Detection and assessment of copy number variation using PacBio long-read and Illumina sequencing in New Zealand dairy cattle.

Single nucleotide polymorphisms have been the DNA variant of choice for genomic prediction, largely because of the ease of single nucleotide polymorphism genotype collection. In contrast, structural variants (SV), which include copy number variants (CNV), translocations, insertions, and inversions, have eluded easy detection and characterization, particularly in nonhuman species. However, evidence increasingly shows that SV not only contribute a substantial proportion of genetic variation but also have significant influence on phenotypes. Here we present the discovery of CNV in a prominent New Zealand dairy bull using long-read PacBio (Pacific Biosciences, Menlo Park, CA) sequencing technology and the Sniffles SV discovery tool (version 0.0.1; https://github.com/fritzsedlazeck/Sniffles). The CNV identified from long reads were compared with CNV discovered in the same bull from Illumina sequencing using CNVnator (read depth-based tool; Illumina Inc., San Diego, CA) as a means of validation. Subsequently, further validation was undertaken using whole-genome Illumina sequencing of 556 cattle representing the wider New Zealand dairy cattle population. Very limited overlap was observed in CNV discovered from the 2 sequencing platforms, in part because of the differences in size of CNV detected. Only a few CNV were therefore able to be validated using this approach. However, the ability to use CNVnator to genotype the 557 cattle for copy number across all regions identified as putative CNV allowed a genome-wide assessment of transmission level of copy number based on pedigree. The more highly transmissible a putative CNV region was observed to be, the more likely the distribution of copy number was multimodal across the 557 sequenced animals. Furthermore, visual assessment of highly transmissible CNV regions provided evidence supporting the presence of CNV across the sequenced animals. This transmission-based approach was able to confirm a subset of CNV that segregates in the New Zealand dairy cattle population. Genome-wide identification and validation of CNV is an important step toward their inclusion in genomic selection strategies.

Leveraging genetically simple traits to identify small-effect variants for complex phenotypes.

BACKGROUND: Polymorphisms underlying complex traits often explain a small part (less than 1 %) of the phenotypic variance (σ2P). This makes identification of mutations underling complex traits difficult and usually only a subset of large-effect loci are identified. One approach to identify more loci is to increase sample size of experiments but here we propose an alternative. The aim of this paper is to use secondary phenotypes for genetically simple traits during the QTL discovery phase for complex traits. We demonstrate this approach in a dairy cattle data set where the complex traits were milk production phenotypes (fat, milk and protein yield; fat and protein percentage in milk) measured on thousands of individuals while secondary (potentially genetically simpler) traits are detailed milk composition traits (measurements of individual protein abundance, mineral and sugar concentrations; and gene expression). RESULTS: Quantitative trait loci (QTL) were identified using 11,527 Holstein cattle with milk production records and up to 444 cows with milk composition traits. There were eight regions that contained QTL for both milk production and a composition trait, including four novel regions. One region on BTAU1 affected both milk yield and phosphorous concentration in milk. The QTL interval included the gene SLC37A1, a phosphorous antiporter. The most significant imputed sequence variants in this region explained 0.001 σ2P for milk yield, and 0.11 σ2P for phosphorus concentration. Since the polymorphisms were non-coding, association mapping for SLC37A1 gene expression was performed using high depth mammary RNAseq data from a separate group of 371 lactating cows. This confirmed a strong eQTL for SLC37A1, with peak association at the same imputed sequence variants that were most significant for phosphorus concentration. Fitting any of these variants as covariables in the association analysis removed the QTL signal for milk production traits. Plausible causative mutations in the casein complex region were also identified using a similar strategy. CONCLUSIONS: Milk production traits in dairy cows are typical complex traits where polymorphisms explain only a small portion of the phenotypic variance. However, here we show that these mutations can have larger effects on secondary traits, such as concentrations of minerals, proteins and sugars in the milk, and expression levels of genes in mammary tissue. These larger effects were used to successfully map variants for milk production traits. Genetically simple traits also provide a direct biological link between possible causal mutations and the effect of these mutations on milk production.

Genetics of complex traits: prediction of phenotype, identification of causal polymorphisms and genetic architecture.

Complex or quantitative traits are important in medicine, agriculture and evolution, yet, until recently, few of the polymorphisms that cause variation in these traits were known. Genome-wide association studies (GWAS), based on the ability to assay thousands of single nucleotide polymorphisms (SNPs), have revolutionized our understanding of the genetics of complex traits. We advocate the analysis of GWAS data by a statistical method that fits all SNP effects simultaneously, assuming that these effects are drawn from a prior distribution. We illustrate how this method can be used to predict future phenotypes, to map and identify the causal mutations, and to study the genetic architecture of complex traits. The genetic architecture of complex traits is even more complex than previously thought: in almost every trait studied there are thousands of polymorphisms that explain genetic variation. Methods of predicting future phenotypes, collectively known as genomic selection or genomic prediction, have been widely adopted in livestock and crop breeding, leading to increased rates of genetic improvement.

Exploiting biological priors and sequence variants enhances QTL discovery and genomic prediction of complex traits.

BACKGROUND: Dense SNP genotypes are often combined with complex trait phenotypes to map causal variants, study genetic architecture and provide genomic predictions for individuals with genotypes but no phenotype. A single method of analysis that jointly fits all genotypes in a Bayesian mixture model (BayesR) has been shown to competitively address all 3 purposes simultaneously. However, BayesR and other similar methods ignore prior biological knowledge and assume all genotypes are equally likely to affect the trait. While this assumption is reasonable for SNP array genotypes, it is less sensible if genotypes are whole-genome sequence variants which should include causal variants. RESULTS: We introduce a new method (BayesRC) based on BayesR that incorporates prior biological information in the analysis by defining classes of variants likely to be enriched for causal mutations. The information can be derived from a range of sources, including variant annotation, candidate gene lists and known causal variants. This information is then incorporated objectively in the analysis based on evidence of enrichment in the data. We demonstrate the increased power of BayesRC compared to BayesR using real dairy cattle genotypes with simulated phenotypes. The genotypes were imputed whole-genome sequence variants in coding regions combined with dense SNP markers. BayesRC increased the power to detect causal variants and increased the accuracy of genomic prediction. The relative improvement for genomic prediction was most apparent in validation populations that were not closely related to the reference population. We also applied BayesRC to real milk production phenotypes in dairy cattle using independent biological priors from gene expression analyses. Although current biological knowledge of which genes and variants affect milk production is still very incomplete, our results suggest that the new BayesRC method was equal to or more powerful than BayesR for detecting candidate causal variants and for genomic prediction of milk traits. CONCLUSIONS: BayesRC provides a novel and flexible approach to simultaneously improving the accuracy of QTL discovery and genomic prediction by taking advantage of prior biological knowledge. Approaches such as BayesRC will become increasing useful as biological knowledge accumulates regarding functional regions of the genome for a range of traits and species.

How old are quantitative trait loci and how widely do they segregate?

The mutations that cause genetic variation in quantitative traits could be old and segregate across many breeds or they could be young and segregate only within one breed. This has implications for our understanding of the evolution of quantitative traits and for genomic prediction to improve livestock. We investigated the age of quantitative trait loci (QTL) for milk production traits identified as segregating in Holstein dairy cattle. We use a multitrait method and found that six of 11 QTL also segregate in Jerseys. Variants identified as Holstein-only QTL were fixed or rare [minor allele frequency (MAF) < 0.05] in Jersey. The age of the QTL mutations appears to vary from perhaps 2000 to 50,000 generations old. The older QTL tend to have high derived allele frequencies and often segregate across both breeds. Holstein-only QTL were often embedded within longer haplotypes, supporting the conclusion that they are typically younger mutations that have occurred more recently than QTL that segregate in both breeds. A reference population for genomic prediction using both Holsteins and Jersey cattle incorrectly predicted a QTL in Jersey cattle when the QTL only segregates in Holsteins. Overcoming this error should help to make genomic prediction more accurate in smaller breeds.

Components of the accuracy of genomic prediction in a multi-breed sheep population.

In genome-wide association studies, failure to remove variation due to population structure results in spurious associations. In contrast, for predictions of future phenotypes or estimated breeding values from dense SNP data, exploiting population structure arising from relatedness can actually increase the accuracy of prediction in some cases, for example, when the selection candidates are offspring of the reference population where the prediction equation was derived. In populations with large effective population size or with multiple breeds and strains, it has not been demonstrated whether and when accounting for or removing variation due to population structure will affect the accuracy of genomic prediction. Our aim in this study was to determine whether accounting for population structure would increase the accuracy of genomic predictions, both within and across breeds. First, we have attempted to decompose the accuracy of genomic prediction into contributions from population structure or linkage disequilibrium (LD) between markers and QTL using a diverse multi-breed sheep (Ovis aries) data set, genotyped for 48,640 SNP. We demonstrate that SNP from a single chromosome can achieve up to 86% of the accuracy for genomic predictions using all SNP. This result suggests that most of the prediction accuracy is due to population structure, because a single chromosome is expected to capture relationships but is unlikely to contain all QTL. We then explored principal component analysis (PCA) as an approach to disentangle the respective contributions of population structure and LD between SNP and QTL to the accuracy of genomic predictions. Results showed that fitting an increasing number of principle components (PC; as covariates) decreased within breed accuracy until a lower plateau was reached. We speculate that this plateau is a measure of the accuracy due to LD. In conclusion, a large proportion of the accuracy for genomic predictions in our data was due to variation associated with population structure. Surprisingly, accounting for this structure generally decreased the accuracy of across breed genomic predictions.

Long-term selection strategies for complex traits using high-density genetic markers.

Selection of animals for breeding ranked on estimated breeding value maximizes genetic gain in the next generation but does not necessarily maximize long-term response. An alternative method, as practiced by plant breeders, is to build a desired genotype by selection on specific loci. Maximal long-term response in animal breeding requires selection on estimated breeding values with constraints on coancestry. In this paper, we compared long-term genetic response using either a genotype building or a genomic estimated breeding value (GEBV) strategy for the Australian Selection Index (ASI), a measure of profit. First, we used real marker effects from the Australian Dairy Herd Improvement Scheme to estimate breeding values for chromosome segments (approximately 25 cM long) for 2,650 Holstein bulls. Second, we selected 16 animals to be founders for a simulated breeding program where, between them, founders contain the best possible combination of 2 segments from 2 animals at each position in the genome. Third, we mated founder animals and their descendants over 30 generations with 2 breeding objectives: (1) to create a population with the "ideal genotype," where the best 2 segments from the founders segregate at each position, or (2) obtain the highest possible response in ASI with coancestry lower than that achieved under breeding objective 1. Results show that genotype building achieved the ideal genotype for breeding objective 1 and obtained a large gain in ASI over the current population (+A$864.99). However, selection on overall GEBV had greater short-term response and almost as much long-term gain (+A$820.42). When coancestry was lowered under breeding objective 2, selection on overall GEBV achieved a higher response in ASI than the genotype building strategy. Selection on overall GEBV seems more flexible in its selection decisions and was therefore better able to precisely control coancestry while maximizing ASI. We conclude that selection on overall GEBV while minimizing average coancestry is the more practical strategy for dairy cattle where selection is for highly polygenic traits, the reproductive rate is relatively low, and there is low tolerance of coancestry. The outcome may be different for traits controlled by few loci of relatively large effects or for different species. In contrast to other simulations, our results indicate that response to selection on overall GEBV may continue for several generations. This is because long-term genetic change in complex traits requires favorable changes to allele frequencies for many loci located throughout the genome.

Reduction of faecal worm egg count, worm numbers and worm fecundity in sheep selected for worm resistance following artificial infection with Teladorsagia circumcincta and Trichostrongylus colubriformis.

We examined the changes to populations of Trichostrongylus colubriformis and Teledorsagia circumcincta in mature sheep selected for reduced faecal worm egg count (WEC). Worm resistant (n=19) and control (n=10) genotype sheep were penned and dosed with a total of 10,000 T. colubriformis and 10,000 T. circumcincta per week for 18-weeks. Sheep genotypes were from lines previously bred over 15 years for either low WEC (resistant) or non-selected (control). Weekly WEC and the proportion of larvae from each species in faecal culture were measured during weeks 3-17. Egg hatchability was assessed on a pooled faecal sample from worm resistant or control genotype sheep at weeks 7, 9, 10, 13, 14 and 17. At week 18 the number of adult and immature worms (early and late 4th, and 5th stage), and indicators of worm fecundity (female worm length and number of eggs in utero) were assessed at necropsy. Results show that resistant sheep had reduced mean WEC to only 18% of the control (P<0.05) and increased the proportion of T. circumcincta larvae in faecal culture during weeks 8-17 (P<0.10). Egg hatch assays indicated a slight reduction in the viability of eggs from worm resistant genotypes at weeks 14 (P<0.05) and 17 (P<0.10). At necropsy, resistant animals had 93% fewer adult T. colubriformis, 44% fewer adult T. circumcincta and had reduced indicators of fecundity in T. circumcincta by up to 40% (P<0.05). We observed no change in the number T. circumcincta worms but an 11% increase in the proportion of early 4th stage T. circumcincta larvae in resistant animals (P<0.05). There were different temporal patterns in WEC and different prediction equations for WEC from necropsy traits for the two sheep genotypes (P<0.05). Thus, our results suggest a changed host-parasite relationship in sheep selected for low WEC. We conclude that lower WEC is achieved through reduced number of adult worms for both species and reduced fecundity for T. circumcincta. These results support the hypotheses that worm resistant sheep with a strong immune function can regulate T. colubriformis by rejecting adult worms but that T. circumcincta is regulated through a combination of suppressed development and reduced female fecundity.

Haemonchus contortus and Trichostrongylus colubriformis did not adapt to long-term exposure to sheep that were genetically resistant or susceptible to nematode infections.

We tested the hypothesis that Haemonchus contortus and Trichostrongylus colubriformis would adapt to long-term exposure to sheep that were either genetically resistant or susceptible to H. contortus. Sheep genotypes were from lines with 10 years prior selection for low (resistant, R) or high (susceptible, S) faecal worm egg count (WEC) following H. contortus infection. Long-term exposure of H. contortus and T.colubriformis to R or S genotypes was achieved using serial passage for up to 30 nematode generations. Thus, we generated four nematode strains; one strain of each species solely exposed to R sheep and one strain of each species solely exposed to S sheep. Considerable host genotype differences in mean WEC during serial passage confirmed adequate nematode selection pressure for both H. contortus (R 4900 eggs per gram (epg), S 19,900 epg) and T. colubriformis (R 5300 epg, S 13,500 epg). Adaptation of nematode strain to host genotype was tested using seven cross-classified tests for H. contortus, and two cross-classified and one outbred genotype test for T. colubriformis. In the cross-classified design, where each strain infects groups of R, S or randomly bred control sheep, parasite adaptation would be indicated by a significant host genotype by nematode strain interaction for traits indicating parasite reproductive success; specifically WEC and, for H. contortus strains, packed cell volume. We found no significant evidence of parasite adaptation to host genotype (P>0.05) for either the H. contortus or T. colubriformis strains. Therefore, we argue that nematodes will not adapt quickly to sheep bred for nematode resistance, where selection is based on low WEC, although selecting sheep using a subset of immune functions may increase adaptation risk. Our results support the hypothesis that nematode resistance is determined by many genes each with relatively small effect. In conclusion, selection of sheep for nematode resistance using WEC should be sustainable in the medium to long-term.

The role of institutional arrangements for more efficient water resources use and allocation.

Water resources management requires an interdisciplinary approach to meet the challenges posed by ever increasing water demand and pollution. This paper discusses in which way appropriate institutional arrangements are complementary to technical interventions that aim to increase more efficient water use and allocation. Specific emphasis is given to water property rights as one of the determining factors in regard to the institutional framework. Issues such as the range of flexibility in designing property rights to water, the implications for pricing, enforcement and sanctioning mechanisms as well as for water trading and information needs are highlighted.

Public health benefit of over-the-counter nicotine medications.

OBJECTIVE: To estimate the impact of allowing non-prescription sales of nicotine medications in the United States on increasing the numbers of smokers quitting. DESIGN: Sales and marketing data were used to compare the use of nicotine medications before and after non-prescription sales, and to estimate the impact of non-prescription sales on quit rates. SETTING: United States. MAIN OUTCOME MEASURES: Number of quit attempts using nicotine replacement therapy (NRT) products, number of smokers who quit smoking with over-the-counter (OTC) NRT or with NRT still sold by prescription, and incremental quits attributable to OTC NRT. RESULTS: Since the US Food and Drug Administration approved nicotine medications for OTC sale in 1996, use of the medications has increased by 152% compared with prior prescription use. With increased use of an efficacious treatment, OTC nicotine medications are estimated to yield from 114,000-304,000 new former smokers annually in the United States. CONCLUSIONS: The broader availability and promotion of effective treatments for tobacco dependence, specifically nicotine gum and patch, increase the number of smokers availing themselves of the medications. This increased use is estimated to contribute substantially to the number of former smokers in the United States.