Exome sequence comparison of seventy-seven multiple myeloma cases identifies potential risk alleles

honors thesisCollege of EngineeringBiomedical EngineeringNicola J. CampMultiple Myeloma (MM) is a heritable cancer of plasma cells with poor prognosis. Although a few genomic risk-loci have been identified for MM, no risk variants have been published that explain MM heritability. We hypothesize MM heritability is due to rare germ-line variants that can be discovered through sequence comparison in high-risk MM cases. To uncover these rare risk-variants we sequenced the exomes (all protein-coding regions) of seventy-seven cases in high-risk pedigrees or diagnosed younger than usual. Alleles variant from a reference genome were prioritized based on sharing between cases and rarity in unaffected controls. Initial prioritization resulted in 7,344 variants which were further prioritized based on proximity to GWAS loci and effect on protein function. Six variants were within a thousand base pairs from a published GWAS locus. Of the six, an intronic SNP in CCND1 is of special interest as CCND1 is somatically altered in the tumors of 30% of MM cases and important in cell cycle progression. One hundred-nine variants were predicted to have high impact on protein function. Four of these variants were seen in additional samples including a frame-shift deletion in HAUS3. This variant is of special interest as HAUS3 regulates cell cycle progression in hematopoietic stem and progenitor cells. The potential risk-variants identified in this study demonstrate rare, genomic variants likely contribute to MM risk and can be identified through sequence comparison. These rare risk-variants could shed light on the genetic factors effecting MM and lead to eventual improved early detection and personalized treatment

Duo Shared Genomic Segment analysis identifies a genome-wide significant risk locus at 18q21.33 in myeloma pedigrees

Aim: High-risk pedigrees (HRPs) are a powerful design to map highly penetrant risk genes. We previously described Shared Genomic Segment (SGS) analysis, a mapping method for single large extended pedigrees that also addresses genetic heterogeneity inherent in complex diseases. SGS identifies shared segregating chromosomal regions that may inherit in only a subset of cases. However, single large pedigrees that are individually powerful (at least 15 meioses between studied cases) are scarce. Here, we expand the SGS strategy to incorporate evidence from two extended HRPs by identifying the same segregating risk locus in both pedigrees and allowing for some relaxation in the size of each HRP.Methods: Duo-SGS is a procedure to combine single-pedigree SGS evidence. It implements statistically rigorous duo-pedigree thresholding to determine genome-wide significance levels that account for optimization across pedigree pairs. Single-pedigree SGS identifies optimal segments shared by case subsets at each locus across the genome, with nominal significance assessed empirically. Duo-SGS combines the statistical evidence for SGS segments at the same genomic location in two pedigrees using Fisher’s method. One pedigree is paired with all others and the best duo-SGS evidence at each locus across the genome is established. Genome-wide significance thresholds are determined through distribution-fitting and the Theory of Large Deviations. We applied the duo-SGS strategy to eleven extended, myeloma HRPs.Results: We identified one genome-wide significant region at 18q21.33 (0.85 Mb, P = 7.3 × 10-9) which contains one gene, CDH20. Thirteen regions were genome-wide suggestive: 1q42.2, 2p16.1, 3p25.2, 5q21.3, 5q31.1, 6q16.1, 6q26, 7q11.23, 12q24.31, 13q13.3, 18p11.22, 18q22.3 and 19p13.12.Conclusion: Our results provide novel risk loci with segregating evidence from multiple HRPs and offer compelling targets and specific segment carriers to focus a future search for functional variants involved in inherited risk formyeloma

Sequencing at lymphoid neoplasm susceptibility loci maps six myeloma risk genes

International audienceAbstract Inherited genetic risk factors play a role in multiple myeloma (MM), yet considerable missing heritability exists. Rare risk variants at genome-wide association study (GWAS) loci are a new avenue to explore. Pleiotropy between lymphoid neoplasms (LNs) has been suggested in family history and genetic studies, but no studies have interrogated sequencing for pleiotropic genes or rare risk variants. Sequencing genetically enriched cases can help discover rarer variants. We analyzed exome sequencing in familial or early-onset MM cases to identify rare, functionally relevant variants near GWAS loci for a range of LNs. A total of 149 distinct and significant LN GWAS loci have been published. We identified six recurrent, rare, potentially deleterious variants within 5 kb of significant GWAS single nucleotide polymorphisms in 75 MM cases. Mutations were observed in BTNL2, EOMES, TNFRSF13B, IRF8, ACOXL and TSPAN32. All six genes replicated in an independent set of 255 early-onset MM or familial MM or precursor cases. Expansion of our analyses to the full length of these six genes resulted in a list of 39 rare and deleterious variants, seven of which segregated in MM families. Three genes also had significant rare variant burden in 733 sporadic MM cases compared with 935 control individuals: IRF8 (P = 1.0 × 10−6), EOMES (P = 6.0 × 10−6) and BTNL2 (P = 2.1 × 10−3). Together, our results implicate six genes in MM risk, provide support for genetic pleiotropy between LN subtypes and demonstrate the utility of sequencing genetically enriched cases to identify functionally relevant variants near GWAS loci

Novel pedigree analysis implicates DNA repair and chromatin remodeling in multiple myeloma risk

<div><p>The high-risk pedigree (HRP) design is an established strategy to discover rare, highly-penetrant, Mendelian-like causal variants. Its success, however, in complex traits has been modest, largely due to challenges of genetic heterogeneity and complex inheritance models. We describe a HRP strategy that addresses intra-familial heterogeneity, and identifies inherited segments important for mapping regulatory risk. We apply this new Shared Genomic Segment (SGS) method in 11 extended, Utah, multiple myeloma (MM) HRPs, and subsequent exome sequencing in SGS regions of interest in 1063 MM / MGUS (monoclonal gammopathy of undetermined significance–a precursor to MM) cases and 964 controls from a jointly-called collaborative resource, including cases from the initial 11 HRPs. One genome-wide significant 1.8 Mb shared segment was found at 6q16. Exome sequencing in this region revealed predicted deleterious variants in <i>USP45</i> (p.Gln691* and p.Gln621Glu), a gene known to influence DNA repair through endonuclease regulation. Additionally, a 1.2 Mb segment at 1p36.11 is inherited in two Utah HRPs, with coding variants identified in <i>ARID1A</i> (p.Ser90Gly and p.Met890Val), a key gene in the SWI/SNF chromatin remodeling complex. Our results provide compelling statistical and genetic evidence for segregating risk variants for MM. In addition, we demonstrate a novel strategy to use large HRPs for risk-variant discovery more generally in complex traits.</p></div

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

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