Exome sequencing and complex disease
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Exome sequencing and complex disease :. practical aspects of rare variant association studies Alice Bouchoms Amaury Vanvinckenroye Maxime Legrand. What is exome sequencing ?. Exon : coding sequence of the DNA Exome sequencing :

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Exome sequencing and complex disease

Exomesequencing and complexdisease :

practical aspects of rare variant association studies

Alice Bouchoms

Amaury Vanvinckenroye

Maxime Legrand


What is exome sequencing

Whatisexomesequencing ?

  • Exon : codingsequence of the DNA

  • Exomesequencing :

    • Aim : to sequence the coding part of the DNA i.e. the exons


Introduction

Introduction

  • GWAS : helpeddiscovercommoncodingvariants

  • Exomesequencing

    • Also rare codingvariants

    • Faster, better

    • large sample ( > 10 000 individuals)

    • Before 2010 : only few publications on PUBMED

    • Now : more than2000 publications on PUBMED

2013

2012

2011


Key questions to ask yourself

Key questions to askyourself


Study design

Study design

  • State objectives

  • Focus on extremeoutcomes

    • Unusualphenotype or traits

    • BUT : CAREFUL : de novo mutations

  • Geographical restrictions ?


Study design1

Study design

  • Sequencingstrategy ?

    • Quality of the sample : 20x or greaterlevel of coverage

      depth of sequencing/person : 60x or greater

    • Non-codingregions : canstillbeusefull

      Determineancestries or estimategenotype

      • 0,2x to 2x


Variant calling

Variant calling

  • Goal : obtainhigh-qualitygenotypes

  • Severalsteps:

    • DNA contamination, DNA fingerprints, good follow-up?

    • Alignmentwithreferencegenome, calibration of base quality score, removal of duplicate reads.


Variant calling1

Variant calling

  • Afterreadsmapping:

    • Samplequalitymetrics (spotting of outlierproperties)

  • Variant calling:

    • Look for differenceswhereoverlapsappear in alignmentwith the referencegenome


Variant calling2

Variant calling

  • Machine-learning-based classifier:

    • Polymorphic variants / artifacts

    • Evaluate metrics : true / false positives

  • Quality metrics on samples

  • Recommendation: min depth of coverage 20X

  • Development of standards for storing sequence data and variant calls


Association analysis

Association analysis

  • Goal: find functional effects of variants

  • Score: indicates the effect on the protein function Separation between variants with high damage and the others

  • If multiple annotations, 3 ways:

    • Focus on the longest transcript

    • Focus on the most deleterious effect

    • Focus on the canonical transcript


Association analysis1

Association analysis

  • Single variant association test

    Check of quality data

  • Usual way of processing rare variants: gather them in groups acting on the same gene to do the analysis


Association analysis2

Association analysis

  • 2 methods for processing groups:

    • Comparison of the number of variantsbetween cases and controls

    • Comparisonwith chance expectations

  • Recommendation: at least a test of eachcategorywithdifferentthresholds

  • If no threshold, variety of frequencycut-offs


Association analysis3

Association analysis

  • Packages available to perform the tests withsubsets of data

  • Example :

    • 1. missense, splice, stop alteringvariants

    • 2. subset of deleteriousvariants

    • 3. splice, stop alteringvariants


Association analysis4

Association analysis

  • No optimal choices for the analysisbecause of variability of variants and of theircharateristicsbetweengenes.

  • Permutation-basedapproaches

    Statisticalsignificance

  • If no permutation-basedthreshold,

    p values ≤ 5 10-7

  • QQ plots to summarize the results


Approaches for follow up

Approaches for follow-up

  • To demonstrate association based on the analysedsamples, additionalsamples are needed.


Approaches for follow up1

Approaches for follow-up

  • Exome chip experiments examine most of the varaints, but not very sensitive to non-European populations.


Approaches for follow up2

Approaches for follow-up

  • Statistical imputation

    Take the base whichhas the highestcorrelationwith the missing one, and assume itis the sameallelethan T (i.e. minor or major).

  • But again, often not possible for mixed populations


Role of functional assays

Role of functionalassays

  • Study the changes in the proteins due to codingvariants

  • Studywhythese changes result in diverse diseases.


Forward genetics

Forwardgenetics

  • Otherapproach to studyfunctionalvariants

  • First look atwhichproteins show changes

  • Thensearch in the DNA sequence for the variant(s)


Discussion

Discussion

  • In other articles :

    • more careful about the samplequality

    • gain of sensitivity in variant calls if made amongseveralsamples

    • indels in variant call are the major source of false positive. Needalignmentalgorithmwhichallowsgapped alignement

    • Check results of association in data bases


Discussion1

Discussion

  • Because of costs, exomesequencingstudies focus on coding part of the genome. Thus not suitable for non-exonicsequence. (stucturalvariants, chromosomalrearrangements)

  • Theseproblemswillbepartiallysolvedby the cut in costs of sequencing


References

references


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