Thomas J. Rea

Deep resequencing reveals excess rare recent variants consistent with explosive population growth

Accurately determining the distribution of rare variants is an important goal of human genetics, but resequencing of a sample large enough for this purpose has been unfeasible until now. Here, we applied Sanger sequencing of genomic PCR amplicons to resequence the diabetes-associated genes KCNJ11 and HHEX in 13,715 people (10,422 European Americans and 3,293 African Americans) and validated amplicons potentially harbouring rare variants using 454 pyrosequencing. We observed far more variation (expected variant-site count ∼578) than would have been predicted on the basis of earlier surveys, which could only capture the distribution of common variants. By comparison with earlier estimates based on common variants, our model shows a clear genetic signal of accelerating population growth, suggesting that humanity harbours a myriad of rare, deleterious variants, and that disease risk and the burden of disease in contemporary populations may be heavily influenced by the distribution of rare variants.

Age-related differences in breast cancer treatment

AbstractBackground: More than half of the cases of breast cancer treated in the United States occur in women over age 65. This study investigates age-related differences in breast cancer therapy. Methods: A retrospective review of all women with primary operable invasive breast cancer treated at the University of Michigan Breast Care Center over a 30-month period showed a total of 77 older patients aged ⩾65 years (median, 71; oldest patient, 92) for whom full information was available regarding comorbidity, tumor stage and histology, and details of surgery, radiation, and chemohormonal therapy and complications. Fifty-one similar younger patients aged 55–64 years (median, 59) were identified for comparison. Patients were classified as either having received standard treatment or nonstandard treatment. Standard therapy was prospectively defined as follows: local/regional—lumpectomy and axillary lymph node dissection plus radiation therapy or modified radical mastectomy; systemic—chemotherapy and/or tamoxifen for stage II disease. A comorbidity score calculated for each patient assigned one point each for nursing home residence, nonambulatory status, recent surgery, and each medical problem requiring drug therapy. Results: When overall treatment (local/regional plus systemic) was assessed, proportionately fewer older patients (55 of 77 versus 47 of 51;p<0.01) received standard treatment. Fewer older than younger patients (62 of 77 versus 50 of 51;p<0.01) received surgical therapy that included an axillary dissection. A smaller proportion of older patients received radiation therapy following lumpectomy and axillary lymph node dissection (26 of 29 versus 19 of 19; N.S.). Overall, only 59 of 77 older patients versus 50 of 51 younger patients (p<0.001) received standard local/regional care. Similar proportions of younger and older patients (19 of 22 and 24 of 30, respectively) received standard systemic therapy for stage II breast cancer, but older patients were less likely to receive chemotherapy than younger patients (7% versus 50%;p<0.001). Treatment-related complications were not age-related but were more frequent in patients receiving standard treatment than in patients receiving nonstandard treatment (45 of 102 versus two of 26;p<0.001). Comorbidity score correlated with the use of nonstandard therapy but not with age. The scores for both older and younger patients receiving overall standard treatment were 0.8 versus 1.5 and 1.4, respectively, in patients receiving nonstandard treatment. Interestingly, explanations for decisions to deviate from standard treatment guidelines were often not identified. Comorbidity was explicitly noted in only one of four younger patients who received nonstandard treatment therapy. In 22 older patients who received nonstandard treatment, comorbidity was cited in eight cases, patient age was cited in six cases, and patient choice was cited in four cases. Follow-up (median, 34 months) did not show that disease-free or overall survival differences were related to age or to treatment (standard versus nonstandard). Conclusions: These data demonstrate age-related variations in breast cancer treatment in a multidisciplinary breast care unit. Lower complication rates and equivalent short-term outcomes in women who received nonstandard therapy suggest good clinical judgment may have played a role in these differences. Although age-related patient preferences and comorbidity are relevant, the age-related attitudes of caregivers must also be taken into account to fully explain these variations.

Consistent Effects of Genes Involved in Reverse Cholesterol Transport on Plasma Lipid and Apolipoprotein Levels in CARDIA Participants

Objective—To identify common variations in genes in the reverse cholesterol transport pathway with nongender-specific influence on plasma lipid and apolipoprotein levels. Methods and Results—An average of 5 single nucleotide polymorphisms (SNPs) were genotyped within each of 45 genomic regions (54 genes) in blacks (1131 females and 812 males) and whites (1102 females and 954 males) from the Coronary Artery Risk Development in Young Adults (CARDIA) study. SNPs and gene-based 3-SNP haplotypes were evaluated for their ability to predict variation in plasma apolipoproteins (apo) A-I and apoB, total cholesterol (TC), high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, and triglycerides (TG). We identified 14 SNPs in 6 candidate gene regions that explained statistically significant variation in the same trait in both genders of at least one race and with evidence of consistent genotype mean trend across gender within race. Haplotype analyses identified 9 candidate gene regions that explained statistically significant variation in one or both races. Conclusion—Four gene regions, ABCA1, APOA1/C3/A4/A5, APOE/C1/C4/C2, and CETP, explained plasma lipoprotein variation most consistently across strata. Other gene regions that influence plasma lipid and apolipoprotein levels within race include CYP7A1, LPL, PPARA, SOAT1, and SREBF2.

The contribution of individual and pairwise combinations of SNPs in the APOA1 and APOC3 genes to interindividual HDL-C variability

Apolipoproteins (apo) A-I and C-III are components of high-density lipoprotein-cholesterol (HDL-C), a quantitative trait negatively correlated with risk of cardiovascular disease (CVD). We analyzed the contribution of individual and pairwise combinations of single nucleotide polymorphisms (SNPs) in the APOA1/APOC3 genes to HDL-C variability to evaluate (1) consistency of published single-SNP studies with our single-SNP analyses; (2) consistency of single-SNP and two-SNP phenotype–genotype relationships across race-, gender-, and geographical location-dependent contexts; and (3) the contribution of single SNPs and pairs of SNPs to variability beyond that explained by plasma apo A-I concentration. We analyzed 45 SNPs in 3,831 young African–American (N=1,858) and European–American (N=1,973) females and males ascertained by the Coronary Artery Risk Development in Young Adults (CARDIA) study. We found three SNPs that significantly impact HDL-C variability in both the literature and the CARDIA sample. Single-SNP analyses identified only one of five significant HDL-C SNP genotype relationships in the CARDIA study that was consistent across all race-, gender-, and geographical location-dependent contexts. The other four were consistent across geographical locations for a particular race–gender context. The portion of total phenotypic variance explained by single-SNP genotypes and genotypes defined by pairs of SNPs was less than 3%, an amount that is miniscule compared to the contribution explained by variability in plasma apo A-I concentration. Our findings illustrate the impact of context-dependence on SNP selection for prediction of CVD risk factor variability.

Complex Adaptive System Models and the Genetic Analysis of Plasma HDL-Cholesterol Concentration

Despite remarkable advances in diagnosis and therapy, ischemic heart disease (IHD) remains a leading cause of morbidity and mortality in industrialized countries. Recent efforts to estimate the influence of genetic variation on IHD risk have focused on predicting individual plasma high-density lipoprotein cholesterol (HDL-C) concentration. Plasma HDL-C concentration (mg/dl), a quantitative risk factor for IHD, has a complex multifactorial etiology that involves the actions of many genes. Single gene variations may be necessary but are not individually sufficient to predict a statistically significant increase in risk of disease. The complexity of phenotype-genotype-environment relationships involved in determining plasma HDL-C concentration has challenged commonly held assumptions about genetic causation and has led to the question of which combination of variations, in which subset of genes, in which environmental strata of a particular population significantly improves our ability to predict high or low risk phenotypes. We document the limitations of inferences from genetic research based on commonly accepted biological models, consider how evidence for real-world dynamical interactions between HDL-C determinants challenges the simplifying assumptions implicit in traditional linear statistical genetic models, and conclude by considering research options for evaluating the utility of genetic information in predicting traits with complex etiologies.

Long-Range Autocorrelations of CpG Islands in the Human Genome

In this paper, we use a statistical estimator developed in astrophysics to study the distribution and organization of features of the human genome. Using the human reference sequence we quantify the global distribution of CpG islands (CGI) in each chromosome and demonstrate that the organization of the CGI across a chromosome is non-random, exhibits surprisingly long range correlations (10 Mb) and varies significantly among chromosomes. These correlations of CGI summarize functional properties of the genome that are not captured when considering variation in any particular separate (and local) feature. The demonstration of the proposed methods to quantify the organization of CGI in the human genome forms the basis of future studies. The most illuminating of these will assess the potential impact on phenotypic variation of inter-individual variation in the organization of the functional features of the genome within and among chromosomes, and among individuals for particular chromosomes.