Proteomic Networks and Related Genetic Variants Associated with Smoking and Chronic Obstructive Pulmonary Disease.

Background: Studies have identified individual blood biomarkers associated with chronic obstructive pulmonary disease (COPD) and related phenotypes. However, complex diseases such as COPD typically involve changes in multiple molecules with interconnections that may not be captured when considering single molecular features. Methods: Leveraging proteomic data from 3,173 COPDGene Non-Hispanic White (NHW) and African American (AA) participants, we applied sparse multiple canonical correlation network analysis (SmCCNet) to 4,776 proteins assayed on the SomaScan v4.0 platform to derive sparse networks of proteins associated with current vs. former smoking status, airflow obstruction, and emphysema quantitated from high-resolution computed tomography scans. We then used NetSHy, a dimension reduction technique leveraging network topology, to produce summary scores of each proteomic network, referred to as NetSHy scores. We next performed genome-wide association study (GWAS) to identify variants associated with the NetSHy scores, or network quantitative trait loci (nQTLs). Finally, we evaluated the replicability of the networks in an independent cohort, SPIROMICS. Results: We identified networks of 13 to 104 proteins for each phenotype and exposure in NHW and AA, and the derived NetSHy scores significantly associated with the variable of interests. Networks included known (sRAGE, ALPP, MIP1) and novel molecules (CA10, CPB1, HIS3, PXDN) and interactions involved in COPD pathogenesis. We observed 7 nQTL loci associated with NetSHy scores, 4 of which remained after conditional analysis. Networks for smoking status and emphysema, but not airflow obstruction, demonstrated a high degree of replicability across race groups and cohorts. Conclusions: In this work, we apply state-of-the-art molecular network generation and summarization approaches to proteomic data from COPDGene participants to uncover protein networks associated with COPD phenotypes. We further identify genetic associations with networks. This work discovers protein networks containing known and novel proteins and protein interactions associated with clinically relevant COPD phenotypes across race groups and cohorts.

Gene expression associations with body mass index in the Multi-Ethnic Study of Atherosclerosis.

BACKGROUND/OBJECTIVES: Obesity, defined as excessive fat accumulation that represents a health risk, is increasing in adults and children, reaching global epidemic proportions. Body mass index (BMI) correlates with body fat and future health risk, yet differs in prediction by fat distribution, across populations and by age. Nonetheless, few genetic studies of BMI have been conducted in ancestrally diverse populations. Gene expression association with BMI was assessed in the Multi-Ethnic Study of Atherosclerosis (MESA) in four self-identified race and ethnicity (SIRE) groups to identify genes associated with obesity. SUBJECTS/METHODS: RNA-sequencing was performed on 1096 MESA participants (37.8% white, 24.3% Hispanic, 28.4% African American, and 9.5% Chinese American) and linear models were used to assess the association of expression from each gene for its effect on BMI, adjusting for age, sex, sequencing center, study site, five expression and four genetic principal components in each self-identified race group. Sample-size-weighted meta-analysis was performed to identify genes with BMI-associated expression across ancestry groups. RESULTS: Within individual SIRE groups, there were zero to three genes whose expression is significantly (p < 1.97 × 10-6) associated with BMI. Across all groups, 45 genes were identified by meta-analysis whose expression was significantly associated with BMI, explaining 29.7% of BMI variation. The 45 genes are expressed in a variety of tissues and cell types and are enriched for obesity-related processes including erythrocyte function, oxygen binding and transport, and JAK-STAT signaling. CONCLUSIONS: We have identified genes whose expression is significantly associated with obesity in a multi-ethnic cohort. We have identified novel genes associated with BMI as well as confirmed previously identified genes from earlier genetic analyses. These novel genes and their biological pathways represent new targets for understanding the biology of obesity as well as new therapeutic intervention to reduce obesity and improve global public health.

Report from the Killer-cell Immunoglobulin-like Receptors (KIR) component of the 17th International HLA and Immunogenetics Workshop.

The goals of the KIR component of the 17th International HLA and Immunogenetics Workshop (IHIW) were to encourage and educate researchers to begin analyzing KIR at allelic resolution, and to survey the nature and extent of KIR allelic diversity across human populations. To represent worldwide diversity, we analyzed 1269 individuals from ten populations, focusing on the most polymorphic KIR genes, which express receptors having three immunoglobulin (Ig)-like domains (KIR3DL1/S1, KIR3DL2 and KIR3DL3). We identified 13 novel alleles of KIR3DL1/S1, 13 of KIR3DL2 and 18 of KIR3DL3. Previously identified alleles, corresponding to 33 alleles of KIR3DL1/S1, 38 of KIR3DL2, and 43 of KIR3DL3, represented over 90% of the observed allele frequencies for these genes. In total we observed 37 KIR3DL1/S1 allotypes, 40 for KIR3DL2 and 44 for KIR3DL3. As KIR allotype diversity can affect NK cell function, this demonstrates potential for high functional diversity worldwide. Allelic variation further diversifies KIR haplotypes. We determined KIR3DL3 ∼ KIR3DL1/S1 ∼ KIR3DL2 haplotypes from five of the studied populations, and observed multiple population-specific haplotypes in each. This included 234 distinct haplotypes in European Americans, 191 in Ugandans, 35 in Papuans, 95 in Egyptians and 86 in Spanish populations. For another 35 populations, encompassing 642,105 individuals we focused on KIR3DL2 and identified another 375 novel alleles, with approximately half of them observed in more than one individual. The KIR allelic level data gathered from this project represents the most comprehensive summary of global KIR allelic diversity to date, and continued analysis will improve understanding of KIR allelic polymorphism in global populations. Further, the wealth of new data gathered in the course of this workshop component highlights the value of collaborative, community-based efforts in immunogenetics research, exemplified by the IHIW.