Additional file 2: Table S1. of Impact of post-alignment processing in variant discovery from whole exome data

Five public exome-seq data in NA12878. Table S2: Change of SNP calling sensitivity and precision rate after local realignment. (PDF 49 kb

Additional file 1: Figure S1. of Impact of post-alignment processing in variant discovery from whole exome data

Change of INDEL calling precision rate following local realignment. Figure S2: Local realignment and BQSR for SAMtools/BCFtools consensus-caller and multiallelic-caller. Figure S3: BQSR changed variant calling sensitivity in NA12878. Figure S4: Change of known and novel SNPs in NA12878 after BQSR. Figure S5: Change of known and novel INDELs in NA12878 by BQSR. Figure S6: Local realignment and BQSR with hg19 versus hg38. (PDF 769 kb

Additional file 2: Table S1. of An analytical workflow for accurate variant discovery in highly divergent regions

Five mappers and five variant callers used in the study. Table S2. SNP and INDEL calling precision rate in simulated data. Table S3. Percent of SNP calling sensitivity for three callers in simulated data. Table S4. INDEL calling sensitivity for two callers in simulated data. Table S5. Known INDELs in the HLA region of NA12878. Table S6. Novel INDELs in the HLA region of NA12878. Table S7. Number of novel SNPs in 22 CLL samples. Table S8. Number of INDELs in 22 CLL samples. (PDF 170 kb

Adequacy of the gamma distribution.

<p>The gamma distribution provides an adequate fit for multiple types of pedigrees. For example, HRP UT-549917 has <i>k</i> = 4.4 and <i>σ</i> = 3.6 with good visual density (a) and CDF (b) fit, with <i>λ</i> = 0.9. (Goodness of fit was estimated with <i>λ</i>, the median of empirical chi-squared distribution divided by the median of the expected chi-squared distribution.) HRP UT-34955 has <i>k</i> = 2.8 and <i>σ</i> = 2.9 with good visual density (c) and CDF (d) fit, with <i>λ</i> = 1.0.</p

Significant or overlapping SGSs and segregating SNVs.

<p>Significant or overlapping SGSs and segregating SNVs.</p

Significant SGS, pedigrees, and segregating SNVs.

<p>In pedigrees, MM cases are fully shaded and MGUS cases are half shaded. Numbers indicate multiple individuals. a) Utah pedigree, 571744, sharing the genome-wide significant SGS. The pedigree is trimmed to allow for viewing (37 MM confirmed cases are known in this pedigree, 3 were ascertained and genotyped). + indicates the genotyped MM cases that are SGS carriers, − indicates genotyped and non-carriers, no carrier status indicates not genotyped. Note–the genealogy extends beyond SEER cancer registry data. MGUS status is unknown in this pedigree. b) Genomic region of significant SGS. c) INSERM pedigree carrying the stop gain SNV marked by “c” in box e. 1 MM and 2 MGUSs carry the SNV. d) Mayo Clinic pedigree carrying the missense SNV marked by “d” in box e. 1 MM and 1 MGUS carry the SNV, but 2 unaffected siblings do not carry the SNV. e) Risk candidate gene, <i>USP45</i>, has 2 segregating SNVs in the ubiquitin C-terminal hydrolase 2 (UCH) domain.</p

SGS with multiple lines of evidence.

<p>a/b) Utah pedigrees carrying the overlapping SGSs on chr1p36.11-p35.1. + indicates the genotyped MM cases that are SGS carriers, − indicates genotyped and non-carriers, no carrier status indicates not genotyped. c) Weill Cornell pedigree with a segregating, missense SNV in <i>ARID1A</i> indicated by “c” in box e. d) Genomic region of overlapping SGS. Dark black genes fall in both regions. e) 2 rare and segregating, missense SNVs were observed in whole-exome sequencing. SNV “b” is carried by the cases indicated with + in box b. SNV “c” in carried by the cases in box c.</p

Genome-wide significance thresholds.

<p>Fitted distributions are stable enough for threshold determination after 100,000 to 300,000 simulations.</p

Adequacy of the gamma distribution.

<p>The gamma distribution provides an adequate fit for multiple types of pedigrees. For example, HRP UT-549917 has <i>k</i> = 4.4 and <i>σ</i> = 3.6 with good visual density (a) and CDF (b) fit, with <i>λ</i> = 0.9. (Goodness of fit was estimated with <i>λ</i>, the median of empirical chi-squared distribution divided by the median of the expected chi-squared distribution.) HRP UT-34955 has <i>k</i> = 2.8 and <i>σ</i> = 2.9 with good visual density (c) and CDF (d) fit, with <i>λ</i> = 1.0.</p

Regional plots of breast cancer association in 1p12-11.2.

<p>Regional plot of association result, recombination hotspots and linkage disequilibrium for the 1p12-11.2:120,505,799–121,481,132 breast cancer susceptibility loci. Association result from a trend test in—log10<i>P</i>values (y axis, left; red diamond, the top ranked breast cancer associated locus in the region; blue diamond, best conditioned analysis results conditioned on rs11249433; black diamonds, genotyped SNPs; gray diamonds, imputed SNPs) of the SNPs are shown according to their chromosomal positions (x axis). Linkage disequilibrium structure based on the 1000 Genomes CEU data (n = 85) was visualized by snp.plotter software. The line graph shows likelihood ratio statistics (y axis, right) for recombination hotspot by SequenceLDhot software based on the background recombination rates inferred by PHASE v2.1. Physical locations are based on hg19. Gene annotation was based on the NCBI RefSeq genes from the UCSC Genome Browser.</p