Old lessons learned anew: family-based methods for detecting genes responsible for quantitative and qualitative traits in the Genetic Analysis Workshop 17 mini-exome sequence data

Simpson, Claire L.; Justice, Cristina M.; Krishnan, Mera; Wojciechowski, Robert; Sung, Heejong; Cai, Jerry; Green, Tiffany; Lewis, Deyana D.; Behneman, Dana; Wilson, Alexander F.; Bailey-Wilson, Joan E.

doi:10.1186/1753-6561-5-s9-s83

Cited by 10 publications

(16 citation statements)

References 8 publications

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“…Simpson et al [2011] found that the nuclear family-based tests of association (ROMP and ASSOC) were more powerful than the linkage methods in detecting variants with causal effects on the quantitative traits Q1 and Q2 but that they also lacked stringent control of type I error.…”

Section: Resultsmentioning

confidence: 99%

Incorporating linkage information into a common disease/rare variant framework

Hinrichs

Suarez

2011

Genetic Epidemiology

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Recent developments in sequencing technology have allowed the investigation of the common disease/rare variant hypothesis. In the Genetic Analysis Workshop 17 data set, we have sequence data on both unrelated individuals and eight large extended pedigrees with simulated quantitative and qualitative phenotypes. Group 11, whose focus was incorporating linkage information, considered several different ways to use the extended pedigrees to identify causal genes and variants. The first issue was the use of standard linkage or identity-by-descent information to identify regions containing causal rare variants. We found that rare variants of large effect segregating through pedigrees were precisely the bailiwick of linkage analysis. For a common disease, we anticipate many risk loci, so a heterogeneity linkage analysis or an analysis of a single pedigree at a time may be useful. The second issue was using pedigree data to identify individuals for sequencing. If one can identify linked regions and even carriers of risk haplotypes, the sequencing will be substantially more efficient. In fact, sequencing only 2.5% of the genome in carefully selected individuals can detect 52% of the risk variants that would be detected through whole-exome sequencing in a large number of unrelated individuals. Finally, we found that linkage information from pedigrees can provide weights for case-control association tests. We also found that pedigree-based association tests have the same issues of binning variants and variant counting as those in tests of unrelated individuals. Clearly, when pedigrees are available, they can provide great assistance in the search for rare variants that influence common disorders.

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Section: Resultsmentioning

confidence: 99%

Incorporating linkage information into a common disease/rare variant framework

Hinrichs

Suarez

2011

Genetic Epidemiology

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“…37 Thus, these linkage results may be very useful in WGS studies of individuals with a family history of PrCa, because they will allow us to prioritize these regions in the search for rare variants with major effects on PrCa risk. [81][82][83] …”

Section: Discussionmentioning

confidence: 99%

Genetic heterogeneity in Finnish hereditary prostate cancer using ordered subset analysis

et al. 2012

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Prostate cancer (PrCa) is the most common male cancer in developed countries and the second most common cause of cancer death after lung cancer. We recently reported a genome-wide linkage scan in 69 Finnish hereditary PrCa (HPC) families, which replicated the HPC9 locus on 17q21-q22 and identified a locus on 2q37. The aim of this study was to identify and to detect other loci linked to HPC. Here we used ordered subset analysis (OSA), conditioned on nonparametric linkage to these loci to detect other loci linked to HPC in subsets of families, but not the overall sample. We analyzed the families based on their evidence for linkage to chromosome 2, chromosome 17 and a maximum score using the strongest evidence of linkage from either of the two loci. Significant linkage to a 5-cM linkage interval with a peak OSA nonparametric allele-sharing LOD score of 4.876 on Xq26.3-q27 (DLOD ¼ 3.193, empirical P ¼ 0.009) was observed in a subset of 41 families weakly linked to 2q37, overlapping the HPCX1 locus. Two peaks that were novel to the analysis combining linkage evidence from both primary loci were identified; 18q12.1-q12.2 (OSA LOD ¼ 2.541, DLOD ¼ 1.651, P ¼ 0.03) and 22q11.1-q11.21 (OSA LOD ¼ 2.395, DLOD ¼ 2.36, P ¼ 0.006), which is close to HPC6. Using OSA allows us to find additional loci linked to HPC in subsets of families, and underlines the complex genetic heterogeneity of HPC even in highly aggregated families.

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“…The strategy has also been both proposed and used in the search for loci contributing to complex traits (Wright et al 1999). This same strategy of using large pedigrees also worked well to identify loci for simulated trait data as part of Genetic Analysis Workshop 17 (Gagnon et al 2011; Simpson et al 2011; Wilson and Ziegler 2011) in which evidence for linkage was clearly identified in the large pedigrees, without a clear comparable signal in sequence data available for unrelated subjects.…”

Section: Advantages Of Family-based Designsmentioning

confidence: 94%

“…The trait loci may represent single genes with relatively high effects, or several closely linked loci, each with moderate effects that together have a large effect (Yazbek et al 2011). Large pedigrees intrinsically have more power for detection of linkage or estimation of effects than do equivalent-sized samples of smaller families (Wijsman and Amos 1997) or unrelated subjects, particularly in the presence of rare variants such as those found in sequence data (Gagnon et al 2011; Simpson et al 2011; Wilson and Ziegler 2011). If pedigrees are sufficiently large, they can individually implicate genomic regions.…”

Section: Advantages Of Family-based Designsmentioning

confidence: 99%

The role of large pedigrees in an era of high-throughput sequencing

Wijsman

2012

Hum Genet

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Rare variation is the current frontier in human genetics. The large pedigree design is practical, efficient, and well-suited for investigating rare variation. In large pedigrees, specific rare variants that co-segregate with a trait will occur in sufficient numbers so that effects can be measured, and evidence for association can be evaluated, by making use of methods that fully use the pedigree information. Evidence from linkage analysis can focus investigation, both reducing the multiple testing burden and expanding the variants that can be evaluated and followed up, as recent studies have shown. The large pedigree design requires only a small fraction of the sample size needed to identify rare variants of interest in population-based designs, and many highly suitable, well-understood, and available statistical and computational tools already exist. Samples consisting of large pedigrees with existing rich phenotype and genome scan data should be prime candidates for high-throughput sequencing in the search of the determinants of complex traits.

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Old lessons learned anew: family-based methods for detecting genes responsible for quantitative and qualitative traits in the Genetic Analysis Workshop 17 mini-exome sequence data

Cited by 10 publications

References 8 publications

Incorporating linkage information into a common disease/rare variant framework

Incorporating linkage information into a common disease/rare variant framework

Genetic heterogeneity in Finnish hereditary prostate cancer using ordered subset analysis

The role of large pedigrees in an era of high-throughput sequencing

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