Limits of resolution of genetic linkage studies: implications for the positional cloning of human disease genes.

Positional cloning studies to identify disease genes are being carried out for many human genetic diseases. Such studies often include a genome-scan linkage analysis to identify the rough chromosomal location of a disease gene, fine structure genetic mapping to define and narrow the chromosomal interval in which the disease gene may be located, and physical mapping and gene identification in the genetically defined interval to clone the disease gene. During the planning of a positional cloning study, it is important to know that, if linkage is found, the genetic interval identified is likely to be sufficiently narrow to be dissected efficiently by methods of physical mapping and gene identification. Thus, we wish to know the limits of resolution of a genetic linkage study. In this paper, I determine for Mendelian diseases the distributions and moments of three measures of linkage resolution: (1) in a set of N chromosomes, the distance between the nearest crossovers that flank a disease locus, (2) the distance between the nearest genetic markers that flank the pair of flanking crossovers after a genome scan, and (3) the distance between the nearest flanking markers after additional randomly placed markers are generated and typed in an identified interval. These results provide explicit sample-size guidelines for future positional cloning studies of Mendelian diseases and make possible a more objective evaluation of whether a proposed positional cloning study is likely to be successful. I also briefly discuss the more difficult problem of linkage resolution for complex genetic diseases.     

Methods for high-density admixture mapping of disease genes

Admixture mapping (also known as "mapping by admixture linkage disequilibrium," or MALD) has been proposed as an efficient approach to localizing disease-causing variants that differ in frequency (because of either drift or selection) between two historically separated populations. Near a disease gene, patient populations descended from the recent mixing of two or more ethnic groups should have an increased probability of inheriting the alleles derived from the ethnic group that carries more disease-susceptibility alleles. The central attraction of admixture mapping is that, since gene flow has occurred recently in modern populations (e.g., in African and Hispanic Americans in the past 20 generations), it is expected that admixture-generated linkage disequilibrium should extend for many centimorgans. High-resolution marker sets are now becoming available to test this approach, but progress will require (a). computational methods to infer ancestral origin at each point in the genome and (b). empirical characterization of the general properties of linkage disequilibrium due to admixture. Here we describe statistical methods to estimate the ancestral origin of a locus on the basis of the composite genotypes of linked markers, and we show that this approach accurately estimates states of ancestral origin along the genome. We apply this approach to show that strong admixture linkage disequilibrium extends, on average, for 17 cM in African Americans. Finally, we present power calculations under varying models of disease risk, sample size, and proportions of ancestry. Studying approximately 2500 markers in approximately 2500 patients should provide power to detect many regions contributing to common disease. A particularly important result is that the power of an admixture mapping study to detect a locus will be nearly the same for a wide range of mixture scenarios: the mixture proportion should be 10%-90% from both ancestral populations.     

Efficient study designs for test of genetic association using sibship data and unrelated cases and controls

Linkage mapping of complex diseases is often followed by association studies between phenotypes and marker genotypes through use of case-control or family-based designs. Given fixed genotyping resources, it is important to know which study designs are the most efficient. To address this problem, we extended the likelihood-based method of Li et al., which assesses whether there is linkage disequilibrium between a disease locus and a SNP, to accommodate sibships of arbitrary size and disease-phenotype configuration. A key advantage of our method is the ability to combine data from different family structures. We consider scenarios for which genotypes are available for unrelated cases, affected sib pairs (ASPs), or only one sibling per ASP. We construct designs that use cases only and others that use unaffected siblings or unrelated unaffected individuals as controls. Different combinations of cases and controls result in seven study designs. We compare the efficiency of these designs when the number of individuals to be genotyped is fixed. Our results suggest that (1) when the disease is influenced by a single gene, the one sibling per ASP-control design is the most efficient, followed by the ASP-control design, and familial cases contribute more association information than singleton cases; (2) when the disease is influenced by multiple genes, familial cases provide more association information than singleton cases, unless the effect of the locus being tested is much smaller than at least one other untested disease locus; and (3) the case-control design can be useful for detecting genes with small effect in the presence of genes with much larger effect. Our findings will be helpful for researchers designing and analyzing complex disease-association studies and will facilitate genotyping resource allocation.     

Mapping by admixture linkage disequilibrium: advances, limitations and guidelines

Mapping by admixture linkage disequilibrium (MALD) is a theoretically powerful, although unproven, approach to mapping genetic variants that are involved in human disease. MALD takes advantage of long-range haplotypes that are generated by gene flow among recently admixed ethnic groups, such as African-Americans and Latinos. Under ideal circumstances, MALD will have more power to detect some genetic variants than other types of genome-wide association study that are carried out among more ethnically homogeneous populations. It will also require 200-500 times fewer markers, providing a significant economic advantage. The MALD approach is now being applied, with results expected in the near future.     

A general test of association for quantitative traits in nuclear families

High-resolution mapping is an important step in the identification of complex disease genes. In outbred populations, linkage disequilibrium is expected to operate over short distances and could provide a powerful fine-mapping tool. Here we build on recently developed methods for linkage-disequilibrium mapping of quantitative traits to construct a general approach that can accommodate nuclear families of any size, with or without parental information. Variance components are used to construct a test that utilizes information from all available offspring but that is not biased in the presence of linkage or familiality. A permutation test is described for situations in which maximum-likelihood estimates of the variance components are biased. Simulation studies are used to investigate power and error rates of this approach and to highlight situations in which violations of multivariate normality assumptions warrant the permutation test. The relationship between power and the level of linkage disequilibrium for this test suggests that the method is well suited to the analysis of dense maps. The relationship between power and family structure is investigated, and these results are applicable to study design in complex disease, especially for late-onset conditions for which parents are usually not available. When parental genotypes are available, power does not depend greatly on the number of offspring in each family. Power decreases when parental genotypes are not available, but the loss in power is negligible when four or more offspring per family are genotyped. Finally, it is shown that, when siblings are available, the total number of genotypes required in order to achieve comparable power is smaller if parents are not genotyped.     

Allelic Association: Linkage Disequilibrium Structure and Gene Mapping

The linkage disequilibrium (LD) structure of the human genome is now well understood and characterised for a number of human populations. The LD structure underpins the design and execution of candidate gene and genome-wide association mapping studies. Successful association mapping studies completed to date provide vital new insights into the genetic influences on common diseases, such as diabetes, some cancers and heart disease. The LD structure also presents new avenues of research into the genetic history of human populations, the effects of natural selection and the impact of recombination on the genomic landscape. This review introduces this exciting and complex field by encompassing this range of topics.     

Detection of disease-associated deletions in case–control studies using SNP genotypes with application to rheumatoid arthritis

Genomic deletions have long been known to play a causative role in microdeletion syndromes. Recent whole-genome genetic studies have shown that deletions can increase the risk for several psychiatric disorders, suggesting that genomic deletions play an important role in the genetic basis of complex traits. However, the association between genomic deletions and common, complex diseases has not yet been systematically investigated in gene mapping studies. Likelihood-based statistical methods for identifying disease-associated deletions have recently been developed for familial studies of parent-offspring trios. The purpose of this study is to develop statistical approaches for detecting genomic deletions associated with complex disease in case–control studies. Our methods are designed to be used with dense single nucleotide polymorphism (SNP) genotypes to detect deletions in large-scale or whole-genome genetic studies. As more and more SNP genotype data for genome-wide association studies become available, development of sophisticated statistical approaches will be needed that use these data. Our proposed statistical methods are designed to be used in SNP-by-SNP analyses and in cluster analyses based on combined evidence from multiple SNPs. We found that these methods are useful for detecting disease-associated deletions and are robust in the presence of linkage disequilibrium using simulated SNP data sets. Furthermore, we applied the proposed statistical methods to SNP genotype data of chromosome 6p for 868 rheumatoid arthritis patients and 1,197 controls from the North American Rheumatoid Arthritis Consortium. We detected disease-associated deletions within the region of human leukocyte antigen in which genomic deletions were previously discovered in rheumatoid arthritis patients.     

Genetic association mapping based on discordant sib pairs: the discordant-alleles test.

Family-based tests of association provide the opportunity to test for an association between a disease and a genetic marker. Such tests avoid false-positive results produced by population stratification, so that evidence for association may be interpreted as evidence for linkage or causation. Several methods that use family-based controls have been proposed, including the haplotype relative risk, the transmission-disequilibrium test, and affected family-based controls. However, because these methods require genotypes on affected individuals and their parents, they are not ideally suited to the study of late-onset diseases. In this paper, we develop several family-based tests of association that use discordant sib pairs (DSPs) in which one sib is affected with a disease and the other sib is not. These tests are based on statistics that compare counts of alleles or genotypes or that test for symmetry in tables of alleles or genotypes. We describe the use of a permutation framework to assess the significance of these statistics. These DSP-based tests provide the same general advantages as parent-offspring trio-based tests, while being applicable to essentially any disease; they may also be tailored to particular hypotheses regarding the genetic model. We compare the statistical properties of our DSP-based tests by computer simulation and illustrate their use with an application to Alzheimer disease and the apolipoprotein E polymorphism. Our results suggest that the discordant-alleles test, which compares the numbers of nonmatching alleles in DSPs, is the most powerful of the tests we considered, for a wide class of disease models and marker types. Finally, we discuss advantages and disadvantages of the DSP design for genetic association mapping.     

Differential admixture in Latin American populations and its impact on the study of colorectal cancer

Genome-wide association studies focused on searching genes responsible for several diseases. Admixture mapping studies proposed a more efficient alternative capable of detecting polymorphisms contributing with a small effect on the disease risk. This method focuses on the higher values of linkage disequilibrium in admixed populations. To test this, we analyzed 10 genomic regions previously defined as related with colorectal cancer among nine populations and studied the variation pattern of haplotypic structures and heterozygosity values on seven categories of SNPs. Both analyses showed differences among chromosomal regions and studied populations. Admixed Latin-American samples generally show intermediate values. Heterozygosity of the SNPs grouped in categories varies more in each gene than in each population. African related populations have more blocks per chromosomal region, coherently with their antiquity. In sum, some similarities were found among Latin American populations, but each chromosomal region showed a particular behavior, despite the fact that the study refers to genes and regions related with one particular complex disease. This study strongly suggests the necessity of developing statistical methods to deal with di- or tri-hybrid populations, as well as to carefully analyze the different historic and demographic scenarios, and the different characteristics of particular chromosomal regions and evolutionary forces.     

Differential allelic expression in the human genome: a robust approach to identify genetic and epigenetic cis-acting mechanisms regulating gene expression

The recent development of whole genome association studies has lead to the robust identification of several loci involved in different common human diseases. Interestingly, some of the strongest signals of association observed in these studies arise from non-coding regions located in very large introns or far away from any annotated genes, raising the possibility that these regions are involved in the etiology of the disease through some unidentified regulatory mechanisms. These findings highlight the importance of better understanding the mechanisms leading to inter-individual differences in gene expression in humans. Most of the existing approaches developed to identify common regulatory polymorphisms are based on linkage/association mapping of gene expression to genotypes. However, these methods have some limitations, notably their cost and the requirement of extensive genotyping information from all the individuals studied which limits their applications to a specific cohort or tissue. Here we describe a robust and high-throughput method to directly measure differences in allelic expression for a large number of genes using the Illumina Allele-Specific Expression BeadArray platform and quantitative sequencing of RT-PCR products. We show that this approach allows reliable identification of differences in the relative expression of the two alleles larger than 1.5-fold (i.e., deviations of the allelic ratio larger than 6040) and offers several advantages over the mapping of total gene expression, particularly for studying humans or outbred populations. Our analysis of more than 80 individuals for 2,968 SNPs located in 1,380 genes confirms that differential allelic expression is a widespread phenomenon affecting the expression of 20% of human genes and shows that our method successfully captures expression differences resulting from both genetic and epigenetic cis-acting mechanisms.     

Ovine alpha-amylase genes: isolation, linkage mapping and association analysis with milk traits

On the basis of comparisons between cattle and sheep genome mapping information the ovine alpha-amylase gene was examined as a possible genetic marker for milk traits in sheep. The objective of the present study was to isolate, map and determine whether this gene is a candidate gene for milk traits. DNA fragments (832 and 2360 bp) corresponding to two different AMY genes were isolated, and one SNP in intron 3 and one GTG deletion in exon 3 of the 2360 bp DNA fragment were found. The 2360 bp ovine AMY DNA fragment was located on chromosome 1 by linkage mapping using the International Mapping Flock. No association was found between estimated breeding values for milk yield, protein and fat contents and AMY genotypes in a daughter design comprising 13 Manchega families with an average of 29 daughters (12-62) per sire.     

Genetic design and statistical power of nested association mapping in maize

We investigated the genetic and statistical properties of the nested association mapping (NAM) design currently being implemented in maize (26 diverse founders and 5000 distinct immortal genotypes) to dissect the genetic basis of complex quantitative traits. The NAM design simultaneously exploits the advantages of both linkage analysis and association mapping. We demonstrated the power of NAM for high-power cost-effective genome scans through computer simulations based on empirical marker data and simulated traits with different complexities. With common-parent-specific (CPS) markers genotyped for the founders and the progenies, the inheritance of chromosome segments nested within two adjacent CPS markers was inferred through linkage. Genotyping the founders with additional high-density markers enabled the projection of genetic information, capturing linkage disequilibrium information, from founders to progenies. With 5000 genotypes, 30-79% of the simulated quantitative trait loci (QTL) were precisely identified. By integrating genetic design, natural diversity, and genomics technologies, this new complex trait dissection strategy should greatly facilitate endeavors to link molecular variation with phenotypic variation for various complex traits.     

QTLs for susceptibility to <Emphasis Type="Italic">Stemphylium vesicarium</Emphasis> in pear

Brown spot is one of the most serious fungal diseases that can affect pear fruits and leaves in the Po valley (Italy). Stemphylium vesicarium is the causal agent of this disease, and several antifungal treatments, repeated throughout the period between bloom and harvest, are needed to control its spread. Many of the most important pear cultivars (such as ‘Abbé Fétel’) are very susceptible to this fungus, while others (such as ‘Bartlett’ and its mutated sports) are known to be resistant. Our research aimed to develop molecular markers linked to this trait. To this end, 92 seedlings derived from an ‘Abbé Fétel’?×?‘Max Red Bartlett’ cross were evaluated for resistance to S. vesicarium for two consecutive years by artificial inoculation with conidia on detached leaves and fruits under controlled conditions in greenhouse. The extent of the lesions was recorded at different time points. A major QTL for susceptibility was located at the lower end of linkage group 15 of ‘Abbé Fétel’. This region was saturated with three SSR markers, and the putative position of a susceptibility gene was also estimated by the single gene mapping approach. This putative gene was located at 2 cM far from the lower end of the linkage group. Molecular markers tightly associated to this locus represent a first step towards the development of MAS (marker-assisted selection) to support the selection of new pear genotypes more resistant to brown spot.     

Phenological dynamics of arrivals and departures on migratory birds in Cracov,Poland, and the environments in the 19th and 20th century

This study presents the results of phenological observations on arrival and departure terms of some migratory birds on the background of the thermal conditions course of Cracov and its vicinity during the 19th and 20th century. On the base of observations carried out in the years 1944–1978 it was found that some migratory birds had a tendency to change their arrival and departure terms. At the end of this period they were noticed to arrive earlier and to depart later than at the beginning. These differences were more marked when compared with data from the 19th century. In the author's opinion these changes observed in the investigated area during the last decades result from a tendency of a warming up of climate in the Northern Hemisphere.Presented at the Eighth International Congress of Biometeorology, 9–14 September 1979, Shefayim, Israel.     

Association mapping of disease loci, by use of a pooled DNA genomic screen.

Genomic screening to map disease loci by association requires automation, pooling of DNA samples, and 3,000-6,000 highly polymorphic, evenly spaced microsatellite markers. Case-control samples can be used in an initial screen, followed by family-based data to confirm marker associations. Association mapping is relevant to genetic studies of complex diseases in which linkage analysis may be less effective and to cases in which multigenerational data are difficult to obtain, including rare or late-onset conditions and infectious diseases. The method can also be used effectively to follow up and confirm regions identified in linkage studies or to investigate candidate disease loci. Study designs can incorporate disease heterogeneity and interaction effects by appropriate subdivision of samples before screening. Here we report use of pooled DNA amplifications-the accurate determination of marker-disease associations for both case-control and nuclear family-based data-including application of correction methods for stutter artifact and preferential amplification. These issues, combined with a discussion of both statistical power and experimental design to define the necessary requirements for detecting of disease loci while virtually eliminating false positives, suggest the feasibility and efficiency of association mapping using pooled DNA screening.     

Design and sample-size considerations in the detection of linkage disequilibrium with a disease locus.

The presence of linkage disequilibrium between closely linked loci can aid in the fine mapping of disease loci. We investigate the power of several designs for sampling individuals with different disease genotypes. As expected, haplotype data provide the greatest power for detecting disequilibrium, but, in the absence of parental information to resolve the phase of double heterozygotes, the most powerful design samples only individuals homozygous at the trait locus. For rare diseases, such a scheme is generally not feasible, and we also provide power and sample-size calculations for designs that sample heterozygotes. The results provide information useful in planning disequilibrium studies.     

A model for linkage analysis with apomixis

Apomixis, or asexual reproduction through seeds, occurs in over 400 species of angiosperms. Although apomixis can favorably perpetuate desired genotypes through successive seed generation, it may also bring about some difficulty for linkage analysis and quantitative trait locus mapping. In this article, we explore the issue of how apomixis affects the precision and power of linkage analysis with molecular markers. We derive a statistical model for estimating the linkage between different markers when some progeny are derived from apomixis. The model was constructed within the maximum likelihood framework and implemented with the EM algorithm. A series of procedures are formulated to test the linkage of markers, the rate of apomixis, and the degree of genetic interference during meiosis. The model was examined and validated through simulation studies. The model will provide a tool for linkage mapping and evolutionary studies for plant species that undergo apomixis.     

家养动物复杂性状基因定位的统计分析和实验设计

复杂性状基因定位的研究是人类、动植物研究中的1个热点领域。在畜禽的研究中,其目的是定位与生产性状、繁殖性状和疾病相关的基因。在人类中,复杂性状基因定位的研究具有极大的挑战性。尽管基因定位的结果积累得很快,但能得以确认的结果却很少。关于畜禽基因定位的研究结果同样也增长很快,目前在鸡、猪、奶牛等物种中几个大尺度的基因定位工作也正在开展中。虽然在不远的将来能够得到新的、可确信的结果,但是如何精确地理解这些复杂性状的基因仍然需要一定的时间。近来,复杂性状基因定位的方法已被用于通过基因表达的数据研究转录调节因子的定位工作中,这是基因定位研究中1个新的领域。基因定位的统计分析和实验设计是基因定位研究中的关键性步骤,研究的目的在于讨论畜禽复杂性状基因定位的统计分析和实验设计的研究进展及今后的发展。     

Sequence analysis of the human hTg737 gene and its polymorphic sites in patients with autosomal recessive polycystic kidney disease

DNA sequence analysis of the human Tg737 gene was performed in 36 patients with the autosomal recessive form of polycystic kidney disease (ARPKD). Coding exons and their adjacent splice sites were screened for mutations. Pathogenic exon or splice region mutations were not identified although one exonic and two intronic polymorphic sites were discovered. These results are in agreement with another study that has recently reported linkage to Chromosome (Chr) 6p21-cen in a set of 16 ARPKD families. STS mapping has localized the gene to a YAC contig that includes D13S175 on chromosome 13q12.1. The polymorphisms found in the hTg737 gene will permit its future evaluation as a candidate gene for other recessive cystic renal diseases and as a modifier gene in human PKD.     

A Monte Carlo method for Bayesian analysis of linkage between single markers and quantitative trait loci. II. A simulation study

A Bayesian approach to the statistical mapping of Quantitative Trait Loci (QTLs) using single markers was implemented via Markov Chain Monte Carlo (MCMC) algorithms for parameter estimation and hypothesis testing. Parameters were estimated by marginal posterior means computed with a Gibbs sampler with data augmentation. Variables sampled included the augmented data (marker-QTL genotypes, polygenic effects), the event of linkage or nonlinkage, and the parameters (allele frequencies, QTL substitution effect, recombination rate, polygenic and residual variances). The analysis was evaluated empirically via application to simulated granddaughter designs consisting of 2000 sons, 20 related sires and their ancestors. Results obtained in this study and preliminary work on multiple linked markers and multiple QTLs support the usefulness of the Bayesian method for the statistical mapping of QTLs.