Positional Cloning of Disease Genes: Advantages of Genetic Isolates

Peltonen, Leena

doi:10.1159/000022892

Cited by 93 publications

(64 citation statements)

References 48 publications

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“…The confounding effects of locus and allele heterogeneity can be reduced by selecting an isolated population (e.g. Peltonen, 2000;Jorde et al 2000), or homogenizing the sample by analyzing subgroups among the subjects separately (Leal & Ott 2000). Alternatively, one can use partitioning approaches, where individuals are assigned into hidden subgroups and different genetic mechanisms are allowed within each group (Whittemore & Halpern 2001;Schaid et al 2001;Sillanpää et al 2001;Shannon et al 2001;Province et al 2001;Schork et al 2001;Grigull et al 2001;Seaman et al 2002;Ritchie et al 2003).…”

Section: Genetic Architecture: Trait Locimentioning

confidence: 99%

Replication in genetic studies of complex traits

Sillanpää

Auranen

2004

Annals of Human Genetics

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SummaryDisappointments in replicating initial findings in gene mapping for complex traits are often attributed to small sample sizes and inadequate techniques to determine the threshold value. This is clearly not the whole truth. More fundamental reasons lie in the inherent heterogeneity related to disease, including genetic heterogeneity, differences in allele frequencies, and context-dependency in genetic architecture. There are also other reasons related to the data collection and analysis. Replication may remain a source of frustration unless more emphasis is put on controlling these sources of heterogeneity between studies.

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Section: Genetic Architecture: Trait Locimentioning

confidence: 99%

Replication in genetic studies of complex traits

Sillanpää

Auranen

2004

Annals of Human Genetics

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“…For many species, however, the lack of a genomic map leads to an inability to separate background LD from other types of LD. Furthermore, events other than admixture, such as population bottlenecks (Lynch and Walsh, 1998) or important demographic changes, could also generate strong LD and increase the occurrence of background LD (Jorde, 2000;Peltonen, 2000;Puffenberger et al, 1994). In this situation, users should not rely on the genomic map alone to decide which loci to use or not.…”

Section: Introductionmentioning

confidence: 99%

Detecting population structure using STRUCTURE software: effect of background linkage disequilibrium

Kaeuffer¹,

Réale²,

Coltman³

et al. 2007

Heredity

105

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STRUCTURE is the most widely used clustering software to detect population genetic structure. The last version of this software (STRUCTURE 2.1) has been enhanced recently to take into account the occurrence of linkage disequilibrium (LD) caused by admixture between populations. This last version, however, still does not consider the effects of strong background LD caused by genetic drift, and which may cause spurious results. STRUCTURE authors have, therefore, suggested a rough threshold value of the distance (1.0 cM) between two loci below which the pair of loci should not be used. Because of the sensitiveness of LD to demographic events, the distance between loci is not always a good indicator of the strength of LD. In this study, we examine the link between genomic distance and the strength of the correlation between loci (r LD ) in a free-ranging population of mouflon (Ovis aries), and we present an empirical test of effect of r LD on the clustering results provided by the linkage model in STRUCTURE. We showed that a high r LD value increases the probability of detecting spurious clustering. We propose to use r LD as an index to base a decision on whether or not to use a pair of loci in a clustering analysis. Keywords: STRUCTURE software; linkage disequilibrium; population structure; clustering Introduction STRUCTURE (Pritchard et al., 2000) is the most widely used clustering software applied to detect population genetic structure, with more than 1000 citations for its first version (Pritchard et al., 2000) and more than 170 citations for its recent enhanced version (Falush et al., 2003) (source: ISI Web of Science database). STRUCTURE generates clusters based on both transient HardyWeinberg disequilibrium (HWD) and linkage disequilibrium (LD) caused by admixture between populations. The program works by clustering individuals in groups, where both linkage and HWD are minimized, and therefore, the presence of LD in the data improves clustering results (Falush et al., 2003). On the other hand, 'strong' LD or departure from Hardy-Weinberg equilibrium could lead to an overestimation of the number of clusters detected (Falush et al., 2003).STRUCTURE deals with two kinds of LD: the first is mixture LD, which occurs across loci even if they are unlinked due to the correlation of allele frequencies 'because individuals with a large component of ancestry in population k have an excess of alleles that are common in k' (Falush et al., 2003). The second is admixture LD, which is 'the correlation that arises between linked markers in recently admixed populations ' (Pritchard and Wen, 2004). This LD occurs because markers are on the same 'chunk' of chromosome that derives from an ancestral population. The 'admixture model' implemented in the latest version of STRUCTURE (STRUCTURE 2.1;Falush et al., 2003) combines admixture LD with map distances between markers to improve clustering results. Falush et al. (2003) defined a third kind of LD: the background LD measured between syntenous loci separated by few cM. Background...

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“…Over the last 10 years, we have developed the Portuguese Island Collection to study the genetics of bipolar disorder and schizophrenia in a relatively homogeneous population [Pato et al, 1997]. Geographically and genetically isolated populations have played a key role in disease gene identification [Peltonen, 2000]. Focused on the Azorean and Madeira Islands, this study has benefited from the unique parallel history of these two archipelagoes (reviewed in Sklar et al [2004]).…”

Section: Introductionmentioning

confidence: 99%

Genome‐wide scan in Portuguese Island families implicates multiple loci in bipolar disorder: Fine mapping adds support on chromosomes 6 and 11

Pato

Kirby

et al. 2004

American J of Med Genetics Pt B

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As part of an extensive study in the Portuguese Island population of families with multiple patients suffering from bipolar disorder and schizophrenia, we performed an initial genome‐wide scan of 16 extended families with bipolar disorder that identified three regions on chromosomes 2, 11, and 19 with genome‐wide suggestive linkage and several other regions, including chromosome 6q, also approached suggestive levels of significance. Dick et al. [2003: Am J Hum Genet 73:107–114] recently reported in a study of 250 families with bipolar disorder a maxLOD score of 3.61 near marker D6S1021 on chromosome 6q. This study replicates this finding having detected a peak NPL = 2.02 (P = 0.025) with the same marker D6S1021(104.7 Mb). Higher‐density mapping provided additional support for loci on chromosome 6 including marker D6S1021 with an NPL = 2.59 (P = 0.0068) and peaking at marker D6S1639 (125 Mb) with an NPL = 3.06 (P = 0.0019). A similar pattern was detected with higher‐density mapping of chromosome 11 with an NPL = 3.15 (P = 0.0014) at marker D11S1883 (63.1 Mb). Simulations at the density of our fine mapping data indicate that less than 1 scan out of 10 would find two such scores genome‐wide in the same scan by chance. Our findings provide additional support for a susceptibility locus for bipolar disorder on 6q, as well as, suggesting the importance of denser scans. Published 2004 Wiley‐Liss, Inc.

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Positional Cloning of Disease Genes: Advantages of Genetic Isolates

Cited by 93 publications

References 48 publications

Replication in genetic studies of complex traits

Replication in genetic studies of complex traits

Detecting population structure using STRUCTURE software: effect of background linkage disequilibrium

Genome‐wide scan in Portuguese Island families implicates multiple loci in bipolar disorder: Fine mapping adds support on chromosomes 6 and 11

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