Explain whole-genome and exome sequencing

6.1.5 · Biology › Genomics

Hum Actually Kya Sequence Kar Rahe Hain?

YEH kyun matter karta hai: Structural variants (bade deletions, duplications, inversions), promoters/enhancers mein regulatory mutations, non-coding RNA genes, aur mitochondrial DNA capture karta hai—woh cheezein jo exome sequencing miss kar deti hai.

YEH kyun matter karta hai: Mendelian disorders diagnose karne ke liye cost-effective hai. Agar koi mutation kisi protein ki amino acid sequence change karta hai (missense, nonsense, frameshift), toh exome sequencing use pakad lega. WGS se kaafi sasta (~$500vs$1000-3000), aur interpret karna bhi aasaan hai (filter karne ke liye kam variants hain).

Exome Capture Kaise Kaam Karta Hai? (First Principles Se Derivation)

Challenge: Next-Gen Sequencing (NGS) mein DNA fragmentation genome ko random ~200-400bp pieces mein tod deta hai. Hum sirf exon fragments kaise nikaalen?

Solution: Biotinylated RNA ya DNA probes use karke hybridization capture.

Step-by-Step Mechanism:

1. Genome fragment karo → Genomic DNA extract karo, use (sonication ya enzymatic tarike se) 200-400bp fragments mein shear karo
2. Adapters add karo → Fragment ends pe sequencing adapters (barcodes/indices ke saath) ligate karo
3. Hybridization → Fragments ko ~300,000-400,000 biotinylated oligonucleotide probes (120-mer sequences) ki library ke saath mix karo jo saare known exon sequences se complementary hain
   • Biotin kyun? Biotin streptavidin se tightly bind karta hai. Yeh "molecular Velcro" hai jo hum capture ke liye use karenge.
4. Capture → Streptavidin-coated magnetic beads add karo. Biotin-probe-exon complexes beads se chipak jaate hain.
5. Wash away → Magnetic separation se saare non-exonic fragments nikal jaate hain (woh probes se bound nahi the, toh wash ho jaate hain)
6. Elution → Heat probe-DNA hybrids ko denature kar deta hai, captured exon fragments release ho jaate hain
7. Sequence → Enriched exonic library NGS sequencer (Illumina, etc.) mein jaati hai

$E=\frac{(\text{exon reads}/\text{total reads}{)}_{\text{after capture}}}{(\text{exon reads}/\text{total reads}{)}_{\text{before capture}}}$

Expected coverage ki derivation:

• Human exome size: $L_{\text{exome}}\approx 30\times 10^6$ bp
• Human genome size: $L_{\text{genome}}\approx 3.2\times 10^9$ bp
• Genome ka fraction jo exome hai: $f=\frac{30\times 10^6}{3.2\times 10^9}\approx 0.009$ (0.9%)

Agar hum $N$ total sequencing reads of length $\ell$ generate karte hain (maan lo 150bp):

• Capture ke bina: Expected exome coverage depth = $\frac{N\cdot \ell \cdot f}{L_{\text{exome}}}$
• Efficiency $\eta$ par capture ke saath (fraction of reads jo on-target hain, typically 0.7-0.9):

$\text{Exome coverage}=\frac{N\cdot \ell \cdot \eta }{L_{\text{exome}}}$

Example: 100 million reads × 150bp × 0.8 on-target efficiency / 30Mbp = exons ki 40× mean coverage.

Approach: Exome sequencing order karo.

Result: SCN2A gene (voltage-gated sodium channel encode karta hai) mein ek de novo missense variant identify kiya. Mutation c.5645G>A (p.Arg1882Gln) exon 28 mein tha.

Exome YEH kyu kaam aaya:

1. Single-gene disorder → causal variant coding region mein hai
2. De novo → inherited nahi, toh parents ke exomes normal hain (trio analysis filter karne mein help karta hai)
3. SCN2A ek known epilepsy gene hai → variant interpretation seedha hai

Exome KAB FAIL hota: Agar patient mein splicing ko affect karne wala deep intronic mutation hota (jaise SCN2A intron 10 mein), toh WES us region ko sequence nahi karta. WGS use pakad leta.

WGS reveal karta hai: Chromosome 16p11.2 par ek 350kb deletion (ek known autism-associated CNV). Yeh deletion multiple genes ko puri tarah se remove kar deta hai.

WES kyun miss kiya:

• Exome sequencing deletions infer karne ke liye read-depth analysis use karta hai (agar kisi gene ke exons mein 50% normal coverage hai, toh heterozygous deletion suspect karo).
• LEKIN: Agar capture probes achhi tarah bind nahi karte, ya deletion kisi aisi gene mein hai jo achhi tarah covered nahi, toh WES ki sensitivity kam hoti hai.
• WGS deleted aur flanking regions mein uniform coverage deta hai → structural variant callers (Manta, Delly) discordant read pairs aur split reads dhundh ke breakpoints detect karte hain.

Math:

• WES coverage non-uniform hoti hai (kuch exons mein 10×, doosron mein 100×, introns mein 0×).
• WGS coverage ~30-40× har jagah hoti hai → 50% drop (40× se 20×) detect karne ki statistical power kaafi zyada hoti hai.

Whole-Genome Sequencing: Hume Extra Kya Milta Hai?

$P(X\ge k)=1-{\sum }_{i=0}^{k-1}\frac{C^i{e}^{-C}}{i!}$

Poisson kyun? Sequencing reads genome mein randomly generate hote hain. Agar mean coverage $C$ hai, toh kisi bhi specific base ko cover karne wale reads ki sankhya Poisson($C$) distribution follow karti hai.

Example: 30× mean coverage par, bases ka kitna fraction ≥10× covered hai?

• $P(X\ge 10)$ jab $C=30$: Hum compute karte hain $1-P(X\le 9)=1-0.0003\approx 99.997\%$

Variant calling ke liye:

• SNVs ke liye confident heterozygote calls hetu ~10-15× coverage chahiye
• Indels ke reliable detection ke liye ~20× chahiye
• Structural variants ke breakpoint resolution ke liye ~30× chahiye

WGS jo WES miss karta hai woh kya detect karta hai:

1. Regulatory variants: Promoters mein mutations (jaise melanoma, glioma mein TERT promoter mutations)
2. Deep intronic variants: Cryptic splice sites create karte hain (jaise neurofibromatosis cause karne wala NF1 intron 31 variant)
3. Non-coding RNA genes: lncRNAs, miRNAs (jaise MIR96 mutations hearing loss cause karte hain)
4. Repeat expansions: Huntington's disease (HTT mein CAG repeats), Fragile X (FMR1 mein CGG repeats)—though short-read WGS struggle karta hai; long-read sequencing better hai
5. Balanced translocations, inversions: Rearrangements jo copy number nahi badlate

WES kyun miss karta hai: Mutation TERT start codon se 124bp upstream hai, promoter mein (non-coding). Standard exome capture probes promoters ko target nahi karte.

Clinical impact: 60-80% glioblastomas mein yeh mutations hoti hain. Yeh prognostic markers aur potential therapeutic targets hain.

Comparison Table: Kab Kaunsa Use Karein?

Bioinformatics Pipeline (Simplified)

WGS aur WES dono similar analysis pipelines use karte hain:

1. Base calling → Raw images (Illumina) ko FASTQ (sequence + quality scores) mein convert karo
2. Alignment → Reads ko reference genome (hg38) se BWA-MEM use karke map karo → BAM file
3. Variant calling → Reference se differences identify karo:
   • SNVs/indels: GATK HaplotypeCaller, FreeBayes
   • CNVs: CNVkit (WES), Manta (WGS)
   • SVs: Delly, Lumpy (sirf WGS)
4. Annotation → Variant effects predict karo (VEP, ANNOVAR): synonymous, missense, frameshift, splice-site
5. Filtering → Common variants (gnomAD mein >1% frequency), low-quality calls remove karo
6. Interpretation → Remaining variants ko patient phenotype se match karo (ACMG criteria: pathogenic, likely pathogenic, VUS, benign)

Filtering cascade:

1. Population frequency: Agar allele frequency $f>0.01$ gnomAD mein hai toh remove karo (rare disease cause karne ki unlikely)
2. Predicted deleteriousness: Rakho agar CADD score $>20$ ho (top 1% most deleterious), ya PolyPhen/SIFT "damaging" predict kare
3. Inheritance pattern: Agar recessive disease suspected hai, sirf homozygous ya compound heterozygous variants rakho
4. Gene-disease association: Sirf un genes mein variants rakho jo patient ke phenotype cause karne ke liye known hain (HPO term matching)

Math: Agar hmare paas 25,000 variants hain:

• Frequency filter ke baad (rare rakho): ~500 variants (98% removed)
• Deleteriousness filter ke baad: ~100 variants (80% removed)
• Inheritance filter ke baad (jaise homozygous recessive): ~5-10 variants
• Gene-phenotype matching ke baad: 0-3 strong candidates

Yeh Sahi Kyun Lagta Hai: Exome = protein-coding genes, diseases broken proteins se hoti hain, toh exome sab kuch pakad lena chahiye.

Steel-Man (Is Idea Mein Merit Kyun Hai): Yeh sach hai ki ~85% known Mendelian disease mutations exons mein hain. Toh exome sequencing classic genetic diseases ke liye high sensitivity rakhta hai.

Reality Check:

1. Coverage gaps: ~2-5% exons poorly captured hote hain (GC-rich regions, high homology wale pseudogenes). Agar causal variant gap mein hai, WES miss kar deta hai.
2. Non-coding diseases: Promoter mutations (TERT, FOXP2), enhancer mutations (limb malformation, β-thalassemia), intronic splicing mutations (deep intronic NF1, Stargardt disease mein ABCA4).
3. Structural variants: Bade deletions, duplications, inversions short reads aur non-uniform coverage se detect karna mushkil hai.
4. Repeat expansions: Huntington's, Fragile X, spinocerebellar ataxias—WES in regions ko achhi tarah sequence nahi karta.

Fix: Agar WES negative hai lekin clinical suspicion high hai:

• WGS consider karo (5-10% diagnostic yield aur badhta hai)
• Targeted repeat expansion testing (Huntington's panel, Fragile X PCR)
• Mitochondrial genome sequencing (agar myopathy/encephalopathy hai)
• RNA sequencing (intronic variants se aberrant splicing detect karta hai)

Exome sequencing un 20,000 important chapters (kitaab ka lagbhag 1%) ki photocopy nikalne jaisa hai. Yeh fast aur sasta hai kyunki tum poori kitaab copy nahi kar rahe. Agar kisi chapter mein koi typo hai (gene mein mutation), photocopy mein woh dikh jayega, aur doctors samajh sakte hain ki tum bimar kyun ho.

Whole-genome sequencing POORI kitaab ki photocopy nikalne jaisa hai—saare 3 billion letters, blank pages, table of contents, index, aur ajeeb decorative margins samete. Yeh zyada cost karta hai aur paper ka ek bada dhera create karta hai, lekin ab tum dekh sakte ho ki kisi ne margins mein secret notes likhe hain (regulatory mutations) ya poore pages phaad diye hain (bade deletions).

Poori kitaab ki zaroorat kab hoti hai? Agar doctor ne sirf chapters (exome) ki photocopy nikali aur typo nahi mila, shayad woh table of contents ya margin note mein hai—kuch aisi cheez jo control karti hai KAB ek chapter padha jaata hai, chapter khud nahi. Tab tumhe use dhundhne ke liye whole-genome sequencing chahiye.

Connections

• Next-Generation Sequencing Technologies – underlying Illumina/Ion Torrent tech
• Variant Calling and Annotation – bioinformatics pipeline details
• Copy Number Variation Detection – WGS vs WES mein CNVs kaise differently dhundhe jaate hain
• RNA Sequencing – DNA sequencing se miss hone wale splicing defects detect karne ka complementary approach
• Long-Read Sequencing – repeat expansions aur phasing ke liye PacBio/Nanopore
• Trio Analysis in Clinical Genetics – de novo variants filter karne ke liye proband + parents compare karna
• ACMG Variant Interpretation Guidelines – variants ko pathogenic classify kaise karein
• Pharmacogenomics – drug metabolism predict karne ke liye WGS use karna
• Population Genomics – common variants filter karne ke liye gnomAD database

#flashcards/biology

Whole-genome sequencing (WGS) kya hai? :: Ek organism ke genome ki complete DNA sequence determine karna (~3.2 billion bp humans mein), jisme saare coding aur non-coding regions, regulatory elements, aur structural variants shamil hain.

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