an approach to testing whether the effect of a candidate gene on a quantitative trait is dominant and for testing whether the effect is recessive. The approach uses parental genotype information in nuclear families to adjust for bias due to population admixture. The approach is applicable regardless of the nature of the sampling. The results of an application of the methods to a candidate mutation for diabetic nephropathy are used for illustration.

The Transmission/Disequilibrium Test and Parental-Genotype Reconstruction: The Reconstruction-Combined Transmission/ Disequilibrium Test

Spielman and Ewens recently proposed a method for testing a marker for linkage with a disease, which combines data from families with and without information on parental genotypes. For some families without parental-genotype information, it may be possible to reconstruct missing parental genotypes from the genotypes of their offspring. The treatment of such a reconstructed family as if parental genotypes have been typed, however, can introduce bias. In the present study, a new method is presented that employs parental-genotype reconstruction and corrects for the biases resulting from reconstruction. The results of an application of this method to a real data set and of a simulation study suggest that this approach may increase the power to detect linkage.

Association of posterior p-values of S.A.G.E. SIBPAL proportion-IBD and Haseman-Elston statistics for ACTHR112.

A common practice among researchers performing linkage studies is the use of equal allele frequencies as input when reporting p-values from computer linkage programs such as S.A.G.E. SIBPAL. Our results, using 5,000 sets from a uniform-prior distribution of allele frequencies, showed that such input may be problematic. Further, we found that the S.A.G.E. SIBPAL test for proportion of alleles shared identical by descent among concordantly affected sib pairs showed a greater percentage of significant p-values with decreasing parental genotype information (Table III), while the S.A.G.E. SIBPAL Haseman-Elston test produced significant p-values comparatively less frequently (Table IV).