The molecular signature of selection underlying human adaptations

Constructing genomic maps of positive selection in humans: where do we go from here?

Identifying targets of positive selection in humans has, until recently, been frustratingly slow, relying on the analysis of individual candidate genes. Genomics, however, has provided the necessary resources to systematically interrogate the entire genome for signatures of natural selection. To date, 21 genome-wide scans for recent or ongoing positive selection have been performed in humans. A key challenge is to begin synthesizing these newly constructed maps of positive selection into a coherent narrative of human evolutionary history and derive a deeper mechanistic understanding of how natural populations evolve. Here, I chronicle the recent history of the burgeoning field of human population genomics, critically assess genome-wide scans for positive selection in humans, identify important gaps in knowledge, and discuss both short- and long-term strategies for traversing the path from the low-resolution, incomplete, and error-prone maps of selection today to the ultimate goal of a detailed molecular, mechanistic, phenotypic, and population genetics characterization of adaptive alleles.

African genetic diversity: implications for human demographic history, modern human origins, and complex disease mapping

Comparative studies of ethnically diverse human populations, particularly in Africa, are important for reconstructing human evolutionary history and for understanding the genetic basis of phenotypic adaptation and complex disease. African populations are characterized by greater levels of genetic diversity, extensive population substructure, and less linkage disequilibrium (LD) among loci compared to non-African populations. Africans also possess a number of genetic adaptations that have evolved in response to diverse climates and diets, as well as exposure to infectious disease. This review summarizes patterns and the evolutionary origins of genetic diversity present in African populations, as well as their implications for the mapping of complex traits, including disease susceptibility.

Effects of raloxifene on Ki-67 and CD34 antigen expression in breast cancer

BACKGROUND

The aim of this study was to evaluate the effect of raloxifene on CD34 and Ki-67 antigen expression in breast cancer specimens from postmenopausal women.

METHODS

Sixteen postmenopausal patients with operable, stage II (>or=3 cm), estrogen receptor-positive breast cancer, who took 60 mg of raloxifene daily for 28 days, participated in this study. Immunohistochemistry was carried out in tumor samples prior to and following raloxifene treatment to evaluate CD34 and Ki-67 protein expression. Angiogenesis was quantified in 10 randomly selected fields per slide, and Ki-67-stained nuclei were counted in 1,000 cells per slide using an image capture and analysis system with 400x magnification. Student's t test for paired samples was used for the statistical analysis of data. Statistical significance was established at p < 0.05.

RESULTS

The mean number of microvessels was 44.44 +/- 3.54 prior to raloxifene therapy and 22.63 +/- 1.61 following therapy (p < 0.001), and the mean percentage of Ki-67-stained nuclei was 19.28 +/- 1.61 and 12.13 +/- 1.48 prior to and following raloxifene treatment, respectively (p < 0.001).

CONCLUSION

Raloxifene significantly reduces CD34 and Ki-67 protein expression in breast carcinoma in postmenopausal women.

Searching for additional disease loci in a genomic region

Our aim is to review methods to optimize detection of all disease genes in a genetic region. As a starting point, we assume there is sufficient evidence from linkage and/or association studies, based on significance levels or replication studies, for the involvement in disease risk of the genetic region under study. For closely linked markers, there will often be multiple associations with disease, and linkage analyses identify a region rather than the specific disease-predisposing gene. Hence, the first task is to identify the primary (major) disease-predisposing gene or genes in a genetic region, and single nucleotide polymorphisms thereof, that is, how to distinguish true associations from those that are just due to linkage disequilibrium with the actual disease-predisposing variants. Then, how do we detect additional disease genes in this genetic region? These two issues are of course very closely interrelated. No existing programs, either individually or in aggregate, can handle the magnitude and complexity of the analyses needed using currently available methods. Further, even with modern computers, one cannot study every possible combination of genetic markers and their haplotypes across the genome, or even within a genetic region. Although we must rely heavily on computers, in the final analysis of multiple effects in a genetic region and/or interaction or independent effects between unlinked genes, manipulation of the data by the individual investigator will play a crucial role. We recommend a multistrategy approach using a variety of complementary methods described below.