1. Nocturnal asthma and the importance of race/ethnicity, genetic ancestry, and the early life microbiome Methods and Application for Health Disparities Research Health Disparities Research Collaborative Albert M. Levin, Ph.D Department of Public Health Sciences Henry Ford Health System

2. Outline Nocturnal asthma Ethnically diverse study of nocturnal asthma in adolescents and adults with asthma ▫ Methods to study genetic admixture Early-life gastrointestinal microbiome and nocturnal asthma ▫ Methods to study the microbiome

3. RAR-Related Orphan Receptor A (RORA) Gene Genome-wide association study of insomnia RORA is a core component gene controlling human circadian rhythms (Sato et al. Neuron 2004) NARG2RORA

4. Moffatt et al. NEJM 2010

5. Nocturnal Asthma Night time awakening due to asthma symptoms Diurnal variation in lung function

6. Nocturnal Asthma Common (30-70%) Increased asthma morbidity ▫ Exacerbations ▫ Use of controller therapy ▫ Poor sleep -> sleepiness during the day  Poor academic performance Increased mortality Few large scale epidemiologic studies performed

7. Study of Asthma Phenotypes and Pharmacogenetic Interactions by Race-Ethnicity Keoki Williams, MD, MPH > 6,000 asthmatics enrolled to date Ethnically diverse ▫ African Americans and European Americans Assess what factors are associated with nocturnal asthma in a ethnically/racially diverse and large sample of asthmatics

8. Definition of Nocturnal Asthma Overnight pulmonary function testing Asthma Control Test question ▫ “How many times in the last four weeks did your asthma symptoms wake you up at night or earlier than usual in the morning”  Not at all  Once or twice  Once per week  2-3 times per week  ≥ 4 times per week ▫ Any vs. none

9. SAPPHIRE Subject Characteristics

10. Factors Associated with nocturnal asthma and differences by race-ethnicity

14. African American asthmatics were 2.56 (95%CI 2.24-2.93) more likely to report nocturnal symptoms than European Americans

15. Number of nights with sleep disturbance P trend <0.001 OR = 4.05 (95%CI 3.04-5.45)

16. Could genetic factors play a role? African American asthmatics were 2.56 (95%CI 2.24-2.93) more likely to report nocturnal symptoms than European Americans African Americans are an admixed population ▫ Genomes are composed of genes from more than 1 ancestral populations ▫ West Africa and Europe Among African Americans, if increasing % West African ancestry (from 0% to 100%) is associated with increasing risk of nocturnal asthma, this would indicate a role of inherited genetic factors.

17. Genetic Admixture

18. Crossing over during meiosis produces admixed chromosomes

19. 1 st Generation 2 nd Generation After Many Generations

20. Genome-wide genetic ancestry Percent of ancestry contributed by one of the ancestral populations for an individual ▫ % W. African = Green/(Green +Blue)*100 W. African European

21. Genetic Admixture

22. Ancestry informative markers (AIMs) Single nucleotide polymorphisms (SNPs; >20 million) ▫ ACGTCACTGT[C/T]GCCTTCGAG AIMs are markers that differ in allele frequency between ancestral populations ▫ West African: C=100% and T=0% ▫ Europe: C=0% and T=100% Each person can have 0, 1, or 2 C alleles (i.e. the W. African allele)

23. Ancestry informative markers (AIMs) AIMs are markers that differ in allele frequency between ancestral populations ▫ West African: C=100% and T=0% ▫ Europe: C=0% and T=100% 2 EuropeanW.African locus ancestry Observed: #W. African alleles AIMS C/T

24. Ancestry informative markers (AIMs) AIMs are markers that differ in allele frequency between ancestral populations ▫ West African: C=100% and T=0% ▫ Europe: C=0% and T=100% 2 EuropeanW.African locus ancestry Observed: #W. African alleles AIMS C/T

25. Ancestry informative markers (AIMs) AIMs are markers that differ in allele frequency between ancestral populations ▫ West African: A=100% and C=0% ▫ Europe: A=0% and C=100% 21 EuropeanW.African locus ancestry Observed: #W. African alleles AIMS C/TA/C

26. Ancestry informative markers (AIMs) AIMs are markers that differ in allele frequency between ancestral populations ▫ West African: A=100% and C=0% ▫ Europe: A=0% and C=100% 21 EuropeanW.African locus ancestry Observed: #W. African alleles AIMS C/TA/C

27. Ancestry informative markers (AIMs) AIMs are markers that differ in allele frequency between ancestral populations ▫ West African: A=100% and G=0% ▫ Europe: A=0% and G=100% 212 EuropeanW.African locus ancestry Observed: #W. African alleles AIMS C/TA/CA/G

28. Ancestry informative markers (AIMs) AIMs are markers that differ in allele frequency between ancestral populations ▫ West African: A=100% and G=0% ▫ Europe: A=0% and G=100% 212 EuropeanW.African locus ancestry Observed: #W. African alleles AIMS C/TA/CA/G

29. Ancestry informative markers (AIMs) AIMs are markers that differ in allele frequency between ancestral populations ▫ West African: G=100% and A=0% ▫ Europe: G=0% and A=100% 2120 EuropeanW.African locus ancestry Observed: #W. African alleles AIMS C/TA/CA/GG/A

30. Ancestry informative markers (AIMs) AIMs are markers that differ in allele frequency between ancestral populations ▫ West African: G=100% and A=0% ▫ Europe: G=0% and A=100% 2120 EuropeanW.African locus ancestry Observed: #W. African alleles AIMS C/TA/CA/GG/A

31. Ancestry informative markers (AIMs) AIMs are markers that differ in allele frequency between ancestral populations ▫ West African: G=100% and T=0% ▫ Europe: G=0% and T=100% 2120 2 EuropeanW.African locus ancestry Observed: #W. African alleles AIMS C/TA/CA/GG/AG/T

32. Ancestry informative markers (AIMs) AIMs are markers that differ in allele frequency between ancestral populations ▫ West African: G=100% and T=0% ▫ Europe: G=0% and T=100% 2120 2 EuropeanW.African locus ancestry Observed: #W. African alleles AIMS C/TA/CA/GG/AG/T

33. Genome-wide African Ancestry in SAPPHIRE African Americans (n=1,040)

34. Genome-wide European Admixture in African Americans Population% European Ancestry New Orleans22.5 ±1.6 Pittsburgh20.2 ±1.6 New York19.8 ±2.1 Maywood, IL18.8 ±1.4 Houston16.9 ±1.6 Detroit16.3 ±2.7 Baltimore15.5 ±2.6 Philadelphia13.8 ±1.9 Charleston, SC11.6 ±1.3 Parra et al. AJHG 1998

35. African Ancestry and Nocturnal Asthma Among African Americans Increasing genome-wide % African ancestry is associated with increased risk of nocturnal asthma ▫ (OR, 3.47; 95% CI, 0.90–13.39; P = 0.069) %African Ancestry Lung Function FEV1 and FVC Nocturnal Asthma Levin et al. (2014) AJRCCM

36. What does it mean? Racial disparity in prevalence of nocturnal asthma is in part explained by heritable genetic variation Evidence that there are unique genetic variation that differentiates non-nocturnal and nocturnal asthma

37. Human Microbiome Project 1.The first phase was to catalog the microoganisms that live on and in the body in healthy adults. 2.Second phase studies are now exploring the role of the microbiome in both health and disease. The Human Microbiome: Our Second Genome Christine C. Johnson, PhD

38. The Human Microbiome “In almost every measure you can think of, we're more microbial than human.” –Lita Proctor, Director of the HMP ▫ Microbial cells outnumber human cells 10:1 ▫ Microbial genetic material outnumber human 100:1 ▫ ~ 3 lbs of microbes in the human gut ▫ ~ 60% of stool dry matter is microbial mass Chuanwu Xi, Biostatistics course presentation Costello et al. (2009) http://www.npr.org/blogs/health/2013/07/22/203659797/staying-healthy-may-mean-learning-to-love-our-microbiomes?ft=1&f=103537970

39. Wayne County Health, Environment, Allergy & Asthma Longitudinal Study (WHEALS) Birth Cohort Pregnant mothers recruited 2003-2007,Detroit Michigan USA (urban/suburban) Racially and socio-economically diverse 1 month study visit: N=110, Median=35 days,IQR=17 days 6 month study visit: N=149, Median=201 days, IQR=37 days N=1,258N=826N=259 4-year Interview asthma and nocturnal symptoms assessed

40. Asthma and nocturnal symptoms Asthma – parental report of a physician diagnosis of asthma Nocturnal symptoms – parental report of waking due to nocturnal cough ▫ Not when sick with cold or chest infection

41. Asthma in WHEALS and MAAP Asthma prevalence 13% (111 of 826) in the cohort ▫ Similar in MAAP sub-cohort (15%) Asthma1-Month N (%) 6-Month N (%) Yes17 (15%)22 (15%) No93 (85%)127 (85%) Total110 (100%)149 (100%)

42. Asthma and Nocturnal Symptoms Nocturnal asthma prevalence 52% (58 of 111) in the cohort ▫ Similar in sub-cohort (51%, 20 of 39) AsthmaNocturnal Cough 1-Month N (%) 6-Month N (%) Yes 8 (47%)12 (55%) No9 (53%)10 (45%)

43. A Brief History of Technology to Study the Microbiome Early studies were culture-dependent, meaning a large amount of bacteria had to be grown in a lab to study it. ▫ The vast majority of microbes have yet to be successfully cultured DNA-based culture-independent methods developed in the 1980’s that resolved both of these issues ▫ Early methods were quite expensive and time consuming ▫ Next-generation high-throughput sequencing in 2005 finally made culture-independent methods accessible Microbiome research is still in its infancy and is rapidly developing. Morgan & Huttenhower (2012)

44. Why? ▫ To determine what bacteria species, and in what abundances, are present in communities ▫ To examine differences between several communities How? ▫ The 16S rRNA gene is common to all bacteria. We count the number of 16S genes present in a sample as a surrogate measure of number of bacterial species present in a sample. ▫ “Tag sequencing” ▫ This saves us from having to sequence the entire genome Intro to Bacterial Sequencing Morgan & Huttenhower (2012)

45. High Rank = More General Low Rank = More Specific Operational Taxonomic Unit (OTU) ▫ Grouping at a taxonomic level, such as genus, species, etc. We typically group at the species level to get the most information Sometimes we only know higher-rank classifications Biological Classification

46. Operational Taxonomic Unit” (OTU) Table Sample 1 Sample 2 Sample 3 Sample 4 OTU 152010 L. acetotolerans OTU 2220150B. acidifaciens OTU 388130A. baumannii OTU 45211630L. camelliae

47. Alpha Diversity vs. Beta Diversity Both act as summary statistics Alpha Diversity: Diversity within a sample ▫ Richness – how many unique species are present? ▫ Evenness – how evenly distributed are they? ▫ Diversity – how many unique species are present and how evenly distributed are they? Beta Diversity: Diversity between samples ▫ Pairwise metrics to build a square distance/dissimilarity matrix between all samples ▫ Scale is usually 0-1, where 1 = very dissimilar, 0 = very similar There are dozens of metric choices

48. Alpha and Beta Diversity Subject ASubject B Richness = 3 Beta Diversity(Subject A, Subject B) = 0

49. Subject ASubject B Richness = 3 Beta Diversity(Subject A, Subject B) = 1 Alpha Diversity High Beta Diversity

50. Statistical Methods Composition differences ▫ PERMANOVA (Canberra distance) Bacterial Community Indices ▫ Richness, evenness, diversity ▫ Kruskal-Wallis (non-parametric t-test) Operational Taxonomic Unit (OTU) tests ▫ Zero-inflated negative binomial ▫ False discovery rate (FDR) “q-value” to account for multiple testing  Q-value < 0.05 (i.e. FDR of 5%) KingdomPhylumClassOrderFamilyGenusSpecies

51. Community Indices – 6 month p=0.024p=0.004p=0.019

52. Compositional Differences – 6-month Asthma with and without nocturnal symptoms (p=0.012)

53. OTUs Associated with Nocturnal Cough: 6-Month Visit

54. Predicted Function of Differential OTUs 27 biological pathways were predicted to be active and also associated with asthma with nocturnal symptoms (q<0.05) ▫ 21 (77.8%) enriched function in nocturnal asthmatics Examples ▫ Circadian Rhythm ▫ Steroid Biosynthesis ▫ Caffeine metabolism ▫ Pesticide degradation (Atrazine and DDT)

55. Conclusions First studies to : ▫ Identify a racial disparity in nocturnal asthma, which is partially explained by genetic admixture ▫ Suggest an association between the early life GI microbiome and nocturnal symptoms in children. Support the growing body of literature linking the microbiome to asthma: ▫ Risk ▫ Severity

56. Acknowledgments Henry Ford Health System Kevin Bobbitt PhD Andrea Cassidy-Bushrow PhD Christine Cole Johnson PhD Suzanne Havstad MA Christine Joseph PhD Haejin Kim MD Kyra Jones MEd Alexandra Sitarik MS Ganesa Wegienka PhD Kim Woodcroft PhD Edward M. Zoratti MD University of California-San Francisco Homer Boushey MD Kei Fujimura PhD Susan Lynch PhD University of Michigan Nicholas Lukacs PhD Georgia Regents University Dennis R. Ownby MD We thank the participants and families of those who have participated in both the SAPPHIRE and WHEALS cohorts. MAAP Investigators Funding National Institute of Allergy and Infectious Diseases Participants SAPPHIRE Investigators L. Keoki Williams MD Karen Wells MS Yun Wang MS

58. Your “11 th Organ System” Though some microbes cause human disease, the vast majority (>99%) of them are beneficial and essential to our health. ▫ Trains the human immune system starting at birth ▫ Aids in digestion ▫ Influences metabolism and fat storage ▫ Stimulates cell growth The human microbiome is a delicate ecosystem, and imbalanced of this system has been linked to: ▫ Asthma, Allergies, Crohn's Disease, Celiac Disease, Obesity, Diabetes, Autism Chuanwu Xi, Biostatistics course presentation Costello et al. (2009) http://www.npr.org/blogs/health/2013/07/22/203659797/staying-healthy-may-mean-learning-to-love-our-microbiomes?ft=1&f=103537970

59. How Do We Go From Raw Sequences to an OTU Table? QIIME: An open source software package for comparison and analysis of microbial communities QIIME takes users from their raw sequences to processed/cleaned/analyzable data, to downstream statistical analysis/visualization/graphics. Wraps many other software packages ▫ Can create a pipeline of several steps in one place http://qiime.org/ Sample 1Sample 2Sample 3Sample 4 OTU 152010L. acetotolerans OTU 2220150B. acidifaciens OTU 388130A. baumannii OTU 45211630L. camelliae ?

60. Obtaining OTUs from Sequences Similar sequences are clustered into groups, which form OTUs Two main clustering strategies: 1.Closed Reference ▫ Throw away clusters based on unknown data ▫ Less bacteria, but “safer” data ▫ We have much more confidence that all taxa clusters are real bacteria, not irregularities in the data 2.Open Reference ▫ Keep clusters based on unknown data ▫ “Novel” data: we have the ability to detect previously undiscovered or unclassified bacteria ▫ Tradeoff: more bacteria = more computation time Do you hypothesize that differences are due to a small number of rare taxa or not?

61. Compositional Differences No evidence of differences in the 1-month visit samples (p=0.59) Evidence of differences in the 6-month visit samples (p=0.004)

62. Compositional Differences Evidence of differences in the 6-month visit samples (p=0.004) Group 1Group 2P-value*R2R2 Nocturnal asthmaticNon-nocturnal asthmatic0.016% Nocturnal non-asthmatic0.105% Non-nocturnal non-asthmatic0.031% Non-nocturnal asthmaticsNocturnal non-asthmatic1.005% Non-nocturnal non-asthmatic0.361% Nocturnal non-asthmaticNon-nocturnal non-asthmatic1.001% *P-values corrected for multiple testing using a Bonferroni correction.

63. Asthma and Nocturnal Symptoms Among non-asthmatics, nocturnal symptom prevalence 10% (69 of 715) in the cohort ▫ Similar in sub-cohort (9%, 19 of 220) AsthmaNocturnal Cough 1-Month N (%) 6-Month N (%) Yes 8 (47%)12 (55%) No9 (53%)10 (45%) NoYes8 (9%)11 (9%) No85 (91%)116 (91%) Total110 (100%)149 (100%)

64. Asthma and Nocturnal Symptoms AsthmaNocturnal Cough 1-Month N (%) 6-Month N (%) Yes 8 (7%)12 (8%) No9 (8%)10 (7%) NoYes8 (7%)11 (7%) No85 (78%)116 (78%) Total110 (100%)149 (100%)