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Troublesome science : the misuse of genetics and genomics in understanding race
It is well established that all human beings today, wherever they live, belong to one single species. Yet even many people who claim to abhor racism take for granted that human \"races\" have a biological reality. From pharmacological researchers to the U.S. government, the dubious tradition of classifying people by race lives on. In Troublesome Science, Rob DeSalle and Ian Tattersall provide a lucid and compelling presentation of how the tools of modern biological science have been misused to sustain the belief in the biological basis of racial classification. Troublesome Science argues that taxonomy, the scientific classification of organisms, provides a cure for such misbegotten mischaracterizations. DeSalle and Tattersall explain how taxonomists do their job, in particular the genomic and morphological techniques they use to identify a species and to understand and organize the relationships among different species and the variants within them. They detail the use of genetic data to trace human origins and look at how scientists have attempted to recognize discrete populations within Homo sapiens. DeSalle and Tattersall demonstrate conclusively that these techniques, when applied correctly to the study of human variety, fail to find genuine differences, striking a blow against pseudoscientific chicanery. While the diversity that exists within our species is a real phenomenon, it nevertheless defeats any systematic attempt to recognize discrete units within it. The stark lines that humans insist on drawing between their own groups and others are nothing but a mixture of imagination and ideology.
Book
Genetic studies of body mass index yield new insights for obesity biology
Obesity is heritable and predisposes to many diseases. To understand the genetic basis of obesity better, here we conduct a genome-wide association study and Metabochip meta-analysis of body mass index (BMI), a measure commonly used to define obesity and assess adiposity, in up to 339,224 individuals. This analysis identifies 97 BMI-associated loci (
P
< 5 × 10
−8
), 56 of which are novel. Five loci demonstrate clear evidence of several independent association signals, and many loci have significant effects on other metabolic phenotypes. The 97 loci account for ∼2.7% of BMI variation, and genome-wide estimates suggest that common variation accounts for >20% of BMI variation. Pathway analyses provide strong support for a role of the central nervous system in obesity susceptibility and implicate new genes and pathways, including those related to synaptic function, glutamate signalling, insulin secretion/action, energy metabolism, lipid biology and adipogenesis.
A genome-wide association study and Metabochip meta-analysis of body mass index (BMI) detects 97 BMI-associated loci, of which 56 were novel, and many loci have effects on other metabolic phenotypes; pathway analyses implicate the central nervous system in obesity susceptibility and new pathways such as those related to synaptic function, energy metabolism, lipid biology and adipogenesis.
Genetic correlates of obesity
In the second of two Articles in this issue from the GIANT Consortium, Elizabeth Speliotes and collegues conducted a genome-wide association study and Metabochip meta-analysis of body mass index (BMI), commonly used to define obesity and assess adiposity, to find 97 BMI-associated loci, of which 56 were novel. Many of these loci have significant effects on other metabolic phenotypes. The 97 loci account for about 2.7% of BMI variation, and genome-wide estimates suggest common variation accounts for more than 20% of BMI variation. Pathway analyses implicate the central nervous system in obesity susceptibility including synaptic function, glutamate signaling, insulin secretion/action, energy metabolism, lipid biology and adipogenesis.
Journal Article
Integrating common and rare genetic variation in diverse human populations
Despite great progress in identifying genetic variants that influence human disease, most inherited risk remains unexplained. A more complete understanding requires genome-wide studies that fully examine less common alleles in populations with a wide range of ancestry. To inform the design and interpretation of such studies, we genotyped 1.6 million common single nucleotide polymorphisms (SNPs) in 1,184 reference individuals from 11 global populations, and sequenced ten 100-kilobase regions in 692 of these individuals. This integrated data set of common and rare alleles, called ‘HapMap 3’, includes both SNPs and copy number polymorphisms (CNPs). We characterized population-specific differences among low-frequency variants, measured the improvement in imputation accuracy afforded by the larger reference panel, especially in imputing SNPs with a minor allele frequency of ≤5%, and demonstrated the feasibility of imputing newly discovered CNPs and SNPs. This expanded public resource of genome variants in global populations supports deeper interrogation of genomic variation and its role in human disease, and serves as a step towards a high-resolution map of the landscape of human genetic variation.
Third-generation HapMap
The International HapMap Consortium, established to develop a haplotype map of the human genome describing the common patterns of DNA sequence variation, has now reached its third incarnation. HapMap1, published in 2005 (go.nature.com/gJisDm), contained more than a million SNP (single nucleotide polymorphism) genotypes generated in 269 individuals from four geographically diverse populations. Two years later, HapMap2 (go.nature.com/WttNWX) added more than 2.1 million SNPs to the original map in the same 269 individuals. With the aim of providing a resource for the latest wave of genome-wide studies focused on disease linkages, HapMap3 casts the net wider. About 1.6 million common SNPs were genotyped in 1,184 individuals from 11 global populations, and ten 100-kilobase regions were sequenced in 692 of these individuals.
Here, the analysis of 'HapMap 3' is reported — a public data set of genomic variants in human populations. The resource integrates common and rare single nucleotide polymorphisms (SNPs) and copy number polymorphisms (CNPs) from 11 global populations, providing insights into population-specific differences among variants. It also demonstrates the feasibility of imputing newly discovered rare SNPs and CNPs.
Journal Article
New genetic loci link adipose and insulin biology to body fat distribution
Body fat distribution is a heritable trait and a well-established predictor of adverse metabolic outcomes, independent of overall adiposity. To increase our understanding of the genetic basis of body fat distribution and its molecular links to cardiometabolic traits, here we conduct genome-wide association meta-analyses of traits related to waist and hip circumferences in up to 224,459 individuals. We identify 49 loci (33 new) associated with waist-to-hip ratio adjusted for body mass index (BMI), and an additional 19 loci newly associated with related waist and hip circumference measures (
P
< 5 × 10
−8
). In total, 20 of the 49 waist-to-hip ratio adjusted for BMI loci show significant sexual dimorphism, 19 of which display a stronger effect in women. The identified loci were enriched for genes expressed in adipose tissue and for putative regulatory elements in adipocytes. Pathway analyses implicated adipogenesis, angiogenesis, transcriptional regulation and insulin resistance as processes affecting fat distribution, providing insight into potential pathophysiological mechanisms.
Genome-wide association meta-analyses of waist-to-hip ratio adjusted for body mass index in more than 224,000 individuals identify 49 loci, 33 of which are new and many showing significant sexual dimorphism with a stronger effect in women; pathway analyses implicate adipogenesis, angiogenesis, transcriptional regulation and insulin resistance as processes affecting fat distribution.
Cardiometabolic traits linked to body fat distribution
In the first of a pair of Articles in this issue from the GIANT Consortium, genome-wide association meta-analyses of waist and hip circumference-related traits in more than 200,000 individuals have been used to identify 49 loci — 33 of them new — associated with waist-to-hip ratio adjusted for body mass index and an additional 19 loci associated with related waist and hip circumference measures. A subset of these loci shows significant sexual dimorphism, with many showing a stronger effect in women. Analyses implicate adipogenesis, angiogenesis, transcriptional regulation and insulin resistance as processes affecting fat distribution, providing insight into potential pathophysiological mechanisms and offer potential targets for interventions in the risks associated with abdominal fat accumulation.
Journal Article
The Genealogy of a Gene
InThe Genealogy of a Gene, Myles Jackson uses the story of the CCR5 gene to investigate the interrelationships among science, technology, and society. Mapping the varied \"genealogy\" of CCR5 -- intellectual property, natural selection, Big and Small Pharma, human diversity studies, personalized medicine, ancestry studies, and race and genomics -- Jackson links a myriad of diverse topics. The history of CCR5 from the 1990s to the present offers a vivid illustration of how intellectual property law has changed the conduct and content of scientific knowledge, and the social, political, and ethical implications of such a transformation. The CCR5 gene began as a small sequence of DNA, became a patented product of a corporation, and then, when it was found to be an AIDS virus co-receptor with a key role in the immune system, it became part of the biomedical research world -- and a potential moneymaker for the pharmaceutical industry. When it was further discovered that a mutation of the gene found in certain populations conferred near-immunity to the AIDS virus, questions about race and genetics arose. Jackson describes these developments in the context of larger issues, including the rise of \"biocapitalism,\" the patentability of products of nature, the difference between U.S. and European patenting approaches, and the relevance of race and ethnicity to medical research.
eBook
Inferring Continuous and Discrete Population Genetic Structure Across Space
An important step in the analysis of genetic data is to describe and categorize natural variation. Individuals that live close together are, on average, more genetically similar than individuals sampled farther apart... A classic problem in population genetics is the characterization of discrete population structure in the presence of continuous patterns of genetic differentiation. Especially when sampling is discontinuous, the use of clustering or assignment methods may incorrectly ascribe differentiation due to continuous processes (e.g., geographic isolation by distance) to discrete processes, such as geographic, ecological, or reproductive barriers between populations. This reflects a shortcoming of current methods for inferring and visualizing population structure when applied to genetic data deriving from geographically distributed populations. Here, we present a statistical framework for the simultaneous inference of continuous and discrete patterns of population structure. The method estimates ancestry proportions for each sample from a set of two-dimensional population layers, and, within each layer, estimates a rate at which relatedness decays with distance. This thereby explicitly addresses the “clines versus clusters” problem in modeling population genetic variation, and remedies some of the overfitting to which nonspatial models are prone. The method produces useful descriptions of structure in genetic relatedness in situations where separated, geographically distributed populations interact, as after a range expansion or secondary contact. We demonstrate the utility of this approach using simulations and by applying it to empirical datasets of poplars and black bears in North America.
Journal Article
ProCARs: Progressive Reconstruction of Ancestral Gene Orders
Background
In the context of ancestral gene order reconstruction from extant genomes, there exist two main computational approaches: rearrangement-based, and homology-based methods. The rearrangement-based methods consist in minimizing a total rearrangement distance on the branches of a species tree. The homology-based methods consist in the detection of a set of potential ancestral contiguity features, followed by the assembling of these features into Contiguous Ancestral Regions (CARs).
Results
In this paper, we present a new homology-based method that uses a progressive approach for both the detection and the assembling of ancestral contiguity features into CARs. The method is based on detecting a set of potential ancestral adjacencies iteratively using the current set of CARs at each step, and constructing CARs progressively using a 2-phase assembling method.
Conclusion
We show the usefulness of the method through a reconstruction of the boreoeutherian ancestral gene order, and a comparison with three other homology-based methods: AnGeS, InferCARs and GapAdj. The program, written in Python, and the dataset used in this paper are available at
http://bioinfo.lifl.fr/procars/
.
Journal Article
Inferring Admixture Histories of Human Populations Using Linkage Disequilibrium
Long-range migrations and the resulting admixtures between populations have been important forces shaping human genetic diversity. Most existing methods for detecting and reconstructing historical admixture events are based on allele frequency divergences or patterns of ancestry segments in chromosomes of admixed individuals. An emerging new approach harnesses the exponential decay of admixture-induced linkage disequilibrium (LD) as a function of genetic distance. Here, we comprehensively develop LD-based inference into a versatile tool for investigating admixture. We present a new weighted LD statistic that can be used to infer mixture proportions as well as dates with fewer constraints on reference populations than previous methods. We define an LD-based three-population test for admixture and identify scenarios in which it can detect admixture events that previous formal tests cannot. We further show that we can uncover phylogenetic relationships among populations by comparing weighted LD curves obtained using a suite of references. Finally, we describe several improvements to the computation and fitting of weighted LD curves that greatly increase the robustness and speed of the calculations. We implement all of these advances in a software package, ALDER, which we validate in simulations and apply to test for admixture among all populations from the Human Genome Diversity Project (HGDP), highlighting insights into the admixture history of Central African Pygmies, Sardinians, and Japanese.
Journal Article
Hidden in Plain Sight — Reconsidering the Use of Race Correction in Clinical Algorithms
Diagnostic algorithms and practice guidelines that adjust or “correct” their outputs on the basis of a patient’s race or ethnicity guide decisions in ways that may direct more attention or resources to white patients than to members of racial and ethnic minorities.
Journal Article