Catalogue Search | MBRL
Are you sure you want to remove the book from the shelf?
{{itemTitle}}
170,168
result(s) for
"Population Genetics"
Sort by:
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
A high-resolution HLA reference panel capturing global population diversity enables multi-ancestry fine-mapping in HIV host response
Fine-mapping to plausible causal variation may be more effective in multi-ancestry cohorts, particularly in the MHC, which has population-specific structure. To enable such studies, we constructed a large (
n
= 21,546) HLA reference panel spanning five global populations based on whole-genome sequences. Despite population-specific long-range haplotypes, we demonstrated accurate imputation at G-group resolution (94.2%, 93.7%, 97.8% and 93.7% in admixed African (AA), East Asian (EAS), European (EUR) and Latino (LAT) populations). Applying HLA imputation to genome-wide association study data for HIV-1 viral load in three populations (EUR, AA and LAT), we obviated effects of previously reported associations from population-specific HIV studies and discovered a novel association at position 156 in HLA-B. We pinpointed the MHC association to three amino acid positions (97, 67 and 156) marking three consecutive pockets (C, B and D) within the HLA-B peptide-binding groove, explaining 12.9% of trait variance.
A high-resolution reference panel based on whole-genome sequencing data enables accurate imputation of
HLA
alleles across diverse populations and fine-mapping of HLA association signals for HIV-1 host response.
Journal Article
How evolution shapes our lives : essays on biology and society
\" It is easy to think of evolution as something that happened long ago, or that occurs only in \"nature,\" or that is so slow that its ongoing impact is virtually nonexistent when viewed from the perspective of a single human lifetime. But we now know that when natural selection is strong, evolutionary change can be very rapid. In this book, some of the world's leading scientists explore the implications of this reality for human life and society. With some twenty-five essays, this volume provides authoritative yet accessible explorations of why understanding evolution is crucial to human life--from dealing with climate change and ensuring our food supply, health, and economic survival to developing a richer and more accurate comprehension of society, culture, and even what it means to be human itself. Combining new essays with ones revised and updated from the acclaimed Princeton Guide to Evolution, this collection addresses the role of evolution in aging, cognition, cooperation, religion, the media, engineering, computer science, and many other areas. The result is a compelling and important book about how evolution matters to humans today. The contributors include Francisco J. Ayala, Dieter Ebert, Elizabeth Hannon, Richard E. Lenski, Tim Lewens, Jonathan B. Losos, Jacob A. Moorad, Mark Pagel, Robert T. Pennock, Daniel E. L. Promislow, Robert C. Richardson, Alan R. Templeton, and Carl Zimmer.\"-- Provided by publisher.
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
Neanderthal man : in search of lost genomes
\"What can we learn from the genes of our closest evolutionary relatives? Neanderthal Man tells the story of geneticist Svante P�a�abo's mission to answer that question, beginning with the study of DNA in Egyptian mummies in the early 1980s and culminating in his sequencing of the Neanderthal genome in 2009. From P�a�abo, we learn how Neanderthal genes offer a unique window into the lives of our hominin relatives and may hold the key to unlocking the mystery of why humans survived while Neanderthals went extinct. Drawing on genetic and fossil clues, P�a�abo explores what is known about the origin of modern humans and their relationship to the Neanderthals and describes the fierce debate surrounding the nature of the two species' interactions. A riveting story about a visionary researcher and the nature of scientific inquiry, Neanderthal Man offers rich insight into the fundamental question of who we are\"-- Provided by publisher.
Book
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
A structural variation reference for medical and population genetics
Structural variants (SVs) rearrange large segments of DNA
1
and can have profound consequences in evolution and human disease
2
,
3
. As national biobanks, disease-association studies, and clinical genetic testing have grown increasingly reliant on genome sequencing, population references such as the Genome Aggregation Database (gnomAD)
4
have become integral in the interpretation of single-nucleotide variants (SNVs)
5
. However, there are no reference maps of SVs from high-coverage genome sequencing comparable to those for SNVs. Here we present a reference of sequence-resolved SVs constructed from 14,891 genomes across diverse global populations (54% non-European) in gnomAD. We discovered a rich and complex landscape of 433,371 SVs, from which we estimate that SVs are responsible for 25–29% of all rare protein-truncating events per genome. We found strong correlations between natural selection against damaging SNVs and rare SVs that disrupt or duplicate protein-coding sequence, which suggests that genes that are highly intolerant to loss-of-function are also sensitive to increased dosage
6
. We also uncovered modest selection against noncoding SVs in
cis
-regulatory elements, although selection against protein-truncating SVs was stronger than all noncoding effects. Finally, we identified very large (over one megabase), rare SVs in 3.9% of samples, and estimate that 0.13% of individuals may carry an SV that meets the existing criteria for clinically important incidental findings
7
. This SV resource is freely distributed via the gnomAD browser
8
and will have broad utility in population genetics, disease-association studies, and diagnostic screening.
A large empirical assessment of sequence-resolved structural variants from 14,891 genomes across diverse global populations in the Genome Aggregation Database (gnomAD) provides a reference map for disease-association studies, population genetics, and diagnostic screening.
Journal Article
Genetic structure and selection in subdivided populations
Various approaches have been developed to evaluate the consequences of spatial structure on evolution in subdivided populations. This book is both a review and new synthesis of several of these approaches, based on the theory of spatial genetic structure. François Rousset examines Sewall Wright's methods of analysis based on F-statistics, effective size, and diffusion approximation; coalescent arguments; William Hamilton's inclusive fitness theory; and approaches rooted in game theory and adaptive dynamics. Setting these in a framework that reveals their common features, he demonstrates how efficient tools developed within one approach can be applied to the others.
Rousset not only revisits classical models but also presents new analyses of more recent topics, such as effective size in metapopulations. The book, most of which does not require fluency in advanced mathematics, includes a self-contained exposition of less easily accessible results. It is intended for advanced graduate students and researchers in evolutionary ecology and population genetics, and will also interest applied mathematicians working in probability theory as well as statisticians.
eBook