Determinants of QTL mapping power in the realized Collaborative Cross

Keele, Gregory R.; Crouse, Wesley L.; Kelada, Samir N. P.; Valdar, William

doi:10.1101/459966

Cited by 15 publications

(31 citation statements)

References 92 publications

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“…Early large-scale studies in incipient CC strains successfully mapped many traits [32] and CC lines were used to map motor performance and body weight [75], energy balance traits [76], exercise physiology [67], toxicology [77], perinatal nutrition in CC RIX lines [78], kidney phenotypes [79], and hematological phenotypes [80]. Although CC mice were intended as a genetic mapping population and were used as such in these early studies, the power of the extant strains is sufficient only to large-effects alleles, typically observed in studies of Mendelian traits [81]. They are therefore useful for reproducible mapping of molecular phenotypes such as cis-expression QTL (eQTL) and epigenetic regulation, which typically exhibit Mendelian genetic variation.…”

Section: Finding Regulatory Mechanisms For Trait Variation In Do Micementioning

confidence: 99%

“…Only large-effect QTL accounting for N50% variance can be mapped with single samples from 50 strains at 80% estimated power. Increased subsampling within strains may enable mapping of moderate effects explaining N20% variance [81]. These effect sizes are typically seen only in highly penetrant Mendelian traits or molecular traits.…”

Section: Experimental Design Considerationsmentioning

confidence: 99%

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High-Diversity Mouse Populations for Complex Traits

et al. 2019

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Contemporary mouse genetic reference populations are a powerful platform to discover complex disease mechanisms. Advanced high-diversity mouse populations include the Collaborative Cross (CC) strains, Diversity Outbred (DO) stock, and their isogenic founder strains. When used in systems genetics and integrative genomics analyses, these populations efficiently harnesses known genetic variation for precise and contextualized identification of complex disease mechanisms. Extensive genetic, genomic, and phenotypic data are already available for these high-diversity mouse populations and a growing suite of data analysis tools have been developed to support research on diverse mice. This integrated resource can be used to discover and evaluate disease mechanisms relevant across species. The Challenge of Complex Disease Complex diseases present compelling biomedical challenges that can be studied using human and nonhuman animal genetics. Although advances in human genetics have identified loci for many heritable complex diseases [1-8], there are several well-known limitations of genomewide association studies (GWASs). (i) For many human disease loci, the biological mechanism of action is unknown. (ii) When little is known about a locus, the path from genetic association to clinically actionable targets is unclear. (iii) Disease process or developmental trajectory is not always obvious from a causal genetic variant. (iv) GWAS results may not generalize across human subpopulations. (v) Medical records and participant phenotyping is often incomplete, imprecise, and retrospective, whereas model organism phenotyping can include in-depth, prospective, and standardized measures. (vi) Sample size requirements are formidable in human GWASs. (vii) Power is insufficient to study interacting genetic loci. (viii) Studies of genetic interaction with development, environment, and sex are largely intractable. Further, heterogeneous diseases like psychiatric disorders manifest with overlapping symptoms, intertwined disease trajectories [9], and complex genetic regulation [9,10]. In summary, the SNP-to-disease association model underlying GWASs cannot readily capture complex disease biology without further biological context. These challenges are often tractable with discovery genetics in model organisms.

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Section: Finding Regulatory Mechanisms For Trait Variation In Do Micementioning

confidence: 99%

Section: Experimental Design Considerationsmentioning

confidence: 99%

High-Diversity Mouse Populations for Complex Traits

et al. 2019

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“…Animal studies allow greater choice over the population's extent of genetic heterogeneity and each population has specific advantages in noise reduction for detection of small effect alleles or a broad survey of very high genetic heterogeneity with variation in nearly every gene in the genome. Minor allele frequencies in even the most complex mouse populations such as the Diversity Outbred (DO) are theoretically 12.5%, providing greater power for detection of variants that influence complex traits in substantially smaller sample sizes than required in human populations . With the exception of large‐scale prospective studies including the Adolescent Brain Cognitive Development and All‐of‐US, studies in humans are generally limited to subjects who are already addicted, restricting the ability to separate premorbid, drug‐naïve traits from the effects of ongoing drug use.…”

Section: Modeling Addiction‐related Behavior Using Laboratory Animalsmentioning

confidence: 99%

Prospects for finding the mechanisms of sex differences in addiction with human and model organism genetic analysis

Datta

Schoenrock

Bubier

et al. 2020

Genes Brain and Behavior

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Despite substantial evidence for sex differences in addiction epidemiology, addiction‐relevant behaviors and associated neurobiological phenomena, the mechanisms and implications of these differences remain unknown. Genetic analysis in model organism is a potentially powerful and effective means of discovering the mechanisms that underlie sex differences in addiction. Human genetic studies are beginning to show precise risk variants that influence the mechanisms of addiction but typically lack sufficient power or neurobiological mechanistic access, particularly for the discovery of the mechanisms that underlie sex differences. Our thesis in this review is that genetic variation in model organisms are a promising approach that can complement these investigations to show the biological mechanisms that underlie sex differences in addiction.

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“…4 Mapping studies in natural populations, such as quantitative trait loci (QTL) studies and genomewide association studies (GWAS), have the potential to allow for the identification of multiple genetic variants influencing a complex trait in the natural genetic context in which the variants occur. [43][44][45] In human populations, while learning, memory and general cognitive function are known to be heritable, 4,20 they are also influenced strongly by a suite of environmental factors, which obviously cannot be systematically controlled in human studies. Assaying learning and memory is often labor-intensive, requiring repeated behavioral trials, 1 making it difficult to assay the large numbers of individuals typically required for high power to detect all but the highest effect QTL.…”

Section: Introductionmentioning

confidence: 99%

“…However, genetic mapping studies have been challenging to perform for learning and memory phenotypes. [43][44][45] There have been a few successes where a causative natural variant for learning or memory has been identified. [43][44][45] In human populations, while learning, memory and general cognitive function are known to be heritable, 4,20 they are also influenced strongly by a suite of environmental factors, which obviously cannot be systematically controlled in human studies.…”

Section: Introductionmentioning

confidence: 99%

Multiple genetic loci affect place learning and memory performance in Drosophila melanogaster

Williams-Simon

Posey

Mitchell

et al. 2019

Genes Brain and Behavior

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Learning and memory are critical functions for all animals, giving individuals the ability to respond to changes in their environment. Within populations, individuals vary, however the mechanisms underlying this variation in performance are largely unknown. Thus, it remains to be determined what genetic factors cause an individual to have high learning ability and what factors determine how well an individual will remember what they have learned. To genetically dissect learning and memory performance, we used the Drosophila synthetic population resource (DSPR), a multiparent mapping resource in the model system Drosophila melanogaster, consisting of a large set of recombinant inbred lines (RILs) that naturally vary in these and othertraits. Fruit flies can be trained in a "heat box" to learn to remain on one side of a chamber (place learning) and can remember this (place memory) over short timescales. Using this paradigm, we measured place learning and memory for~49 000 individual flies from over 700 DSPR RILs. We identified 16 different loci across the genome that significantly affect place learning and/or memory performance, with 5 of these loci affecting both traits. To identify transcriptomic differences associated with performance, we performed RNA-Seq on pooled samples of seven high performing and seven low performing RILs for both learning and memory and identified hundreds of genes with differences in expression in the two sets. Integrating our transcriptomic results with the mapping results allowed us to identify nine promising candidate genes, advancing our understanding of the genetic basis underlying natural variation in learning and memory performance.K E Y W O R D S differential gene expression, Drosophila melanogaster, learning, memory, multiparental population, QTL

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Determinants of QTL mapping power in the realized Collaborative Cross

Cited by 15 publications

References 92 publications

High-Diversity Mouse Populations for Complex Traits

High-Diversity Mouse Populations for Complex Traits

Prospects for finding the mechanisms of sex differences in addiction with human and model organism genetic analysis

Multiple genetic loci affect place learning and memory performance in Drosophila melanogaster

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