Inferring the Demographic History of African Farmers and Pygmy Hunter–Gatherers Using a Multilocus Resequencing Data Set

The transition from hunting and gathering to farming involved a major cultural innovation that has spread rapidly over most of the globe in the last ten millennia. In sub-Saharan Africa, hunter–gatherers have begun to shift toward an agriculture-based lifestyle over the last 5,000 years. Only a few populations still base their mode of subsistence on hunting and gathering. The Pygmies are considered to be the largest group of mobile hunter–gatherers of Africa. They dwell in equatorial rainforests and are characterized by their short mean stature. However, little is known about the chronology of the demographic events—size changes, population splits, and gene flow—ultimately giving rise to contemporary Pygmy (Western and Eastern) groups and neighboring agricultural populations. We studied the branching history of Pygmy hunter–gatherers and agricultural populations from Africa and estimated separation times and gene flow between these populations. We resequenced 24 independent noncoding regions across the genome, corresponding to a total of ∼33 kb per individual, in 236 samples from seven Pygmy and five agricultural populations dispersed over the African continent. We used simulation-based inference to identify the historical model best fitting our data. The model identified included the early divergence of the ancestors of Pygmy hunter–gatherers and farming populations ∼60,000 years ago, followed by a split of the Pygmies' ancestors into the Western and Eastern Pygmy groups ∼20,000 years ago. Our findings increase knowledge of the history of the peopling of the African continent in a region lacking archaeological data. An appreciation of the demographic and adaptive history of African populations with different modes of subsistence should improve our understanding of the influence of human lifestyles on genome diversity

Genome-wide association study of Tourette Syndrome

Tourette Syndrome (TS) is a developmental disorder that has one of the highest familial recurrence rates among neuropsychiatric diseases with complex inheritance. However, the identification of definitive TS susceptibility genes remains elusive. Here, we report the first genome-wide association study (GWAS) of TS in 1285 cases and 4964 ancestry-matched controls of European ancestry, including two European-derived population isolates, Ashkenazi Jews from North America and Israel, and French Canadians from Quebec, Canada. In a primary meta-analysis of GWAS data from these European ancestry samples, no markers achieved a genome-wide threshold of significance (p<5 × 10−8); the top signal was found in rs7868992 on chromosome 9q32 within COL27A1 (p=1.85 × 10−6). A secondary analysis including an additional 211 cases and 285 controls from two closely-related Latin-American population isolates from the Central Valley of Costa Rica and Antioquia, Colombia also identified rs7868992 as the top signal (p=3.6 × 10−7 for the combined sample of 1496 cases and 5249 controls following imputation with 1000 Genomes data). This study lays the groundwork for the eventual identification of common TS susceptibility variants in larger cohorts and helps to provide a more complete understanding of the full genetic architecture of this disorder

Rational Inferences about Departures from Hardy-Weinberg Equilibrium

Previous studies have explored the use of departure from Hardy-Weinberg equilibrium (DHW) for fine mapping Mendelian disorders and for general fine mapping. Other studies have used Hardy-Weinberg tests for genotyping quality control. To enable investigators to make rational decisions about whether DHW is due to genotyping error or to underlying biology, we developed an analytic framework and software to determine the parameter values for which DHW might be expected for common diseases. We show analytically that, for a general disease model, the difference between population and Hardy-Weinberg–expected genotypic frequencies (Δ) at the susceptibility locus is a function of the susceptibility-allele frequency (q), heterozygote relative risk (β), and homozygote relative risk (γ). For unaffected control samples, Δ is a function of risk in nonsusceptible homozygotes (α), the population prevalence of disease (K(P)), q, β, and γ. We used these analytic functions to calculate Δ and the number of cases or controls needed to detect DHW for a range of genetic models consistent with common diseases (1.1 ⩽ γ ⩽ 10 and 0.005 ⩽ K(P) ⩽ 0.2). Results suggest that significant DHW can be expected in relatively small samples of patients over a range of genetic models. We also propose a goodness-of-fit test to aid investigators in determining whether a DHW observed in the context of a case-control study is consistent with a genetic disease model. We illustrate how the analytic framework and software can be used to help investigators interpret DHW in the context of association studies of common diseases

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dence, especially the truncated p values, reduce this problem. Conclusion: We identified regions modestly linked with type 2 diabetes by summarizing results from 23 autosomal genome scans

Correlation of Intergenerational Family Sizes Suggests a Genetic Component of Reproductive Fitness

Reproductive fitness is a complex phenotype that is a direct measure of Darwinian selection. Estimation of the genetic contribution to this phenotype in human populations is confounded by within-family correlations of sociocultural, economic, and other nongenetic factors that influence family sizes. Here, we report an intergenerational correlation in reproductive success in the Hutterites, a human population that is relatively homogeneous with respect to sociocultural factors that influence fertility. We introduce an estimator of this correlation that takes into account the presence of multiple parent-offspring pairs from the same nuclear family. Statistical significance of the estimated correlation is assessed by a permutation test that maintains the overall structure of the pedigree. Further, temporal trends in fertility within this population are accounted for. Applying these methods to the S-Leut Hutterites yields a correlation in effective family size of 0.29 between couples and their sons and 0.18 between couples and their daughters, with empirical P<1×10-6 and P=.0041, respectively. Similar results were obtained for completed families (0.31 between couples and their sons and 0.23 between couples and their daughters; empirical P<1×10-6 and P=.00059, respectively). We interpret these results as indicating a significant genetic component to reproductive fitness in the Hutterites

REPORT Correlation of Intergenerational Family Sizes Suggests a Genetic Component of Reproductive Fitness

Reproductive fitness is a complex phenotype that is a direct measure of Darwinian selection. Estimation of the genetic contribution to this phenotype in human populations is confounded by within-family correlations of sociocultural, economic, and other nongenetic factors that influence family sizes. Here, we report an intergenerational correlation in reproductive success in the Hutterites, a human population that is relatively homogeneous with respect to sociocultural factors that influence fertility. We introduce an estimator of this correlation that takes into account the presence of multiple parent-offspring pairs from the same nuclear family. Statistical significance of the estimated correlation is assessed by a permutation test that maintains the overall structure of the pedigree. Further, temporal trends in fertility within this population are accounted for. Applying these methods to the S-Leut Hutterites yields a correlation in effective family size of 0.29 between couples and their sons and 0.18 between couples and their daughters, with empirical P ! 1 # and , respectively. Similar results were obtained for completed families (0.31 between couples and their 56 10 P p .0041 sons and 0.23 between couples and their daughters; empirical and , respectively). We interpret 56 P ! 1 # 10 P p .00059 these results as indicating a significant genetic component to reproductive fitness in the Hutterites

Spoiling the Whole Bunch: Quality Control Aimed at Preserving the Integrity of High-Throughput Genotyping

False-positive or false-negative results attributable to undetected genotyping errors and confounding factors present a constant challenge for genome-wide association studies (GWAS) given the low signals associated with complex phenotypes and the noise associated with high-throughput genotyping. In the context of the genetics of kidneys in diabetes (GoKinD) study, we identify a source of error in genotype calling and demonstrate that a standard battery of quality-control (QC) measures is not sufficient to detect and/or correct it. We show that, if genotyping and calling are done by plate (batch), even a few DNA samples of marginally acceptable quality can profoundly alter the allele calls for other samples on the plate. In turn, this leads to significant differential bias in estimates of allele frequency between plates and, potentially, to false-positive associations, particularly when case and control samples are not sufficiently randomized to plates. This problem may become widespread as investigators tap into existing public databases for GWAS control samples. We describe how to detect and correct this bias by utilizing additional sources of information, including raw signal-intensity data