A simple method for calculating the statistical power for detecting a QTL located in a marker interval

Fine mapping of the Cepaea nemoralis shell colour and mid-banded loci using a high-density linkage map

Molluscs are a highly speciose phylum that exhibits an astonishing array of colours and patterns, yet relatively little progress has been made in identifying the underlying genes that determine phenotypic variation. One prominent example is the land snail Cepaea nemoralis for which classical genetic studies have shown that around nine loci, several physically linked and inherited together as a 'supergene', control the shell colour and banding polymorphism. As a first step towards identifying the genes involved, we used whole-genome resequencing of individuals from a laboratory cross to construct a high-density linkage map, and then trait mapping to identify 95% confidence intervals for the chromosomal region that contains the supergene, specifically the colour locus (C), and the unlinked mid-banded locus (U). The linkage map is made up of 215,593 markers, ordered into 22 linkage groups, with one large group making up ~27% of the genome. The C locus was mapped to a ~1.3 cM region on linkage group 11, and the U locus was mapped to a ~0.7 cM region on linkage group 15. The linkage map will serve as an important resource for further evolutionary and population genomic studies of C. nemoralis and related species, as well as the identification of candidate genes within the supergene and for the mid-banding phenotype.

Compatibility between snails and schistosomes: insights from new genetic resources, comparative genomics, and genetic mapping

The freshwater snail Biomphalaria glabrata is an important intermediate host of the parasite Schistosoma mansoni that causes human intestinal schistosomiasis. To better understand vector snail biology and help advance innovative snail control strategies, we have developed a new snail model consisting of two homozygous B. glabrata lines (iM line and iBS90) with sharply contrasting schistosome-resistance phenotypes. We produced and compared high-quality genome sequences for iM line and iBS90 which were assembled from 255 (N50 = 22.7 Mb) and 346 (N50 = 19.4 Mb) scaffolds, respectively. Using F2 offspring bred from the two lines and the newly generated iM line genome, we constructed 18 linkage groups (representing the 18 haploid chromosomes) covering 96% of the genome and identified three new QTLs (quantitative trait loci), two involved in snail resistance/susceptibility and one relating to body pigmentation. This study provides excellent genomic resources for unveiling complex vector snail biology, reveals genomic difference between resistant and susceptible lines, and offers novel insights into genetic mechanism of the compatibility between snail and schistosome.

Mapping QTL associated with partial resistance to Aphanomyces root rot in pea (Pisum sativum L.) using a 13.2 K SNP array and SSR markers

KEY MESSAGE

A stable and major QTL, which mapped to an approximately 20.0 cM region on pea chromosome 4, was identified as the most consistent region conferring partial resistance to Aphanomyces euteiches. Aphanomyces root rot (ARR), caused by Aphanomyces euteiches Drechs., is a destructive soilborne disease of field pea (Pisum Sativum L.). No completely resistant pea germplasm is available, and current ARR management strategies rely on partial resistance and fungicidal seed treatments. In this study, an F8 recombinant inbred line population of 135 individuals from the cross 'Reward' (susceptible) × '00-2067' (tolerant) was evaluated for reaction to ARR under greenhouse conditions with the A. euteiches isolate Ae-MDCR1 and over 2 years in a field nursery in Morden, Manitoba. Root rot severity, foliar weight, plant vigor and height were used as estimates of tolerance to ARR. Genotyping was conducted with a 13.2 K single-nucleotide polymorphism (SNP) array and 222 simple sequence repeat (SSR) markers. Statistical analyses of the phenotypic data indicated significant (P < 0.001) genotypic effects and significant G × E interactions (P < 0.05) in all experiments. After filtering, 3050 (23.1%) of the SNP and 30 (13.5%) of the SSR markers were retained for linkage analysis, which distributed 2999 (2978 SNP + 21 SSR) of the markers onto nine linkage groups representing the seven chromosomes of pea. Mapping of quantitative trait loci (QTL) identified 8 major-effect (R2 > 20%), 13 moderate-effect (10% < R2 < 20%) effect and 6 minor-effect (R2 < 10%) QTL. A genomic region on chromosome 4, delimited by the SNP markers PsCam037549_22628_1642 and PsCam026054_14999_2864, was identified as the most consistent region responsible for partial resistance to A. euteiches isolate Ae-MDCR1. Other genomic regions important for resistance were of the order chromosome 5, 6 and 7.

Development and Validation of an Open Access SNP Array for Nile Tilapia (Oreochromis niloticus)

Tilapia are among the most important farmed fish species worldwide, and are fundamental for the food security of many developing countries. Several genetically improved Nile tilapia (Oreochromis niloticus) strains exist, such as the iconic Genetically Improved Farmed Tilapia (GIFT), and breeding programs typically follow classical pedigree-based selection. The use of genome-wide single-nucleotide polymorphism (SNP) data can enable an understanding of the genetic architecture of economically important traits and the acceleration of genetic gain via genomic selection. Due to the global importance and diversity of Nile tilapia, an open access SNP array would be beneficial for aquaculture research and production. In the current study, a ∼65K SNP array was designed based on SNPs discovered from whole-genome sequence data from a GIFT breeding nucleus population and the overlap with SNP datasets from wild fish populations and several other farmed Nile tilapia strains. The SNP array was applied to clearly distinguish between different tilapia populations across Asia and Africa, with at least ∼30,000 SNPs segregating in each of the diverse population samples tested. It is anticipated that this SNP array will be an enabling tool for population genetics and tilapia breeding research, facilitating consistency and comparison of results across studies.

Fifteen years of quantitative trait loci studies in fish: challenges and future directions

Understanding the genetic basis of phenotypic variation is a major challenge in biology. Here, we systematically evaluate 146 quantitative trait loci (QTL) studies on teleost fish over the last 15 years to investigate (i) temporal trends and (ii) factors affecting QTL detection and fine-mapping. The number of fish QTL studies per year increased over the review period and identified a cumulative number of 3632 putative QTLs. Most studies used linkage-based mapping approaches and were conducted on nonmodel species with limited genomic resources. A gradual and moderate increase in the size of the mapping population and a sharp increase in marker density from 2011 onwards were observed; however, the number of QTLs and variance explained by QTLs changed only minimally over the review period. Based on these findings, we discuss the causative factors and outline how larger sample sizes, phenomics, comparative genomics, epigenetics and software development could improve both the quantity and quality of QTLs in future genotype-phenotype studies. Given that the technical limitations on DNA sequencing have mostly been overcome in recent years, a renewed focus on these and other study design factors will likely lead to significant improvements in QTL studies in the future.

Genetic linkage of distinct adaptive traits in sympatrically speciating crater lake cichlid fish

Our understanding of how biological diversity arises is limited, especially in the case of speciation in the face of gene flow. Here we investigate the genomic basis of adaptive traits, focusing on a sympatrically diverging species pair of crater lake cichlid fishes. We identify the main quantitative trait loci (QTL) for two eco-morphological traits: body shape and pharyngeal jaw morphology. These traits diverge in parallel between benthic and limnetic species in the repeated adaptive radiations of this and other fish lineages. Remarkably, a single chromosomal region contains the highest effect size QTL for both traits. Transcriptomic data show that the QTL regions contain genes putatively under selection. Independent population genomic data corroborate QTL regions as areas of high differentiation between the sympatric sister species. Our results provide empirical support for current theoretical models that emphasize the importance of genetic linkage and pleiotropy in facilitating rapid divergence in sympatry.

Genetic architecture of parallel pelvic reduction in ninespine sticklebacks

Teleost fish genomes are known to be evolving faster than those of other vertebrate taxa. Thus, fish are suited to address the extent to which the same vs. different genes are responsible for similar phenotypic changes in rapidly evolving genomes of evolutionary independent lineages. To gain insights into the genetic basis and evolutionary processes behind parallel phenotypic changes within and between species, we identified the genomic regions involved in pelvic reduction in Northern European ninespine sticklebacks (Pungitius pungitius) and compared them to those of North American ninespine and threespine sticklebacks (Gasterosteus aculeatus). To this end, we conducted quantitative trait locus (QTL) mapping using 283 F₂ progeny from an interpopulation cross. Phenotypic analyses indicated that pelvic reduction is a recessive trait and is inherited in a simple Mendelian fashion. Significant QTL for pelvic spine and girdle lengths were identified in the region of the Pituitary homeobox transcription factor 1 (Pitx1) gene, also responsible for pelvic reduction in threespine sticklebacks. The fact that no QTL was observed in the region identified in the mapping study of North American ninespine sticklebacks suggests that an alternative QTL for pelvic reduction has emerged in this species within the past 1.6 million years after the split between Northern European and North American populations. In general, our study provides empirical support for the view that alternative genetic mechanisms that lead to similar phenotypes can evolve over short evolutionary time scales.

The power of QTL mapping with RILs

QTL (quantitative trait loci) mapping is commonly used to identify genetic regions responsible to important phenotype variation. A common strategy of QTL mapping is to use recombinant inbred lines (RILs), which are usually established by several generations of inbreeding of an F1 population (usually up to F6 or F7 populations). As this inbreeding process involves a large amount of labor, we are particularly interested in the effect of the number of inbreeding generations on the power of QTL mapping; a part of the labor could be saved if a smaller number of inbreeding provides sufficient power. By using simulations, we investigated the performance of QTL mapping with recombinant inbred lines (RILs). As expected, we found that the power of F4 population could be almost comparable to that of F6 and F7 populations. A potential problem in using F4 population is that a large proportion of RILs are heterozygotes. We here introduced a new method to partly relax this problem. The performance of this method was verified by simulations with a wide range of parameters including the size of the segregation population, recombination rate, genome size and the density of markers. We found our method works better than the commonly used standard method especially when there are a number of heterozygous markers. Our results imply that in most cases, QTL mapping does not necessarily require RILs at F6 or F7 generations; rather, F4 (or even F3) populations would be almost as useful as F6 or F7 populations. Because the cost to establish a number of RILs for many generations is enormous, this finding will cause a reduction in the cost of QTL mapping, thereby accelerating gene mapping in many species.

Small- and large-effect quantitative trait locus interactions underlie variation in yeast sporulation efficiency

Quantitative trait loci (QTL) with small effects on phenotypic variation can be difficult to detect and analyze. Because of this a large fraction of the genetic architecture of many complex traits is not well understood. Here we use sporulation efficiency in Saccharomyces cerevisiae as a model complex trait to identify and study small-effect QTL. In crosses where the large-effect quantitative trait nucleotides (QTN) have been genetically fixed we identify small-effect QTL that explain approximately half of the remaining variation not explained by the major effects. We find that small-effect QTL are often physically linked to large-effect QTL and that there are extensive genetic interactions between small- and large-effect QTL. A more complete understanding of quantitative traits will require a better understanding of the numbers, effect sizes, and genetic interactions of small-effect QTL.

Disentangling prenatal and postnatal maternal genetic effects reveals persistent prenatal effects on offspring growth in mice

Mothers are often the most important determinant of traits expressed by their offspring. These "maternal effects" (MEs) are especially crucial in early development, but can also persist into adulthood. They have been shown to play a role in a diversity of evolutionary and ecological processes, especially when genetically based. Although the importance of MEs is becoming widely appreciated, we know little about their underlying genetic basis. We address the dearth of genetic data by providing a simple approach, using combined genotype information from parents and offspring, to identify "maternal genetic effects" (MGEs) contributing to natural variation in complex traits. Combined with experimental cross-fostering, our approach also allows for the separation of pre- and postnatal MGEs, providing rare insights into prenatal effects. Applying this approach to an experimental mouse population, we identified 13 ME loci affecting body weight, most of which (12/13) exhibited prenatal effects, and nearly half (6/13) exhibiting postnatal effects. MGEs contributed more to variation in body weight than the direct effects of the offsprings' own genotypes until mice reached adulthood, but continued to represent a major component of variation through adulthood. Prenatal effects always contributed more variation than postnatal effects, especially for those effects that persisted into adulthood. These results suggest that MGEs may be an important component of genetic architecture that is generally overlooked in studies focused on direct mapping from genotype to phenotype. Our approach can be used in both experimental and natural populations, providing a widely practicable means of expanding our understanding of MGEs.

An investigation of the power for separating closely linked QTL in experimental populations

SummaryHu & Xu (2008) developed a statistical method for computing the statistical power for detecting a quantitative trait locus (QTL) located in a marker interval. Their method is based on the regression interval mapping method and allows experimenters to effectively investigate the power for detecting a QTL in a population. This paper continues to work on the power analysis of separating multiple-linked QTLs. We propose simple formulae to calculate the power of separating closely linked QTLs located in marker intervals. The proposed formulae are simple functions of information numbers, variance inflation factors and genetic parameters of a statistical model in a population. Both regression and maximum likelihood interval mappings suitable for detecting QTL in the marker intervals are considered. In addition, the issue of separating linked QTLs in the progeny populations from an F2 subject to further self and/or random mating is also touched upon. One of the primary keys to our approach is to derive the genotypic distributions of three and four loci for evaluating the correlation structures between pairwise unobservable QTLs in the model across populations. The proposed formulae allow us to predict the power of separation when several factors, such as sample sizes, sizes and directions of QTL effects, distances between QTLs, interval sizes and relative QTL positions in the intervals, are considered together at a time in different experimental populations. Numerical justifications and Monte Carlo simulations were provided for confirmation and illustration.

Genetic mapping of quantitative trait loci for milk production in sheep

A backcross pedigree using dairy East Friesian rams and non-dairy Dorset ewes was established specifically to map quantitative trait loci (QTL) affecting milk production in sheep. Ninety nine microsatellite markers of an initial set of 120 were successfully genotyped and informative on 188 animals of this backcross pedigree. Test-day milk records on individual ewes were used to estimate several milk yield related traits, including peak milk yield and cumulative milk yield to 50 (MY50), 100 (MY100) and 250 days (MY250). These traits, as well as estimated breeding value of backcross ewes extracted from the genetic evaluation file of the entire flock, were used in interval mapping. Ovine chromosomes 2, 12, 18, 20 and 24 were identified to harbour putative QTL for different measures of milk production. The QTL on Ovis aries chromosomes (OAR) 2 and 20 mapped to locations where similar trait QTL have already been mapped in other studies, whereas QTL on OAR 12, 18 and 24 were unique to our backcross pedigree and have not been reported previously. In addition, all identified QTL regions were syntenic with bovine chromosomal segments revealed to harbour QTL affecting milk production traits, providing supporting evidence for the QTL identified here.

Combining two Meishan F2 crosses improves the detection of QTL on pig chromosomes 2, 4 and 6

Background

In pig, a number of experiments have been set up to identify QTL and a multitude of chromosomal regions harbouring genes influencing traits of interest have been identified. However, the mapping resolution remains limited in most cases and the detected QTL are rather inaccurately located. Mapping accuracy can be improved by increasing the number of phenotyped and genotyped individuals and/or the number of informative markers. An alternative approach to overcome the limited power of individual studies is to combine data from two or more independent designs.

Methods

In the present study we report a combined analysis of two independent design (a French and a Dutch F2 experimental designs), with 2000 F2 individuals. The purpose was to further map QTL for growth and fatness on pig chromosomes 2, 4 and 6. Using QTL-map software, uni- and multiple-QTL detection analyses were applied separately on the two pedigrees and then on the combination of the two pedigrees.

Results

Joint analyses of the combined pedigree provided (1) greater significance of shared QTL, (2) exclusion of false suggestive QTL and (3) greater mapping precision for shared QTL.

Conclusions

Combining two Meishan x European breeds F2 pedigrees improved the mapping of QTL compared to analysing pedigrees separately. Our work was facilitated by the access to raw phenotypic data and DNA of animals from both pedigrees and the combination of the two designs with the addition of new markers allowed us to fine map QTL without phenotyping additional animals.

Clock genes localize to quantitative trait loci for stage-specific growth in juvenile coho salmon, Oncorhynchus kisutch

In most organisms, an internal circadian clock coordinates the expression of biological rhythms and enables individuals to anticipate and respond to the seasonally changing environment. There is remarkable conservation of function in the molecular machinery underlying this circadian clock across taxa with 4 canonical proteins interacting to form an autoregulatory feedback loop: CLOCK, CRYPTOCHROME, PERIOD, and BMAL. We mapped duplicated copies of Clock and Cryptochrome in coho salmon (Oncorhynchus kisutch) to determine if these genes localize to quantitative trait loci (QTL) for hatch timing, weight, length, and growth rate measured throughout the juvenile life-history stage. We found that Cryptochrome2b mapped to a QTL region for growth (measured at 304 days post-hatching) on linkage group OKI06. The percentage of variation (PEV) explained by this QTL was 15.2%. Cryptochrome2b was also associated with a marginally nonsignificant QTL for length (measured at 395 days post-hatching). OtsClock1b mapped to a QTL region for growth rate (PEV 10.1%) and length (PEV 10.5%) on linkage group OKI24 (measured at 479 days posthatching). Neither gene localized to QTL for hatch timing or weight. Our findings indicate that the growth rate and length QTL associated with OtsClock1b and Cryptochrome2b are development stage-specific and may result from temporally differentiated gene expression patterns.

Quantitative trait locus analysis of hatch timing, weight, length and growth rate in coho salmon, Oncorhynchus kisutch

In quantitative genetics, multivariate statistical approaches are increasingly used to describe genetic correlations in natural populations, yet the exact genetic relationship between phenotype and genotype is often unknown. Quantitative trait locus (QTL) analyses can be used to describe the molecular basis of this relationship. In salmonids, growth and development are important fitness traits that are phenotypically correlated with each other and with other life-history traits, and an understanding of the molecular basis of these relationships is valuable for future evolutionary studies. Here, a QTL analysis using an outbred cross was initiated to determine the molecular basis of phenotypic correlations between such growth traits in coho salmon (Oncorhynchus kisutch), an important fish species distributed throughout the North Pacific Ocean. Fifty-three QTL for growth rate, length and weight at eight time periods were located on seven linkage groups (OKI03, OKI06, OKI18, OKI19, OKI23, OKI24 and an unnamed linkage group) or associated with five unlinked markers (Omm1159, Omm1367/i, Omy325UoG, OmyRGT55TUF and OtsG422UCD). One QTL for hatch timing was associated with the marker, Omm1241. All QTL were of minor effect, explaining no more than 20% of the observed variation in phenotypic value. Several instances of colocalization of QTL weight, length and growth rate were observed, suggesting a genetic basis for phenotypic correlations observed between these traits. This study lays the foundation for future QTL mapping efforts, for detailed examinations of the genetic basis of phenotypic correlations between growth traits, and for exploring the adaptive significance of growth traits in natural populations.

Mapping quantitative trait loci (QTL) in sheep. II. Meta-assembly and identification of novel QTL for milk production traits in sheep

An (Awassi × Merino) × Merino backcross family of 172 ewes was used to map quantitative trait loci (QTL) for different milk production traits on a framework map of 200 loci across all autosomes. From five previously proposed mathematical models describing lactation curves, the Wood model was considered the most appropriate due to its simplicity and its ability to determine ovine lactation curve characteristics. Derived milk traits for milk, fat, protein and lactose yield, as well as percentage composition and somatic cell score were used for single and two-QTL approaches using maximum likelihood estimation and regression analysis. A total of 15 significant (P < 0.01) and additional 25 suggestive (P < 0.05) QTL were detected across both single QTL methods and all traits. In preparation of a meta-analysis, all QTL results were compared with a meta-assembly of QTL for milk production traits in dairy ewes from various public domain sources and can be found on the ReproGen ovine gbrowser http://crcidp.vetsci.usyd.edu.au/cgi-bin/gbrowse/oaries_genome/. Many of the QTL for milk production traits have been reported on chromosomes 1, 3, 6, 16 and 20. Those on chromosomes 3 and 20 are in strong agreement with the results reported here. In addition, novel QTL were found on chromosomes 7, 8, 9, 14, 22 and 24. In a cross-species comparison, we extended the meta-assembly by comparing QTL regions of sheep and cattle, which provided strong evidence for synteny conservation of QTL regions for milk, fat, protein and somatic cell score data between cattle and sheep.