High-throughput sequence identification of gene coding variants within alcohol-related QTLs

Low initial response to alcohol has been shown to be among the best predictors of development of alcoholism. A similar phenotypic measure, difference in initial sensitivity to ethanol, has been used for the genetic selection of two mouse strains, the Inbred Long-Sleep (ILS) and Inbred Short-Sleep (ISS) mice, and for the subsequent identification of four quantitative trait loci (QTLs) for alcohol sensitivity. We now report the application of high throughput comparative gene sequencing in the search for genes underlying these four QTLs. To carry out this search, over 1.7 million bases of comparative DNA sequence were generated from 68 candidate genes within the QTL intervals, corresponding to a survey of over 36,000 amino acids. Eight central nervous system genes, located within these QTLs, were identified that contain a total of 36 changes in protein coding sequence. Some of these coding variants are likely to contribute to the phenotypic variation between ILS/ISS animals, including sensitivity to alcohol, providing specific new genetic targets potentially important to the neuronal actions of alcohol.     

Genome characterization of the selected long- and short-sleep mouse lines

The Inbred Long- and Short-Sleep (ILS, ISS) mouse lines were selected for differences in acute ethanol sensitivity using the loss of righting response (LORR) as the selection trait. The lines show an over tenfold difference in LORR and, along with a recombinant inbred panel derived from them (the LXS), have been widely used to dissect the genetic underpinnings of acute ethanol sensitivity. Here we have sequenced the genomes of the ILS and ISS to investigate the DNA variants that contribute to their sensitivity difference. We identified ~2.7 million high-confidence SNPs and small indels and ~7000 structural variants between the lines; variants were found to occur in 6382 annotated genes. Using a hidden Markov model, we were able to reconstruct the genome-wide ancestry patterns of the eight inbred progenitor strains from which the ILS and ISS were derived, and found that quantitative trait loci that have been mapped for LORR were slightly enriched for DNA variants. Finally, by mapping and quantifying RNA-seq reads from the ILS and ISS to their strain-specific genomes rather than to the reference genome, we found a substantial improvement in a differential expression analysis between the lines. This work will help in identifying and characterizing the DNA sequence variants that contribute to the difference in ethanol sensitivity between the ILS and ISS and will also aid in accurate quantification of RNA-seq data generated from the LXS RIs.     

Exome sequencing and arrayCGH detection of gene sequence and copy number variation between ILS and ISS mouse strains

It has been well documented that genetic factors can influence predisposition to develop alcoholism. While the underlying genomic changes may be of several types, two of the most common and disease associated are copy number variations (CNVs) and sequence alterations of protein coding regions. The goal of this study was to identify CNVs and single-nucleotide polymorphisms that occur in gene coding regions that may play a role in influencing the risk of an individual developing alcoholism. Toward this end, two mouse strains were used that have been selectively bred based on their differential sensitivity to alcohol: the Inbred long sleep (ILS) and Inbred short sleep (ISS) mouse strains. Differences in initial response to alcohol have been linked to risk for alcoholism, and the ILS/ISS strains are used to investigate the genetics of initial sensitivity to alcohol. Array comparative genomic hybridization (arrayCGH) and exome sequencing were conducted to identify CNVs and gene coding sequence differences, respectively, between ILS and ISS mice. Mouse arrayCGH was performed using catalog Agilent 1 × 244 k mouse arrays. Subsequently, exome sequencing was carried out using an Illumina HiSeq 2000 instrument. ArrayCGH detected 74 CNVs that were strain-specific (38 ILS/36 ISS), including several ISS-specific deletions that contained genes implicated in brain function and neurotransmitter release. Among several interesting coding variations detected by exome sequencing was the gain of a premature stop codon in the alpha-amylase 2B (AMY2B) gene specifically in the ILS strain. In total, exome sequencing detected 2,597 and 1,768 strain-specific exonic gene variants in the ILS and ISS mice, respectively. This study represents the most comprehensive and detailed genomic comparison of ILS and ISS mouse strains to date. The two complementary genome-wide approaches identified strain-specific CNVs and gene coding sequence variations that should provide strong candidates to contribute to the alcohol-related phenotypic differences associated with these strains.     

Mapping quantitative trait loci mediating sensitivity to etomidate

Long- and Short-Sleep (LS and SS) mice were selectively bred for differences in ethanol-induced loss of the righting reflex (LORR) and have been found to differ in LORR induced by various anesthetic agents. We used a two-stage mapping strategy to identify quantitative trait loci (QTLs) affecting duration of LORR caused by the general anesthetic etomidate and brain levels of etomidate (BEL) following regain of the righting reflex. Analysis of recombinant-inbred strains derived from a cross between LS and SS mice (LSXSS) yielded a heritability estimate of 0.23 for etomidate-induced LORR and identified one marker that showed suggestive linkage for a QTL, on mouse Chromosome (chr) 12. Mapping in an F(2) population derived from a cross between inbred LS and SS (ILS and ISS) revealed a significant QTL for etomidate-induced LORR on Chr 12, and two significant QTLs mediating BEL on Chrs 6 and 12. Several QTLs showing suggestive linkage for etomidate-induced LORR and BEL were also identified in the F(2) population. Brain levels of etomidate in the RI and F(2) mice suggested that differences in LORR were due to differential central nervous system sensitivity, rather than differential etomidate metabolism. Interestingly, the region on Chr 7 has also been identified as a region influencing ethanol-induced LORR, suggesting the possibility of a common genetic mechanism mediating etomidate and ethanol sensitivity. These QTL regions need to be further narrowed before the testing of candidate genes is feasible.     

Development of congenics for hypnotic sensitivity to ethanol by QTL-marker-assisted counter selection

Mouse strains congenic for individual quantitative trait loci (QTLs) conferring hypnotic sensitivity to ethanol were constructed by backcrossing genotypically selected ILS × ISS N₂ individuals to either inbred Long Sleep (ILS) or inbred Short Sleep (ISS) mice. We used a novel ``speed congenic' approach in which N<sub>2</sub> mice were genotyped for markers flanking each of the five originally identified QTLs. Genotypic selection for ISS regions at four of the five QTLs, and for ILS/ISS at the fifth QTL, allowed rapid fixation of the genetic background. We call this strategy ``QTL-Marker-Assisted Counter Selection' or QMACS. By the N₄ generation, phenotypic assessments showed that in some sublines the QTL had not been captured; these sublines were discarded and positive lines split to create new replicate sublines. One QTL, on Chromosome (Chr) 8, was not confirmed. At the N₈, virtually all sublines on the remaining QTLs retained the phenotypic difference between heterozygotes and ISS homozygotes. Small numbers of interim congenics were produced at the N₆ and later generations in which the ILS QTL was made homozygous on the ISS background; as expected, these congenic mice showed an increased sleep time. For later backcrosses (after the N₄), the parents were selected on the basis of phenotype as well as genotype. The parent-offspring correlation over all QTLs was significant, supporting the use of phenotypic selection in congenic construction. Received: 15 September 1998 / Accepted: 8 October 1998     

Quantitative trait loci for sensitivity to ethanol intoxication in a C57BL/6J?×?129S1/SvImJ inbred mouse cross

Individual variation in sensitivity to acute ethanol (EtOH) challenge is associated with alcohol drinking and is a predictor of alcohol abuse. Previous studies have shown that the C57BL/6J (B6) and 129S1/SvImJ (S1) inbred mouse strains differ in responses on certain measures of acute EtOH intoxication. To gain insight into genetic factors contributing to these differences, we performed quantitative trait locus (QTL) analysis of measures of EtOH-induced ataxia (accelerating rotarod), hypothermia, and loss of righting reflex (LORR) duration in a B6×S1 F2 population. We confirmed that S1 showed greater EtOH-induced hypothermia (specifically at a high dose) and longer LORR compared to B6. QTL analysis revealed several additive and interacting loci for various phenotypes, as well as examples of genotype interactions with sex. QTLs for different EtOH phenotypes were largely non-overlapping, suggesting separable genetic influences on these behaviors. The most compelling main-effect QTLs were for hypothermia on chromosome 16 and for LORR on chromosomes 4 and 6. Several QTLs overlapped with loci repeatedly linked to EtOH drinking in previous mouse studies. The architecture of the traits we examined was complex but clearly amenable to dissection in future studies. Using integrative genomics strategies, plausible functional and positional candidates may be found. Uncovering candidate genes associated with variation in these phenotypes in this population could ultimately shed light on genetic factors underlying sensitivity to EtOH intoxication and risk for alcoholism in humans.     

Genetic dissection of quantitative trait locus for ethanol sensitivity in long- and short-sleep mice

Interval-specific congenic strains (ISCS) allow fine mapping of a quantitative trait locus (QTL), narrowing its confidence interval by an order of magnitude or more. In earlier work, we mapped four QTL specifying differential ethanol sensitivity, assessed by loss of righting reflex because of ethanol (LORE), in the inbred long-sleep (ILS) and inbred short-sleep (ISS) strains, accounting for approximately 50% of the genetic variance for this trait. Subsequently, we generated reciprocal congenic strains in which each full QTL interval from ILS was bred onto the ISS background and vice versa. An earlier paper reported construction and results of the ISCS on the ISS background; here, we describe this process and report results on the ILS background. We developed multiple ISCS for each Lore QTL in which the QTL interval was broken into a number of smaller intervals. For each of the four QTL regions (chromosomes 1, 2, 11 and 15), we were successful in reducing the intervals significantly. Multiple, positive strains were overlapped to generate a single, reduced interval. Subsequently, this reduced region was overlaid on previous reductions from the ISS background congenics, resulting in substantial reductions in all QTL regions by approximately 75% from the initial mapping study. Genes with sequence or expression polymorphisms in the reduced intervals are potential candidates; evidence for these is presented. Genetic background effects can be important in detection of single QTL; combining this information with the generation of congenics on both backgrounds, as described here, is a powerful approach for fine mapping QTL.     

The Human Obesity Gene Map: The 2003 Update

This is the tenth update of the human obesity gene map, incorporating published results up to the end of October 2003 and continuing the previous format. Evidence from single‐gene mutation obesity cases, Mendelian disorders exhibiting obesity as a clinical feature, quantitative trait loci (QTLs) from human genome‐wide scans and animal crossbreeding experiments, and association and linkage studies with candidate genes and other markers is reviewed. Transgenic and knockout murine models relevant to obesity are also incorporated (N = 55). As of October 2003, 41 Mendelian syndromes relevant to human obesity have been mapped to a genomic region, and causal genes or strong candidates have been identified for most of these syndromes. QTLs reported from animal models currently number 183. There are 208 human QTLs for obesity phenotypes from genome‐wide scans and candidate regions in targeted studies. A total of 35 genomic regions harbor QTLs replicated among two to five studies. Attempts to relate DNA sequence variation in specific genes to obesity phenotypes continue to grow, with 272 studies reporting positive associations with 90 candidate genes. Fifteen such candidate genes are supported by at least five positive studies. The obesity gene map shows putative loci on all chromosomes except Y. Overall, more than 430 genes, markers, and chromosomal regions have been associated or linked with human obesity phenotypes. The electronic version of the map with links to useful sites can be found at http:obesitygene.pbrc.edu .     

The Human Obesity Gene Map: The 2002 Update

This is the ninth update of the human obesity gene map, incorporating published results through October 2002 and continuing the previous format. Evidence from single‐gene mutation obesity cases, Mendelian disorders exhibiting obesity as a clinical feature, quantitative trait loci (QTLs) from human genome‐wide scans and various animal crossbreeding experiments, and association and linkage studies with candidate genes and other markers is reviewed. For the first time, transgenic and knockout murine models exhibiting obesity as a phenotype are incorporated (N = 38). As of October 2002, 33 Mendelian syndromes relevant to human obesity have been mapped to a genomic region, and the causal genes or strong candidates have been identified for 23 of these syndromes. QTLs reported from animal models currently number 168; there are 68 human QTLs for obesity phenotypes from genome‐wide scans. Additionally, significant linkage peaks with candidate genes have been identified in targeted studies. Seven genomic regions harbor QTLs replicated among two to five studies. Attempts to relate DNA sequence variation in specific genes to obesity phenotypes continue to grow, with 222 studies reporting positive associations with 71 candidate genes. Fifteen such candidate genes are supported by at least five positive studies. The obesity gene map shows putative loci on all chromosomes except Y. More than 300 genes, markers, and chromosomal regions have been associated or linked with human obesity phenotypes. The electronic version of the map with links to useful sites can be found at http:obesitygene.pbrc.edu .     

Looking into flowering time in almond (Prunus dulcis (Mill) D. A. Webb): the candidate gene approach

Blooming time is one of the most important agronomic traits in almond. Biochemical and molecular events underlying flowering regulation must be understood before methods to stimulate late flowering can be developed. Attempts to elucidate the genetic control of this process have led to the identification of a major gene (Lb) and quantitative trait loci (QTLs) linked to observed phenotypic differences, but although this gene and these QTLs have been placed on the Prunus reference genetic map, their sequences and specific functions remain unknown. The aim of our investigation was to associate these loci with known genes using a candidate gene approach. Two almond cDNAs and eight Prunus expressed sequence tags were selected as candidate genes (CGs) since their sequences were highly identical to those of flowering regulatory genes characterized in other species. The CGs were amplified from both parental lines of the mapping population using specific primers. Sequence comparison revealed DNA polymorphisms between the parental lines, mainly of the single nucleotide type. Polymorphisms were used to develop co-dominant cleaved amplified polymorphic sequence markers or length polymorphisms based on insertion/deletion events for mapping the candidate genes on the Prunus reference map. Ten candidate genes were assigned to six linkage groups in the Prunus genome. The positions of two of these were compatible with the regions where two QTLs for blooming time were detected. One additional candidate was localized close to the position of the Evergrowing gene, which determines a non-deciduous behaviour in peach.     

The Human Obesity Gene Map: The 2004 Update

This paper presents the eleventh update of the human obesity gene map, which incorporates published results up to the end of October 2004. Evidence from single‐gene mutation obesity cases, Mendelian disorders exhibiting obesity as a clinical feature, transgenic and knockout murine models relevant to obesity, quantitative trait loci (QTLs) from animal cross‐breeding experiments, association studies with candidate genes, and linkages from genome scans is reviewed. As of October 2004, 173 human obesity cases due to single‐gene mutations in 10 different genes have been reported, and 49 loci related to Mendelian syndromes relevant to human obesity have been mapped to a genomic region, and causal genes or strong candidates have been identified for most of these syndromes. There are 166 genes which, when mutated or expressed as transgenes in the mouse, result in phenotypes that affect body weight and adiposity. The number of QTLs reported from animal models currently reaches 221. The number of human obesity QTLs derived from genome scans continues to grow, and we have now 204 QTLs for obesity‐related phenotypes from 50 genome‐wide scans. A total of 38 genomic regions harbor QTLs replicated among two to four studies. The number of studies reporting associations between DNA sequence variation in specific genes and obesity phenotypes has also increased considerably with 358 findings of positive associations with 113 candidate genes. Among them, 18 genes are supported by at least five positive studies. The obesity gene map shows putative loci on all chromosomes except Y. Overall, >600 genes, markers, and chromosomal regions have been associated or linked with human obesity phenotypes. The electronic version of the map with links to useful publications and genomic and other relevant sites can be found at http:obesitygene.pbrc.edu .     

Variation in Galr1 expression determines susceptibility to exocitotoxin-induced cell death in mice

Inbred strains of mice differ in their susceptibility to excitotoxin-induced cell death, but the genetic basis of individual variation in differential susceptibility is unknown. Previously, we identified a highly significant quantitative trait locus (QTL) on chromosome 18 that influenced susceptibility to kainic acid-induced cell death ( Sicd1 ). Comparison of susceptibility to seizure-induced cell death between reciprocal congenic lines for Sicd1 and parental background mice indicates that genes influencing this trait were captured in both strains. Two positional gene candidates, Galr1 and Mbp , map to 55 cM, where the Sicd1 QTL had been previously mapped. Thus, this study was undertaken to determine if Galr1 and/or Mbp could be considered as candidate genes. Genomic sequence comparison of these two functional candidate genes from the C57BL/6J (resistant at Sicd1 ) and the FVB/NJ (susceptible at Sicd1 ) strains showed no single-nucleotide polymorphisms. However, expression studies confirmed that Galr1 shows significant differential expression in the congenic and parental inbred strains. Galr1 expression was downregulated in the hippocampus of C57BL/6J mice and FVB.B6- Sicd1 congenic mice when compared with FVB/NJ or B6.FVB- Sicd1 congenic mice. A survey of Galr1 expression among other inbred strains showed a significant effect such that 'susceptible' strains showed a reduction in Galr1 expression as compared with 'resistant' strains. In contrast, no differences in Mbp expression were observed. In summary, these results suggest that differential expression of Galr1 may contribute to the differences in susceptibility to seizure-induced cell death between cell death-resistant and cell death-susceptible strains.     

The human obesity gene map: the 2005 update

This paper presents the 12th update of the human obesity gene map, which incorporates published results up to the end of October 2005. Evidence from single-gene mutation obesity cases, Mendelian disorders exhibiting obesity as a clinical feature, transgenic and knockout murine models relevant to obesity, quantitative trait loci (QTL) from animal cross-breeding experiments, association studies with candidate genes, and linkages from genome scans is reviewed. As of October 2005, 176 human obesity cases due to single-gene mutations in 11 different genes have been reported, 50 loci related to Mendelian syndromes relevant to human obesity have been mapped to a genomic region, and causal genes or strong candidates have been identified for most of these syndromes. There are 244 genes that, when mutated or expressed as transgenes in the mouse, result in phenotypes that affect body weight and adiposity. The number of QTLs reported from animal models currently reaches 408. The number of human obesity QTLs derived from genome scans continues to grow, and we now have 253 QTLs for obesity-related phenotypes from 61 genome-wide scans. A total of 52 genomic regions harbor QTLs supported by two or more studies. The number of studies reporting associations between DNA sequence variation in specific genes and obesity phenotypes has also increased considerably, with 426 findings of positive associations with 127 candidate genes. A promising observation is that 22 genes are each supported by at least five positive studies. The obesity gene map shows putative loci on all chromosomes except Y. The electronic version of the map with links to useful publications and relevant sites can be found at http://obesitygene.pbrc.edu.     

Ranking candidate genes in rat models of type 2 diabetes

Background  

Rat models are frequently used to find genomic regions that contribute to complex diseases, so called quantitative trait loci (QTLs). In general, the genomic regions found to be associated with a quantitative trait are rather large, covering hundreds of genes. To help selecting appropriate candidate genes from QTLs associated with type 2 diabetes models in rat, we have developed a web tool called Candidate Gene Capture (CGC), specifically adopted for this disorder.     

Differences in DBA/1J and DBA/2J reveal lipid QTL genes

Recent advances in mouse genomics have revealed considerable variation in the form of single-nucleotide polymorphisms (SNPs) among common inbred strains. This has made it possible to characterize closely related strains and to identify genes that differ; such genes may be causal for quantitative phenotypes. The mouse strains DBA/1J and DBA/2J differ by just 5.6% at the SNP level. These strains exhibit differences in a number of metabolic and lipid phenotypes, such as plasma levels of triglycerides (TGs) and HDL. A cross between these strains revealed multiple quantitative trait loci (QTLs) in 294 progeny. We identified significant TG QTLs on chromosomes (Chrs) 1, 2, 3, 4, 8, 9, 10, 11, 12, 13, 14, 16, and 19, and significant HDL QTLs on Chrs 3, 9, and 16. Some QTLs mapped to chromosomes with limited variability between the two strains, thus facilitating the identification of candidate genes. We suggest that Tshr is the QTL gene for Chr 12 TG and HDL levels and that Ihh may account for the TG QTL on Chr 1. This cross highlights the advantage of crossing closely related strains for subsequent identification of QTL genes.     

Mapping quantitative trait loci using binned genotypes

Precise mapping of quantitative trait loci(QTLs)is critical for assessing genetic effects and identifying candidate genes for quantitative traits.Interval and composite interval mappings have been the methods of choice for several decades,which have provided tools for identifying genomic regions harboring causal genes for quantitative traits.Historically,the concept was developed on the basis of sparse marker maps where genotypes of loci within intervals could not be observed.Currently,genomes of many organisms have been saturated with markers due to the new sequencing technologies.Genotyping by sequencing usually generates hundreds of thousands of single nucleotide polymorphisms(SNPs),which often include the causal polymorphisms.The concept of interval no longer exists,prompting the necessity of a norm change in QTL mapping technology to make use of the high-volume genomic data.Here we developed a statistical method and a software package to map QTLs by binning markers into haplotype blocks,called bins.The new method detects associations of bins with quantitative traits.It borrows the mixed model methodology with a polygenic control from genome-wide association studies(GWAS)and can handle all kinds of experimental populations under the linear mixed model(LMM)framework.We tested the method using both simulated data and data from populations of rice.The results showed that this method has higher power than the current methods.An R package named binQTL is available from GitHub.     

Mapping of quantitative trait loci (QTLs) for oil yield using SSRs and gene-based markers in African oil palm (<Emphasis Type="Italic">Elaeis guineensis</Emphasis> Jacq.)

The identification of quantitative trait loci (QTLs) based on anchor markers, especially candidate genes that control a trait of interest, has been noted to increase the power of QTL detection. Since these markers can be scored as co-dominant data, they are also valuable for comparing and integrating the QTL linkage maps from diverse mapping populations. To estimate the position and effects of QTLs linked to oil yield traits in African oil palm, co-dominant microsatellites (SSR) and candidate gene-based sequence polymorphisms were applied to construct a linkage map for a progeny showing large differences in oil yield components. The progeny was genotyped for 97 SSR markers, 93 gene-linked markers, and 12 non-gene-linked SNP markers. From these, 190 segregating loci could be arranged into 31 linkage groups while 12 markers remained unmapped. Using the single marker linkage, interval mapping and multiple QTL methods, 16 putative QTLs on seven linkage groups affecting important oil yield related traits such as fresh fruit bunch yield (FFB), ratio of oil per fruit (OF), oil per bunch (OB), fruit per bunch (FB) and wet mesocarp per fruit (WMF) could be identified in the segregating population with estimated values for explained variance ranging from 12.4 % to 54.5 %. Markers designed from some candidate genes involved in lipid biosynthesis were found to be mapped near significant QTLs for various economic yield traits. Associations between QTLs and potential candidate genes are discussed.