Genome scan meta-analysis of schizophrenia and bipolar disorder,part I: Methods and power analysis

This is the first of three articles on a meta-analysis of genome scans of schizophrenia (SCZ) and bipolar disorder (BPD) that uses the rank-based genome scan meta-analysis (GSMA) method. Here we used simulation to determine the power of GSMA to detect linkage and to identify thresholds of significance. We simulated replicates resembling the SCZ data set (20 scans; 1,208 pedigrees) and two BPD data sets using very narrow (9 scans; 347 pedigrees) and narrow (14 scans; 512 pedigrees) diagnoses. Samples were approximated by sets of affected sibling pairs with incomplete parental data. Genotypes were simulated and nonparametric linkage (NPL) scores computed for 20 180-cM chromosomes, each containing six 30-cM bins, with three markers/bin (or two, for some scans). Genomes contained 0, 1, 5, or 10 linked loci, and we assumed relative risk to siblings (lambda(sibs)) values of 1.15, 1.2, 1.3, or 1.4. For each replicate, bins were ranked within-study by maximum NPL scores, and the ranks were averaged (R(avg)) across scans. Analyses were repeated with weighted ranks ((sqrt)N[genotyped cases] for each scan). Two P values were determined for each R(avg): P(AvgRnk) (the pointwise probability) and P(ord) (the probability, given the bin's place in the order of average ranks). GSMA detected linkage with power comparable to or greater than the underlying NPL scores. Weighting for sample size increased power. When no genomewide significant P values were observed, the presence of linkage could be inferred from the number of bins with nominally significant P(AvgRnk), P(ord), or (most powerfully) both. The results suggest that GSMA can detect linkage across multiple genome scans.     

Genome scan meta-analysis of schizophrenia and bipolar disorder,part II: Schizophrenia

Schizophrenia is a common disorder with high heritability and a 10-fold increase in risk to siblings of probands. Replication has been inconsistent for reports of significant genetic linkage. To assess evidence for linkage across studies, rank-based genome scan meta-analysis (GSMA) was applied to data from 20 schizophrenia genome scans. Each marker for each scan was assigned to 1 of 120 30-cM bins, with the bins ranked by linkage scores (1 = most significant) and the ranks averaged across studies (R(avg)) and then weighted for sample size (N(sqrt)[affected casess]). A permutation test was used to compute the probability of observing, by chance, each bin's average rank (P(AvgRnk)) or of observing it for a bin with the same place (first, second, etc.) in the order of average ranks in each permutation (P(ord)). The GSMA produced significant genomewide evidence for linkage on chromosome 2q (PAvgRnk<.000417). Two aggregate criteria for linkage were also met (clusters of nominally significant P values that did not occur in 1,000 replicates of the entire data set with no linkage present): 12 consecutive bins with both P(AvgRnk) and P(ord)<.05, including regions of chromosomes 5q, 3p, 11q, 6p, 1q, 22q, 8p, 20q, and 14p, and 19 consecutive bins with P(ord)<.05, additionally including regions of chromosomes 16q, 18q, 10p, 15q, 6q, and 17q. There is greater consistency of linkage results across studies than has been previously recognized. The results suggest that some or all of these regions contain loci that increase susceptibility to schizophrenia in diverse populations.     

Systemic lupus erythematosus susceptibility loci defined by genome scan meta-analysis

To date, several susceptibility loci for systemic lupus erythematosus (SLE) have been identified by individual genome-wide scans, but many of these loci have shown inconsistent results across studies. Additionally, many individual studies are at the lower limit of acceptable power recommended for declaring significant linkage. The genome search meta-analysis (GSMA) has been proposed as a valid and robust method for combining several genome scan results. The aim of this study is to investigate whether there is any consistent evidence of linkage across multiple studies, and to identify novel SLE susceptibility loci by using GSMA method. Twelve genome scan results generated from nine independent studies have been used for the present GSMA. All together, the data consists of 605 families with 1,355 SLE affected individuals from three self-reported ethnicities; Caucasian, African-American, and Hispanic. For each study, the genome was divided into 120 bins (30 cM) and ranked according to the maximum evidence of linkage within each bin. The ranks were summed and averaged across studies following which the significance was assessed by the permutation tests. The present study identified two genomic locations at 6p22.3–6p21.1 and 16p12.3–16q12.2 that met genome-wide significance (p<0.000417). The identified region at 6p22.3–6p21.1 contains the HLA region. The combined p-values using Fisher’s method also supported the significance in these regions. Clustering of significant adjacent bins was observed for chromosomes 6 and 16. Additionally, there are 12 other bins with two point-wise p-values (Psumrnk and Pord) <0.05, suggesting that these bin regions are highly likely to contain SLE susceptibility loci. Among them, present GSMA also identified two novel regions at 4q32.1–4q34.3 and 13q13.2–13q22.2. However, separate analysis using only Caucasian populations identified the strongest evidence for linkage at chromosome 6p21.1–6q15 (Psumrnk=0.00021). One interesting novel region suggests that 3q22.1–3q25.33 (Psumrnk=0.01376) may be an ethnicity-specific SLE linkage. In summary, the present GSMA have identified two statistically significant genomic regions that reconfirmed the SLE linkage at chromosomes 6 and 16.     

Novel non-HLA-susceptible regions determined by meta-analysis of four genomewide scans for ankylosing spondylitis

We identified novel non-HLA-susceptible regions for ankylosing spondylitis (AS) by applying the genome-search-metaanalysis (GSMA) method to combine the previous four AS genomewide scan studies including 479 families with 1175 affected individuals. Three original genomescans were mainly analysed for Caucasian families and one analysed for Han Mongolian families. Ten bins had both Psumrnk and Pord <0.05, suggesting these bins most likely contain AS-linked loci. The 10 bins are 6.2, 16.3, 6.1, 3.3, 6.3, 16.4, 10.5, 17.1, 2.5 and 2.9. The most significant result of linkage was on chromosome 6p22.3-p21.1 (bin 6.2, Psumrnk <0.000417), where HLA loci are located. By addition of a genome scan of Chinese origin, our GSMA result further confirmed the HLA loci as the greatest susceptible region to AS and suggested that non-HLA loci chromosome 16q, 3p, 10q, 2p, 2q and 17p, may also contain AS-linked loci. The novel loci identified in our result give hints to further studies.     

Identification of shared genetic susceptibility locus for coronary artery disease, type 2 diabetes and obesity: a meta-analysis of genome-wide studies

ABSTRACT: Type 2 diabetes (2DM), obesity, and coronary artery disease (CAD) are frequently coexisted being as key components of metabolic syndrome. Whether there is shared genetic background underlying these diseases remained unclear. We performed a meta-analysis of 35 genome screens for 2DM, 36 for obesity or body mass index (BMI)-defined obesity, and 21 for CAD using genome search meta-analysis (GSMA), which combines linkage results to identify regions with only weak evidence and provide genetic interactions among different diseases. For each study, 120 genomic bins of approximately 30 cM were defined and ranked according to the best linkage evidence within each bin. For each disease, bin 6.2 achieved genomic significanct evidence, and bin 9.3, 10.5, 16.3 reached suggestive level for 2DM. Bin 11.2 and 16.3, and bin 10.5 and 9.3, reached suggestive evidence for obesity and CAD respectively. In pooled all three diseases, bin 9.3 and 6.5 reached genomic significant and suggestive evidence respectively, being relatively much weaker for 2DM/CAD or 2DM/obesity or CAD/obesity. Further, genomewide significant evidence was observed of bin 16.3 and 4.5 for 2DM/obesity, which is decreased when CAD was added. These findings indicated that bin 9.3 and 6.5 are most likely to be shared by 2DM, obesity and CAD. And bin 16.3 and 4.5 are potentially common regions to 2DM and obesity only. The observed shared susceptibility regions imply a partly overlapping genetic aspects of disease development. Fine scanning of these regions will definitely identify more susceptibility genes and causal variants.     

Data acquisition for meta-analysis of genome-wide linkage studies using the genome search meta-analysis method

BACKGROUND: The Genome Search Meta-Analysis (GSMA) method enables researchers to pool results across genome-wide linkage studies, to increase the power to detect linkage. Results from individual studies must be extracted, with the maximum evidence for linkage placed into bins, usually of 30 cM width, and ranked within the study. Ranks are then summed across studies, with high summed ranks potentially showing evidence for linkage in the meta-analysis. OBJECTIVES: In this paper we study the properties of the GSMA method considering two different issues: (1) data binning from genome-wide results when indexed markers or graphs are available, based on either predefined boundary markers, or equal-length bins; (2) the use of selected instead of genome-wide results, using simulation to estimate power and type I error rates of GSMA. This is relevant when published papers show only summary results (e.g. with NPL score >1). Results: Using digitizing software to extract linkage statistics from graphs and assigning equal bin length is accurate, with the resulting ranking of bins similar to those defined through boundary markers. Simulation results show that power can fall substantially when genome-wide results are not available, particularly when only results from a single marker are available in a linked region. However there is no increase in false positive findings. CONCLUSIONS: The GSMA method is robust across different bin definitions and methods of data presentation and extraction. Using studies based on only the top ranked bins does not produce false positive results, but lacks power to detect genes conferring a modest increase in risk. Therefore, we advise that effort should be made to obtain genome-wide results from investigators or from published papers to avoid limiting the utility of the GSMA.     

Fine mapping and SNP analysis of positional candidates at the preeclampsia susceptibility locus (PREG1) on chromosome 2

Genome scans in Icelandic, Australian and New Zealand, and Finnish families have localized putative susceptibility loci for preeclampsia/ eclampsia to chromosome 2. The locus mapped in the Australian and New Zealand study (designated PREG1) was thought to be the same locus as that identified in the Icelandic study. In both these studies, two distinct quantitative trait locus (QTL) regions were evident on chromosome 2. Here, we describe our fine mapping of the PREG1 locus and a genetic analysis of two positional candidate genes. Twenty-five additional microsatellite markers were genotyped within the 74-cM linkage region defined by the combined Icelandic and Australian and New Zealand genome scans. The overall position and shape of the localization evidence obtained using nonparametric multipoint analysis did not change from that seen previously in our 10-cM resolution genome scan; two peaks were displayed, one on chromosome 2p at marker D2S388 (107.46 cM) and the other on chromosome 2q at 151.5 cM at marker D2S2313. Using the robust two-point linkage analysis implemented in the Analyze program, all 25 markers gave positive LOD scores with significant evidence of linkage being seen at marker D2S2313 (151.5 cM), achieving a LOD score of 3.37 under a strict diagnostic model. Suggestive evidence of linkage was seen at marker D2S388 (107.46 cM) with a LOD score of 2.22 under the general diagnostic model. Two candidate genes beneath the peak on chromosome 2p were selected for further analysis using public single nucleotide polymorphisms (SNPs) within these genes. Maximum LOD scores were obtained for an SNP in TACR1 (LOD = 3.5) and for an SNP in TCF7L1 (LOD = 3.33), both achieving genome-wide significance. However, no evidence of association was seen with any of the markers tested. These data strongly support the presence of a susceptibility gene on chromosome 2p11-12 and substantiate the possibility of a second locus on chromosome 2q23.     

Contrasting genome organisation: two regions of the Brassica oleracea genome compared with collinear regions of the Arabidopsis thaliana genome.

Brassica crop species are of worldwide importance and are closely related to the model plant Arabidopsis thaliana for which the complete genome sequence has recently been established. We investigated collinearity of marker order by comparing two contrasting regions of the Brassica oleracea genome with homologous regions of A. thaliana. Although there is widespread replication of marker loci in both A. thaliana and B. oleracea, we found that a combination of genetic markers mapped in B. oleracea, including RFLPs, CAPS, and SSRs allowed comparison and interpretation of medium-scale chromosomal organisation and rearrangements. The interpretation of data was facilitated by hybridising probes onto the whole A. thaliana genome, as represented by BAC contigs. Twenty marker loci were sampled from the whole length of the shortest B. oleracea linkage group, 06, and 21 from a 30.4-cM section of the longest linkage group, 03. There is evidence of locus duplication on linkage group 06. Locus order is well conserved between a putative duplicated region of 10.5 cM and a discrete region comprising 25 cM of A. thaliana chromosome I. This was supported by evidence from seven paralogous loci, three of which were duplicated in a 30.6-cM region of linkage group 06. The pattern of locus order for the remainder of linkage group 06 and the sampled section of linkage group 03 was more complex when compared with the A. thaliana genome. Although there was some conservation of locus order between markers on linkage group 03 and approximately 9 cM of A. thaliana chromosome I, this was superimposed upon a complex pattern of additional loci that were replicated in both A. thaliana and B. oleracea. The results are discussed in the context of the ability to use collinear information to assist map-based cloning.     

Localization of Müllerian mimicry genes on a dense linkage map of Heliconius erato

We report a dense genetic linkage map of Heliconius erato, a neotropical butterfly that has undergone a remarkable adaptive radiation in warningly colored mimetic wing patterns. Our study exploited natural variation segregating in a cross between H. erato etylus and H. himera to localize wing color pattern loci on a dense linkage map containing amplified fragment length polymorphisms (AFLP), microsatellites, and single-copy nuclear loci. We unambiguously identified all 20 autosomal linkage groups and the sex chromosome (Z). The map spanned a total of 1430 Haldane cM and linkage groups varied in size from 26.3 to 97.8 cM. The average distance between markers was 5.1 cM. Within this framework, we localized two major color pattern loci to narrow regions of the genome. The first gene, D, responsible for red/orange elements, had a most likely placement in a 6.7-cM region flanked by two AFLP markers on the end of a large 87.5-cM linkage group. The second locus, Sd, affects the melanic pattern on the forewing and was found within a 6.3-cM interval between flanking AFLP loci. This study complements recent linkage analysis of H. erato's comimic, H. melpomene, and forms the basis for marker-assisted physical mapping and for studies into the comparative genetic architecture of wing-pattern mimicry in Heliconius.     

Genome scan meta-analysis of schizophrenia and bipolar disorder,part III: Bipolar disorder

Genome scans of bipolar disorder (BPD) have not produced consistent evidence for linkage. The rank-based genome scan meta-analysis (GSMA) method was applied to 18 BPD genome scan data sets in an effort to identify regions with significant support for linkage in the combined data. The two primary analyses considered available linkage data for “very narrow” (i.e., BP-I and schizoaffective disorder–BP) and “narrow” (i.e., adding BP-II disorder) disease models, with the ranks weighted for sample size. A “broad” model (i.e., adding recurrent major depression) and unweighted analyses were also performed. No region achieved genomewide statistical significance by several simulation-based criteria. The most significant P values (<.01) were observed on chromosomes 9p22.3-21.1 (very narrow), 10q11.21-22.1 (very narrow), and 14q24.1-32.12 (narrow). Nominally significant P values were observed in adjacent bins on chromosomes 9p and 18p-q, across all three disease models on chromosomes 14q and 18p-q, and across two models on chromosome 8q. Relatively few BPD pedigrees have been studied under narrow disease models relative to the schizophrenia GSMA data set, which produced more significant results. There was no overlap of the highest-ranked regions for the two disorders. The present results for the very narrow model are promising but suggest that more and larger data sets are needed. Alternatively, linkage might be detected in certain populations or subsets of pedigrees. The narrow and broad data sets had considerable power, according to simulation studies, but did not produce more highly significant evidence for linkage. We note that meta-analysis can sometimes provide support for linkage but cannot disprove linkage in any candidate region.     

A genomewide scan for loci predisposing to type 2 diabetes in a U.K. population (the Diabetes UK Warren 2 Repository): analysis of 573 pedigrees provides independent replication of a susceptibility locus on chromosome 1q

Improved molecular understanding of the pathogenesis of type 2 diabetes is essential if current therapeutic and preventative options are to be extended. To identify diabetes-susceptibility genes, we have completed a primary (418-marker, 9-cM) autosomal-genome scan of 743 sib pairs (573 pedigrees) with type 2 diabetes who are from the Diabetes UK Warren 2 repository. Nonparametric linkage analysis of the entire data set identified seven regions showing evidence for linkage, with allele-sharing LOD scores > or =1.18 (P< or =.01). The strongest evidence was seen on chromosomes 8p21-22 (near D8S258 [LOD score 2.55]) and 10q23.3 (near D10S1765 [LOD score 1.99]), both coinciding with regions identified in previous scans in European subjects. This was also true of two lesser regions identified, on chromosomes 5q13 (D5S647 [LOD score 1.22] and 5q32 (D5S436 [LOD score 1.22]). Loci on 7p15.3 (LOD score 1.31) and 8q24.2 (LOD score 1.41) are novel. The final region showing evidence for linkage, on chromosome 1q24-25 (near D1S218 [LOD score 1.50]), colocalizes with evidence for linkage to diabetes found in Utah, French, and Pima families and in the GK rat. After dense-map genotyping (mean marker spacing 4.4 cM), evidence for linkage to this region increased to a LOD score of 1.98. Conditional analyses revealed nominally significant interactions between this locus and the regions on chromosomes 10q23.3 (P=.01) and 5q32 (P=.02). These data, derived from one of the largest genome scans undertaken in this condition, confirm that individual susceptibility-gene effects for type 2 diabetes are likely to be modest in size. Taken with genome scans in other populations, they provide both replication of previous evidence indicating the presence of a diabetes-susceptibility locus on chromosome 1q24-25 and support for the existence of additional loci on chromosomes 5, 8, and 10. These data should accelerate positional cloning efforts in these regions of interest.     

HEGESMA: genome search meta-analysis and heterogeneity testing

SUMMARY: Heterogeneity and genome search meta-analysis (HEGESMA) is a comprehensive software for performing genome scan meta-analysis, a quantitative method to identify genetic regions (bins) with consistently increased linkage score across multiple genome scans, and for testing the heterogeneity of the results of each bin across scans. The program provides as an output the average of ranks and three heterogeneity statistics, as well as corresponding significance levels. Statistical inferences are based on Monte Carlo permutation tests. The program allows both unweighted and weighted analysis, with the weights for each study as specified by the user. Furthermore, the program performs heterogeneity analyses restricted to the bins with similar average ranks. AVAILABILITY: http://biomath.med.uth.gr.     

The development of a bin mapping population and the selective mapping of 103 markers in the diploid Fragaria reference map.

We have identified a set of plants (the bin set) to permit "selective" or "bin" mapping using the diploid strawberry mapping population FV x FN, derived from the F2 cross F. vesca 815 x F. nubicola 601, which has been used to develop the Fragaria reference map. The bin set consists of 8 plants: the F. vesca 815 parent, the F1 hybrid individual, and 6 seedlings of the F2 population. This bin set divides the 578 cM of the diploid Fragaria genome into 46 bins, the largest mapping bin being 26 cM in length and the average bin size being 12.6 cM. To validate the FV x FN bin set, we used it to locate 103 loci into bins on the FV x FN map. These loci comprised 61 previously described SSRs, 38 new SSRs developed in this investigation from Fragaria x ananassa genomic DNA, EST and gene sequences, and 4 ripening-related genes developed for Prunus. The 103 markers were located to bins on all 7 linkage groups of the Fragaria map and a new mapping bin was identified with the novel markers, demonstrating that the map covers the majority of the diploid Fragaria genome and that the 6 bin-set seedlings selected were appropriate for bin mapping using this progeny.     

Linkage mapping of the locus responsible for male pseudohermaphroditism (mp) on rat chromosome 7.

TF is a mutant rat strain showing male pseudohermaphroditism controlled by an autosomal single recessive gene (mp). The affected rats show lack of Leydig cells and androgen deficiency. In this study, we performed linkage analysis using F(2) progeny of crosses between TF and BN strains to determine the chromosomal localization of the mp locus. The mp locus was mapped in a 4 cM region of the distal region of rat chromosome 7 between D7Rat3 and D7Rat115 or D7Rat94. Comparison of the linkage map with corresponding regions of the published rat genome sequence revealed several candidate genes for the mp mutation, including the Dhh, Tegt, Gdp3, and Amhr2 genes.     

A whole-genome screen of a quantitative trait of age-related maculopathy in sibships from the Beaver Dam Eye Study

Age-related maculopathy (ARM) is a leading cause of visual impairment among the elderly in Western populations. To identify ARM-susceptibility loci, we genotyped a subset of subjects from the Beaver Dam (WI) Eye Study and performed a model-free genomewide linkage analysis for markers linked to a quantitative measure of ARM. We initially genotyped 345 autosomal markers in 325 individuals (N=263 sib pairs) from 102 pedigrees. Ten regions suggestive of linkage with ARM were observed on chromosomes 3, 5, 6, 12, 15, and 16. Prior to fine mapping, the most significant regions were an 18-cM region on chromosome 12, near D12S1300 (P=.0159); a region on chromosome 3, near D3S1763, with a P value of.0062; and a 6-cM region on chromosome 16, near D16S769, with a P value of.0086. After expanding our analysis to include 25 additional fine-mapping markers, we found that a 14-cM region on chromosome 12, near D12S346 (located at 106.89 cM), showed the strongest indication of linkage, with a P value of.004. Three other regions, on chromosomes 5, 6, and 15, that were nominally significant at P< or =.01 are also appropriate for fine mapping.     

A note on generalized Genome Scan Meta-Analysis statistics

Background  

Wise et al. introduced a rank-based statistical technique for meta-analysis of genome scans, the Genome Scan Meta-Analysis (GSMA) method. Levinson et al. recently described two generalizations of the GSMA statistic: (i) a weighted version of the GSMA statistic, so that different studies could be ascribed different weights for analysis; and (ii) an order statistic approach, reflecting the fact that a GSMA statistic can be computed for each chromosomal region or bin width across the various genome scan studies.     

A genome-wide linkage analysis of alcoholism on microsatellite and single-nucleotide polymorphism data,using alcohol dependence phenotypes and electroencephalogram measures

The Collaborative Study on the Genetics of Alcoholism (COGA) is a large-scale family study designed to identify genes that affect the risk for alcoholism and alcohol-related phenotypes. We performed genome-wide linkage analyses on the COGA data made available to participants in the Genetic Analysis Workshop 14 (GAW 14). The dataset comprised 1,350 participants from 143 families. The samples were analyzed on three technologies: microsatellites spaced at 10 cM, Affymetrix GeneChip Human Mapping 10 K Array (HMA10K) and Illumina SNP-based Linkage III Panel. We used ALDX1 and ALDX2, the COGA definitions of alcohol dependence, as well as electrophysiological measures TTTH1 and ECB21 to detect alcoholism susceptibility loci. Many chromosomal regions were found to be significant for each of the phenotypes at a p-value of 0.05. The most significant region for ALDX1 is on chromosome 7, with a maximum LOD score of 2.25 for Affymetrix SNPs, 1.97 for Illumina SNPs, and 1.72 for microsatellites. The same regions on chromosome 7 (96-106 cM) and 10 (149-176 cM) were found to be significant for both ALDX1 and ALDX2. A region on chromosome 7 (112-153 cM) and a region on chromosome 6 (169-185 cM) were identified as the most significant regions for TTTH1 and ECB21, respectively. We also performed linkage analysis on denser maps of markers by combining the SNPs datasets from Affymetrix and Illumina. Adding the microsatellite data to the combined SNP dataset improved the results only marginally. The results indicated that SNPs outperform microsatellites with the densest marker sets performing the best.     

Linkage analysis of complex diseases using microsatellites and single-nucleotide polymorphisms: application to alcoholism

The efficacy of linkage studies using microsatellites and single-nucleotide polymorphisms (SNPs) was evaluated. Analyzed data were supplied by the Collaborative Study on the Genetics of Alcoholism (COGA). Alcoholism was analyzed together with a simulated trait caused by a gene of known position, through a nonparametric linkage test (NPL). For the alcoholism trait, four densities of SNPs (1 SNP per 0.2 cM, 0.5 cM, 1 cM and 2 cM) showed higher peaks of NPL z scores and smaller significant p-values than the usual 10-cM density of microsatellites. However, the two highest densities of SNPs had unstable z score signals, and therefore were difficult to interpret. Analyzing a simulated trait with the same markers in the same pedigrees, we confirmed the higher power of all four densities of SNPs compared to the 10-cM microsatellites panel, although the existence of other confounding peaks was confirmed for maps that are denser than 1 SNP/cM. We further showed that estimating the gene position using SNPs is far less biased than using the usual panel of microsatellites (biases of 0-2 cM for SNPs vs. 8.9 cM for microsatellites). We conclude that using dense maps of SNPs in linkage analysis is more powerful and less biased than using the 10-cM maps of microsatellites. However, linkage signals can be unstable and difficult to interpret when several SNPs are genotyped per centimorgan. The power and accuracy of 1 SNP/cM or 1 SNP/2 cM may be sufficient in a genome-wide linkage scan while denser maps may be most useful in fine-gene mapping studies exploiting linkage disequilibrium.     

Identification of QTLs for eight agronomically important traits using an ultra-high-density map based on SNPs generated from high-throughput sequencing in sorghum under contrasting photoperiods

The productivity of sorghum is mainly determined by agronomically important traits. The genetic bases of these traits have historically been dissected and analysed through quantitative trait locus (QTL) mapping based on linkage maps with low-throughput molecular markers, which is one of the factors that hinder precise and complete information about the numbers and locations of the genes or QTLs controlling the traits. In this study, an ultra-high-density linkage map based on high-quality single nucleotide polymorphisms (SNPs) generated from low-coverage sequences (~0.07 genome sequence) in a sorghum recombinant inbred line (RIL) population was constructed through new sequencing technology. This map consisted of 3418 bin markers and spanned 1591.4 cM of genome size with an average distance of 0.5 cM between adjacent bins. QTL analysis was performed and a total of 57 major QTLs were detected for eight agronomically important traits under two contrasting photoperiods. The phenotypic variation explained by individual QTLs varied from 3.40% to 33.82%. The high accuracy and quality of this map was evidenced by the finding that genes underlying two cloned QTLs, Dw3 for plant height (chromosome 7) and Ma1 for flowering time (chromosome 6), were localized to the correct genomic regions. The close associations between two genomic regions on chromosomes 6 and 7 with multiple traits suggested the existence of pleiotropy or tight linkage. Several major QTLs for heading date, plant height, numbers of nodes, stem diameter, panicle neck length, and flag leaf width were detected consistently under both photoperiods, providing useful information for understanding the genetic mechanisms of the agronomically important traits responsible for the change of photoperiod.     

Bivariate linkage analysis of the insulin resistance syndrome phenotypes on chromosome 7q

Metabolic abnormalities of the insulin resistance syndrome (IRS) have been shown to aggregate in families and to exhibit trait-pair correlations, suggesting a common genetic component. A broad region on chromosome 7q has been implicated in several studies to contain loci that cosegregate with IRS-related traits. However, it is not clear whether such loci have any common genetic (pleiotropic) influences on the correlated traits. Also, it is not clear whether the chromosomal regions contain more than one locus influencing the IRS-related phenotypes. In this study we present evidence for linkage of five IRS-related traits [body mass index (BMI), waist circumference (WC), In split proinsulin (LSPI), In triglycerides (LTG), and high-density lipoprotein cholesterol (HDLC)] to a region at 7q11.23. Subsequently, to gain further insight into the genetic component(s) mapping to this region, we explored whether linkage of these traits is due to pleiotropic effects using a bivariate linkage analytical technique, which has been shown to localize susceptibility regions with precision. Four hundred forty individuals from 27 Mexican American families living in Texas were genotyped for 19 highly polymorphic markers on chromosome 7. Multipoint variance component linkage analysis was used to identify genetic location(s) influencing IRS-related traits of obesity (BMI and WC), dyslipidemia (LTG and HDLC), and insulin levels (LSPI); the analysis identified a broad chromosomal region spanning approximately 24 cM. To gain more precision in localization, we used a bivariate linkage approach for each trait pair. These analyses suggest localization of most of these bivariate traits to an approximately 6-cM region near marker D7S653 [7q11.23, 103-109 cM; a maximum bivariate LOD of 4.51 was found for the trait pair HDLC and LSPI (the LODeq score is 3.94)]. We observed evidence of pleiotropic effects in this region on obesity and insulin-related trait pairs.