This review explores the limitations of self-reported race, ethnicity, and genetic ancestry in biomedical research. Various terminologies are used to classify human differences in genomic research including race, ethnicity, and ancestry. Although race and ethnicity are related, race refers to a person’s physical appearance, such as skin color and eye color. Ethnicity, on the other hand, refers to communality in cultural heritage, language, social practice, traditions, and geopolitical factors. Genetic ancestry inferred using ancestry informative markers (AIMs) is based on genetic/genomic data. Phenotype-based race/ethnicity information and data computed using AIMs often disagree. For example, self-reporting African Americans can have drastically different levels of African or European ancestry. Genetic analysis of individual ancestry shows that some self-identified African Americans have up to 99% of European ancestry, whereas some self-identified European Americans have substantial admixture from African ancestry. Similarly, African ancestry in the Latino population varies between 3% in Mexican Americans to 16% in Puerto Ricans. The implication of this is that, in African American or Latino populations, self-reported ancestry may not be as accurate as direct assessment of individual genomic information in predicting treatment outcomes. To better understand human genetic variation in the context of health disparities, we suggest using “ancestry” (or biogeographical ancestry) to describe actual genetic variation, “race” to describe health disparity in societies characterized by racial categories, and “ethnicity” to describe traditions, lifestyle, diet, and values. We also suggest using ancestry informative markers for precise characterization of individuals’ biological ancestry. Understanding the sources of human genetic variation and the causes of health disparities could lead to interventions that would improve the health of all individuals.

Previous work with model transgenic plants has demonstrated that cellular accumulation of mannitol can alleviate abiotic stress. Here, we show that ectopic expression of the mtlD gene for the biosynthesis of mannitol in wheat improves tolerance to water stress and salinity. Wheat (Triticum aestivum L. cv Bobwhite) was transformed with the mtlD gene of Escherichia coli. Tolerance to water stress and salinity was evaluated using calli and T 2 plants transformed with (ϩmtlD) or without (ϪmtlD) mtlD. Calli were exposed to Ϫ1.0 MPa of polyethylene glycol 8,000 or 100 mm NaCl. T 2 plants were stressed by withholding water or by adding 150 mm NaCl to the nutrient medium. Fresh weight of ϪmtlD calli was reduced by 40% in the presence of polyethylene glycol and 37% under NaCl stress. Growth of ϩmtlD calli was not affected by stress. In ϪmtlD plants, fresh weight, dry weight, plant height, and flag leaf length were reduced by 70%, 56%, 40%, and 45% compared with 40%, 8%, 18%, and 29%, respectively, in ϩmtlD plants. Salt stress reduced shoot fresh weight, dry weight, plant height, and flag leaf length by 77%, 73%, 25%, and 36% in ϪmtlD plants, respectively, compared with 50%, 30%, 12%, and 20% in ϩmtlD plants. However, the amount of mannitol accumulated in the callus and mature fifth leaf (1.7-3.7 mol g Ϫ1 fresh weight in the callus and 0.6-2.0 mol g Ϫ1 fresh weight in the leaf) was too small to protect against stress through osmotic adjustment. We conclude that the improved growth performance of mannitol-accumulating calli and mature leaves was due to other stress-protective functions of mannitol, although this study cannot rule out possible osmotic effects in growing regions of the plant.Water stress and salinity are major abiotic factors that limit crop productivity in drought-prone areas. One way of increasing productivity in stressful environments is to breed crops that are more tolerant to stress. However, success in breeding for tolerance has been limited because (a) tolerance to stress is controlled by many genes, and their simultaneous selection is difficult (Richards, 1996;Yeo, 1998;Flowers et al., 2000); (b) tremendous effort is required to eliminate undesirable genes that are also incorporated during breeding (Richards, 1996); and (c) there is a lack of efficient selection procedures particularly under field conditions (Ribaut et al., 1997). Genetic engineering offers an alternative approach for developing tolerant crops. Unlike classical breeding, genetic engineering is a faster and more precise means of achieving improved tolerance (Cushman and Bohnert, 2000) because it avoids the transfer of unwanted chromosomal regions. Moreover, through genetic engineering, multiple genes can be assembled and simultaneously introduced to the crop of interest. There are many functional targets for engineering tolerance to water stress and salinity, one of them being accumulation of osmoprotectants (Rathinasabapathi, 2000).The osmolyte mannitol is normally synthesized in numerous plant species, but not in wheat (Triticum aes...

Organisms frequently encounter different environmental conditions. The physiological and behavioral responses to these conditions depend on the genetic make up of individuals. Genotype generally remains constant from one environment to another, although occasional spontaneous mutations may occur which cause it to change. However, when the same genotype is subjected to different environments, it can produce a wide range of phenotypes. These phenotypic variations are attributable to the effect of the environment on the expression and function of genes influencing the trait. Changes in the relative performance of genotypes across different environments are referred to as genotype–environment interactions (GEI). A general argument for research on the impact of GEI in common diseases is that it provides insights into disease processes at the population, individual and molecular levels. In humans, GEI is complicated by multiple factors including phenocopies, genocopies, epigenetics and imprinting. A better understanding of GEI is essential if patients are to make informed health choices guided by their genomic information. In this article, we clarify the role of the environment on phenotype, we describe how human population structure can obscure the resolution of GEI and we discuss how emerging biobanks across the globe can be coordinated to further our understanding of genotype–phenotype associations within the context of varying environment.

The photosynthetic organs of the barley spike (lemma, palea, and awn) are considered resistant to drought. However, there is little information about gene expression in the spike organs under drought conditions. We compared response of the transcriptome of the lemma, palea, awn, and seed to drought stress using the Barley1 Genome Array. Barley plants were exposed to drought treatment for 4 days at the grain-filling stage by withholding water. At the end of the stress, relative water content of the lemma, palea, and awn dropped from 85% to 60%. Nevertheless, the water content of the seed only decreased from 89% to 81%. Transcript abundance followed the water status of the spike organs; the awn had more drought-regulated genes followed by lemma and palea, and the seed showed very little change in gene expression. Despite expressing more drought-associated genes, many genes for amino acid, amino acid derivative, and carbohydrate metabolism, as well as for photosynthesis, respiration, and stress response, were down-regulated in the awn compared with the lemma, palea, and seed. This suggests that the lemma and the palea are more resistant to drought stress compared with the awn.