Genome-wide association studies (GWAS) have been widely used in the genetic dissection of complex traits. However, there are still limits in current GWAS statistics. For example, (1) almost all the existing methods do not estimate additive and dominance effects in quantitative trait nucleotide (QTN) detection; (2) the methods for detecting QTN-by-environment interaction (QEI) are not straightforward and do not estimate additive and dominance effects as well as additive-by-environment and dominance-by-environment interaction effects, leading to unreliable results; and (3) no or too simple polygenic background controls have been employed in QTN-by-QTN interaction (QQI) detection. As a result, few studies of QEI and QQI for complex traits have been reported based on multiple-environment experiments. Recently, new statistical tools, including 3VmrMLM, have been developed to address these needs in GWAS. In 3VmrMLM, all the trait-associated effects, including QTN, QEI and QQI related effects, are compressed into a single effect-related vector, while all the polygenic backgrounds are compressed into a single polygenic effect matrix. These compressed parameters can be accurately and efficiently estimated through a unified mixed model analysis. To further validate these new GWAS methods, particularly 3VmrMLM, they should be rigorously tested in real data of various plants and a wide range of other species.

In the past 10 years, contemporary geneticists using new molecular tools have been able to resolve complex traits into individual genetic components and describe each such component in detail. Molecular Dissection of Complex Traits summarizes the state of the art in molecular analysis of complex traits (QTL mapping), placing new developments in thi

This book introduces the basic concepts and methods that are useful in the statistical analysis and modeling of the DNA-based marker and phenotypic data that arise in agriculture, forestry, experimental biology, and other fields. It concentrates on the linkage analysis of markers, map construction and quantitative trait locus (QTL) mapping, and assumes a background in regression analysis and maximum likelihood approaches. The strength of this book lies in the construction of general models and algorithms for linkage analysis, as well as in QTL mapping in any kind of crossed pedigrees initiated with inbred lines of crops.

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

In the past 10 years, contemporary geneticists using new molecular tools have been able to resolve complex traits into individual genetic components and describe each such component in detail. Molecular Dissection of Complex Traits summarizes the state of the art in molecular analysis of complex traits (QTL mapping), placing new developments in thi

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

As per the reports of FAO, the human population will rise to 9 billion by the end of 2050 and 70% of more food must be produced over the next three decades to feed the additional population. The breeding approaches for crop improvement programs are dependent on the availability and accessibility of genetic variation, either spontaneous or induced by the mutagens. Plant breeders, agronomists, and geneticists are under constant pressure to expand food production by employing innovative breeding strategies to enhance yield, adaptability, nutrition, resistance to biotic and abiotic stresses. In conventional breeding approaches, introgression of genes in crop varieties is laborious and time-consuming. Nowadays, new innovative plant breeding techniques such as molecular breeding and plant biotechnology, supplement the traditional breeding approaches to achieve the desired goals of enhanced food production. With the advent of recent molecular tools like genomics, transgenics, molecular marker-assisted back-crossing, TILLING, Eco-TILLING, gene editing, CRISPR CAS, non-targeted protein abundant comparative proteomics, genome wide association studies have made possible mapping of important QTLs, insertion of transgenes, reduction of linkage drags, and manipulation of genome. In general, conventional and modern plant breeding approaches would be strategically ideal for developing new elite crop varieties to meet the feeding requirement of the increasing world population. This book highlights the latest progress in the field of plant breeding, and their applicability in crop improvement. The basic concept of this 2-volume work is to assess the use of modern breeding strategies in supplementing the conventional breeding toward the development of elite crop varieties, for obtaining desired goals of food production.