Genetic architecture of body size in mammals

Much of the heritability for human stature is caused by mutations of small-to-medium effect. This is because detrimental pleiotropy restricts large-effect mutations to very low frequencies

Comparing linkage and association analyses in sheep points to a better way of doing GWAS

Genome wide association studies (GWAS) have largely succeeded family-based linkage studies in livestock and human populations as the preferred method to map loci for complex or quantitative traits. However, the type of results produced by the two analyses contrast sharply due to differences in linkage disequilibrium (LD) imposed by the design of studies. In this paper, we demonstrate that association and linkage studies are in agreement provided that (i) the effects from both studies are estimated appropriately as random effects, (ii) all markers are fitted simultaneously and (iii) appropriate adjustments are made for the differences in LD between the study designs. We demonstrate with real data that linkage results can be predicted by the sum of association effects. Our association study captured most of the linkage information because we could predict the linkage results with moderate accuracy. We suggest that the ability of common single nucleotide polymorphism (SNP) to capture the genetic variance in a population will depend on the effective population size of the study organism. The results provide further evidence for many loci of small effect underlying complex traits. The analysis suggests a more informed method for GWAS is to fit statistical models where all SNPs are analysed simultaneously and as random effects

Adaptation of gastrointestinal nematode parasites to host genotype: single locus simulation models

Background: Breeding livestock for improved resistance to disease is an increasingly important selection goal. However, the risk of pathogens adapting to livestock bred for improved disease resistance is difficult to quantify. Here, we explore the possibility of gastrointestinal worms adapting to sheep bred for low faecal worm egg count using computer simulation. Our model assumes sheep and worm genotypes interact at a single locus, such that the effect of an A allele in sheep is dependent on worm genotype, and the B allele in worms is favourable for parasitizing the A allele sheep but may increase mortality on pasture. We describe the requirements for adaptation and test if worm adaptation (1) is slowed by non-genetic features of worm infections and (2) can occur with little observable change in faecal worm egg count. Results: Adaptation in worms was found to be primarily influenced by overall worm fitness, viz. the balance between the advantage of the B allele during the parasitic stage in sheep and its disadvantage on pasture. Genetic variation at the interacting locus in worms could be from de novo or segregating mutations, but de novo mutations are rare and segregating mutations are likely constrained to have (near) neutral effects on worm fitness. Most other aspects of the worm infection we modelled did not affect the outcomes. However, the host-controlled mechanism to reduce faecal worm egg count by lowering worm fecundity reduced the selection pressure on worms to adapt compared to other mechanisms, such as increasing worm mortality. Temporal changes in worm egg count were unreliable for detecting adaptation, despite the steady environment assumed in the simulations. Conclusions: Adaptation of worms to sheep selected for low faecal worm egg count requires an allele segregating in worms that is favourable in animals with improved resistance but less favourable in other animals. Obtaining alleles with this specific property seems unlikely. With support from experimental data, we conclude that selection for low faecal worm egg count should be stable over a short time frame (e.g. 20 years). We are further exploring model outcomes with multiple loci and comparing outcomes to other control strategies

Understanding verbal fluency in healthy aging, Alzheimer’s disease, and Parkinson’s disease

This is the author's accepted manuscript. This article may not exactly replicate the final version published in the APA journal. It is not the copy of record.• Objective: Verbal fluency measures are frequently part of batteries designed to assess executive function, but are also used to assess semantic processing ability or word knowledge. The goal of the present study was to identify the cognitive components underlying fluency performance. • Method: Healthy young and older adults, adults with Parkinson’s disease, and adults with Alzheimer’s disease performed letter, category, and action fluency tests. Performance was assessed in terms of number of items generated, clustering, and the time course of output. A series of neuropsychological assessments were also administered to index verbal ability, working memory, executive function, and processing speed as correlates of fluency performance. • Results: Findings indicated that regardless of the particular performance measure, young adults performed the best and adults with Alzheimer’s disease performed most poorly, with healthy older adults and adults with Parkinson’s disease performing at intermediate levels. The exception was the action fluency task, where adults with Parkinson’s disease performed most poorly. The time course of fluency performance was characterized in terms of slope and intercept parameters and related to neuropsychological constructs. Speed of processing was found to be the best predictor of performance, rather than the efficiency of executive function or semantic knowledge. • Conclusions: Together, these findings demonstrate that the pattern of fluency performance looks generally the same regardless of how performance is measured. In addition, the primary role of processing speed in performance suggests that the use of fluency tasks as measures of executive function or verbal ability warrants reexamination.This work was conducted with grant support from the Kansas City Life Sciences Institute. Additional support was provided by the Digital Electronics Core of the Center for Biobehavioral Neurosciences in Communication Disorders, grant number P30 DC-005803, for assistance with the development of the digital ink assessment

Genetic influence on within-person longitudinal change in anthropometric traits in the UK Biobank

The causes of temporal fluctuations in adult traits are poorly understood. Here, we investigate the genetic determinants of within-person trait variability of 8 repeatedly measured anthropometric traits in 50,117 individuals from the UK Biobank. We found that within-person (non-directional) variability had a SNP-based heritability of 2–5% for height, sitting height, body mass index (BMI) and weight (P ≤ 2.4 × 10−3). We also analysed longitudinal trait change and show a loss of both average height and weight beyond about 70 years of age. A variant tracking the Alzheimer’s risk APOE-E4 allele (rs429358) was significantly associated with weight loss (β = −0.047 kg per yr, s.e. 0.007, P = 2.2 × 10−11), and using 2-sample Mendelian Randomisation we detected a relationship consistent with causality between decreased lumbar spine bone mineral density and height loss (bxy = 0.011, s.e. 0.003, P = 3.5 × 10−4). Finally, population-level variance quantitative trait loci (vQTL) were consistent with within-person variability for several traits, indicating an overlap between trait variability assessed at the population or individual level. Our findings help elucidate the genetic influence on trait-change within an individual and highlight disease risks associated with these changes

Genome-wide association analyses identify 143 risk variants and putative regulatory mechanisms for type 2 diabetes

Type 2 diabetes (T2D) is a very common disease in humans. Here we conduct a meta-analysis of genome-wide association studies (GWAS) with ~16 million genetic variants in 62,892 T2D cases and 596,424 controls of European ancestry. We identify 139 common and 4 rare variants associated with T2D, 42 of which (39 common and 3 rare variants) are independent of the known variants. Integration of the gene expression data from blood (n = 14,115 and 2765) with the GWAS results identifies 33 putative functional genes for T2D, 3 of which were targeted by approved drugs. A further integration of DNA methylation (n = 1980) and epigenomic annotation data highlight 3 genes (CAMK1D, TP53INP1, and ATP5G1) with plausible regulatory mechanisms, whereby a genetic variant exerts an effect on T2D through epigenetic regulation of gene expression. Our study uncovers additional loci, proposes putative genetic regulatory mechanisms for T2D, and provides evidence of purifying selection for T2D-associated variants

Genome-wide association analyses identify 143 risk variants and putative regulatory mechanisms for type 2 diabetes

Type 2 diabetes (T2D) is a very common disease in humans. Here we conduct a meta-analysis of genome-wide association studies (GWAS) with ~16 million genetic variants in 62,892 T2D cases and 596,424 controls of European ancestry. We identify 139 common and 4 rare variants associated with T2D, 42 of which (39 common and 3 rare variants) are independent of the known variants. Integration of the gene expression data from blood (n = 14,115 and 2765) with the GWAS results identifies 33 putative functional genes for T2D, 3 of which were targeted by approved drugs. A further integration of DNA methylation (n = 1980) and epigenomic annotation data highlight 3 genes (CAMK1D, TP53INP1, and ATP5G1) with plausible regulatory mechanisms, whereby a genetic variant exerts an effect on T2D through epigenetic regulation of gene expression. Our study uncovers additional loci, proposes putative genetic regulatory mechanisms for T2D, and provides evidence of purifying selection for T2D-associated variants.</p

Sheep Updates 2006 - part 2

This session covers six papers from different authors: GENETICS 1. Novel selection traits - what are the possible side effects?, Darryl Smith, Kathryn Kemper, South Australian Research and Development Institute, David Rutley, University of Adelaide. 2. Genetic Changes in the Australian Merino since 1900, Sheep Genetics Australia Technical Committee, R.R. Woolaston Pullenvale, Queensland, D.J. Brown, Animal Genetics and Breeding Unit*, University of New England, K.D. Atkins, A.E. Casey, NSW Department of Primary Industries, A.J. Ball, Meat and Livestock Australia, University of New England 3. Influence of Sire Growth Estimated Breeding Value (EBV0 on Progeny Growth, David Hopkins, David Stanley, Leonie Martin, NSW Department Primary Industries, Centre for Sheep Meat Development, Arthur Gilmour, Remy van de Ven, NSW Department Primary Industries, Orange Agricultural Institute FINISHING 4. Predicting Input Sensitivity on Lamb Feedlot Profitability by Using Feedlot Calculator, David Stanley, NSW Department Primary Industries, Centre for Sheep Meat Development, Geoff Duddy, NSW Department Primary Industries, Yanco Agricultural Institute, Steve Semple, NSW Department Primary Industries, Orange Agricultural Institute, David Hopkins, NSW Department Primary Industries, Centre for Sheep Meat Development 5. Annual ryegrass toxicity (ARGT) in WA - 2006, David Kessell, Meat & Livestock Australia ARGT Project, Northam, WA 6. Poor ewe nutrition during pregnancy increases fatness of their progeny, Andrew Thompson, Department of Primary Industries, Victori

Genetic architecture reconciles linkage and association studies of complex traits

Linkage studies have successfully mapped loci underlying monogenic disorders, but mostly failed when applied to common diseases. Conversely, genome-wide association studies (GWASs) have identified replicable associations between thousands of SNPs and complex traits, yet capture less than half of the total heritability. In the present study we reconcile these two approaches by showing that linkage signals of height and body mass index (BMI) from 119,000 sibling pairs colocalize with GWAS-identified loci. Concordant with polygenicity, we observed the following: a genome-wide inflation of linkage test statistics; that GWAS results predict linkage signals; and that adjusting phenotypes for polygenic scores reduces linkage signals. Finally, we developed a method using recombination rate-stratified, identity-by-descent sharing between siblings to unbiasedly estimate heritability of height (0.76 ± 0.05) and BMI (0.55 ± 0.07). Our results imply that substantial heritability remains unaccounted for by GWAS-identified loci and this residual genetic variation is polygenic and enriched near these loci.</p