Genomic evidence for shared common ancestry of East African hunting-gathering populations and insights into local adaptation.

Anatomically modern humans arose in Africa ∼300,000 years ago, but the demographic and adaptive histories of African populations are not well-characterized. Here, we have generated a genome-wide dataset from 840 Africans, residing in western, eastern, southern, and northern Africa, belonging to 50 ethnicities, and speaking languages belonging to four language families. In addition to agriculturalists and pastoralists, our study includes 16 populations that practice, or until recently have practiced, a hunting-gathering (HG) lifestyle. We observe that genetic structure in Africa is broadly correlated not only with geography, but to a lesser extent, with linguistic affiliation and subsistence strategy. Four East African HG (EHG) populations that are geographically distant from each other show evidence of common ancestry: the Hadza and Sandawe in Tanzania, who speak languages with clicks classified as Khoisan; the Dahalo in Kenya, whose language has remnant clicks; and the Sabue in Ethiopia, who speak an unclassified language. Additionally, we observed common ancestry between central African rainforest HGs and southern African San, the latter of whom speak languages with clicks classified as Khoisan. With the exception of the EHG, central African rainforest HGs, and San, other HG groups in Africa appear genetically similar to neighboring agriculturalist or pastoralist populations. We additionally demonstrate that infectious disease, immune response, and diet have played important roles in the adaptive landscape of African history. However, while the broad biological processes involved in recent human adaptation in Africa are often consistent across populations, the specific loci affected by selective pressures more often vary across populations.

Risk-adjusted Outcomes of Clinically Relevant Pancreatic Fistula Following Pancreatoduodenectomy: A Model for Performance Evaluation.

OBJECTIVE: To evaluate surgical performance in pancreatoduodenectomy using clinically relevant postoperative pancreatic fistula (CR-POPF) occurrence as a quality indicator. BACKGROUND: Accurate assessment of surgeon and institutional performance requires (1) standardized definitions for the outcome of interest and (2) a comprehensive risk-adjustment process to control for differences in patient risk. METHODS: This multinational, retrospective study of 4301 pancreatoduodenectomies involved 55 surgeons at 15 institutions. Risk for CR-POPF was assessed using the previously validated Fistula Risk Score, and pancreatic fistulas were stratified by International Study Group criteria. CR-POPF variability was evaluated and hierarchical regression analysis assessed individual surgeon and institutional performance. RESULTS: There was considerable variability in both CR-POPF risk and occurrence. Factors increasing the risk for CR-POPF development included increasing Fistula Risk Score (odds ratio 1.49 per point, P < 0.00001) and octreotide (odds ratio 3.30, P < 0.00001). When adjusting for risk, performance outliers were identified at the surgeon and institutional levels. Of the top 10 surgeons (≥15 cases) for nonrisk-adjusted performance, only 6 remained in this high-performing category following risk adjustment. CONCLUSIONS: This analysis of pancreatic fistulas following pancreatoduodenectomy demonstrates considerable variability in both the risk and occurrence of CR-POPF among surgeons and institutions. Disparities in patient risk between providers reinforce the need for comprehensive, risk-adjusted modeling when assessing performance based on procedure-specific complications. Furthermore, beyond inherent patient risk factors, surgical decision-making influences fistula outcomes.

Patterns of ancestry, signatures of natural selection, and genetic association with stature in Western African pygmies.

African Pygmy groups show a distinctive pattern of phenotypic variation, including short stature, which is thought to reflect past adaptation to a tropical environment. Here, we analyze Illumina 1M SNP array data in three Western Pygmy populations from Cameroon and three neighboring Bantu-speaking agricultural populations with whom they have admixed. We infer genome-wide ancestry, scan for signals of positive selection, and perform targeted genetic association with measured height variation. We identify multiple regions throughout the genome that may have played a role in adaptive evolution, many of which contain loci with roles in growth hormone, insulin, and insulin-like growth factor signaling pathways, as well as immunity and neuroendocrine signaling involved in reproduction and metabolism. The most striking results are found on chromosome 3, which harbors a cluster of selection and association signals between approximately 45 and 60 Mb. This region also includes the positional candidate genes DOCK3, which is known to be associated with height variation in Europeans, and CISH, a negative regulator of cytokine signaling known to inhibit growth hormone-stimulated STAT5 signaling. Finally, pathway analysis for genes near the strongest signals of association with height indicates enrichment for loci involved in insulin and insulin-like growth factor signaling.

Genetic adaptation to high altitude in the Ethiopian highlands.

BACKGROUND: Genomic analysis of high-altitude populations residing in the Andes and Tibet has revealed several candidate loci for involvement in high-altitude adaptation, a subset of which have also been shown to be associated with hemoglobin levels, including EPAS1, EGLN1, and PPARA, which play a role in the HIF-1 pathway. Here, we have extended this work to high- and low-altitude populations living in Ethiopia, for which we have measured hemoglobin levels. We genotyped the Illumina 1M SNP array and employed several genome-wide scans for selection and targeted association with hemoglobin levels to identify genes that play a role in adaptation to high altitude. RESULTS: We have identified a set of candidate genes for positive selection in our high-altitude population sample, demonstrated significantly different hemoglobin levels between high- and low-altitude Ethiopians and have identified a subset of candidate genes for selection, several of which also show suggestive associations with hemoglobin levels. CONCLUSIONS: We highlight several candidate genes for involvement in high-altitude adaptation in Ethiopia, including CBARA1, VAV3, ARNT2 and THRB. Although most of these genes have not been identified in previous studies of high-altitude Tibetan or Andean population samples, two of these genes (THRB and ARNT2) play a role in the HIF-1 pathway, a pathway implicated in previous work reported in Tibetan and Andean studies. These combined results suggest that adaptation to high altitude arose independently due to convergent evolution in high-altitude Amhara populations in Ethiopia.

Colloquium paper: working toward a synthesis of archaeological, linguistic, and genetic data for inferring African population history.

Although Africa is the origin of modern humans, the pattern and distribution of genetic variation and correlations with cultural and linguistic diversity in Africa have been understudied. Recent advances in genomic technology, however, have led to genomewide studies of African samples. In this article, we discuss genetic variation in African populations contextualized with what is known about archaeological and linguistic variation. What emerges from this review is the importance of using independent lines of evidence in the interpretation of genetic and genomic data in the reconstruction of past population histories.

A Bayesian approach to efficient differential allocation for resampling-based significance testing.

BACKGROUND: Large-scale statistical analyses have become hallmarks of post-genomic era biological research due to advances in high-throughput assays and the integration of large biological databases. One accompanying issue is the simultaneous estimation of p-values for a large number of hypothesis tests. In many applications, a parametric assumption in the null distribution such as normality may be unreasonable, and resampling-based p-values are the preferred procedure for establishing statistical significance. Using resampling-based procedures for multiple testing is computationally intensive and typically requires large numbers of resamples. RESULTS: We present a new approach to more efficiently assign resamples (such as bootstrap samples or permutations) within a nonparametric multiple testing framework. We formulated a Bayesian-inspired approach to this problem, and devised an algorithm that adapts the assignment of resamples iteratively with negligible space and running time overhead. In two experimental studies, a breast cancer microarray dataset and a genome wide association study dataset for Parkinson's disease, we demonstrated that our differential allocation procedure is substantially more accurate compared to the traditional uniform resample allocation. CONCLUSION: Our experiments demonstrate that using a more sophisticated allocation strategy can improve our inference for hypothesis testing without a drastic increase in the amount of computation on randomized data. Moreover, we gain more improvement in efficiency when the number of tests is large. R code for our algorithm and the shortcut method are available at http://people.pcbi.upenn.edu/~lswang/pub/bmc2009/.