Evidence of a novel quantitative-trait locus for obesity on chromosome 4p in Mexican Americans

Although several genomewide scans have identified quantitative-trait loci influencing several obesity-related traits in humans, genes influencing normal variation in obesity phenotypes have not yet been identified. We therefore performed a genome scan of body mass index (BMI) on Mexican Americans, a population prone to obesity and diabetes, using a variance-components linkage analysis to identify loci that influence BMI. We used phenotypic data from 430 individuals (26% diabetics, 59% females, mean age +/- SD = 43 +/- 17 years, mean BMI +/- SD = 30.0 +/- 6.7, mean leptin (ng/ml) +/- SD = 22.1 +/- 17.1) distributed across 27 low-income Mexican American pedigrees who participated in the San Antonio Family Diabetes Study (SAFDS) for whom a 10-15-cM map is available. In this genomewide search, after accounting for the covariate effects of age, sex, diabetes, and leptin, we identified a genetic region exhibiting the most highly significant evidence for linkage (LOD 4.5) with BMI on chromosome 4p (4p15.1) at 42 cM, near marker D4S2912. This linkage result has been confirmed in an independent linkage study of severe obesity in Utah pedigrees. Two strong positional candidates, the human peroxisome proliferator-activated receptor gamma coactivator 1 (PPARGC1) and cholecystokinin A receptor (CCKAR) with major roles in the development of obesity, are located in this region. In conclusion, we identified a major genetic locus influencing BMI on chromosome 4p in Mexican Americans.     

Candidate-gene screening and association analysis at the autism-susceptibility locus on chromosome 16p: evidence of association at GRIN2A and ABAT

Autism is a highly heritable neurodevelopmental disorder whose underlying genetic causes have yet to be identified. To date, there have been eight genome screens for autism, two of which identified a putative susceptibility locus on chromosome 16p. In the present study, 10 positional candidate genes that map to 16p11-13 were examined for coding variants: A2BP1, ABAT, BFAR, CREBBP, EMP2, GRIN2A, MRTF-B, SSTR5, TBX6, and UBN1. Screening of all coding and regulatory regions by denaturing high-performance liquid chromatography identified seven nonsynonymous changes. Five of these mutations were found to cosegregate with autism, but the mutations are not predicted to have deleterious effects on protein structure and are unlikely to represent significant etiological variants. Selected variants from candidate genes were genotyped in the entire International Molecular Genetics Study of Autism Consortium collection of 239 multiplex families and were tested for association with autism by use of the pedigree disequilibrium test. Additionally, genotype frequencies were compared between 239 unrelated affected individuals and 192 controls. Patterns of linkage disequilibrium were investigated, and the transmission of haplotypes across candidate genes was tested for association. Evidence of single-marker association was found for variants in ABAT, CREBBP, and GRIN2A. Within these genes, 12 single-nucleotide polymorphisms (SNPs) were subsequently genotyped in 91 autism trios (one affected individual and two unaffected parents), and the association was replicated within GRIN2A (Fisher's exact test, P<.0001). Logistic regression analysis of SNP data across GRIN2A and ABAT showed a trend toward haplotypic differences between cases and controls.     

Sequence diversity in 36 candidate genes for cardiovascular disorders.

Two strategies involving whole-genome association studies have been proposed for the identification of genes involved in complex diseases. The first one seeks to characterize all common variants of human genes and to test their association with disease. The second one seeks to develop dense maps of single-nucleotide polymorphisms (SNPs) and to detect susceptibility genes through linkage disequilibrium. We performed a molecular screening of the coding and/or flanking regions of 36 candidate genes for cardiovascular diseases. All polymorphisms identified by this screening were further genotyped in 750 subjects of European descent. In the whole set of genes, the lengths explored spanned 53.8 kb in the 5' regions, 68.4 kb in exonic regions, and 13 kb in the 3' regions. The strength of linkage disequilibrium within candidate regions suggests that genomewide maps of SNPs might be efficient ways to identify new disease-susceptibility genes, provided that the maps are sufficiently dense. However, the relatively large number of polymorphisms within coding and regulatory regions of candidate genes raises the possibility that several of them might be functional and that the pattern of genotype-phenotype association might be more complex than initially envisaged, as actually has been observed in some well-characterized genes. These results argue in favor of both genomewide association studies and detailed studies of the overall sequence variation of candidate genes, as complementary approaches.     

Juvenile hemochromatosis locus maps to chromosome 1q

Juvenile hemochromatosis (JH) is an autosomal recessive disorder that leads to severe iron loading in the 2d to 3d decade of life. Affected members in families with JH do not show linkage to chromosome 6p and do not have mutations in the HFE gene that lead to the common hereditary hemochromatosis. In this study we performed a genomewide search to map the JH locus in nine families: six consanguineous and three with multiple affected patients. This strategy allowed us to identify the JH locus on the long arm of chromosome 1. A maximum LOD score of 5.75 at a recombination fraction of 0 was detected with marker D1S498, and a LOD score of 5. 16 at a recombination fraction of 0 was detected for marker D1S2344. Homozygosity mapping in consanguineous families defined the limits of the candidate region in an approximately 4-cM interval between markers D1S442 and D1S2347. Analysis of genes mapped in this interval excluded obvious candidates. The JH locus does not correspond to the chromosomal localization of any known gene involved in iron metabolism. These findings provide a means to recognize, at an early age, patients in affected families. They also provide a starting point for the identification of the affected gene by positional cloning.     

Evidence for a susceptibility gene for autism on chromosome 2 and for genetic heterogeneity

Although there is considerable evidence for a strong genetic component to idiopathic autism, several genomewide screens for susceptibility genes have been performed with limited concordance of linked loci, reflecting either numerous genes of weak effect and/or sample heterogeneity. Because decreasing sample heterogeneity would increase the power to identify genes, the effect on evidence for linkage of restricting a sample of autism-affected relative pairs to those with delayed onset (at age >36 mo) of phrase speech (PSD, for phrase speech delay) was studied. In the second stage of a two-stage genome screen for susceptibility loci involving 95 families with two or more individuals with autism or related disorders, a maximal multipoint heterogeneity LOD score (HLOD) of 1.96 and a maximal multipoint nonparametric linkage (NPL) score of 2.39 was seen on chromosome 2q. Restricting the analysis to the subset of families (n=49) with two or more individuals having a narrow diagnosis of autism and PSD generated a maximal multipoint HLOD score of 2.99 and an NPL score of 3.32. The increased scores in the restricted sample, together with evidence for heterogeneity in the entire sample, indicate that the restricted sample comprises a population that is more genetically homogeneous, which could therefore increase the likelihood of positional cloning of susceptibility loci.     

Integration of expression profiles and genetic mapping data to identify candidate genes in intracranial aneurysm.

Intracranial aneurysm (IA) is a complex genetic disease for which, to date, 10 loci have been identified by linkage. Identification of the risk-conferring genes in the loci has proven difficult, since the regions often contain several hundreds of genes. An approach to prioritize positional candidate genes for further studies is to use gene expression data from diseased and nondiseased tissue. Genes that are not expressed, either in diseased or nondiseased tissue, are ranked as unlikely to contribute to the disease. We demonstrate an approach for integrating expression and genetic mapping data to identify likely pathways involved in the pathogenesis of a disease. We used expression profiles for IAs and nonaneurysmal intracranial arteries (IVs) together with the 10 reported linkage intervals for IA. Expressed genes were analyzed for membership in Kyoto Encyclopedia of Genes and Genomes (KEGG) biological pathways. The 10 IA loci harbor 1,858 candidate genes, of which 1,561 (84%) were represented on the microarrays. We identified 810 positional candidate genes for IA that were expressed in IVs or IAs. Pathway information was available for 294 of these genes and involved 32 KEGG biological function pathways represented on at least 2 loci. A likelihood-based score was calculated to rank pathways for involvement in the pathogenesis of IA. Adherens junction, MAPK, and Notch signaling pathways ranked high. Integration of gene expression profiles with genetic mapping data for IA provides an approach to identify candidate genes that are more likely to function in the pathology of IA.     

Database of cattle candidate genes and genetic markers for milk production and mastitis

A cattle database of candidate genes and genetic markers for milk production and mastitis has been developed to provide an integrated research tool incorporating different types of information supporting a genomic approach to study lactation, udder development and health. The database contains 943 genes and genetic markers involved in mammary gland development and function, representing candidates for further functional studies. The candidate loci were drawn on a genetic map to reveal positional overlaps. For identification of candidate loci, data from seven different research approaches were exploited: (i) gene knockouts or transgenes in mice that result in specific phenotypes associated with mammary gland (143 loci); (ii) cattle QTL for milk production (344) and mastitis related traits (71); (iii) loci with sequence variations that show specific allele-phenotype interactions associated with milk production (24) or mastitis (10) in cattle; (iv) genes with expression profiles associated with milk production (207) or mastitis (107) in cattle or mouse; (v) cattle milk protein genes that exist in different genetic variants (9); (vi) miRNAs expressed in bovine mammary gland (32) and (vii) epigenetically regulated cattle genes associated with mammary gland function (1). Fourty-four genes found by multiple independent analyses were suggested as the most promising candidates and were further in silico analysed for expression levels in lactating mammary gland, genetic variability and top biological functions in functional networks. A miRNA target search for mammary gland expressed miRNAs identified 359 putative binding sites in 3'UTRs of candidate genes.     

A strategy to identify positional candidate genes conferring Marek''s disease resistance by integrating DNA microarrays and genetic mapping

Marker-assisted selection (MAS) to enhance genetic resistance to Marek's disease (MD), a herpesvirus-induced T cell cancer in chicken, is an attractive alternative to augment control with vaccines. Our earlier studies indicate that there are many quantitative trait loci (QTL) containing one or more genes that confer genetic resistance to MD. Unfortunately, it is difficult to sufficiently resolve these QTL to identify the causative gene and generate tightly linked markers. One possible solution is to identify positional candidate genes by virtue of gene expression differences between MD resistant and susceptible chicken using deoxyribonucleic acid (DNA) microarrays followed by genetic mapping of the differentially-expressed genes. In this preliminary study, we show that DNA microarrays containing approximately 1200 genes or expressed sequence tags (ESTs) are able to reproducibly detect differences in gene expression between the inbred ADOL lines 63 (MD resistant) and 72 (MD susceptible) of uninfected and Marek's disease virus (MDV)-infected peripheral blood lymphocytes. Microarray data were validated by quantitative polymerase chain reaction (PCR) and found to be consistent with previous literature on gene induction or immune response. Integration of the microarrays with genetic mapping data was achieved with a sample of 15 genes. Twelve of these genes had mapped human orthologues. Seven genes were located on the chicken linkage map as predicted by the human-chicken comparative map, while two other genes defined a new conserved syntenic group. More importantly, one of the genes with differential expression is known to confer genetic resistance to MD while another gene is a prime positional candidate for a QTL.     

Multiple genes for essential-hypertension susceptibility on chromosome 1q

Essential hypertension, defined as elevated levels of blood pressure (BP) without any obvious cause, is a major risk factor for coronary heart disease, stroke, and renal disease. BP levels and susceptibility to development of essential hypertension are partially determined by genetic factors that are poorly understood. Similar to other efforts to understand complex, non-Mendelian phenotypes, genetic dissection of hypertension-related traits employs genomewide linkage analyses of families and association studies of patient cohorts, to uncover rare and common disease alleles, respectively. Family-based mapping studies of elevated BP cover the large intermediate ground for identification of genes with common variants of significant effect. Our genomewide linkage and candidate-gene-based association studies demonstrate that a replicated linkage peak for BP regulation on human chromosome 1q, homologous to mouse and rat quantitative trait loci for BP, contains at least three genes associated with BP levels in multiple samples: ATP1B1, RGS5, and SELE. Individual variants in these three genes account for 2-5-mm Hg differences in mean systolic BP levels, and the cumulative effect reaches 8-10 mm Hg. Because the associated alleles in these genes are relatively common (frequency >5%), these three genes are important contributors to elevated BP in the population at large.     

Genetic dissection of intermale aggressive behavior in BALB/cJ and A/J mice

Aggressive behaviors are disabling, treatment refractory, and sometimes lethal symptoms of several neuropsychiatric disorders. However, currently available treatments for patients are inadequate, and the underlying genetics and neurobiology of aggression is only beginning to be elucidated. Inbred mouse strains are useful for identifying genomic regions, and ultimately the relevant gene variants (alleles) in these regions, that affect mammalian aggressive behaviors, which, in turn, may help to identify neurobiological pathways that mediate aggression. The BALB/cJ inbred mouse strain exhibits relatively high levels of intermale aggressive behaviors and shows multiple brain and behavioral phenotypes relevant to neuropsychiatric syndromes associated with aggression. The A/J strain shows very low levels of aggression. We hypothesized that a cross between BALB/cJ and A/J inbred strains would reveal genomic loci that influence the tendency to initiate intermale aggressive behavior. To identify such loci, we conducted a genomewide scan in an F2 population of 660 male mice bred from BALB/cJ and A/J inbred mouse strains. Three significant loci on chromosomes 5, 10 and 15 that influence aggression were identified. The chromosome 5 and 15 loci are completely novel, and the chromosome 10 locus overlaps an aggression locus mapped in our previous study that used NZB/B1NJ and A/J as progenitor strains. Haplotype analysis of BALB/cJ, NZB/B1NJ and A/J strains showed three positional candidate genes in the chromosome 10 locus. Future studies involving fine genetic mapping of these loci as well as additional candidate gene analysis may lead to an improved biological understanding of mammalian aggressive behaviors.     

Gene-centric genomewide association study via entropy

Genes are the functional units in most organisms. Compared to genetic variants located outside genes, genic variants are more likely to affect disease risk. The development of the human HapMap project provides an unprecedented opportunity for genetic association studies at the genomewide level for elucidating disease etiology. Currently, most association studies at the single-nucleotide polymorphism (SNP) or the haplotype level rely on the linkage information between SNP markers and disease variants, with which association findings are difficult to replicate. Moreover, variants in genes might not be sufficiently covered by currently available methods. In this article, we present a gene-centric approach via entropy statistics for a genomewide association study to identify disease genes. The new entropy-based approach considers genic variants within one gene simultaneously and is developed on the basis of a joint genotype distribution among genetic variants for an association test. A grouping algorithm based on a penalized entropy measure is proposed to reduce the dimension of the test statistic. Type I error rates and power of the entropy test are evaluated through extensive simulation studies. The results indicate that the entropy test has stable power under different disease models with a reasonable sample size. Compared to single SNP-based analysis, the gene-centric approach has greater power, especially when there is more than one disease variant in a gene. As the genomewide genic SNPs become available, our entropy-based gene-centric approach would provide a robust and computationally efficient way for gene-based genomewide association study.     

Identification of Rare Causal Variants in Sequence-Based Studies: Methods and Applications to VPS13B,a Gene Involved in Cohen Syndrome and Autism

Pinpointing the small number of causal variants among the abundant naturally occurring genetic variation is a difficult challenge, but a crucial one for understanding precise molecular mechanisms of disease and follow-up functional studies. We propose and investigate two complementary statistical approaches for identification of rare causal variants in sequencing studies: a backward elimination procedure based on groupwise association tests, and a hierarchical approach that can integrate sequencing data with diverse functional and evolutionary conservation annotations for individual variants. Using simulations, we show that incorporation of multiple bioinformatic predictors of deleteriousness, such as PolyPhen-2, SIFT and GERP++ scores, can improve the power to discover truly causal variants. As proof of principle, we apply the proposed methods to VPS13B, a gene mutated in the rare neurodevelopmental disorder called Cohen syndrome, and recently reported with recessive variants in autism. We identify a small set of promising candidates for causal variants, including two loss-of-function variants and a rare, homozygous probably-damaging variant that could contribute to autism risk.     

A major predisposition locus for severe obesity, at 4p15-p14

Although the predisposition to morbid obesity is heritable, the identities of the disease-causing genes are largely unknown. Therefore, we have conducted a genomewide search with 628 markers, using multigenerational Utah pedigrees to identify genes involved in predisposition to obesity. In the genomewide search, we identified a highly significant linkage to high body-mass index in female patients, at D4S2632, with a multipoint heterogeneity LOD (HLOD) score of 6.1 and a nonparametric linkage (NPL) score of 5.3. To further delineate the linkage, we increased both the marker density around D4S2632 and the size of our pedigree data set. As a result, the linkage evidence increased to a multipoint HLOD score of 9.2 (at D4S3350) and an NPL score of 11.3. Evidence from almost half of the families in this analysis support this linkage, and therefore the gene in this region might account for a significant percentage of the genetic predisposition to severe obesity in females. However, further studies are necessary to clarify the effect that this gene has in males and in the general population.     

The use of mouse models to unravel genetic architecture of physical activity: a review

The discovery of genetic variants that underlie a complex phenotype is challenging. One possible approach to facilitate this endeavor is to identify quantitative trait loci (QTL) that contribute to the phenotype and consequently unravel the candidate genes within these loci. Each proposed candidate locus contains multiple genes and, therefore, further analysis is required to choose plausible candidate genes. One of such methods is to use comparative genomics in order to narrow down the QTL to a region containing only a few genes. We illustrate this strategy by applying it to genetic findings regarding physical activity (PA) in mice and human. Here, we show that PA is a complex phenotype with a strong biological basis and complex genetic architecture. Furthermore, we provide considerations for the translatability of this phenotype between species. Finally, we review studies which point to candidate genetic regions for PA in humans (genetic association and linkage studies) or use mouse models of PA (QTL studies) and we identify candidate genetic regions that overlap between species. On the basis of a large variety of studies in mice and human, statistical analysis reveals that the number of overlapping regions is not higher than expected on a chance level. We conclude that the discovery of new candidate genes for complex phenotypes, such as PA levels, is hampered by various factors, including genetic background differences, phenotype definition and a wide variety of methodological differences between studies .     

Candidate locus for a nuclear modifier gene for maternally inherited deafness

Maternally inherited deafness associated with the A1555G mutation in the mitochondrial 12S ribosomal RNA (rRNA) gene appears to require additional environmental or genetic changes for phenotypic expression. Aminoglycosides have been identified as one such environmental factor. In one large Arab-Israeli pedigree with congenital hearing loss in some of the family members with the A1555G mutation and with no exposure to aminoglycosides, biochemical evidence has suggested the role of nuclear modifier gene(s), but a genomewide search has indicated the absence of a single major locus having such an effect. Thus it has been concluded that the penetrance of the mitochondrial mutation appears to depend on additive effects of several nuclear genes. We have now investigated 10 multiplex Spanish and Italian families with 35 members with the A1555G mutation and sensorineural deafness. Parametric analysis of a genomewide screen again failed to identify significant evidence for linkage to a single autosomal locus. However, nonparametric analysis supported the role of the chromosomal region around marker D8S277. The combined maximized allele-sharing LOD score of 3.1 in Arab-Israeli/Spanish/Italian families represents a highly suggestive linkage result. We suggest that this region should be considered a candidate for containing the first human nuclear modifier gene for a mitochondrial DNA disorder. The locus operates in Arab-Israeli, Spanish, and Italian families, resulting in the deafness phenotype on a background of the mitochondrial A1555G mutation. No obvious candidate genes are located in this region.     

Integration of text- and data-mining using ontologies successfully selects disease gene candidates

Genome-wide techniques such as microarray analysis, Serial Analysis of Gene Expression (SAGE), Massively Parallel Signature Sequencing (MPSS), linkage analysis and association studies are used extensively in the search for genes that cause diseases, and often identify many hundreds of candidate disease genes. Selection of the most probable of these candidate disease genes for further empirical analysis is a significant challenge. Additionally, identifying the genes that cause complex diseases is problematic due to low penetrance of multiple contributing genes. Here, we describe a novel bioinformatic approach that selects candidate disease genes according to their expression profiles. We use the eVOC anatomical ontology to integrate text-mining of biomedical literature and data-mining of available human gene expression data. To demonstrate that our method is successful and widely applicable, we apply it to a database of 417 candidate genes containing 17 known disease genes. We successfully select the known disease gene for 15 out of 17 diseases and reduce the candidate gene set to 63.3% (±18.8%) of its original size. This approach facilitates direct association between genomic data describing gene expression and information from biomedical texts describing disease phenotype, and successfully prioritizes candidate genes according to their expression in disease-affected tissues.     

Computational disease gene identification: a concert of methods prioritizes type 2 diabetes and obesity candidate genes

Genome-wide experimental methods to identify disease genes, such as linkage analysis and association studies, generate increasingly large candidate gene sets for which comprehensive empirical analysis is impractical. Computational methods employ data from a variety of sources to identify the most likely candidate disease genes from these gene sets. Here, we review seven independent computational disease gene prioritization methods, and then apply them in concert to the analysis of 9556 positional candidate genes for type 2 diabetes (T2D) and the related trait obesity. We generate and analyse a list of nine primary candidate genes for T2D genes and five for obesity. Two genes, LPL and BCKDHA, are common to these two sets. We also present a set of secondary candidates for T2D (94 genes) and for obesity (116 genes) with 58 genes in common to both diseases.     

Ethnic-affiliation estimation by use of population-specific DNA markers.

During the past 10 years, DNA analysis has revolutionized the determination of identity in a forensic context. Statements about the biological identity of two human DNA samples now can be made with complete confidence. Although DNA markers are very powerful for distinguishing among individuals, most offer little power to distinguish ethnicity or to support any statement about the physical characteristics of an individual. Through a search of the literature and of unpublished data on allele frequencies we have identified a panel of population-specific genetic markers that enable robust ethnic-affiliation estimation for major U.S. resident populations. In this report, we identify these loci and present their levels of allele-frequency differential between ethnically defined samples, and we demonstrate, using log-likelihood analysis, that this panel of markers provides significant statistical power for ethnic-affiliation estimation. In addition to their use in forensic ethnic-affiliation estimation, population-specific genetic markers are very useful in both population- and individual-level admixture estimation and in mapping genes by use of the linkage disequilibrium created when populations hybridize.     

Use of multivariate linkage analysis for dissection of a complex cognitive trait

Replication of linkage results for complex traits has been exceedingly difficult, owing in part to the inability to measure the precise underlying phenotype, small sample sizes, genetic heterogeneity, and statistical methods employed in analysis. Often, in any particular study, multiple correlated traits have been collected, yet these have been analyzed independently or, at most, in bivariate analyses. Theoretical arguments suggest that full multivariate analysis of all available traits should offer more power to detect linkage; however, this has not yet been evaluated on a genomewide scale. Here, we conduct multivariate genomewide analyses of quantitative-trait loci that influence reading- and language-related measures in families affected with developmental dyslexia. The results of these analyses are substantially clearer than those of previous univariate analyses of the same data set, helping to resolve a number of key issues. These outcomes highlight the relevance of multivariate analysis for complex disorders for dissection of linkage results in correlated traits. The approach employed here may aid positional cloning of susceptibility genes in a wide spectrum of complex traits.