Elizabeth E. Marchani

Genetic similarities within and between human populations

The proportion of human genetic variation due to differences between populations is modest, and individuals from different populations can be genetically more similar than individuals from the same population. Yet sufficient genetic data can permit accurate classification of individuals into populations. Both findings can be obtained from the same data set, using the same number of polymorphic loci. This article explains why. Our analysis focuses on the frequency, ω, with which a pair of random individuals from two different populations is genetically more similar than a pair of individuals randomly selected from any single population. We compare ω to the error rates of several classification methods, using data sets that vary in number of loci, average allele frequency, populations sampled, and polymorphism ascertainment strategy. We demonstrate that classification methods achieve higher discriminatory power than ω because of their use of aggregate properties of populations. The number of loci analyzed is the most critical variable: with 100 polymorphisms, accurate classification is possible, but ω remains sizable, even when using populations as distinct as sub-Saharan Africans and Europeans. Phenotypes controlled by a dozen or fewer loci can therefore be expected to show substantial overlap between human populations. This provides empirical justification for caution when using population labels in biomedical settings, with broad implications for personalized medicine, pharmacogenetics, and the meaning of race.

Human population genetic structure and diversity inferred from polymorphic L1 (LINE-1) and Alu insertions

Background/Aims: The L1 retrotransposable element family is the most successful self-replicating genomic parasite of the human genome. L1 elements drive replication of Alu elements, and both have had far-reaching impacts on the human genome. We use L1 and Alu insertion polymorphisms to analyze human population structure. Methods: We genotyped 75 recent, polymorphic L1 insertions in 317 individuals from 21 populations in sub-Saharan Africa, East Asia, Europe and the Indian subcontinent. This is the first sample of L1 loci large enough to support detailed population genetic inference. We analyzed these data in parallel with a set of 100 polymorphic Alu insertion loci previously genotyped in the same individuals. Results and Conclusion: The data sets yield congruent results that support the recent African origin model of human ancestry. A genetic clustering algorithm detects clusters of individuals corresponding to continental regions. The number of loci sampled is critical: with fewer than 50 typical loci, structure cannot be reliably discerned in these populations. The inclusion of geographically intermediate populations (from India) reduces the distinctness of clustering. Our results indicate that human genetic variation is neither perfectly correlated with geographic distance (purely clinal) nor independent of distance (purely clustered), but a combination of both: stepped clinal.

Culture creates genetic structure in the Caucasus: Autosomal, mitochondrial, and Y-chromosomal variation in Daghestan

BackgroundNear the junction of three major continents, the Caucasus region has been an important thoroughfare for human migration. While the Caucasus Mountains have diverted human traffic to the few lowland regions that provide a gateway from north to south between the Caspian and Black Seas, highland populations have been isolated by their remote geographic location and their practice of patrilocal endogamy. We investigate how these cultural and historical differences between highland and lowland populations have affected patterns of genetic diversity. We test 1) whether the highland practice of patrilocal endogamy has generated sex-specific population relationships, and 2) whether the history of migration and military conquest associated with the lowland populations has left Central Asian genes in the Caucasus, by comparing genetic diversity and pairwise population relationships between Daghestani populations and reference populations throughout Europe and Asia for autosomal, mitochondrial, and Y-chromosomal markers.ResultsWe found that the highland Daghestani populations had contrasting histories for the mitochondrial DNA and Y-chromosome data sets. Y-chromosomal haplogroup diversity was reduced among highland Daghestani populations when compared to other populations and to highland Daghestani mitochondrial DNA haplogroup diversity. Lowland Daghestani populations showed Turkish and Central Asian affinities for both mitochondrial and Y-chromosomal data sets. Autosomal population histories are strongly correlated to the pattern observed for the mitochondrial DNA data set, while the correlation between the mitochondrial DNA and Y-chromosome distance matrices was weak and not significant.ConclusionThe reduced Y-chromosomal diversity exhibited by highland Daghestani populations is consistent with genetic drift caused by patrilocal endogamy. Mitochondrial and Y-chromosomal phylogeographic comparisons indicate a common Near Eastern origin of highland populations. Lowland Daghestani populations show varying influence from Near Eastern and Central Asian populations.

Evidence for three loci modifying age-at-onset of Alzheimer's disease in early-onset PSEN2 families

Families with early‐onset Alzheimers disease (AD) sharing a single PSEN2 mutation exhibit a wide range of age‐at‐onset, suggesting that modifier loci segregate within these families. While APOE is known to be an age‐at‐onset modifier, it does not explain all of this variation. We performed a genome scan within nine such families for loci influencing age‐at‐onset, while simultaneously controlling for variation in the primary PSEN2 mutation (N141I) and APOE. We found significant evidence of linkage between age‐at‐onset and chromosome 1q23.3 (P < 0.001) when analysis included all families, and to chromosomes 1q23.3 (P < 0.001), 17p13.2 (P = 0.0002), 7q33 (P = 0.017), and 11p14.2 (P = 0.017) in a single large pedigree. Simultaneous analysis of these four chromosomes maintained strong evidence of linkage to chromosomes 1q23.3 and 17p13.2 when all families were analyzed, and to chromosomes 1q23.3, 7q33, and 17p13.2 within the same single pedigree. Inclusion of major gene covariates proved essential to detect these linkage signals, as all linkage signals dissipated when PSEN2 and APOE were excluded from the model. The four chromosomal regions with evidence of linkage all coincide with previous linkage signals, associated SNPs, and/or candidate genes identified in independent AD study populations. This study establishes several candidate regions for further analysis and is consistent with an oligogenic model of AD risk and age‐at‐onset. More generally, this study also demonstrates the value of searching for modifier loci in existing datasets previously used to identify primary causal variants for complex disease traits.

Identification of Rare Variants from Exome Sequence in a Large Pedigree with Autism

We carried out analyses with the goal of identifying rare variants in exome sequence data that contribute to disease risk for a complex trait. We analyzed a large, 47-member, multigenerational pedigree with 11 cases of autism spectrum disorder, using genotypes from 3 technologies representing increasing resolution: a multiallelic linkage marker panel, a dense diallelic marker panel, and variants from exome sequencing. Genome-scan marker genotypes were available on most subjects, and exome sequence data was available on 5 subjects. We used genome-scan linkage analysis to identify and prioritize the chromosome 22 region of interest, and to select subjects for exome sequencing. Inheritance vectors (IVs) generated by Markov chain Monte Carlo analysis of multilocus marker data were the foundation of most analyses. Genotype imputation used IVs to determine which sequence variants reside on the haplotype that co-segregates with the autism diagnosis. Together with a rare-allele frequency filter, we identified only one rare variant on the risk haplotype, illustrating the potential of this approach to prioritize variants. The associated gene, MYH9, is biologically unlikely, and we speculate that for this complex trait, the key variants may lie outside the exome.

The N141I Mutation in PSEN2: Implications for the Quintessential Case of Alzheimer Disease

OBJECTIVE To connect a new family with early-onset Alzheimer disease (EOAD) in Germany to the American Volga German pedigrees. DESIGN Pedigree molecular genetic analysis. SETTING University Medical Centers in Fulda and Giessen, Germany, and in Seattle, Washington. RESULTS The families from Fulda, Germany, and the American Volga German families with EOAD share the same N141I PSEN2 mutation on an identical haplotypic background. This establishes that the N141I mutation occurred prior to emigration of the families from the Hesse region to Russia in the 1760s, and documents that relatives of the original immigrant families are presently living in Germany with the mutation and the disease. CONCLUSION A family with the N141I mutation in PSEN2 that presently lives in Germany has been connected to the haplotype that carries the same mutation in pedigrees descended from the Volga Germans. This raises the possibility that the original patient with Alzheimer disease (Auguste D.), who had EOAD and lived in this same region of Germany, may also have had the PSEN2 N141I mutation.

Estimation and visualization of identity-by-descent within pedigrees simplifies interpretation of complex trait analysis.

Linkage analysis identifies markers that appear to be co-inherited with a trait within pedigrees. The inheritance of a chromosomal segment may be probabilistically reconstructed, with missing data complicating inference. Inheritance patterns are further obscured in the analysis of complex traits, where variants in one or more genes may contribute to phenotypic variation within a pedigree. In this case, determining which relatives share a trait variant is not simple. We describe how to represent these patterns of inheritance for marker loci. We summarize how to sample patterns of inheritance consistent with genotypic and pedigree data using gl_auto, available in MORGAN v3.0. We describe identification of classes of equivalent inheritance patterns with the program IBDgraph. We finally provide an example of how these programs may be used to simplify interpretation of linkage analysis of complex traits in general pedigrees. We borrow information across loci in a parametric linkage analysis of a large pedigree. We explore the contribution of each equivalence class to a linkage signal, illustrate estimated patterns of identity-by-descent sharing, and identify a haplotype tagging the chromosomal segment driving the linkage signal. Haplotype carriers are more likely to share the linked trait variant, and can be prioritized for subsequent DNA sequencing.

Estimating the age of retrotransposon subfamilies using maximum likelihood

We present a maximum likelihood model to estimate the age of retrotransposon subfamilies. This method is designed around a master gene model which assumes a constant retrotransposition rate. The statistical properties of this model and an ad hoc estimation procedure are compared using two simulated data sets. We also test whether each estimation procedure is robust to violation of the master gene model. According to our results, both estimation procedures are accurate under the master gene model. While both methods tend to overestimate ages under the intermediate model, the maximum likelihood estimate is significantly less inflated than the ad hoc estimate. We estimate the ages of two subfamilies of human-specific LINE-I insertions using both estimation procedures. By calculating confidence intervals around the maximum likelihood estimate, our model can both provide an estimate of retrotransposon subfamily age and describe the range of subfamily ages consistent with the data.

Genome scan of age-at-onset in the NIMH Alzheimer disease sample uncovers multiple loci, along with evidence of both genetic and sample heterogeneity

Alzheimers disease (AD) is a common neurodegenerative disorder of late life with a complex genetic basis. Although several genes are known to play a role in rare early onset AD, only the APOE gene is known to have a high contribution to risk of the common late‐onset form of the disease (LOAD, onset >60 years). APOE genotypes vary in their AD risk as well as age‐at‐onset distributions, and it is likely that other loci will similarly affect AD age‐at‐onset. Here we present the first analysis of age‐at‐onset in the NIMH LOAD sample that allows for both a multilocus trait model and genetic heterogeneity among the contributing sites, while at the same time accommodating age censoring, effects of known genetic covariates, and full pedigree and marker information. The results provide evidence for genomic regions not previously implicated in this data set, including regions on chromosomes 7q, 15, and 19p. They also affirm evidence for loci on chromosomes 1q, 6p, 9q, 11, and, of course, the APOE locus on 19q, all of which have been reported previously in the same sample. The analyses failed to find evidence for linkage to chromosome 10 with inclusion of unaffected subjects and extended pedigrees. Several regions implicated in these analyses in the NIMH sample have been previously reported in genome scans of other AD samples. These results, therefore, provide independent confirmation of AD loci in family‐based samples on chromosomes 1q, 7q, 19p, and suggest that further efforts towards identifying the underlying causal loci are warranted.

Genome scan in familial late-onset Alzheimer's disease: a locus on chromosome 6 contributes to age-at-onset.

Alzheimers disease (AD) is a common, genetically complex, fatal neurodegenerative disorder of late life. Although several genes are known to play a role in early‐onset AD, identification of the genetic basis of late onset AD (LOAD) has been challenging, with only the APOE gene known to have a high contribution to both AD risk and age‐at‐onset. Here, we present the first genome‐scan analysis of the complete, well‐characterized University of Washington LOAD sample of 119 pedigrees, using age‐at‐onset as the trait of interest. The analysis approach used allows for a multilocus trait model while at the same time accommodating age censoring, effects of APOE as a known genetic covariate, and full pedigree and marker information. The results provide strong evidence for linkage of loci contributing to age‐at‐onset to genomic regions on chromosome 6q16.3, and to 19q13.42 in the region of the APOE locus. There was evidence for interaction between APOE and the locus on chromosome 6q and suggestive evidence for linkage to chromosomes 11p13, 15q12‐14, and 19p13.12. These results provide the first independent confirmation of an AD age‐at‐onset locus on chromosome 6 and suggest that further efforts towards identifying the underlying causal locus or loci are warranted.