Complex trait mapping in isolated populations: Are specific statistical methods required?

Strategies for phasing and imputation in a population isolate

In the search for genetic associations with complex traits, population isolates offer the advantage of reduced genetic and environmental heterogeneity. In addition, cost-efficient next-generation association approaches have been proposed in these populations where only a subsample of representative individuals is sequenced and then genotypes are imputed into the rest of the population. Gene mapping in such populations thus requires high-quality genetic imputation and preliminary phasing. To identify an effective study design, we compare by simulation a range of phasing and imputation software and strategies. We simulated 1,115,604 variants on chromosome 10 for 477 members of the large complex pedigree of Campora, a village within the established isolate of Cilento in southern Italy. We assessed the phasing performance of identical by descent based software ALPHAPHASE and SLRP, LD-based software SHAPEIT2, SHAPEIT3, and BEAGLE, and new software EAGLE that combines both methodologies. For imputation we compared IMPUTE2, IMPUTE4, MINIMAC3, BEAGLE, and new software PBWT. Genotyping errors and missing genotypes were simulated to observe their effects on the performance of each software. Highly accurate phased data were achieved by all software with SHAPEIT2, SHAPEIT3, and EAGLE2 providing the most accurate results. MINIMAC3, IMPUTE4, and IMPUTE2 all performed strongly as imputation software and our study highlights the considerable gain in imputation accuracy provided by a genome sequenced reference panel specific to the population isolate.

Consangunity and psychosis in Algeria. A family study

Several studies have affirmed the existence of a strong and complex genetic component in the determination of psychotic disorders. However, the genetic architecture of these disorders remains poorly understood. GWAS studies conducted over the past decade have identified some associations to low effect, and the major part of this heritability remains unexplained, thus calling into question the hypothesis of “common disease – common variant” for model involving a large number of rare variants. Family studies of extended pedigrees selected from geographical isolate can be a powerful approach in identifying rare genetic variants of complex diseases such as psychotic disorders. Here, we studied four multigenerational families in which co-exist psychotic and mood disorders and a high rate of consanguinity, identified in the northwest of Algeria. This case-control study aimed to characterize new rare genetic variants responsible for psychosis. These families have received complete clinical and genealogical investigations, genome wide analysis that were performed in the laboratory of medical genetics in the university hospital of Geneva. A genome wide research CNVs using Agilent Human Genome CGH Microarray Kit 44 K, covering 45 subjects including 20 patients and in a control population of 55 individuals. Three CNVs that had never been reported to date have been identified in one of four families and validated by two techniques. It is the dup 4q26, and 16q23.1 del del21q21. These CNVs are transmitted by either parent line, suggesting a cumulative effect on the risk of psychotic disorders. Further analyzes using pan-genomic linkage analysis using GWAS chip (Illumina Human 660 W-Quad v1.0 Breadchip) and complete WES (by GAIIx Illumina/HiSeq 2000) were performed in some related individuals to search other mutations may explain the appearance of the phenotype in this population.

GENLIB: an R package for the analysis of genealogical data

BACKGROUND

Founder populations have an important role in the study of genetic diseases. Access to detailed genealogical records is often one of their advantages. These genealogical data provide unique information for researchers in evolutionary and population genetics, demography and genetic epidemiology. However, analyzing large genealogical datasets requires specialized methods and software. The GENLIB software was developed to study the large genealogies of the French Canadian population of Quebec, Canada. These genealogies are accessible through the BALSAC database, which contains over 3 million records covering the whole province of Quebec over four centuries. Using this resource, extended pedigrees of up to 17 generations can be constructed from a sample of present-day individuals.

RESULTS

We have extended and implemented GENLIB as a package in the R environment for statistical computing and graphics, thus allowing optimal flexibility for users. The GENLIB package includes basic functions to manage genealogical data allowing, for example, extraction of a part of a genealogy or selection of specific individuals. There are also many functions providing information to describe the size and complexity of genealogies as well as functions to compute standard measures such as kinship, inbreeding and genetic contribution. GENLIB also includes functions for gene-dropping simulations. The goal of this paper is to present the full functionalities of GENLIB. We used a sample of 140 individuals from the province of Quebec (Canada) to demonstrate GENLIB's functions. Ascending genealogies for these individuals were reconstructed using BALSAC, yielding a large pedigree of 41,523 individuals. Using GENLIB's functions, we provide a detailed description of these genealogical data in terms of completeness, genetic contribution of founders, relatedness, inbreeding and the overall complexity of the genealogical tree. We also present gene-dropping simulations based on the whole genealogy to investigate identical-by-descent sharing of alleles and chromosomal segments of different lengths and estimate probabilities of identical-by-descent sharing.

CONCLUSIONS

The R package GENLIB provides a user friendly and flexible environment to analyze extensive genealogical data, allowing an efficient and easy integration of different types of data, analytical methods and additional developments and making this tool ideal for genealogical analysis.

Permutation testing in the presence of polygenic variation

This article discusses problems with and solutions to performing valid permutation tests for quantitative trait loci in the presence of polygenic effects. Although permutation testing is a popular approach for determining statistical significance of a test statistic with an unknown distribution--for instance, the maximum of multiple correlated statistics or some omnibus test statistic for a gene, gene-set, or pathway--naive application of permutations may result in an invalid test. The risk of performing an invalid permutation test is particularly acute in complex trait mapping where polygenicity may combine with a structured population resulting from the presence of families, cryptic relatedness, admixture, or population stratification. I give both analytical derivations and a conceptual understanding of why typical permutation procedures fail and suggest an alternative permutation-based algorithm, MVNpermute, that succeeds. In particular, I examine the case where a linear mixed model is used to analyze a quantitative trait and show that both phenotype and genotype permutations may result in an invalid permutation test. I provide a formula that predicts the amount of inflation of the type 1 error rate depending on the degree of misspecification of the covariance structure of the polygenic effect and the heritability of the trait. I validate this formula by doing simulations, showing that the permutation distribution matches the theoretical expectation, and that my suggested permutation-based test obtains the correct null distribution. Finally, I discuss situations where naive permutations of the phenotype or genotype are valid and the applicability of the results to other test statistics.

Population-based and family-based designs to analyze rare variants in complex diseases

Genotyping of rare variants on a large scale is now possible using next-generation sequencing. Sample selection is a crucial step in designing the genetic study of a complex disease, and knowledge of the efficiency and limitations of population-based and family-based designs can help researchers make the appropriate choice. The nine contributions to Group 5 of Genetic Analysis Workshop 17 evaluate population-based and family-based designs by comparing the results obtained with various methods applied to the mini-exome simulations. These simulations consisted of 200 replicates composed of unrelated individuals and eight extended pedigrees with genotypes and various phenotypes. The methods tested for association with a population-based and/or a family-based design, tested for linkage with a family-based design, or estimated heritability. We summarize the strengths and weaknesses of both designs. Although population-based designs seem more suitable for detecting the effect of multiple rare variants, family-based designs can potentially enrich the sample in rare variants, for which the effect would be concealed at the population level. However, as of today, the main limitation is still the high cost of next-generation sequencing.

Increased power of mixed models facilitates association mapping of 10 loci for metabolic traits in an isolated population

The potential benefits of using population isolates in genetic mapping, such as reduced genetic, phenotypic and environmental heterogeneity, are offset by the challenges posed by the large amounts of direct and cryptic relatedness in these populations confounding basic assumptions of independence. We have evaluated four representative specialized methods for association testing in the presence of relatedness; (i) within-family (ii) within- and between-family and (iii) mixed-models methods, using simulated traits for 2906 subjects with known genome-wide genotype data from an extremely isolated population, the Island of Kosrae, Federated States of Micronesia. We report that mixed models optimally extract association information from such samples, demonstrating 88% power to rank the true variant as among the top 10 genome-wide with 56% achieving genome-wide significance, a >80% improvement over the other methods, and demonstrate that population isolates have similar power to non-isolate populations for observing variants of known effects. We then used the mixed-model method to reanalyze data for 17 published phenotypes relating to metabolic traits and electrocardiographic measures, along with another 8 previously unreported. We replicate nine genome-wide significant associations with known loci of plasma cholesterol, high-density lipoprotein, low-density lipoprotein, triglycerides, thyroid stimulating hormone, homocysteine, C-reactive protein and uric acid, with only one detected in the previous analysis of the same traits. Further, we leveraged shared identity-by-descent genetic segments in the region of the uric acid locus to fine-map the signal, refining the known locus by a factor of 4. Finally, we report a novel associations for height (rs17629022, P< 2.1 × 10⁻⁸).

Comparing population structure as inferred from genealogical versus genetic information

Algorithms for inferring population structure from genetic data (ie, population assignment methods) have shown to effectively recognize genetic clusters in human populations. However, their performance in identifying groups of genealogically related individuals, especially in scanty-differentiated populations, has not been tested empirically thus far. For this study, we had access to both genealogical and genetic data from two closely related, isolated villages in southern Italy. We found that nearly all living individuals were included in a single pedigree, with multiple inbreeding loops. Despite F(st) between villages being a low 0.008, genetic clustering analysis identified two clusters roughly corresponding to the two villages. Average kinship between individuals (estimated from genealogies) increased at increasing values of group membership (estimated from the genetic data), showing that the observed genetic clusters represent individuals who are more closely related to each other than to random members of the population. Further, average kinship within clusters and F(st) between clusters increases with increasingly stringent membership threshold requirements. We conclude that a limited number of genetic markers is sufficient to detect structuring, and that the results of genetic analyses faithfully mirror the structuring inferred from detailed analyses of population genealogies, even when F(st) values are low, as in the case of the two villages. We then estimate the impact of observed levels of population structure on association studies using simulated data.

Case-control association testing in the presence of unknown relationships

Genome-wide association studies result in inflated false-positive results when unrecognized cryptic relatedness exists. A number of methods have been proposed for testing association between markers and disease with a correction for known pedigree-based relationships. However, in most case-control studies, relationships are generally unknown, yet the design is predicated on the assumption of at least ancestral relatedness among cases. Here, we focus on adjusting cryptic relatedness when the genealogy of the sample is unknown, particularly in the context of samples from isolated populations where cryptic relatedness may be problematic. We estimate cryptic relatedness using maximum-likelihood methods and use a corrected chi(2) test with estimated kinship coefficients for testing in the context of unknown cryptic relatedness. Estimated kinship coefficients characterize precisely the relatedness between truly related people, but are biased for unrelated pairs. The proposed test substantially reduces spurious positive results, producing a uniform null distribution of P-values. Especially with missing pedigree information, estimated kinship coefficients can still be used to correct non-independence among individuals. The corrected test was applied to real data sets from genetic isolates and created a distribution of P-value that was close to uniform. Thus, the proposed test corrects the non-uniform distribution of P-values obtained with the uncorrected test and illustrates the advantage of the approach on real data.

Identification of novel susceptibility loci for Guam neurodegenerative disease: challenges of genome scans in genetic isolates

Amyotrophic lateral sclerosis/parkinsonism-dementia complex (ALS/PDC) is a fatal neurodegenerative disease found in the Chamorro people of Guam and other Pacific Island populations. The etiology is unknown, although both genetic and environmental factors appear important. To identify loci for ALS/PDC, we conducted both genome-wide linkage and association analyses, using approximately 400 microsatellite markers, in the largest sample assembled to date, comprising a nearly complete sample of all living and previously sampled deceased cases. A single, large, complex pedigree was ascertained from a village on Guam, with smaller families and a case-control sample ascertained from the rest of Guam by population-based neurological screening and archival review. We found significant evidence for two regions with novel ALS/PDC loci on chromosome 12 and supportive evidence for the involvement of the MAPT region on chromosome 17. D12S1617 on 12p gave the strongest evidence of linkage (maximum LOD score, Z(max) = 4.03) in our initial scan, with additional support in the complete case-control sample in the form of evidence of allelic association at this marker and another nearby marker. D12S79 on 12q also provided significant evidence of linkage (Z(max) = 3.14) with support from flanking markers. Our results suggest that ALS/PDC may be influenced by as many as three loci, while illustrating challenges that are intrinsic in genetic analyses of isolated populations, as well as analytical strategies that are useful in this context. Elucidation of the genetic basis of ALS/PDC should improve our understanding of related neurodegenerative disorders including Alzheimer disease, Parkinson disease, frontotemporal dementia and ALS.

Genome-wide association studies in an isolated founder population from the Pacific Island of Kosrae

It has been argued that the limited genetic diversity and reduced allelic heterogeneity observed in isolated founder populations facilitates discovery of loci contributing to both Mendelian and complex disease. A strong founder effect, severe isolation, and substantial inbreeding have dramatically reduced genetic diversity in natives from the island of Kosrae, Federated States of Micronesia, who exhibit a high prevalence of obesity and other metabolic disorders. We hypothesized that genetic drift and possibly natural selection on Kosrae might have increased the frequency of previously rare genetic variants with relatively large effects, making these alleles readily detectable in genome-wide association analysis. However, mapping in large, inbred cohorts introduces analytic challenges, as extensive relatedness between subjects violates the assumptions of independence upon which traditional association test statistics are based. We performed genome-wide association analysis for 15 quantitative traits in 2,906 members of the Kosrae population, using novel approaches to manage the extreme relatedness in the sample. As positive controls, we observe association to known loci for plasma cholesterol, triglycerides, and C-reactive protein and to a compelling candidate loci for thyroid stimulating hormone and fasting plasma glucose. We show that our study is well powered to detect common alleles explaining >/=5% phenotypic variance. However, no such large effects were observed with genome-wide significance, arguing that even in such a severely inbred population, common alleles typically have modest effects. Finally, we show that a majority of common variants discovered in Caucasians have indistinguishable effect sizes on Kosrae, despite the major differences in population genetics and environment.

Isolated populations and complex disease gene identification

Isolated populations can be useful for the identification of genes underlying common complex diseases.

The utility of genetically isolated populations (population isolates) in the mapping and identification of genes is not only limited to the study of rare diseases; isolated populations also provide a useful resource for studies aimed at improved understanding of the biology underlying common diseases and their component traits. Well characterized human populations provide excellent study samples for many different genetic investigations, ranging from genome-wide association studies to the characterization of interactions between genes and the environment.

Identity-by-descent estimation and mapping of qualitative traits in large, complex pedigrees

Computing identity-by-descent sharing between individuals connected through a large, complex pedigree is a computationally demanding task that often cannot be done using exact methods. What I present here is a rapid computational method for estimating, in large complex pedigrees, the probability that pairs of alleles are IBD given the single-point genotype data at that marker for all individuals. The method can be used on pedigrees of essentially arbitrary size and complexity without the need to divide the individuals into separate subpedigrees. I apply the method to do qualitative trait linkage mapping using the nonparametric sharing statistic S(pairs). The validity of the method is demonstrated via simulation studies on a 13-generation 3028-person pedigree with 700 genotyped individuals. An analysis of an asthma data set of individuals in this pedigree finds four loci with P-values <10(-3) that were not detected in prior analyses. The mapping method is fast and can complete analyses of approximately 150 affected individuals within this pedigree for thousands of markers in a matter of hours.

HapMap tagSNP transferability in multiple populations: general guidelines

Linkage disequilibrium (LD) has received much attention recently because of its value in localizing disease-causing genes. Due to the extensive LD between neighboring loci in the human genome, it is believed that a subset of the single nucleotide polymorphisms in a region (tagSNPs) can be selected to capture most of the remaining SNP variants. In this study, we examined LD patterns and HapMap tagSNP transferability in more than 300 individuals. A South Indian sample and an African Mbuti Pygmy population sample were included to evaluate the performance of HapMap tagSNPs in geographically distinct and genetically isolated populations. Our results show that HapMap tagSNPs selected with r(2) >= 0.8 can capture more than 85% of the SNPs in populations that are from the same continental group. Combined tagSNPs from HapMap CEU and CHB+JPT serve as the best reference for the Indian sample. The HapMap YRI are a sufficient reference for tagSNP selection in the Pygmy sample. In addition to our findings, we reviewed over 25 recent studies of tagSNP transferability and propose a general guideline for selecting tagSNPs from HapMap populations.

Fine mapping of a locus for nonsyndromic mental retardation on chromosome 19p13

Mental retardation (MR) occurs in approximately 3% of the population and therefore significantly impacts public health. Despite this relatively high prevalence, the specific causes of MR remain unknown in most cases, although both genetic and environmental factors are known to contribute. We describe a consanguineous family with autosomal recessive (AR) nonsyndromic MR (NSMR). Because the consanguinity of this family is complex, we explore alternative approaches for generating accurate estimates of the evidence for linkage in this family, and demonstrate evidence for linkage to chromosome 19p13 (lod score ranging from 1.2 to 3.5, depending on assumptions of allele frequencies). Fine mapping of the linked region defined a critical region of 3.6 Mb, which overlaps with a previously reported gene (CC2D1A) for MR. However, no mutations in the coding region of this gene are present in the family we describe. These results suggest that another gene causing autosomal recessive nonsyndromic MR (ARNSMR) is located within this genomic region.

Genomewide rapid association using mixed model and regression: a fast and simple method for genomewide pedigree-based quantitative trait loci association analysis

For pedigree-based quantitative trait loci (QTL) association analysis, a range of methods utilizing within-family variation such as transmission-disequilibrium test (TDT)-based methods have been developed. In scenarios where stratification is not a concern, methods exploiting between-family variation in addition to within-family variation, such as the measured genotype (MG) approach, have greater power. Application of MG methods can be computationally demanding (especially for large pedigrees), making genomewide scans practically infeasible. Here we suggest a novel approach for genomewide pedigree-based quantitative trait loci (QTL) association analysis: genomewide rapid association using mixed model and regression (GRAMMAR). The method first obtains residuals adjusted for family effects and subsequently analyzes the association between these residuals and genetic polymorphisms using rapid least-squares methods. At the final step, the selected polymorphisms may be followed up with the full measured genotype (MG) analysis. In a simulation study, we compared type 1 error, power, and operational characteristics of the proposed method with those of MG and TDT-based approaches. For moderately heritable (30%) traits in human pedigrees the power of the GRAMMAR and the MG approaches is similar and is much higher than that of TDT-based approaches. When using tabulated thresholds, the proposed method is less powerful than MG for very high heritabilities and pedigrees including large sibships like those observed in livestock pedigrees. However, there is little or no difference in empirical power of MG and the proposed method. In any scenario, GRAMMAR is much faster than MG and enables rapid analysis of hundreds of thousands of markers.

Patterns of linkage disequilibrium between SNPs in a Sardinian population isolate and the selection of markers for association studies

OBJECTIVE

In isolated populations, 'background' linkage disequilibrium (LD) has been shown to extend over large genetic distances. This and their reduced environmental and genetic heterogeneity has stimulated interest in their potential for association mapping. We compared LD unit map distances with pair-wise measurements of LD in a dense single nucleotide polymorphism (SNP) set.

METHODS

We genotyped 771 SNPs in an 8 Mb segment of chromosome 22 on 101 individuals from the isolated village of Talana, Sardinia, and compared with outbred European populations.

RESULTS

Heterozygosity was remarkably similar in both populations. In contrast, the extent of LD observed was quite different. The decay of LD with distance is slower in the isolate. The differences in LD map lengths suggest that useful LD extends up to three times farther in the Sardinian population; smaller differences are seen with pairwise LD metrics. While LD map length slightly decreases with average relatedness, cryptic relatedness does not explain the decrease in LD map length. Haplotypes, block boundaries, and patterns of LD are similar in both populations, suggesting a shared distribution of recombination hotspots.

CONCLUSIONS

About 15% fewer haplotype tagging SNPs need to be genotyped in the isolate, and possibly 70% fewer if selecting SNPs evenly spaced on the metric LD map.

Family based studies and genetic epidemiology: theory and practice

Family based studies have underpinned many successes in uncovering the causes of monogenic and oligogenic diseases. Now research is focussing on the identification and characterisation of genes underlying common diseases and it is widely accepted that these studies will require large population based samples. Population based family study designs have the potential to facilitate the analysis of the effects of both genes and environment. These types of studies integrate the population based approaches of classic epidemiology and the methods enabling the analysis of correlations between relatives sharing both genes and environment. The extent to which such studies are feasible will depend upon population- and disease-specific factors. To review this topic, a symposium was held to present and discuss the costs, requirements and advantages of population based family study designs. This article summarises the features of the meeting held at The University of Sheffield, August 2006.

Campora: a young genetic isolate in South Italy

Genetic isolates have been successfully used in the study of complex traits, mainly because due to their features, they allow a reduction in the complexity of the genetic models underlying the trait. The aim of the present study is to describe the population of Campora, a village in the South of Italy, highlighting its properties of a genetic isolate. Both historical evidence and multi-locus genetic data (genomic and mitochondrial DNA polymorphisms) have been taken into account in the analyses. The extension of linkage disequilibrium (LD) regions has been evaluated on autosomes and on a region of the X chromosome. We defined a study sample population on the basis of the genealogy and exogamy data. We found in this population a few different mitochondrial and Y chromosome haplotypes and we ascertained that, similarly to other isolated populations, in Campora LD extends over wider region compared to large and genetically heterogeneous populations. These findings indicate a conspicuous genetic homogeneity in the genome. Finally, we found evidence for a recent population bottleneck that we propose to interpret as a demographic crisis determined by the plague of the 17th century. Overall our findings demonstrate that Campora displays the genetic characteristics of a young isolate.

Chasing genes for mood disorders and schizophrenia in genetically isolated populations

Major affective disorders and schizophrenia are among the most common brain diseases worldwide and their predisposition is influenced by a complex interaction of genetic and environmental factors. So far, traditional linkage mapping studies for these complex disorders have not achieved the same success as the positional cloning of genes for Mendelian diseases. The struggle to identify susceptibility genes for complex disorders has stimulated the development of alternative approaches, including studies in genetically isolated populations. Since isolated populations are likely to have both a reduced number of genetic vulnerability factors and environmental background and are therefore considered to be more homogeneous compared to outbred populations, the use of isolated populations in genetic studies is expected to improve the chance of finding susceptibility loci and genes. Here we review the role of isolated populations, based on linkage and association studies, in the identification of susceptibility genes for bipolar disorder and schizophrenia.

New susceptibility locus for hypertension on chromosome 8q by efficient pedigree-breaking in an Italian isolate

Essential hypertension (EH) affects a large proportion of the adult population in Western countries and is a major risk factor for cardiovascular diseases. EH is a multifactorial disease with a complex genetic component. To tackle the complexity of this genetic component, we have initiated a study of Campora, an isolated village in South Italy. A random sample of 389 adults was genotyped for a very dense microsatellite genome scan and phenotyped for EH. Of this sample, 173 affected individuals were all related through a 2,180-member pedigree and could be integrated within a linkage analysis. The complexity of the pedigree prevented its direct use for a non-parametric linkage (NPL) analysis. Therefore, the method proposed by Falchi et al. [2004, Am. J. Hum. Genet., 75, 1015-1031] was used for automatic pedigree-breaking. We identified a new locus for EH on chromosome 8q22-23 and detected linkage with two known loci for EH: 1q42-43 and 4p16. Simulations showed that the linkage with 8q22-23 is highly genome-wide significant, even when accounting for the breaking of the pedigree. An extension to qualitative traits of another pedigree-breaking approach [Pankratz et al., 2001, Genet. Epidemiol., 21 (Suppl. 1), S258-S263] also detected a significant linkage on 8q22-23 using a remarkably different set of sub-pedigrees and helped to refine the location of the linkage signal. This work both identifies a new locus strongly linked to hypertension and shows that the power of linkage analysis can be improved by the appropriate use of efficient pedigree-breaking strategies.

Combinatorial Mismatch Scan (CMS) for loci associated with dementia in the Amish

BACKGROUND

Population heterogeneity may be a significant confounding factor hampering detection and verification of late onset Alzheimer's disease (LOAD) susceptibility genes. The Amish communities located in Indiana and Ohio are relatively isolated populations that may have increased power to detect disease susceptibility genes.

METHODS

We recently performed a genome scan of dementia in this population that detected several potential loci. However, analyses of these data are complicated by the highly consanguineous nature of these Amish pedigrees. Therefore we applied the Combinatorial Mismatch Scanning (CMS) method that compares identity by state (IBS) (under the presumption of identity by descent (IBD)) sharing in distantly related individuals from such populations where standard linkage and association analyses are difficult to implement. CMS compares allele sharing between individuals in affected and unaffected groups from founder populations. Comparisons between cases and controls were done using two Fisher's exact tests, one testing for excess in IBS allele frequency and the other testing for excess in IBS genotype frequency for 407 microsatellite markers.

RESULTS

In all, 13 dementia cases and 14 normal controls were identified who were not related at least through the grandparental generation. The examination of allele frequencies identified 24 markers (6%) nominally (p < or = 0.05) associated with dementia; the most interesting (empiric p < or = 0.005) markers were D3S1262, D5S211, and D19S1165. The examination of genotype frequencies identified 21 markers (5%) nominally (p < or = 0.05) associated with dementia; the most significant markers were both located on chromosome 5 (D5S1480 and D5S211). Notably, one of these markers (D5S211) demonstrated differences (empiric p < or = 0.005) under both tests.

CONCLUSION

Our results provide the initial groundwork for identifying genes involved in late-onset Alzheimer's disease within the Amish community. Genes identified within this isolated population will likely play a role in a subset of late-onset AD cases across more general populations. Regions highlighted by markers demonstrating suggestive allelic and/or genotypic differences will be the focus of more detailed examination to characterize their involvement in dementia.

Highly discrepant proportions of female and male Scandinavian and British Isles ancestry within the isolated population of the Faroe Islands

The Faroe Islands in the North Atlantic Ocean are inhabited by a small population, whose origin is thought to date back to the Viking Age. Historical, archaeological and linguistic evidence indicates that the present population of the Faroe Islands may have a mixture of Scandinavian and British Isles ancestry. In the present study we used 122 new and 19 previously published hypervariable region I sequences of the mitochondrial control region to analyse the genetic diversity of the Faroese population and compare it with other populations in the North Atlantic region. The analyses suggested that the Faroese mtDNA pool has been affected by genetic drift, and is among the most homogenous and isolated in the North Atlantic region. This will have implications for attempts to locate genes for complex disorders. To obtain estimates of Scandinavian vs British Isles ancestry proportions, we applied a frequency-based admixture approach taking private haplotypes into account by the use of phylogenetic information. While previous studies have suggested an excess of Scandinavian ancestry among the male settlers of the Faroe Islands, the current study indicates an excess of British Isles ancestry among the female settlers of the Faroe Islands. Compared to other admixed populations of the North Atlantic region, the population of the Faroe Islands appears to have the highest level of asymmetry in Scandinavian vs British Isles ancestry proportions among female and male settlers of the archipelago.

A genome-wide search for alleles and haplotypes associated with autism and related pervasive developmental disorders on the Faroe Islands

The involvement of genetic factors in the etiology of autism has been clearly established. We undertook a genome-wide search for regions containing susceptibility genes for autism in 12 subjects with childhood autism and related pervasive developmental disorders (PDDs) and 44 controls from the relatively isolated population of the Faroe Islands. In total, 601 microsatellite markers distributed throughout the human genome with an average distance of 5.80 cM were genotyped, including 502 markers in the initial scan. The Faroese population structure and genetic relatedness of cases and controls were also evaluated. Based on a combined approach, including an assumption-free test as implemented in CLUMP, Fisher's exact test for specific alleles and haplotypes, and IBD(0) probability calculations, we found association between autism and microsatellite markers in regions on 2q, 3p, 6q, 15q, 16p, and 18q. The most significant finding was on 3p25.3 (P(T1)=0.00003 and P(T4)=0.00007), which was also supported by other genetic studies. Furthermore, no evidence of population substructure was found, and a higher degree of relatedness among cases could not be detected, decreasing the risk of inflated P-values. Our data suggest that markers in these regions are in linkage disequilibrium with genes involved in the etiology of autism, and we hypothesize susceptibility genes for autism and related PDDs to be localized within these regions.