A test for linkage and association in general pedigrees: the pedigree disequilibrium test

Family-based tests of linkage disequilibrium typically are based on nuclear-family data including affected individuals and their parents or their unaffected siblings. A limitation of such tests is that they generally are not valid tests of association when data from related nuclear families from larger pedigrees are used. Standard methods require selection of a single nuclear family from any extended pedigrees when testing for linkage disequilibrium. Often data are available for larger pedigrees, and it would be desirable to have a valid test of linkage disequilibrium that can use all potentially informative data. In this study, we present the pedigree disequilibrium test (PDT) for analysis of linkage disequilibrium in general pedigrees. The PDT can use data from related nuclear families from extended pedigrees and is valid even when there is population substructure. Using computer simulations, we demonstrated validity of the test when the asymptotic distribution is used to assess the significance, and examined statistical power. Power simulations demonstrate that, when extended pedigree data are available, substantial gains in power can be attained by use of the PDT rather than existing methods that use only a subset of the data. Furthermore, the PDT remains more powerful even when there is misclassification of unaffected individuals. Our simulations suggest that there may be advantages to using the PDT even if the data consist of independent families without extended family information. Thus, the PDT provides a general test of linkage disequilibrium that can be widely applied to different data structures.     

Identifying pedigrees segregating at a major locus for a quantitative trait: an efficient strategy for linkage analysis

Having found evidence for segregation at a major locus for a quantitative trait, a logical next step is to identify those pedigrees in which major-locus segregation is occurring. If the quantitative trait is a risk factor for an associated disease, identifying such segregating pedigrees can be important in classifying families by etiology, in risk assessment, and in suggesting treatment modalities. Identifying segregating pedigrees can also be helpful in selecting pedigrees to include in a subsequent linkage study to map the major locus. Here, we describe a strategy to identify pedigrees segregating at a major locus for a quantitative trait. We apply this pedigree selection strategy to simulated data generated under a major-locus or mixed model with a rare dominant allele and sampled according to one of several fixed-structure or sequential sampling designs. We demonstrate that for the situations considered, the pedigree selection strategy is sensitive and specific and that a linkage study based only on the pedigrees classified as segregating extracts essentially all the linkage information in the entire sample of pedigrees. Our results suggest that for large-scale linkage studies involving many genetic markers, the savings from this strategy can be substantial and that, compared with fixed-structure sampling, sequential sampling of pedigrees can greatly improve the efficiency for linkage analysis of a quantitative trait.     

Collection of pedigree data for genetic analysis in isolate populations

Pedigree data are useful for a wealth of research purposes in human population biology and genetics. The collection of extended pedigrees represents the most powerful sampling design for quantitative genetic and linkage studies of both normal and disease-related quantitative traits. In this paper we outline an approach for collecting pedigree data in stable isolate populations. As an example, the pedigree for the Jirel population, which was obtained using the methods presented, is described. The Jirel pedigree contains 2,000 study participants and more than 62,000 pairwise relationships that are informative for genetic analysis. Once such pedigrees are genetically characterized by a genome scan for a given trait, they become an invaluable resource for future genetic studies of any quantitative trait.     

Nationwide Health Data Management System: a novel approach for integrating biomarker measurements with comprehensive health records in large populations studies

The nation-wide electronic health record database acts as an interoperable repository of health data obtained throughout citizen contacts with health care providers. This system improves accessibility for citizens and researchers to health data with the ability to assign context to disease development. In that system, individual patients who are members of the large population-based health database can be assessed as individuals or as a population in prospective studies of prospective diseases.     

Analysis of genealogical structure of populations. I. A method of collecting genealogical data in numerical form

A method for collecting genealogical data with respect to an individual, a family, and members of the whole population is suggested. The essence of vertical pedigree construction consists of the same type of steps for filling in data (in the fixed order which excludes skips in the enumeration of lines of descent) about the father and the mother of the next ancestor. Each number in the received ordered list of ancestors uniquely determines a path (line of descent) to the given pedigree member. The path is explicitly described by a sequence of digits 0 and 1 (that corresponds to the sequence of fathers and mothers in the line of descent) at binary notation of this number. As a result, a pedigree is presented as a set of numbered rows that contain information, which uniquely identifies direct ancestors as individual persons. Results of joining separate pedigrees are recorded as a family list that contains lists of children for each parental pair. A pair of parents (more exactly, pointers of their families in the previous generation and numbers of pair members in their families) plays the role of the family "heading." Such a family list permits one to trace lines of descent and relationships for any population members presented in the list. It contains all genealogical information within the bounds of the study in a compact form. Here the process of collection requires considerably less time than traditional graphic representation of pedigrees. In addition, due to repeated checks of data during accumulation of material, error is minimized. Using pedigrees that have been collected, it is possible to calculate the coefficient of inbreeding manually. In connection with the wide prevalence of personal computers at present, it is also important that the data received are in fact ready to direct input to a computer for further automated data processing.     

Estimating ethnic admixture from pedigree data

This paper introduces a likelihood method of estimating ethnic admixture that uses individuals, pedigrees, or a combination of individuals and pedigrees. For each founder of a pedigree, admixture proportions are calculated by conditioning on the pedigree-wide genotypes at all ancestry-informative markers. These estimates are then propagated down the pedigree to the nonfounders by a simple averaging process. The large-sample standard errors of the founders' proportions can be similarly transformed into standard errors for the admixture proportions of the descendants. These standard errors are smaller than the corresponding standard errors when each individual is treated independently. Both hard and soft information on a founder's ancestry can be accommodated in this scheme, which has been implemented in the genetic software package Mendel. The utility of the method is demonstrated on simulated data and a real data example involving Mexican families of mixed Amerindian and Spanish ancestry.     

Increase in linkage information by stratification of pedigree data into gold-standard and standard diagnoses: application to the NIMH Alzheimer Disease Genetics Initiative Dataset

Patients diagnosed with a standard clinical method (subject to misclassification error) are often combined with patients diagnosed with a gold-standard method (with zero or very small misclassification error) in family-based studies of complex disease. For example, non-autopsied patients (NAP) are often included along with autopsy-proven (AP) patients in family-based studies of complex diseases, such as Alzheimer's disease (AD). Theoretical and simulation studies suggest that certain misclassification errors can result in severe reduction of power in genetic linkage and association analyses and that phenotype (or diagnostic) error can produce misleading results. Morton's test for heterogeneity can identify genomic regions where error may have led to loss in power. We applied this test to pedigree data from the NIMH Alzheimer's Disease Genetics Initiative Database separated into AP and NAP pedigrees. Morton's test identified one highly significant region of heterogeneity on chromosome 2. The source of the heterogeneity was due to significant indication of linkage in the AP pedigrees at position 109 cM (p value = 6.68 x 10(-5)) with no indication in the NAP pedigrees. Furthermore, Morton's test showed no evidence for heterogeneity on chromosome 19 in early-onset pedigrees that showed highly significant evidence for linkage in other published reports. These results suggest that supplementing linkage analysis with Morton's test can be usefully applied to genetic data sets that have AP and NAP samples, or other sample mixtures that include a 'gold standard' subgroup with reduced error rate, to increase power to detect linkage in the presence of diagnostic misclassification.     

Estimating the power of a proposed linkage study: a practical computer simulation approach.

I describe a simulation method to estimate the power to detect linkage given a set of pedigrees of known structure and for which family history data may be available. This method can be applied to autosomal and X-linked dominant diseases; depending on the pedigrees under consideration, it will often be applicable for autosomal and X-linked recessive diseases. This power calculation can most usefully be undertaken after family history data are gathered, but prior to examination and testing of pedigree members to obtain marker information. Of key importance, the power calculation is straightforward to carry out and not too time-consuming; it is practical even on a microcomputer. The result of the power calculation is an objective answer to the question: Will my families be sufficient to demonstrate linkage?     

Ignoring intermarker linkage disequilibrium induces false-positive evidence of linkage for consanguineous pedigrees when genotype data is missing for any pedigree member

Missing genotype data can increase false-positive evidence for linkage when either parametric or nonparametric analysis is carried out ignoring intermarker linkage disequilibrium (LD). Previously it was demonstrated by Huang et al. [1] that no bias occurs in this situation for affected sib-pairs with unrelated parents when either both parents are genotyped or genotype data is available for two additional unaffected siblings when parental genotypes are missing. However, this is not the case for autosomal recessive consanguineous pedigrees, where missing genotype data for any pedigree member within a consanguinity loop can increase false-positive evidence of linkage. False-positive evidence for linkage is further increased when cryptic consanguinity is present. The amount of false-positive evidence for linkage, and which family members aid in its reduction, is highly dependent on which family members are genotyped. When parental genotype data is available, the false-positive evidence for linkage is usually not as strong as when parental genotype data is unavailable. For a pedigree with an affected proband whose first-cousin parents have been genotyped, further reduction in the false-positive evidence of linkage can be obtained by including genotype data from additional affected siblings of the proband or genotype data from the proband's sibling-grandparents. For the situation, when parental genotypes are unavailable, false-positive evidence for linkage can be reduced by including genotype data from either unaffected siblings of the proband or the proband's married-in-grandparents in the analysis.     

Inference of relationships in population data using identity-by-descent and identity-by-state

It is an assumption of large, population-based datasets that samples are annotated accurately whether they correspond to known relationships or unrelated individuals. These annotations are key for a broad range of genetics applications. While many methods are available to assess relatedness that involve estimates of identity-by-descent (IBD) and/or identity-by-state (IBS) allele-sharing proportions, we developed a novel approach that estimates IBD0, 1, and 2 based on observed IBS within windows. When combined with genome-wide IBS information, it provides an intuitive and practical graphical approach with the capacity to analyze datasets with thousands of samples without prior information about relatedness between individuals or haplotypes. We applied the method to a commonly used Human Variation Panel consisting of 400 nominally unrelated individuals. Surprisingly, we identified identical, parent-child, and full-sibling relationships and reconstructed pedigrees. In two instances non-sibling pairs of individuals in these pedigrees had unexpected IBD2 levels, as well as multiple regions of homozygosity, implying inbreeding. This combined method allowed us to distinguish related individuals from those having atypical heterozygosity rates and determine which individuals were outliers with respect to their designated population. Additionally, it becomes increasingly difficult to identify distant relatedness using genome-wide IBS methods alone. However, our IBD method further identified distant relatedness between individuals within populations, supported by the presence of megabase-scale regions lacking IBS0 across individual chromosomes. We benchmarked our approach against the hidden Markov model of a leading software package (PLINK), showing improved calling of distantly related individuals, and we validated it using a known pedigree from a clinical study. The application of this approach could improve genome-wide association, linkage, heterozygosity, and other population genomics studies that rely on SNP genotype data.     

Evaluation of birth defect histories obtained through maternal interviews.

Etiologic studies of birth defects often use family history information provided by parents of patients. The validity of this information has not been adequately assessed. Using data from the Atlanta Birth Defects Case-Control study, we evaluated sensitivity, specificity, and positive predictive value of mothers' responses regarding the presence of birth defects in their offspring. A total of 4929 mothers of infants with major structural defects ascertained by the Metropolitan Atlanta Congenital Defects Program and a total of 3,029 mothers of normal infants were asked whether their babies had had a birth defect or a health problem diagnosed during the first year of life. Interviewers and coders of maternal responses were blinded to the case-control status of infants. Sensitivity (the proportion of case mothers who gave responses that could be coded as denoting a major birth defect) was 61%. Specificity (the proportion of control mothers who gave responses that could not be coded as denoting a major birth defect) was 98%. The positive predictive value (the proportion of mothers who gave a major-birth-defect response who in fact had babies with major birth defects) was estimated as 47%. Sensitivity, specificity, and positive predictive value varied by maternal sociodemographic factors such as race and education, as well as by type of defect. These results suggest that family history data obtained through maternal interviews should be cautiously interpreted and, if not properly validated, may alter estimates of recurrence risks.     

Monitoring and managing genetic variation in group breeding populations without individual pedigrees

The genetic management of captive populations to conserve genetic variation is currently based on analyses of individual pedigrees to infer inbreeding and kinship coefficients and values of individuals as breeders. Such analyses require that individual pedigrees are known and individual pairing (mating) can be controlled. Many species in captivity, however, breed in groups due to various reasons, such as space constraints and fertility considerations for species living naturally in social groups, and thus have no pedigrees available for the traditional genetic analyses and management. In the absence of individual pedigree, such group breeding populations can still be genetically monitored, evaluated and managed by suitable population genetics models using population level information (such as census data). This article presents a simple genetic model of group breeding populations to demonstrate how to estimate the genetic variation maintained within and among populations and to optimise management based on these estimates. A numerical example is provided to illustrate the use of the proposed model. Some issues relevant to group breeding, such as the development and robustness evaluation of the population genetics model appropriate for a particular species under specific management and recording systems and the genetic monitoring with markers, are also briefly discussed.     

Further data supporting linkage between cystic fibrosis and the met oncogene and haplotype analysis with met and pJ3.11

The linkage of cystic fibrosis (CF) and the polymorphic DNA markers pJ3.11, met, 7C22, DOCR1-917, COL1A2, and TCRB have jointly localized the mutation causing CF to chromosome 7q2.1-3.1. We report further linkage data with two polymorphic markers at the met oncogene locus, pmetH and pmetD, which supports the tight linkage found by White et al. between CF and met. One family shows evidence for meiotic recombination between CF and met. Analysis of haplotypes in CF pedigrees collected for linkage studies combined with data from single affected families requesting prenatal diagnosis (Farrall et al., Lancet i:1402-1404, 1986) shows CF and met to be in linkage equilibrium in our population while pJ3.11-CF haplotypes show a deviation from the equilibrium frequencies.     

Linking teratogen information service and birth defects registry databases to improve knowledge of birth defect status

BACKGROUND: Although teratogen information services (TISs) obtain maternal exposure information from their callers, such services often do not know if the pregnancies were affected by a birth defect. This study attempted to improve the completeness of this information for Texas Teratogen Information Service (TTIS) callers by linking their records with the Texas Birth Defects Registry (TBDR) and Texas birth certificates (TBCs). METHODS: A total of 344 expectant mothers called TTIS with expected dates of delivery between 1 January 2000 and 31 December 2001. These pregnancies were linked with TBDR and TBC data. The percentages of pregnancies with known birth defect information both before and after the linkage were compared. RESULTS: The TTIS originally collected birth defect status information for 101 of the 344 callers (29.4%) and 0.6% of all 344 callers or 2.0% of callers with birth defect status information had a pregnancy affected by a birth defect. Linking TTIS records with TBDR and TBC data helped to raise the percentage of callers with birth defect status information from 29.4% to 71.5%. Among those callers, the percentage known to have birth defects increased from 2.0% to 4.1%. The sensitivity of TTIS follow-up calls in identifying birth defects was 50%, and the specificity was 100%. CONCLUSIONS: Linking TTIS caller records with TBDR and TBC data significantly increased both the percentage of pregnancies with birth defect status information and the percentage of pregnancies identified as affected by birth defects. Such linkage may be a good approach by which TISs can increase the completeness of their birth defect status information.     

Comparisons of methods for linkage analysis and haplotype reconstruction using extended pedigree data

We compare and contrast the performance of SIMPLE, a Monte Carlo based software, with that of several other methods for linkage and haplotype analyses, focusing on the simulated data from the New York City population. First, a whole-genome scan study based on the microsatellite markers was performed using GENEHUNTER. Because GENEHUNTER had to drop individuals for many of the pedigrees, we performed a follow-up study focusing on several regions of interest using SIMPLE, which can handle all pedigrees in their entirety. Second, 3 haplotyping programs, including that in SIMPLE, were used to reconstruct haplotypic configurations in pedigrees. SIMPLE emerges clearly as a preferred tool, as it can handle large pedigrees and produces haplotypic configurations without double recombinant haplotypes. For this study, we had knowledge of the simulating models at the time we performed the analysis.     

Accounting for linkage disequilibrium among markers in linkage analysis: impact of haplotype frequency estimation and molecular haplotypes for a gene in a candidate region for Alzheimer's disease

OBJECTIVES: Linkage disequilibrium (LD) between closely spaced SNPs can be accommodated in linkage analysis by specifying the multi-SNP haplotype frequencies, if known. Phased haplotypes in candidate regions can provide gold standard haplotype frequency estimates, and may be of inherent interest as markers. We evaluated the effects of different methods of haplotype frequency estimation, and the use of marker phase information, on linkage analysis of a multi-SNP cluster in a candidate region for Alzheimer's disease (AD). METHODS: We performed parametric linkage analysis of a five-SNP cluster in extended pedigrees to compare the use of: (1) haplotype frequencies estimated by molecular phase determination, maximum likelihood estimation, or by assuming linkage equilibrium (LE); (2) AD families or controls as the frequency source; and (3) unphased or molecularly phased SNP data. RESULTS: There was moderate to strong pairwise LD among the five SNPs. Falsely assuming LE substantially inflated the LOD score, but the method of haplotype frequency estimation and particular sample used made little difference provided that LD was accommodated. Use of phased haplotypes produced a modest increase in the LOD score over unphased SNPs. CONCLUSIONS: Ignoring LD between markers can lead to substantially inflated evidence for linkage in LOD score analysis of extended pedigrees with missing data. Use of marker phase information in linkage analysis may be important in disease studies where the costs of family recruitment and phenotyping greatly exceed the costs of phase determination.     

Constructing a population-based research database from routine maternal screening records: a resource for studying alloimmunization in pregnant women

Background

Although screening for maternal red blood cell antibodies during pregnancy is a standard procedure, the prevalence and clinical consequences of non-anti-D immunization are poorly understood. The objective was to create a national database of maternal antibody screening results that can be linked with population health registers to create a research resource for investigating these issues.

Study Design and Methods

Each birth in the Swedish Medical Birth Register was uniquely identified and linked to the text stored in routine maternal antibody screening records in the time window from 9 months prior to 2 weeks after the delivery date. These text records were subjected to a computerized search for specific antibodies using regular expressions. To illustrate the research potential of the resulting database, selected antibody prevalence rates are presented as tables and figures, and the complete data (from more than 60 specific antibodies) presented as online moving graphical displays.

Results

More than one million (1,191,761) births with valid screening information from 1982–2002 constitute the study population. Computerized coverage of screening increased steadily over time and varied by region as electronic records were adopted. To ensure data quality, we restricted analysis to birth records in areas and years with a sustained coverage of at least 80%, representing 920,903 births from 572,626 mothers in 17 of the 24 counties in Sweden. During the study period, non-anti-D and anti-D antibodies occurred in 76.8/10,000 and 14.1/10,000 pregnancies respectively, with marked differences between specific antibodies over time.

Conclusion

This work demonstrates the feasibility of creating a nationally representative research database from the routine maternal antibody screening records from an extended calendar period. By linkage with population registers of maternal and child health, such data are a valuable resource for addressing important clinical questions, such as the etiological significance of non-anti-D antibodies.     

Collection, use, and protection of population-based birth defects surveillance data in the united states

Birth defects surveillance systems collect population-based birth defects data from multiple sources to track trends in prevalence, identify risk factors, refer affected families to services, and evaluate prevention efforts. Strong state and federal public health and legal mandates are in place to govern the collection and use of these data. Despite the prima facie appeal of "opt-in" and similar strategies to those who view data collection as a threat to privacy, the use of these strategies in lieu of population-based surveillance can severely limit the ability of public health agencies to accurately access the health status of a group within a defined geographical area. With the need for population-based data central to their mission, birth defects programs around the country take their data stewardship role seriously, recognizing both moral and legal obligations to protect the data by employing numerous safeguards. Birth defects surveillance systems are shaped by the needs of the community they are designed to serve, with the goal of preventing birth defects or alleviating the burdens associated with them.     

Combining markers into haplotypes can improve population structure inference

High-throughput genotyping and sequencing technologies can generate dense sets of genetic markers for large numbers of individuals. For most species, these data will contain many markers in linkage disequilibrium (LD). To utilize such data for population structure inference, we investigate the use of haplotypes constructed by combining the alleles at single-nucleotide polymorphisms (SNPs). We introduce a statistic derived from information theory, the gain of informativeness for assignment (GIA), which quantifies the additional information for assigning individuals to populations using haplotype data compared to using individual loci separately. Using a two-loci-two-allele model, we demonstrate that combining markers in linkage equilibrium into haplotypes always leads to nonpositive GIA, suggesting that combining the two markers is not advantageous for ancestry inference. However, for loci in LD, GIA is often positive, suggesting that assignment can be improved by combining markers into haplotypes. Using GIA as a criterion for combining markers into haplotypes, we demonstrate for simulated data a significant improvement of assigning individuals to candidate populations. For the many cases that we investigate, incorrect assignment was reduced between 26% and 97% using haplotype data. For empirical data from French and German individuals, the incorrectly assigned individuals can, for example, be decreased by 73% using haplotypes. Our results can be useful for challenging population structure and assignment problems, in particular for studies where large-scale population-genomic data are available.     

A user-friendly Hypercard interface for human linkage analysis

The availability of a large number of highly informative geneticmarkers has made human linkage analysis faster and easier toperform. However, current linkage analysis software does notprovide an organizational database into which a large body oflinkage data can be easily stored and manipulated. This manualentry and editing of linkage data is often time consuming andprone to typing errors. In addition, the large number of allelesin many of these markers must be reduced in order to performlinkage analysis with multiple loci across large genetic distances.This reduction in allele number is often difficult and confusing,especially in large pedigrees. We have taken advantage of theMacintosh-based Hypercard program to develop an interface withwhich linkage data can be easily stored, retrieved and edited.For each family, the components of the pedigree, including IDnumbers, sex and affection status, only need to be entered once.The program (Linkage Interface) retrieves this information eachtime the data from a new polymorphic marker is entered. LinkageInterface has flexible editing capabilities that allow the userto change any portion of the pedigree, including the additionor deletion of family members, without affecting previouslyentered genotype data. Linkage Interface can also analyze boththe pedigree and marker data and will detect any inconsistenciesin inheritance patterns. In addition, the program can reducethe number of alleles for a polynwrphic marker. Linkage Interfacewill then compare the ‘reduced’ data to the originalmarker data and assists in maintaining all informative meiosesby pointing out which meioses have become non-informative. Oncepolymorphic marker data are entered, the pedigree data, includingthe marker genotypes, are easily exported to a text file. Thistext file can be transferred to an IBM-compatible computer fordirect use with DOS-based linkage programs.