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Understanding The Genetic Architecture Of Complex Traits Through Meta Analysis
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Book Synopsis Understanding the Genetic Architecture of Complex Traits Through Meta-analysis by : Kodi Taraszka
Download or read book Understanding the Genetic Architecture of Complex Traits Through Meta-analysis written by Kodi Taraszka and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Exploring how genetic architecture shapes complex traits and diseases is a central premise of human genetics. Over the years, genome-wide association studies (GWAS) have enabled the discovery of numerous genetic variants associated with a variety of complex traits. In addition to the large array of traits analyzed, GWAS in diverse ancestral populations have also seen a significant increase in sample sizes. These efforts led to tens of thousands of publicly available GWAS summary statistics whose known correlation structure could be leveraged for further discovery. In this dissertation, I present two novel methods for the meta-analysis of GWAS summary statistics as well as conduct a pan-cancer meta-analysis of somatic variant burden. For one method, I present a likelihood ratio test for the joint analysis of genetically correlated traits and provide a per trait interpretation framework of the omnibus association. For the other method, I present a Bayesian framework that improves fine mapping of significant associations for one trait by leveraging the complementary information from distinct ancestral backgrounds. In addition to these methods, I analyzed how clinical and polygenic germline features influence somatic variant burden within and across cancer types.
Book Synopsis Statistical Methods to Understand the Genetic Architecture of Complex Traits by : Farhad Hormozdiari
Download or read book Statistical Methods to Understand the Genetic Architecture of Complex Traits written by Farhad Hormozdiari and published by . This book was released on 2016 with total page 239 pages. Available in PDF, EPUB and Kindle. Book excerpt: Genome-wide association studies (GWAS) have successfully identified thousands of risk loci for complex traits. Identifying these variants requires annotating all possible variations between any two individuals, followed by detecting the variants that affect the disease status or traits. High-throughput sequencing (HTS) advancements have made it possible to sequence cohort of individuals in an efficient manner both in term of cost and time. However, HTS technologies have raised many computational challenges. I first propose an efficient method to recover dense genotype data by leveraging low sequencing and imputation techniques. Then, I introduce a novel statistical method (CNVeM) to identify Copy-number variations (CNVs) loci using HTS data. CNVeM was the first method that incorporates multi-mapped reads, which are discarded by all existing methods. Unfortunately, among all GWAS variants only a handful of them have been successfully validated to be biologically causal variants. Identifying causal variants can aid us to understand the biological mechanism of traits or diseases. However, detecting the causal variants is challenging due to linkage disequilibrium (LD) and the fact that some loci contain more than one causal variant. In my thesis, I will introduce CAVIAR (CAusal Variants Identification in Associated Regions) that is a new statistical method for fine mapping. The main advantage of CAVIAR is that we predict a set of variants for each locus that will contain all of the true causal variants with a high confidence level (e.g. 95%) even when the locus contains multiple causal variants. Next, I aim to understand the underlying mechanism of GWAS risk loci. A standard approach to uncover the mechanism of GWAS risk loci is to integrate results of GWAS and expression quantitative trait loci (eQTL) studies; we attempt to identify whether or not a significant GWAS variant also influences expression at a nearby gene in a specific tissue. However, detecting the same variant being causal in both GWAS and eQTL is challenging due to complex LD structure. I will introduce eCAVIAR (eQTL and GWAS CAusal Variants Identification in Associated Regions), a statistical method to compute the probability that the same variant is responsible for both the GWAS and eQTL signal, while accounting for complex LD structure. We integrate Glucose and Insulin-related traits meta-analysis with GTEx to detect the target genes and the most relevant tissues. Interestingly, we observe that most loci do not colocalize between GWAS and eQTL. Lastly, I propose an approach called phenotype imputation that allows one to perform GWAS on a phenotype that is difficult to collect. In our approach, we leverage the correlation structure between multiple phenotypes to impute the uncollected phenotype. I demonstrate that we can analytically calculate the statistical power of association test using imputed phenotype, which can be helpful for study design purposes
Book Synopsis Assessing Rare Variation in Complex Traits by : Eleftheria Zeggini
Download or read book Assessing Rare Variation in Complex Traits written by Eleftheria Zeggini and published by Springer. This book was released on 2015-08-13 with total page 262 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is unique in covering a wide range of design and analysis issues in genetic studies of rare variants, taking advantage of collaboration of the editors with many experts in the field through large-scale international consortia including the UK10K Project, GO-T2D and T2D-GENES. Chapters provide details of state-of-the-art methodology for rare variant detection and calling, imputation and analysis in samples of unrelated individuals and families. The book also covers analytical issues associated with the study of rare variants, such as the impact of fine-scale population structure, and with combining information on rare variants across studies in a meta-analysis framework. Genetic association studies have in the last few years substantially enhanced our understanding of factors underlying traits of high medical importance, such as body mass index, lipid levels, blood pressure and many others. There is growing empirical evidence that low-frequency and rare variants play an important role in complex human phenotypes. This book covers multiple aspects of study design, analysis and interpretation for complex trait studies focusing on rare sequence variation. In many areas of genomic research, including complex trait association studies, technology is in danger of outstripping our capacity to analyse and interpret the vast amounts of data generated. The field of statistical genetics in the whole-genome sequencing era is still in its infancy, but powerful methods to analyse the aggregation of low-frequency and rare variants are now starting to emerge. The chapter Functional Annotation of Rare Genetic Variants is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.
Book Synopsis Computational Approaches to Understanding the Genetic Architecture of Complex Traits by : Brielin C. Brown
Download or read book Computational Approaches to Understanding the Genetic Architecture of Complex Traits written by Brielin C. Brown and published by . This book was released on 2016 with total page 90 pages. Available in PDF, EPUB and Kindle. Book excerpt: Advances in DNA sequencing technology have resulted in the ability to generate genetic data at costs unimaginable even ten years ago. This has resulted in a tremendous amount of data, with large studies providing genotypes of hundreds of thousands of individuals at millions of genetic locations. This rapid increase in the scale of genetic data necessitates the development of computational methods that can analyze this data rapidly without sacrificing statistical rigor. The low cost of DNA sequencing also provides an opportunity to tailor medical care to an individuals unique genetic signature. However, this type of precision medicine is limited by our understanding of how genetic variation shapes disease. Our understanding of so- called complex diseases is particularly poor, and most identified variants explain only a tiny fraction of the variance in the disease that is expected to be due to genetics. This is further complicated by the fact that most studies of complex disease go directly from genotype to phenotype, ignoring the complex biological processes that take place in between. Herein, we discuss several advances in the field of complex trait genetics. We begin with a review of computational and statistical methods for working with genotype and phenotype data, as well as a discussion of methods for analyzing RNA-seq data in effort to bridge the gap between genotype and phenotype. We then describe our methods for 1) improving power to detect common variants associated with disease, 2) determining the extent to which different world populations share similar disease genetics and 3) identifying genes which show differential expression between the two haplotypes of a single individual. Finally, we discuss opportunities for future investigation in this field.
Book Synopsis Efficient Methods for Understanding the Genetic Architecture of Complex Traits by : Yue N/A Wu
Download or read book Efficient Methods for Understanding the Genetic Architecture of Complex Traits written by Yue N/A Wu and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Understanding the genetic architecture of complex traits is a central goal of modern human genetics.Recent efforts focused on building large-scale biobanks, that collect genetic and trait data on large numbers of individuals, present exciting opportunities for understanding genetic architecture. However, these datasets also pose several statistical and computational challenges. In this dissertation, we consider a series of statistical models that allow us to infer aspects of the genetic architecture of single and multiple traits. Inference in these models is computationally challenging due to the size of the genetic data -- consisting of millions of genetic variants measured across hundreds of thousands of individuals.We propose a series of scalable computational methods that can perform efficient inference in these models and apply these methods to data from the UK Biobank to showcase their utility.
Book Synopsis Genetic Dissection of Complex Traits by : D.C. Rao
Download or read book Genetic Dissection of Complex Traits written by D.C. Rao and published by Academic Press. This book was released on 2008-04-23 with total page 788 pages. Available in PDF, EPUB and Kindle. Book excerpt: The field of genetics is rapidly evolving and new medical breakthroughs are occuring as a result of advances in knowledge of genetics. This series continually publishes important reviews of the broadest interest to geneticists and their colleagues in affiliated disciplines. Five sections on the latest advances in complex traits Methods for testing with ethical, legal, and social implications Hot topics include discussions on systems biology approach to drug discovery; using comparative genomics for detecting human disease genes; computationally intensive challenges, and more
Book Synopsis Analysis of Complex Disease Association Studies by : Eleftheria Zeggini
Download or read book Analysis of Complex Disease Association Studies written by Eleftheria Zeggini and published by Academic Press. This book was released on 2010-11-17 with total page 353 pages. Available in PDF, EPUB and Kindle. Book excerpt: According to the National Institute of Health, a genome-wide association study is defined as any study of genetic variation across the entire human genome that is designed to identify genetic associations with observable traits (such as blood pressure or weight), or the presence or absence of a disease or condition. Whole genome information, when combined with clinical and other phenotype data, offers the potential for increased understanding of basic biological processes affecting human health, improvement in the prediction of disease and patient care, and ultimately the realization of the promise of personalized medicine. In addition, rapid advances in understanding the patterns of human genetic variation and maturing high-throughput, cost-effective methods for genotyping are providing powerful research tools for identifying genetic variants that contribute to health and disease. This burgeoning science merges the principles of statistics and genetics studies to make sense of the vast amounts of information available with the mapping of genomes. In order to make the most of the information available, statistical tools must be tailored and translated for the analytical issues which are original to large-scale association studies. Analysis of Complex Disease Association Studies will provide researchers with advanced biological knowledge who are entering the field of genome-wide association studies with the groundwork to apply statistical analysis tools appropriately and effectively. With the use of consistent examples throughout the work, chapters will provide readers with best practice for getting started (design), analyzing, and interpreting data according to their research interests. Frequently used tests will be highlighted and a critical analysis of the advantages and disadvantage complimented by case studies for each will provide readers with the information they need to make the right choice for their research. Additional tools including links to analysis tools, tutorials, and references will be available electronically to ensure the latest information is available. Easy access to key information including advantages and disadvantage of tests for particular applications, identification of databases, languages and their capabilities, data management risks, frequently used tests Extensive list of references including links to tutorial websites Case studies and Tips and Tricks
Book Synopsis Population Genetic Analyses of Gene Expression and Complex Traits by : Emily Catherine Glassberg
Download or read book Population Genetic Analyses of Gene Expression and Complex Traits written by Emily Catherine Glassberg and published by . This book was released on 2018 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: A central goal of human genetics is to understand how the genome contributes to complex traits like height, BMI, and risk for many common diseases. This question is particularly challenging to answer in that analyses of human complex traits rely largely on observational data. In this dissertation, I present several projects that utilize statistical models and careful interpretation of data to infer features of latent process from observational data. First, I aim to improve our understanding of human complex trait architecture by exploring the effects of selective constraint on gene expression. I demonstrate that, while there is clear evidence of widespread constraint on gene expression, this selection is weak. Much of the variance of gene expression across individuals is, therefore, attributable to common genetic variants. Consistent with existing literature, this suggests that the genetic architecture of complex traits is highly polygenic. I also consider the role of previously under-studied contributors to genetic variance (e.g., epistatic interactions, somatic variation, and short tandem repeats) in complex traits. In addition, I apply the same careful consideration of confounders and data interpretation to measure, and understand possible drivers of, gendered differences in behavior between men and women scientists at academic meetings.
Book Synopsis Genetic Architecture and Evolution of Complex Traits and Diseases in Diverse Human Populations by : Mashaal Sohail
Download or read book Genetic Architecture and Evolution of Complex Traits and Diseases in Diverse Human Populations written by Mashaal Sohail and published by Frontiers Media SA. This book was released on 2022-03-31 with total page 108 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Molecular and Computational Approaches to Identification of Genes Underlying Complex Traits by : Martin L. Jirout
Download or read book Molecular and Computational Approaches to Identification of Genes Underlying Complex Traits written by Martin L. Jirout and published by . This book was released on 2008 with total page 236 pages. Available in PDF, EPUB and Kindle. Book excerpt: Understanding the genetic architecture of complex traits is of great interest to the biomedical community. HXB/BXH recombinant inbred (RI) strains, derived from the spontaneously hypertensive rat (SHR) and normotensive Brown Norway (BN. Lx), are an important genomic resource for complex trait analysis by means of genetic linkage mapping. The power and accuracy of quantitative trait locus (QTL) analysis critically depends on the quality of the genetic map. To maximize the potential of the HXB/BXH RI strains for complex trait mapping, the latest available genotype information was used to construct a new genetic linkage map. Further, gene expression profiling and biochemical phenotyping in the adrenal glands of the HXB/BXH rats was performed to address the possible link between the dysregulated catecholamine biosynthesis in the SHR and the development of hypertension. Expression levels and enzyme activities of the two main catecholamine biosynthetic enzymes, Dbh and Pnmt, were found to be regulated from their genic regions (i.e., in cis). Pnmt re-sequencing revealed promoter polymorphisms, which resulted in a decreased response of the transfected SHR promoter to glucocorticoid stimulation. Dbh activity was negatively correlated with systolic blood pressure in RI strains, and Pnmt activity was negatively correlated with heart rate. These heritable changes in enzyme expression suggest primary genetic mechanisms for regulation of catecholamine action and blood pressure control in the SHR. In a separate analysis, genetic determinants of gene expression in the adrenal gland were explored. The adrenal transcriptome assayed via microarrays was subjected to expression quantitative trait locus (eQTL) mapping. Significant clustering of trans-eQTLs was observed, implying that groups of genes are jointly regulated from a single locus. A novel multivariate distance-matrix regression analysis (MDMR) method was applied to identify cis-eQTL genes whose expression profiles strongly correlate with those of the trans-eQTL cluster genes. The resulting genes, Rbm16 and Prp4b, are involved in pre-mRNA processing and as such present leading candidates for further studies aimed at better understanding of the quantitative genetics of gene expression. In conclusion, an important genomic resource was enhanced and then utilized to identify genetic loci controlling key aspects of catecholamine physiology, and differences in global gene expression.
Book Synopsis Computational Genetic Approaches for Understanding the Genetic Basis of Complex Traits by : Eun Yong Kang
Download or read book Computational Genetic Approaches for Understanding the Genetic Basis of Complex Traits written by Eun Yong Kang and published by . This book was released on 2013 with total page 273 pages. Available in PDF, EPUB and Kindle. Book excerpt: Recent advances in genotyping and sequencing technology have enabled researchers to collect an enormous amount of high-dimensional genotype data. These large scale genomic data provide unprecedented opportunity for researchers to study and analyze the genetic factors of human complex traits. One of the major challenges in analyzing these high-throughput genomic data is requirements for effective and efficient computational methodologies. In this thesis, I introduce several methodologies for analyzing these genomic data which facilitates our understanding of the genetic basis of complex human traits. First, I introduce a method for inferring biological networks from high-throughput data containing both genetic variation information and gene expression profiles from genetically distinct strains of an organism. For this problem, I use causal inference techniques to infer the presence or absence of causal relationships between yeast gene expressions in the framework of graphical causal models. In particular, I utilize prior biological knowledge that genetic variations affect gene expressions, but not vice versa, which allow us to direct the subsequent edges between two gene expression levels. The prediction of a presence of causal relationship as well as the absence of causal relationship between gene expressions can facilitate distinguishing between direct and indirect effects of variation on gene expression levels. I demonstrate the utility of our approach by applying it to data set containing 112 yeast strains and the proposed method identifies the known "regulatory hotspot" in yeast. Second, I introduce efficient pairwise identity by descent (IBD) association mapping method, which utilizes importance sampling to improve efficiency and enables approximation of extremely small p-values. Two individuals are IBD at a locus if they have identical alleles inherited from a common ancestor. One popular approach to find the association between IBD status and disease phenotype is the pairwise method where one compares the IBD rate of case/case pairs to the background IBD rate to detect excessive IBD sharing between cases. One challenge of the pairwise method is computational efficiency. In the pairwise method, one uses permutation to approximate p-values because it is difficult to analytically obtain the asymptotic distribution of the statistic. Since the p-value threshold for genome-wide association studies (GWAS) is necessarily low due to multiple testing, one must perform a large number of permutations which can be computationally demanding. I present Fast-Pairwise to overcome the computational challenges of the traditional pairwise method by utilizing importance sampling to improve efficiency and enable approximation of extremely small p-values. Using the WTCCC type 1 diabetes data, I show that Fast-Pairwise can successfully pinpoint a gene known to be associated to the disease within the MHC region. Finally, I introduce a novel meta analytic approach to identify gene-by-environment interactions by aggregating the multiple studies with varying environmental conditions. Identifying environmentally specific genetic effects is a key challenge in understanding the structure of complex traits. Model organisms play a crucial role in the identification of such gene-by-environment interactions, as a result of the unique ability to observe genetically similar individuals across multiple distinct environments. Many model organism studies examine the same traits but, under varying environmental conditions. These studies when examined in aggregate provide an opportunity to identify genomic loci exhibiting environmentally-dependent effects. In this project, I jointly analyze multiple studies with varying environmental conditions using a meta-analytic approach based on a random effects model to identify loci involved in gene-by-environment interactions. Our approach is motivated by the observation that methods for discovering gene-by-environment interactions are closely related to random effects models for meta-analysis. We show that interactions can be interpreted as heterogeneity and can be detected without utilizing the traditional uni- or multi-variate approaches for discovery of gene-by-environment interactions. I apply our new method to combine 17 mouse studies containing in aggregate 4,965 distinct animals. We identify 26 significant loci involved in High-density lipoprotein (HDL) cholesterol, many of which show significant evidence of involvement in gene-by-environment interactions.
Book Synopsis Molecular Mechanisms Underlying the Genetic Architecture of Complex Traits by : Nicholas Alexander Sinnott-Armstrong
Download or read book Molecular Mechanisms Underlying the Genetic Architecture of Complex Traits written by Nicholas Alexander Sinnott-Armstrong and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis focuses on using genetics to better connect complex trait associations to the molecular and cellular processes they affect. Rather than performing top-down post-genome wide association studies (post-GWAS), which has become increasingly popular in recent years as a way to dissect the factors contributing to individual risk loci, we focus here on bottom-up post-GWAS: can we use traits for which the molecular processes governing their concentrations are already well understood, and identify variants affecting them which then might lead to disease or other downstream effects? I hope that you believe this is possible, and useful, in reading these selected works which compose my dissertation.
Book Synopsis Modeling the Genetic Architecture of Complex Traits by : Han Hao
Download or read book Modeling the Genetic Architecture of Complex Traits written by Han Hao and published by . This book was released on 2016 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Quantitative trait loci (QTL) mapping is the process of detecting genetic variants that regulate complex traits. Among all complex traits with continuous values, function-valued traits that undergo developmental processes are commonly seen in growth analysis, shape analysis, network analysis, and clinical trials. Functional mapping is a statistical tool for mapping QTLs involved with function-valued phenotypic traits. The utility of functional mapping can be displayed when the phenotypic traits represent developmental processes and can be modeled by either parametric or nonparametric curves.In this dissertation, we focus on the application of functional mapping and the development of computational frameworks in different types of function-valued traits. In Chapter 2, we deal with the situation where the trait values follow a parametric trend. A Richard curve is used to model rice height growth, and QTLs are examined for their association with the onset, offset and duration of the developmentaol process using a Gaussian mixture model. In Chapter 3, the trait values do not have a parametric form. The contour of mei leaves is modeled using Fourier basis expansion, and QTLs are examined for their association with shape development using a high-dimensional analysis of variance (HANOVA) approach. In Chapter 4 and Chapter 5, we study the interactions between two function-valued traits. The growth of two interacting traits is modeled with ordinary differential equation (ODE) systems, and QTLs are examined for their association with the type and intensity of such interactions using a generalized profiling approach.
Book Synopsis Approaches to Mapping the Genetic Architecture of Complex Traits in Humans by : Rathi Suresh
Download or read book Approaches to Mapping the Genetic Architecture of Complex Traits in Humans written by Rathi Suresh and published by . This book was released on 2007 with total page 312 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Methods and Models for the Analysis of Genetic Variation Across Species Using Large-scale Genomic Data by : Tanya Ngoc Phung
Download or read book Methods and Models for the Analysis of Genetic Variation Across Species Using Large-scale Genomic Data written by Tanya Ngoc Phung and published by . This book was released on 2018 with total page 213 pages. Available in PDF, EPUB and Kindle. Book excerpt: Understanding how different evolutionary processes shape genetic variation within and between species is an important question in population genetics. The advent of next generation sequencing has allowed for many theories and hypotheses to be tested explicitly with data. However, questions such as what evolutionary processes affect neutral divergence (DNA differences between species) or genetic variation in different regions of the genome (such as on autosomes versus sex chromosomes) or how many genetic variants contribute to complex traits are still outstanding. In this dissertation, I utilized different large-scale genomic datasets and developed statistical methods to determine the role of natural selection on genetic variation between species, sex-biased evolutionary processes on shaping patterns of genetic variation on the X chromosome and autosomes, and how population history, mutation, and natural selection interact to control complex traits. First, I used genome-wide divergence data between multiple pairs of species ranging in divergence time to show that natural selection has reduced divergence at neutral sites that are linked to those under direct selection. To determine explicitly whether and to what extent linked selection and/or mutagenic recombination could account for the pattern of neutral divergence across the genome, I developed a statistical method and applied it to human-chimp neutral divergence dataset. I showed that a model including both linked selection and mutagenic recombination resulted in the best fit to the empirical data. However, the signal of mutagenic recombination could be coming from biased gene conversion. Comparing genetic diversity between the X chromosome and the autosomes could provide insights into whether and how sex-biased processes have affected genetic variation between different genomic regions. For example, X/A diversity ratio greater than neutral expectation could be due to more X chromosomes than expected and could be a result of mating practices such as polygamy where there are more reproducing females than males. I next utilized whole-genome sequences from dogs and wolves and found that X/A diversity is lower than neutral expectation in both dogs and wolves in ancient time-scales, arguing for evolutionary processes resulting in more males reproducing compared to females. However, within breed dogs, patterns of population differentiation suggest that there have been more reproducing females, highlighting effects from breeding practices such as popular sire effect where one male can father many offspring with multiple females. In medical genetics, a complete understanding of the genetic architecture is essential to unravel the genetic basis of complex traits. While genome wide association studies (GWAS) have discovered thousands of trait-associated variants and thus have furthered our understanding of the genetic architecture, key parameters such as the number of causal variants and the mutational target size are still under-studied. Further, the role of natural selection in shaping the genetic architecture is still not entirely understood. In the last chapter, I developed a computational method called InGeAr to infer the mutational target size and explore the role of natural selection on affecting the variant's effect on the trait. I found that the mutational target size differs from trait to trait and can be large, up to tens of megabases. In addition, purifying selection is coupled with the variant's effect on the trait. I discussed how these results support the omnigenic model of complex traits. In summary, in this dissertation, I utilized different types of large genomic dataset, from genome-wide divergence data to whole genome sequence data to GWAS data to develop models and statistical methods to study how different evolutionary processes have shaped patterns of genetic variation across the genome.
Book Synopsis Biosocial Surveys by : National Research Council
Download or read book Biosocial Surveys written by National Research Council and published by National Academies Press. This book was released on 2008-01-06 with total page 429 pages. Available in PDF, EPUB and Kindle. Book excerpt: Biosocial Surveys analyzes the latest research on the increasing number of multipurpose household surveys that collect biological data along with the more familiar interviewerâ€"respondent information. This book serves as a follow-up to the 2003 volume, Cells and Surveys: Should Biological Measures Be Included in Social Science Research? and asks these questions: What have the social sciences, especially demography, learned from those efforts and the greater interdisciplinary communication that has resulted from them? Which biological or genetic information has proven most useful to researchers? How can better models be developed to help integrate biological and social science information in ways that can broaden scientific understanding? This volume contains a collection of 17 papers by distinguished experts in demography, biology, economics, epidemiology, and survey methodology. It is an invaluable sourcebook for social and behavioral science researchers who are working with biosocial data.
Book Synopsis Breeding for Quantitative Traits in Plants by : Rex Novero Bernardo
Download or read book Breeding for Quantitative Traits in Plants written by Rex Novero Bernardo and published by . This book was released on 2019 with total page 422 pages. Available in PDF, EPUB and Kindle. Book excerpt:
