Genetic linkage analysis in the age of whole-genome sequencing

Ott, Jürg; Wang, Jing; Leal, Suzanne M.

doi:10.1038/nrg3908

Cited by 246 publications

(190 citation statements)

References 86 publications

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“…However, in some coding regions, the segments sequenced by WES included only a small number of SNVs, resulting in a low information content and accounting for the small proportion of linked coding regions (7.4%) detected only by GWSA. Clearly, with the decreasing cost of whole-genome sequencing (48), optimal approaches will, in the future, involve linkage analysis together with other analyses of whole-genome sequencing data (49). However, it is currently possible to use WES data for PCA after the application of the appropriate QC filters and adjustment for population substructure to estimate homozygosity rates by ROH and perform reliable linkage analyses, particularly for coding regions.…”

Section: Discussionmentioning

confidence: 99%

Whole-exome sequencing to analyze population structure, parental inbreeding, and familial linkage

Belkadi

Pedergnana

Cobat

et al. 2016

Proc. Natl. Acad. Sci. U.S.A.

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Principal component analysis (PCA), homozygosity rate estimations, and linkage studies in humans are classically conducted through genome-wide single-nucleotide variant arrays (GWSA). We compared whole-exome sequencing (WES) and GWSA for this purpose. We analyzed 110 subjects originating from different regions of the world, including North Africa and the Middle East, which are poorly covered by public databases and have high consanguinity rates. We tested and applied a number of quality control (QC) filters. Compared with GWSA, we found that WES provided an accurate prediction of population substructure using variants with a minor allele frequency > 2% (correlation = 0.89 with the PCA coordinates obtained by GWSA). WES also yielded highly reliable estimates of homozygosity rates using runs of homozygosity with a 1,000-kb window (correlation = 0.94 with the estimates provided by GWSA). Finally, homozygosity mapping analyses in 15 families including a single offspring with high homozygosity rates showed that WES provided 51% less genome-wide linkage information than GWSA overall but 97% more information for the coding regions. At the genome-wide scale, 76.3% of linked regions were found by both GWSA and WES, 17.7% were found by GWSA only, and 6.0% were found by WES only. For coding regions, the corresponding percentages were 83.5%, 7.4%, and 9.1%, respectively. With appropriate QC filters, WES can be used for PCA and adjustment for population substructure, estimating homozygosity rates in individuals, and powerful linkage analyses, particularly in coding regions.exome sequencing | genotyping array | population structure | homozygosity mapping | linkage analysis W hole-exome sequencing (WES) has become the leading strategy for uncovering germ-line exome variants in humans. A number of gene-and variant-level methods have been proposed for the analysis of WES data to select candidate variants in rare Mendelian disorders and more common traits (1-13). These analyses benefit from the use of additional information, such as familial linkage, homozygosity rate, and ethnic background, which are commonly used in the study of inherited diseases (14-17). Genomewide single-nucleotide variant array (GWSAs) are the gold standard method for linkage analysis, because they provide maximal linkage information for the whole genome (18). GWSAs are also classically used to estimate homozygosity rate in patients, confirming or sometimes, revealing parental consanguinity through the inbreeding coefficient parameter F in particular (19,20). Population stratification can be an issue in the analysis of population-based genetic data, including WES, particularly for association studies (21-24). Population structures have been widely determined by GWSA (25, 26) in European (27), African (28, 29), Asian (30), Jewish (31), Mexican (32), and other populations (33). These analyses are mostly based on principal component analysis (PCA) (34), which can also be used to confirm or reveal the ethnicity of an individual patient (or his or her parents).U...

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Section: Discussionmentioning

confidence: 99%

Whole-exome sequencing to analyze population structure, parental inbreeding, and familial linkage

Belkadi

Pedergnana

Cobat

et al. 2016

Proc. Natl. Acad. Sci. U.S.A.

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“…22,23 However, to our knowledge, our paper is the first methodological one proving the robustness of linkage analysis performed on WES genotype data. Although application of linkage analysis, or IBD detection, directly on WES genotype data has already successfully reduced the search space of the causative disease gene in a few studies, [24][25][26][27] the conditions of validity of this strategy still remained uncertain.…”

Section: Discussionmentioning

confidence: 99%

Can whole-exome sequencing data be used for linkage analysis?

et al. 2015

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Whole-exome sequencing (WES) has become the strategy of choice to identify causal variants in monogenic disorders. However, the list of candidate variants can be quite large, including false positives generated by sequencing errors. To reduce this list of candidate variants to the most relevant ones, a cost-effective strategy would be to focus on regions of linkage identified through linkage analysis conducted with common polymorphisms present in WES data. However, the non-uniform exon coverage of the genome and the lack of knowledge on the power of this strategy have largely precluded its use so far. To compare the performance of linkage analysis conducted with WES and SNP chip data in different situations, we performed simulations on two pedigree structures with, respectively, a dominant and a recessive trait segregating. We found that the performance of the two sets of markers at excluding regions of the genome were very similar, and there was no real gain at using SNP chip data compared with using the common SNPs extracted from WES data. When analyzing the real WES data available for these two pedigrees, we found that the linkage information derived from the WES common polymorphisms was able to reduce by half the list of candidate variants identified by a simple filtering approach. Conducting linkage analysis with WES data available on pedigrees and excluding among the candidate variants those that fall in excluded linkage regions is thus a powerful and costeffective strategy to reduce the number of false-positive candidate variants.

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“…While subjective well-being measures are not accurately measured, if one accounts for measurement error in this analysis, it was found to only increase the amount of explained variation of additive genetic effects to range from 12 to 18%. 43 More recent developments for sequencing and linkage analysis that have been introduced in the genetics literature include Ott et al (2015) and Pabinger et al (2014), but to the best of our knowledge have yet to be used by economists. 44 For example, Lee and Shaikh (2014) and Lehrer et al (2016) provide a set of methodological tools to analyze heterogeneity in causal effects that can additionally incorporate corrections for multiple testing.…”

Section: Discussionmentioning

confidence: 99%

Are genetic markers of interest for economic research?

Lehrer

Ding

2017

IZA J Labor Policy

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The idea that genetic differences may explain a multitude of individual-level outcomes studied by economists is far from controversial. Since more datasets now contain measures of genetic variation, it is reasonable to postulate that incorporating genomic data in economic analyses will become more common. However, there remains much debate among academics as to, first, whether ignoring genetic differences in empirical analyses biases the resulting estimates. Second, several critics argue that since genetic characteristics are immutable, the incorporation of these variables into economic analysis will not yield much policy guidance. In this paper, we revisit these concerns and survey the main avenues by which empirically oriented economic researchers have utilized measures of genetic markers to improve our understanding of economic phenomena. We discuss the strengths, limitations, and potential of existing approaches and conclude by highlighting several prominent directions forward for future research.JEL Classification: I12, J19, I26

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Genetic linkage analysis in the age of whole-genome sequencing

Cited by 246 publications

References 86 publications

Whole-exome sequencing to analyze population structure, parental inbreeding, and familial linkage

Whole-exome sequencing to analyze population structure, parental inbreeding, and familial linkage

Can whole-exome sequencing data be used for linkage analysis?

Are genetic markers of interest for economic research?

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