Sex-specific quantitative trait loci affecting longevity in Drosophila melanogaster

Senescence, the decline in survivorship and fertility with increasing age, is a near-universal property of organisms. Senescence and limited lifespan are thought to arise because weak natural selection late in life allows the accumulation of mutations with deleterious late-age effects that are either neutral (the mutation accumulation hypothesis) or beneficial (the antagonistic pleiotropy hypothesis) early in life. Analyses of Drosophila spontaneous mutations, patterns of segregating variation and covariation, and lines selected for late-age fertility have implicated both classes of mutation in the evolution of aging, but neither their relative contributions nor the properties of individual loci that cause aging in nature are known. To begin to dissect the multiple genetic causes of quantitative variation in lifespan, we have conducted a genome-wide screen for quantitative trait loci (QTLs) affecting lifespan that segregate among a panel of recombinant inbred lines using a dense molecular marker map. Five autosomal QTLs were mapped by composite interval mapping and by sequential multiple marker analysis. The QTLs had large sex-specific effects on lifespan and age-specific effects on survivorship and mortality and mapped to the same regions as candidate genes with fertility, cellular aging, stress resistance and male-specific effects. Late age-of-onset QTL effects are consistent with the mutation accumulation hypothesis for the evolution of senescence, and sex-specific QTL effects suggest a novel mechanism for maintaining genetic variation for lifespan.

Identification of multiple quantitative trait loci linked to prion disease incubation period in mice

Polymorphisms in the prion protein gene are known to affect prion disease incubation times and susceptibility in humans and mice. However, studies with inbred lines of mice show that large differences in incubation times occur even with the same amino acid sequence of the prion protein, suggesting that other genes may contribute to the observed variation. To identify these loci we analyzed 1,009 animals from an F2 intercross between two strains of mice, CAST/Ei and NZW/OlaHSd, with significantly different incubation periods when challenged with RML scrapie prions. Interval mapping identified three highly significantly linked regions on chromosomes 2, 11, and 12; composite interval mapping suggests that each of these regions includes multiple linked quantitative trait loci. Suggestive evidence for linkage was obtained on chromosomes 6 and 7. The sequence conservation between the mouse and human genome suggests that identification of mouse prion susceptibility alleles may have direct relevance to understanding human susceptibility to bovine spongiform encephalopathy (BSE) infection, as well as identifying key factors in the molecular pathways of prion pathogenesis. However, the demonstration of other major genetic effects on incubation period suggests the need for extreme caution in interpreting estimates of variant Creutzfeldt–Jakob disease epidemic size utilizing existing epidemiological models.

Quantitative trait loci and metabolic pathways

The interpretation of quantitative trait locus (QTL) studies is limited by the lack of information on metabolic pathways leading to most economic traits. Inferences about the roles of the underlying genes with a pathway or the nature of their interaction with other loci are generally not possible. An exception is resistance to the corn earworm Helicoverpa zea (Boddie) in maize (Zea mays L.) because of maysin, a C-glycosyl flavone synthesized in silks via a branch of the well characterized flavonoid pathway. Our results using flavone synthesis as a model QTL system indicate: (i) the importance of regulatory loci as QTLs, (ii) the importance of interconnecting biochemical pathways on product levels, (iii) evidence for “channeling” of intermediates, allowing independent synthesis of related compounds, (iv) the utility of QTL analysis in clarifying the role of specific genes in a biochemical pathway, and (v) identification of a previously unknown locus on chromosome 9S affecting flavone level. A greater understanding of the genetic basis of maysin synthesis and associated corn earworm resistance should lead to improved breeding strategies. More broadly, the insights gained in relating a defined genetic and biochemical pathway affecting a quantitative trait should enhance interpretation of the biological basis of variation for other quantitative traits.

Quantitative trait loci and metabolic pathways: genetic control of the concentration of maysin, a corn earworm resistance factor, in maize silks.

Interpretation of quantitative trait locus (QTL) studies of agronomic traits is limited by lack of knowledge of biochemical pathways leading to trait expression. To more fully elucidate the biological significance of detected QTL, we chose a trait that is the product of a well-characterized pathway, namely the concentration of maysin, a C-glycosyl flavone, in silks of maize, Zea mays L. Maysin is a host-plant resistance factor against the corn earworm, Helicoverpa zea (Boddie). We determined silk maysin concentrations and restriction fragment length polymorphism genotypes at flavonoid pathway loci or linked markers for 285 F2 plants derived from the cross of lines GT114 and GT119. Single-factor analysis of variance indicated that the p1 region on chromosome 1 accounted for 58.0% of the phenotypic variance and showed additive gene action. The p1 locus is a transcription activator for portions of the flavonoid pathway. A second QTL, represented by marker umc 105a near the brown pericarp1 locus on chromosome 9, accounted for 10.8% of the variance. Gene action of this region was dominant for low maysin, but was only expressed in the presence of a functional p1 allele. The model explaining the greatest proportion of phenotypic variance (75.9%) included p1, umc105a, umc166b (chromosome 1), r1 (chromosome 10), and two epistatic interaction terms, p1 x umc105a and p1 x r1. Our results provide evidence that regulatory loci have a central role and that there is a complex interplay among different branches of the flavonoid pathway in the expression of this trait.

Interactions between quantitative trait loci in soybean in which trait variation at one locus is conditional upon a specific allele at another.

A large recombinant inbred population of soybean has been characterized for 220 restriction fragment-length polymorphism (RFLP) markers. Values for agronomic traits also have been measured. Quantitative trait loci (QTL) for height, yield, and maturity were located by their linkage to RFLP markers. QTL controlling large amounts of trait variation were analyzed for the dependence of trait variation on particular alleles at a second locus by comparing cumulative distributions of the trait for each genotype (four genotypes per pair of loci). Interesting pairs of loci were analyzed statistically with maximum likelihood and Monte Carlo comparison of additive and epistatic models. For each locus affecting height, variation was conditional upon the presence of a particular allele at a second unlinked locus that itself explained little or no trait variation. The results show that interactions between QTL are frequent and control large effects. Interactions distinguished between different QTL in a single linkage group and between QTL that affect different traits closely linked to one RFLP marker--i.e., distinguished between pleiotropy and closely linked genes. The implications for the evolution of inbreeding plants and for the construction of agronomic breeding strategies are discussed.

Resistance gene analogues in sugarcane and sorghum and their association with quantitative trait loci for rust resistance

Fifty-four different sugarcane resistance gene analogue (RGA) sequences were isolated, characterized, and used to identify molecular markers linked to major disease-resistance loci in sugarcane. Ten RGAs were identified from a sugarcane stem expressed sequence tag (EST) library; the remaining 44 were isolated from sugarcane stem, leaf, and root tissue using primers designed to conserved RGA motifs. The map location of 31 of the RGAs was determined in sugarcane and compared with the location of quantitative trait loci (QTL) for brown rust resistance. After 2 years of phenotyping, 3 RGAs were shown to generate markers that were significantly associated with resistance to this disease. To assist in the understanding of the complex genetic structure of sugarcane, 17 of the 31 RGAs were also mapped in sorghum. Comparative mapping between sugarcane and sorghum revealed syntenic localization of several RGA clusters. The 3 brown rust associated RGAs were shown to map to the same linkage group (LG) in sorghum with 2 mapping to one region and the third to a region previously shown to contain a major rust-resistance QTL in sorghum. These results illustrate the value of using RGAs for the identification of markers linked to disease resistance loci and the value of simultaneous mapping in sugarcane and sorghum.

Analysis of expression quantitative trait loci in D. melanogaster

Expression Quantitative Trait Loci (eQTL) analysis allows for the identification of genetic variation associated with variation in gene expression. It is often unclear however, which of the associated variants are causal, and by what mechanism. Integrating functional genomic data with eQTL data can provide insight into the impact of natural variation in the population, and the nature of the transcriptional machinery itself. In this thesis, I integrate functional genomic data with eQTL data derived from both 5’ CAGE and 3’ TagXseq expression assays, in developing embryos. I first use both datasets to analyse the transcription landscape in embryonic D., melanogaster, and then carry out an analysis of sequence motifs associated with transcription factor binding sites, promoters, and 3’ polyadenylation sites. Finally, I integrate functional genomic data, including these novel sequence motifs, to shed light on the mechanisms of gene expression variation in D.,melanogaster. I am able to demonstrate that some variants effecting gene regulation in Drosophila are found within haplotypes which buffer their effects.

Gene expression variation and expression quantitative trait mapping of human chromosome 21 genes.

Inter-individual differences in gene expression are likely to account for an important fraction of phenotypic differences, including susceptibility to common disorders. Recent studies have shown extensive variation in gene expression levels in humans and other organisms, and that a fraction of this variation is under genetic control. We investigated the patterns of gene expression variation in a 25 Mb region of human chromosome 21, which has been associated with many Down syndrome (DS) phenotypes. Taqman real-time PCR was used to measure expression variation of 41 genes in lymphoblastoid cells of 40 unrelated individuals. For 25 genes found to be differentially expressed, additional analysis was performed in 10 CEPH families to determine heritabilities and map loci harboring regulatory variation. Seventy-six percent of the differentially expressed genes had significant heritabilities, and genomewide linkage analysis led to the identification of significant eQTLs for nine genes. Most eQTLs were in trans, with the best result (P=7.46 x 10(-8)) obtained for TMEM1 on chromosome 12q24.33. A cis-eQTL identified for CCT8 was validated by performing an association study in 60 individuals from the HapMap project. SNP rs965951 located within CCT8 was found to be significantly associated with its expression levels (P=2.5 x 10(-5)) confirming cis-regulatory variation. The results of our study provide a representative view of expression variation of chromosome 21 genes, identify loci involved in their regulation and suggest that genes, for which expression differences are significantly larger than 1.5-fold in control samples, are unlikely to be involved in DS-phenotypes present in all affected individuals.

FastEpistasis: a high performance computing solution for quantitative trait epistasis.

Motivation: Genome-wide association studies have become widely used tools to study effects of genetic variants on complex diseases. While it is of great interest to extend existing analysis methods by considering interaction effects between pairs of loci, the large number of possible tests presents a significant computational challenge. The number of computations is further multiplied in the study of gene expression quantitative trait mapping, in which tests are performed for thousands of gene phenotypes simultaneously. Results: We present FastEpistasis, an efficient parallel solution extending the PLINK epistasis module, designed to test for epistasis effects when analyzing continuous phenotypes. Our results show that the algorithm scales with the number of processors and offers a reduction in computation time when several phenotypes are analyzed simultaneously. FastEpistasis is capable of testing the association of a continuous trait with all single nucleotide polymorphism ( SNP) pairs from 500 000 SNPs, totaling 125 billion tests, in a population of 5000 individuals in 29, 4 or 0.5 days using 8, 64 or 512 processors.

A hyperactive quantitative trait locus allele of Arabidopsis BRX contributes to natural variation in root growth vigor.

Quantitative trait loci analysis of natural Arabidopsis thaliana accessions is increasingly exploited for gene isolation. However, to date this has mostly revealed deleterious mutations. Among them, a loss-of-function allele identified the root growth regulator BREVIS RADIX (BRX). Here we present evidence that BRX and the paralogous BRX-LIKE (BRXL) genes are under selective constraint in monocotyledons as well as dicotyledons. Unexpectedly, however, whereas none of the Arabidopsis orthologs except AtBRXL1 could complement brx null mutants when expressed constitutively, nearly all monocotyledon BRXLs tested could. Thus, BRXL proteins seem to be more diversified in dicotyledons than in monocotyledons. This functional diversification was correlated with accelerated rates of sequence divergence in the N-terminal regions. Population genetic analyses of 30 haplotypes are suggestive of an adaptive role of AtBRX and AtBRXL1. In two accessions, Lc-0 and Lov-5, seven amino acids are deleted in the variable region between the highly conserved C-terminal, so-called BRX domains. Genotyping of 42 additional accessions also found this deletion in Kz-1, Pu2-7, and Ws-0. In segregating recombinant inbred lines, the Lc-0 allele (AtBRX(Lc-0)) conferred significantly enhanced root growth. Moreover, when constitutively expressed in the same regulatory context, AtBRX(Lc-0) complemented brx mutants more efficiently than an allele without deletion. The same was observed for AtBRXL1, which compared with AtBRX carries a 13 amino acid deletion that encompasses the deletion found in AtBRX(Lc-0). Thus, the AtBRX(Lc-0) allele seems to contribute to natural variation in root growth vigor and provides a rare example of an experimentally confirmed, hyperactive allelic variant.

Mutations in the bovine ABCG2 and the ovine MSTN gene added to the few quantitative trait nucleotides identified in farm animals: a mini-review

The progress in molecular genetics in animal breeding is moderately effective as compared to traditional animal breeding using quantitative genetic approaches. There is an extensive disparity between the number of reported quantitative trait loci (QTLs) and their linked genetic variations in cattle, pig, and chicken. The identification of causative mutations affecting quantitative traits is still very challenging and hampered by the cloudy relationship between genotype and phenotype. There are relatively few reports in which a successful identification of a causative mutation for an animal production trait was demonstrated. The examples that have attracted considerable attention from the animal breeding community are briefly summarized and presented in a table. In this mini-review, the recent progress in mapping quantitative trait nucleotides (QTNs) are reviewed, including the ABCG2 gene mutation that underlies a QTL for fat and protein content and the ovine MSTN gene mutation that causes muscular hypertrophy in Texel sheep. It is concluded that the progress in molecular genetics might facilitate the elucidation of the genetic architecture of QTLs, so that also the high-hanging fruits can be harvested in order to contribute to efficient and sustainable animal production.

POOR PERFORMANCE OF BOOTSTRAP CONFIDENCE INTERVALS FOR THE LOCATION OF A QUANTITATIVE TRAIT LOUCS

The aim of many genetic studies is to locate the genomic regions (called quantitative trait loci, QTLs) that contribute to variation in a quantitative trait (such as body weight). Confidence intervals for the locations of QTLs are particularly important for the design of further experiments to identify the gene or genes responsible for the effect. Likelihood support intervals are the most widely used method to obtain confidence intervals for QTL location, but the non-parametric bootstrap has also been recommended. Through extensive computer simulation, we show that bootstrap confidence intervals are poorly behaved and so should not be used in this context. The profile likelihood (or LOD curve) for QTL location has a tendency to peak at genetic markers, and so the distribution of the maximum likelihood estimate (MLE) of QTL location has the unusual feature of point masses at genetic markers; this contributes to the poor behavior of the bootstrap. Likelihood support intervals and approximate Bayes credible intervals, on the other hand, are shown to behave appropriately.

Sequential Quantitative Trait Locus Mapping in Experimental Crosses

The etiology of complex diseases is heterogeneous. The presence of risk alleles in one or more genetic loci affects the function of a variety of intermediate biological pathways, resulting in the overt expression of disease. Hence, there is an increasing focus on identifying the genetic basis of disease by sytematically studying phenotypic traits pertaining to the underlying biological functions. In this paper we focus on identifying genetic loci linked to quantitative phenotypic traits in experimental crosses. Such genetic mapping methods often use a one stage design by genotyping all the markers of interest on the available subjects. A genome scan based on single locus or multi-locus models is used to identify the putative loci. Since the number of quantitative trait loci (QTLs) is very likely to be small relative to the number of markers genotyped, a one-stage selective genotyping approach is commonly used to reduce the genotyping burden, whereby markers are genotyped solely on individuals with extreme trait values. This approach is powerful in the presence of a single quantitative trait locus (QTL) but may result in substantial loss of information in the presence of multiple QTLs. Here we investigate the efficiency of sequential two stage designs to identify QTLs in experimental populations. Our investigations for backcross and F2 crosses suggest that genotyping all the markers on 60% of the subjects in Stage 1 and genotyping the chromosomes significant at 20% level using additional subjects in Stage 2 and testing using all the subjects provides an efficient approach to identify the QTLs and utilizes only 70% of the genotyping burden relative to a one stage design, regardless of the heritability and genotyping density. Complex traits are a consequence of multiple QTLs conferring main effects as well as epistatic interactions. We propose a two-stage analytic approach where a single-locus genome scan is conducted in Stage 1 to identify promising chromosomes, and interactions are examined using the loci on these chromosomes in Stage 2. We examine settings under which the two-stage analytic approach provides sufficient power to detect the putative QTLs.