Development of highly multiplex targeted proteomics assays in biofluids using the Stellar mass spectrometer.

The development of targeted assays that monitor biomedically relevant proteins is an important step in bridging discovery experiments to large scale clinical studies. Targeted assays are currently unable to scale to hundreds or thousands of targets. We demonstrate the generation of large-scale assays using a novel hybrid nominal mass instrument. The scale of these assays is achievable with the Stellar™ mass spectrometer through the accommodation of shifting retention times by real-time alignment, while being sensitive and fast enough to handle many concurrent targets. Assays were constructed using precursor information from gas-phase fractionated (GPF) data-independent acquisition (DIA). We demonstrate the ability to schedule methods from an orbitrap and linear ion trap acquired GPF DIA library and compare the quantification of a matrix-matched calibration curve from orbitrap DIA and linear ion trap parallel reaction monitoring (PRM). Two applications of these proposed workflows are shown with a cerebrospinal fluid (CSF) neurodegenerative disease protein PRM assay and with a Mag-Net enriched plasma extracellular vesicle (EV) protein survey PRM assay.

Evaluating the Performance of the Astral Mass Analyzer for Quantitative Proteomics Using Data-Independent Acquisition.

We evaluate the quantitative performance of the newly released Asymmetric Track Lossless (Astral) analyzer. Using data-independent acquisition, the Thermo Scientific Orbitrap Astral mass spectrometer quantifies 5 times more peptides per unit time than state-of-the-art Thermo Scientific Orbitrap mass spectrometers, which have long been the gold standard for high-resolution quantitative proteomics. Our results demonstrate that the Orbitrap Astral mass spectrometer can produce high-quality quantitative measurements across a wide dynamic range. We also use a newly developed extracellular vesicle enrichment protocol to reach new depths of coverage in the plasma proteome, quantifying over 5000 plasma proteins in a 60 min gradient with the Orbitrap Astral mass spectrometer.

Evaluating the Performance of the Astral Mass Analyzer for Quantitative Proteomics Using Data Independent Acquisition.

We evaluate the quantitative performance of the newly released Asymmetric Track Lossless (Astral) analyzer. Using data independent acquisition, the Thermo Scientific™ Orbitrap™ Astral™ mass spectrometer quantifies 5 times more peptides per unit time than state-of-the-art Thermo Scientific™ Orbitrap™ mass spectrometers, which have long been the gold standard for high resolution quantitative proteomics. Our results demonstrate that the Orbitrap Astral mass spectrometer can produce high quality quantitative measurements across a wide dynamic range. We also use a newly developed extra-cellular vesicle enrichment protocol to reach new depths of coverage in the plasma proteome, quantifying over 5,000 plasma proteins in a 60-minute gradient with the Orbitrap Astral mass spectrometer.

Meta-analysis of up to 622,409 individuals identifies 40 novel smoking behaviour associated genetic loci.

Smoking is a major heritable and modifiable risk factor for many diseases, including cancer, common respiratory disorders and cardiovascular diseases. Fourteen genetic loci have previously been associated with smoking behaviour-related traits. We tested up to 235,116 single nucleotide variants (SNVs) on the exome-array for association with smoking initiation, cigarettes per day, pack-years, and smoking cessation in a fixed effects meta-analysis of up to 61 studies (up to 346,813 participants). In a subset of 112,811 participants, a further one million SNVs were also genotyped and tested for association with the four smoking behaviour traits. SNV-trait associations with P < 5 × 10-8 in either analysis were taken forward for replication in up to 275,596 independent participants from UK Biobank. Lastly, a meta-analysis of the discovery and replication studies was performed. Sixteen SNVs were associated with at least one of the smoking behaviour traits (P < 5 × 10-8) in the discovery samples. Ten novel SNVs, including rs12616219 near TMEM182, were followed-up and five of them (rs462779 in REV3L, rs12780116 in CNNM2, rs1190736 in GPR101, rs11539157 in PJA1, and rs12616219 near TMEM182) replicated at a Bonferroni significance threshold (P < 4.5 × 10-3) with consistent direction of effect. A further 35 SNVs were associated with smoking behaviour traits in the discovery plus replication meta-analysis (up to 622,409 participants) including a rare SNV, rs150493199, in CCDC141 and two low-frequency SNVs in CEP350 and HDGFRP2. Functional follow-up implied that decreased expression of REV3L may lower the probability of smoking initiation. The novel loci will facilitate understanding the genetic aetiology of smoking behaviour and may lead to the identification of potential drug targets for smoking prevention and/or cessation.

Exome Chip Meta-analysis Fine Maps Causal Variants and Elucidates the Genetic Architecture of Rare Coding Variants in Smoking and Alcohol Use.

BACKGROUND: Smoking and alcohol use have been associated with common genetic variants in multiple loci. Rare variants within these loci hold promise in the identification of biological mechanisms in substance use. Exome arrays and genotype imputation can now efficiently genotype rare nonsynonymous and loss of function variants. Such variants are expected to have deleterious functional consequences and to contribute to disease risk. METHODS: We analyzed ∼250,000 rare variants from 16 independent studies genotyped with exome arrays and augmented this dataset with imputed data from the UK Biobank. Associations were tested for five phenotypes: cigarettes per day, pack-years, smoking initiation, age of smoking initiation, and alcoholic drinks per week. We conducted stratified heritability analyses, single-variant tests, and gene-based burden tests of nonsynonymous/loss-of-function coding variants. We performed a novel fine-mapping analysis to winnow the number of putative causal variants within associated loci. RESULTS: Meta-analytic sample sizes ranged from 152,348 to 433,216, depending on the phenotype. Rare coding variation explained 1.1% to 2.2% of phenotypic variance, reflecting 11% to 18% of the total single nucleotide polymorphism heritability of these phenotypes. We identified 171 genome-wide associated loci across all phenotypes. Fine mapping identified putative causal variants with double base-pair resolution at 24 of these loci, and between three and 10 variants for 65 loci. Twenty loci contained rare coding variants in the 95% credible intervals. CONCLUSIONS: Rare coding variation significantly contributes to the heritability of smoking and alcohol use. Fine-mapping genome-wide association study loci identifies specific variants contributing to the biological etiology of substance use behavior.

Inhibiting the Evolution of Antibiotic Resistance.

Efforts to battle antimicrobial resistance (AMR) are generally focused on developing novel antibiotics. However, history shows that resistance arises regardless of the nature or potency of new drugs. Here, we propose and provide evidence for an alternate strategy to resolve this problem: inhibiting evolution. We determined that the DNA translocase Mfd is an "evolvability factor" that promotes mutagenesis and is required for rapid resistance development to all antibiotics tested across highly divergent bacterial species. Importantly, hypermutator alleles that accelerate AMR development did not arise without Mfd, at least during evolution of trimethoprim resistance. We also show that Mfd's role in AMR development depends on its interactions with the RNA polymerase subunit RpoB and the nucleotide excision repair protein UvrA. Our findings suggest that AMR development can be inhibited through inactivation of evolvability factors (potentially with "anti-evolution" drugs)-in particular, Mfd-providing an unexplored route toward battling the AMR crisis.

Multiple nonglycemic genomic loci are newly associated with blood level of glycated hemoglobin in East Asians.

Glycated hemoglobin A1c (HbA1c) is used as a measure of glycemic control and also as a diagnostic criterion for diabetes. To discover novel loci harboring common variants associated with HbA1c in East Asians, we conducted a meta-analysis of 13 genome-wide association studies (GWAS; N = 21,026). We replicated our findings in three additional studies comprising 11,576 individuals of East Asian ancestry. Ten variants showed associations that reached genome-wide significance in the discovery data set, of which nine (four novel variants at TMEM79 [P value = 1.3 × 10(-23)], HBS1L/MYB [8.5 × 10(-15)], MYO9B [9.0 × 10(-12)], and CYBA [1.1 × 10(-8)] as well as five variants at loci that had been previously identified [CDKAL1, G6PC2/ABCB11, GCK, ANK1, and FN3KI]) showed consistent evidence of association in replication data sets. These variants explained 1.76% of the variance in HbA1c. Several of these variants (TMEM79, HBS1L/MYB, CYBA, MYO9B, ANK1, and FN3K) showed no association with either blood glucose or type 2 diabetes. Among individuals with nondiabetic levels of fasting glucose (<7.0 mmol/L) but elevated HbA1c (≥6.5%), 36.1% had HbA1c <6.5% after adjustment for these six variants. Our East Asian GWAS meta-analysis has identified novel variants associated with HbA1c as well as demonstrated that the effects of known variants are largely transferable across ethnic groups. Variants affecting erythrocyte parameters rather than glucose metabolism may be relevant to the use of HbA1c for diagnosing diabetes in these populations.

Joint effects of known type 2 diabetes susceptibility loci in genome-wide association study of Singapore Chinese: the Singapore Chinese health study.

BACKGROUND: Genome-wide association studies (GWAS) have identified genetic factors in type 2 diabetes (T2D), mostly among individuals of European ancestry. We tested whether previously identified T2D-associated single nucleotide polymorphisms (SNPs) replicate and whether SNPs in regions near known T2D SNPs were associated with T2D within the Singapore Chinese Health Study. METHODS: 2338 cases and 2339 T2D controls from the Singapore Chinese Health Study were genotyped for 507,509 SNPs. Imputation extended the genotyped SNPs to 7,514,461 with high estimated certainty (r(2)>0.8). Replication of known index SNP associations in T2D was attempted. Risk scores were computed as the sum of index risk alleles. SNPs in regions ± 100 kb around each index were tested for associations with T2D in conditional fine-mapping analysis. RESULTS: Of 69 index SNPs, 20 were genotyped directly and genotypes at 35 others were well imputed. Among the 55 SNPs with data, disease associations were replicated (at p<0.05) for 15 SNPs, while 32 more were directionally consistent with previous reports. Risk score was a significant predictor with a 2.03 fold higher risk CI (1.69-2.44) of T2D comparing the highest to lowest quintile of risk allele burden (p = 5.72 × 10(-14)). Two improved SNPs around index rs10923931 and 5 new candidate SNPs around indices rs10965250 and rs1111875 passed simple Bonferroni corrections for significance in conditional analysis. Nonetheless, only a small fraction (2.3% on the disease liability scale) of T2D burden in Singapore is explained by these SNPs. CONCLUSIONS: While diabetes risk in Singapore Chinese involves genetic variants, most disease risk remains unexplained. Further genetic work is ongoing in the Singapore Chinese population to identify unique common variants not already seen in earlier studies. However rapid increases in T2D risk have occurred in recent decades in this population, indicating that dynamic environmental influences and possibly gene by environment interactions complicate the genetic architecture of this disease.

Genome-wide testing of putative functional exonic variants in relationship with breast and prostate cancer risk in a multiethnic population.

Rare variation in protein coding sequence is poorly captured by GWAS arrays and has been hypothesized to contribute to disease heritability. Using the Illumina HumanExome SNP array, we successfully genotyped 191,032 common and rare non-synonymous, splice site, or nonsense variants in a multiethnic sample of 2,984 breast cancer cases, 4,376 prostate cancer cases, and 7,545 controls. In breast cancer, the strongest associations included either SNPs in or gene burden scores for genes LDLRAD1, SLC19A1, FGFBP3, CASP5, MMAB, SLC16A6, and INS-IGF2. In prostate cancer, one of the most associated SNPs was in the gene GPRC6A (rs2274911, Pro91Ser, OR = 0.88, P = 1.3 × 10(-5)) near to a known risk locus for prostate cancer; other suggestive associations were noted in genes such as F13A1, ANXA4, MANSC1, and GP6. For both breast and prostate cancer, several of the most significant associations involving SNPs or gene burden scores (sum of minor alleles) were noted in genes previously reported to be associated with a cancer-related phenotype. However, only one of the associations (rs145889899 in LDLRAD1, p = 2.5 × 10(-7) only seen in African Americans) for overall breast or prostate cancer risk was statistically significant after correcting for multiple comparisons. In addition to breast and prostate cancer, other cancer-related traits were examined (body mass index, PSA level, and alcohol drinking) with a number of known and potentially novel associations described. In general, these findings do not support there being many protein coding variants of moderate to high risk for breast and prostate cancer with odds ratios over a range that is probably required for protein coding variation to play a truly outstanding role in risk heritability. Very large sample sizes will be required to better define the role of rare and less penetrant coding variation in prostate and breast cancer disease genetics.

Hormone metabolism genes and mammographic density in Singapore Chinese women.

BACKGROUND: Female steroid hormone levels and exogenous hormone use influence breast cancer risk. We investigated the association between genetic variation in the hormone metabolism and signaling pathway and mammographic density, a strong predictor of breast cancer risk. METHODS: We genotyped 161 SNPs in 15 hormone metabolism pathway gene regions and evaluated mammographic density in 2,038 Singapore Chinese women. Linear regression analysis was used to investigate single-nucleotide polymorphism (SNP) and mammographic density association. An overall pathway summary was obtained using the adaptive ranked truncated product test. RESULTS: We did not find any of the individually tested SNPs to be associated with mammographic density after a multiple testing correction. There was no evidence of an overall effect on mammographic density of genetic variation in the hormone metabolism pathway. CONCLUSIONS: In this cross-sectional study, genetic variation in hormone metabolism pathway was not associated with mammographic density in Singapore Chinese women. IMPACT: Consistent with existing data from Caucasian populations, polymorphisms in hormone pathway genes are not likely to be strong predictors of mammographic density in Asian women.

Genetic variation in transforming growth factor beta 1 and mammographic density in Singapore Chinese women.

TGF-ß plays a critical role in normal mammary development and morphogenesis. Decreased TGF-ß signaling has been associated with increased mammographic density. Percent mammographic density (PMD) adjusted for age and body mass index (BMI) is a strong risk factor and predictor of breast cancer risk. PMD is highly heritable, but few genetic determinants have been identified. We investigated the association between genetic variation in TGFB1 and PMD using a cross-sectional study of 2,038 women who were members of the population-based Singapore Chinese Health Study cohort. We assessed PMD using a computer-assisted method. We used linear regression to examine the association between nine tagging single-nucleotide polymorphisms (SNP) of TGFB1 and PMD and their interaction with parity, adjusting for age, BMI, and dialect group. We calculated P values adjusted for correlated tests (P(ACT)) to account for multiple testing. The strongest association was observed for rs2241716. Adjusted PMD was higher by 1.5% per minor allele (P(ACT) = 0.04). When stratifying by parity, this association was limited to nulliparous women. For nulliparous women, adjusted PMD was higher by 8.6% per minor allele (P(ACT) = 0.003; P for interaction with parity = 0.002). Three additional TGFB1 tagging SNPs, which were in linkage disequilibrium with rs2241716, were statistically significantly associated with adjusted PMD (P(ACT) < 0.05) for nulliparous women. However, none of these three SNPs showed statistically significant association after adjusting for rs2241716. Our data support that TGFB1 genetic variation may be an important genetic determinant of mammographic density measure that predicts breast cancer risk, particularly in nulliparous women.

Genetic variation in peroxisome proliferator-activated receptor gamma, soy, and mammographic density in Singapore Chinese women.

BACKGROUND: PPARγ is a transcription factor important for adipogenesis and adipocyte differentiation. Data from animal studies suggest that PPARγ may be involved in breast tumorigenesis, but results from epidemiologic studies on the association between PPARγ variation and breast cancer risk have been mixed. Recent data suggest that soy isoflavones can activate PPARγ. We investigated the interrelations of soy, PPARγ, and mammographic density, a biomarker of breast cancer risk in a cross-sectional study of 2,038 women who were members of the population-based Singapore Chinese Health Study Cohort. METHODS: We assessed mammographic density using a computer-assisted method. We used linear regression to examine the association between 26 tagging single-nucleotide polymorphisms (SNP) of PPARγ and their interaction with soy intake and mammographic density. To correct for multiple testing, we calculated P values adjusted for multiple correlated tests (P(ACT)). RESULTS: Out of the 26 tested SNPs in the PPARγ, seven SNPs were individually shown to be statistically significantly associated with mammographic density (P(ACT) = 0.008-0.049). A stepwise regression procedure identified that only rs880663 was independently associated with mammographic density which decreased by 1.89% per-minor allele (P(ACT) = 0.008). This association was significantly stronger in high-soy consumers as mammographic density decreased by 3.97% per-minor allele of rs880663 in high-soy consumers (P(ACT) = 0.006; P for interaction with lower soy intake = 0.017). CONCLUSIONS: Our data support that PPARγ genetic variation may be important in determining mammographic density, particularly in high-soy consumers. IMPACT: Our findings may help to identify molecular targets and lifestyle intervention for future prevention research.

Genetic variation in insulin-like growth factor 2 may play a role in ovarian cancer risk.

The insulin-like growth factor (IGF) signaling axis plays an important role in cancer biology. We hypothesized that genetic variation in this pathway may influence risk of ovarian cancer. A three-center study of non-Hispanic whites including 1880 control women, 1135 women with invasive epithelial ovarian cancer and 321 women with borderline epithelial ovarian tumors was carried out to test the association between tag single-nucleotide polymorphisms (tSNPs) (n=58) in this pathway and risk of ovarian cancer. We found no association between variation in IGF1, IGFBP1 or IGFBP3 and risk of invasive disease, whereas five tSNPs in IGF2 were associated with risk of invasive epithelial ovarian cancer at P<0.05 and followed-up one of the associated SNPs. We conducted genotyping in 3216 additional non-Hispanic white cases and 5382 additional controls and were able to independently replicate our initial findings. In the combined set of studies, rs4320932 was associated with a 13% decreased risk of ovarian cancer per copy of the minor allele carried (95% confidence interval 0.81-0.93, P-trend=7.4 × 10(-5)). No heterogeneity of effect across study centers was observed (p(het)=0.25). IGF2 is emerging as an important gene for ovarian cancer; additional genotyping is warranted to further confirm these associations with IGF2 and to narrow down the region harboring the causal SNP.

Genetic variation in the progesterone receptor gene and risk of endometrial cancer: a haplotype-based approach.

BACKGROUND: It is well established that estrogen increases endometrial cancer risk, whereas progesterone opposes the estrogen effects. The PROGINS allele of the progesterone receptor (PGR) gene reduces the function of PGR and has been associated with increased risk of the endometrioid type ovarian cancer. We investigated whether genetic variation in PGR is also associated with endometrial cancer risk using a haplotype-based approach. METHODS: We pooled data from two endometrial cancer case-control studies that were nested within two prospective cohorts, the Multiethnic Cohort Study and the California Teachers Study. Seventeen haplotype-tagging single nucleotide polymorphisms (SNPs) across four linkage disequilibrium (LD) blocks spanning the PGR locus were genotyped in 583 incident cases and 1936 control women. Odds ratios (ORs) and 95% confidence intervals (CIs) associated with each haplotype were estimated using conditional logistic regression, stratified by age and ethnicity. RESULTS: Genetic variation in LD block 3 of the PGR locus was associated with endometrial cancer risk (P(global test) = 0.002), with haplotypes 3C, 3D and 3F associated with 31-34% increased risk. Among whites (383 cases/840 controls), genetic variation in all four blocks was associated with increased endometrial cancer risk (P(global test) = 0.010, 0.013, 0.005 and 0.020). Haplotypes containing the PROGINS allele and several haplotypes in blocks 1, 3 and 4 were associated with 34-77% increased risk among whites. SNP analyses for whites suggested that rs608995, partially linked to the PROGINS allele (r(2) = 0.6), was associated with increased risk (OR = 1.30, 95% CI = 1.06-1.59). CONCLUSIONS: Our results suggest that genetic variation in the PGR region is associated with endometrial cancer risk.

Screening and association testing of common coding variation in steroid hormone receptor co-activator and co-repressor genes in relation to breast cancer risk: the Multiethnic Cohort.

BACKGROUND: Only a limited number of studies have performed comprehensive investigations of coding variation in relation to breast cancer risk. Given the established role of estrogens in breast cancer, we hypothesized that coding variation in steroid receptor coactivator and corepressor genes may alter inter-individual response to estrogen and serve as markers of breast cancer risk. METHODS: We sequenced the coding exons of 17 genes (EP300, CCND1, NME1, NCOA1, NCOA2, NCOA3, SMARCA4, SMARCA2, CARM1, FOXA1, MPG, NCOR1, NCOR2, CALCOCO1, PRMT1, PPARBP and CREBBP) suggested to influence transcriptional activation by steroid hormone receptors in a multiethnic panel of women with advanced breast cancer (n = 95): African Americans, Latinos, Japanese, Native Hawaiians and European Americans. Association testing of validated coding variants was conducted in a breast cancer case-control study (1,612 invasive cases and 1,961 controls) nested in the Multiethnic Cohort. We used logistic regression to estimate odds ratios for allelic effects in ethnic-pooled analyses as well as in subgroups defined by disease stage and steroid hormone receptor status. We also investigated effect modification by established breast cancer risk factors that are associated with steroid hormone exposure. RESULTS: We identified 45 coding variants with frequencies > or = 1% in any one ethnic group (43 non-synonymous variants). We observed nominally significant positive associations with two coding variants in ethnic-pooled analyses (NCOR2: His52Arg, OR = 1.79; 95% CI, 1.05-3.05; CALCOCO1: Arg12His, OR = 2.29; 95% CI, 1.00-5.26). A small number of variants were associated with risk in disease subgroup analyses and we observed no strong evidence of effect modification by breast cancer risk factors. Based on the large number of statistical tests conducted in this study, the nominally significant associations that we observed may be due to chance, and will need to be confirmed in other studies. CONCLUSION: Our findings suggest that common coding variation in these candidate genes do not make a substantial contribution to breast cancer risk in the general population. Cataloging and testing of coding variants in coactivator and corepressor genes should continue and may serve as a valuable resource for investigations of other hormone-related phenotypes, such as inter-individual response to hormonal therapies used for cancer treatment and prevention.

Comprehensive association testing of common genetic variation in DNA repair pathway genes in relationship with breast cancer risk in multiple populations.

Genetic association studies of multiple populations investigate a wider range of risk alleles than studies of a single ethnic group. In this study, we developed a multiethnic tagging strategy, exploiting differences in linkage disequilibrium (LD) structure between populations, to comprehensively capture common genetic variation across 60 genes spanning multiple DNA repair pathways, in five racial/ethnic populations. Over 2600 SNPs were genotyped in each population and single- and multi-marker predictors of common alleles were selected to capture the LD patterns specific to each group. Coding variants (n = 211) were genotyped to test whether combinations of putative functional variants in DNA repair pathway genes could have cumulative effects on risk. Tests of association were conducted in a multiethnic breast cancer study (2093 cases and 2303 controls), with validation of the top allelic associations (P