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.

Brief communication: the Thule migration: rejecting population histories using computer simulation.

Locked within our genetic code are the histories of our genes and the genes of our ancestors. Deciphering a population's history from genetic data often involves lengthy investigations of many loci for many individuals. We test hypothetical population histories of the Thule expansion using a new coalescent simulation method that uses little more than mitochondrial haplogroup data. This new methodology rejects a severe bottleneck at expansion and reveals the range of probable population histories on which to focus future research.

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, omega, 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 omega 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 omega 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 omega 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.