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More than 12 % of women will be diagnosed with breast cancer in their lifetime. Although there have been tremendous advances in elucidating genetic risk factors underlying both familial and sporadic breast cancer, much of the genetic contribution to breast cancer etiology remains unknown. The discovery of BRCA1 and BRCA2 over 20 years ago remains the seminal event in the field and has paved the way for the discovery of other high-penetrance susceptibility genes by linkage analysis. The advent of genome-wide association studies made possible the next wave of discoveries, in which over 80 low-penetrance and moderate-penetrance variants were identified. Although these studies were highly successful at discovering variants associated with both familial and sporadic breast cancer, the variants identified to date explain only 50 % of the heritability of breast cancer. In this review, we look back at the investigative strategies that have led to our current understanding of breast cancer genetics, consider the challenges of performing association studies in heterogeneous complex diseases such as breast cancer, and look ahead toward the types of study designs that may lead to the identification of the genetic variation accounting for the remaining missing heritability.

Genome-wide association is a promising approach to identify common genetic variants that predispose to human disease 1 , 2 , 3 , 4 . Because of the high cost of genotyping hundreds of thousands of markers on thousands of subjects, genome-wide association studies often follow a staged design in which a proportion (π samples ) of the available samples are genotyped on a large number of markers in stage 1, and a proportion (π samples ) of these markers are later followed up by genotyping them on the remaining samples in stage 2. The standard strategy for analyzing such two-stage data is to view stage 2 as a replication study and focus on findings that reach statistical significance when stage 2 data are considered alone 2 . We demonstrate that the alternative strategy of jointly analyzing the data from both stages almost always results in increased power to detect genetic association, despite the need to use more stringent significance levels, even when effect sizes differ between the two stages. We recommend joint analysis for all two-stage genome-wide association studies, especially when a relatively large proportion of the samples are genotyped in stage 1 (π samples ≥ 0.30), and a relatively large proportion of markers are selected for follow-up in stage 2 (π markers ≥ 0.01).

Pathogenic heterozygous variants in PHOX2B are associated with congenital central hypoventilation syndrome (CCHS), which is characterized by autonomic nervous system dysregulation severely affecting respiratory control. The interpretation of PHOX2B missense variation is challenging due to their rarity and the lack of available functional evidence. Consequently, most PHOX2B missense variants are classified as variants of uncertain significance (VUSs), complicating the timely diagnosis and clinical management of the condition. To generate an improved model for assessments of PHOX2B missense variants, a methodology was derived to evaluate all PHOX2B missense variants in the literature and public/private databases according to a consensus classification framework and assigned pathogenicity classifications. Pathogenicity prediction scores from the in silico prediction tools CADD, REVEL, BayesDel, and AlphaMissense were obtained for all variants. A weighted logistic regression in a multiple imputation framework was performed to assess the strength of evidence supporting application of ACMG/AMP guidelines′ PP3/BP4 criteria. CADD, REVEL, and BayesDel meet the predictive strengths for PP3/BP4 recommended by the Clinical Genome Resource (ClinGen). Based on their areas under the curve and low proportions of variants with indeterminate pathogenicity predictions, BayesDel and REVEL were the strongest predictive tools and should be utilized for routine PHOX2B missense variant assessment with this study′s calculated score thresholds for PP3/BP4 strength levels. Furthermore, the positional distribution of pathogenic and benign variants was analyzed to assess potential hotspots or critical functional domains in PHOX2B, and pathogenic variants were found to cluster in the homeodomain. The enrichment of pathogenic variation was substantiated by the prediction tools, supporting the use of the PM1 criterion for variants in the homeodomain. This calibration of existing computational prediction tools for PHOX2B missense variant classification and recognition of the homeodomain variants will enable fewer VUS classifications in favor of conclusive results, aiding in these individuals′ care.

Melanoma is the deadliest form of skin cancer and presents a significant health care burden in many countries. In addition to ultraviolet radiation in sunlight, the main causal factor for melanoma, genetic factors also play an important role in melanoma susceptibility. Although genome-wide association studies have identified many single nucleotide polymorphisms associated with melanoma, little is known about the proportion of disease risk attributable to these loci and their distribution throughout the genome. Here, we investigated the genetic architecture of melanoma in 1,888 cases and 990 controls of European non-Hispanic ancestry. We estimated the overall narrow-sense heritability of melanoma to be 0.18 (P < 0.03), indicating that genetics contributes significantly to the risk of sporadically-occurring melanoma. We then demonstrated that only a small proportion of this risk is attributable to known risk variants, suggesting that much remains unknown of the role of genetics in melanoma. To investigate further the genetic architecture of melanoma, we partitioned the heritability by chromosome, minor allele frequency, and functional annotations. We showed that common genetic variation contributes significantly to melanoma risk, with a risk model defined by a handful of genomic regions rather than many risk loci distributed throughout the genome. We also demonstrated that variants affecting gene expression in skin account for a significant proportion of the heritability, and are enriched among melanoma risk loci. Finally, by incorporating skin color into our analyses, we observed both a shift in significance for melanoma-associated loci and an enrichment of expression quantitative trait loci among melanoma susceptibility variants. These findings suggest that skin color may be an important modifier of melanoma risk. We speculate that incorporating skin color and other non-genetic factors into genetic studies may allow for an improved understanding of melanoma susceptibility and guide future investigations to identify melanoma risk genes.

We determined differential gene expression in response to high glucose in lymphoblastoid cell lines derived from matched individuals with type 1 diabetes with and without retinopathy. Those genes exhibiting the largest difference in glucose response were assessed for association with diabetic retinopathy in a genome-wide association study meta-analysis. Expression quantitative trait loci (eQTLs) of the glucose response genes were tested for association with diabetic retinopathy. We detected an enrichment of the eQTLs from the glucose response genes among small association p-values and identified folliculin ( FLCN ) as a susceptibility gene for diabetic retinopathy. Expression of FLCN in response to glucose was greater in individuals with diabetic retinopathy. Independent cohorts of individuals with diabetes revealed an association of FLCN eQTLs with diabetic retinopathy. Mendelian randomization confirmed a direct positive effect of increased FLCN expression on retinopathy. Integrating genetic association with gene expression implicated FLCN as a disease gene for diabetic retinopathy. One of the side effects of diabetes is loss of vision from diabetic retinopathy, which is caused by injury to the light sensing tissue in the eye, the retina. Almost all individuals with diabetes develop diabetic retinopathy to some extent, and it is the leading cause of irreversible vision loss in working-age adults in the United States. How long a person has been living with diabetes, the extent of increased blood sugars and genetics all contribute to the risk and severity of diabetic retinopathy. Unfortunately, virtually no genes associated with diabetic retinopathy have yet been identified. When a gene is activated, it produces messenger molecules known as mRNA that are used by cells as instructions to produce proteins. The analysis of mRNA molecules, as well as genes themselves, can reveal the role of certain genes in disease. The studies of all genes and their associated mRNAs are respectively called genomics and transcriptomics. Genomics reveals what genes are present, while transcriptomics shows how active genes are in different cells. Skol et al. developed methods to study genomics and transcriptomics together to help discover genes that cause diabetic retinopathy. Genes involved in how cells respond to high blood sugar were first identified using cells grown in the lab. By comparing the activity of these genes in people with and without retinopathy the study identified genes associated with an increased risk of retinopathy in diabetes. In people with retinopathy, the activity of the folliculin gene (FLCN) increased more in response to high blood sugar. This was further verified with independent groups of people and using computer models to estimate the effect of different versions of the folliculin gene. The methods used here could be applied to understand complex genetics in other diseases. The results provide new understanding of the effects of diabetes. They may also help in the development of new treatments for diabetic retinopathy, which are likely to improve on the current approach of using laser surgery or injections into the eye.

Patients undergoing radiation treatment for Hodgkins's lymphoma are at increased risk of developing secondary malignancies with time. This genome-wide analysis identifies genetic polymorphisms associated with increased risk of secondary malignancies in treated children. The risk alleles result in decreased radiation-mediated induction of PRDM1, a tumor suppressor transcription factor, leading to impaired repression of oncogenic drivers such as MYC. Survivors of pediatric Hodgkin's lymphoma are at risk for radiation therapy–induced second malignant neoplasms (SMNs). We identified two variants at chromosome 6q21 associated with SMNs in survivors of Hodgkin's lymphoma treated with radiation therapy as children but not as adults. The variants comprise a risk locus associated with decreased basal expression of PRDM1 (encoding PR domain containing 1, with ZNF domain) and impaired induction of the PRDM1 protein after radiation exposure. These data suggest a new gene-exposure interaction that may implicate PRDM1 in the etiology of radiation therapy-induced SMNs.

Paediatric B-cell precursor acute lymphoblastic leukaemia (BCP-ALL) is the most common cancer of childhood, yet little is known about BCP-ALL predisposition. In this study, in 2,187 cases of European ancestry and 5,543 controls, we discover and replicate a locus indexed by rs77728904 at 9p21.3 associated with BCP-ALL susceptibility ( P combined =3.32 × 10 −15 , OR=1.72) and independent from rs3731217, the previously reported ALL-associated variant in this region. Of correlated SNPs tagged by this locus, only rs662463 is significant in African Americans, suggesting it is a plausible causative variant. Functional analysis shows that rs662463 is a cis -eQTL for CDKN2B , with the risk allele associated with lower expression, and suggests that rs662463 influences BCP-ALL risk by regulating CDKN2B expression through CEBPB signalling. Functional analysis of rs3731217 suggests it is associated with BCP-ALL by acting within a splicing regulatory element determining CDKN2A exon 3 usage ( P =0.01). These findings provide new insights into the critical role of the CDKN2 locus in BCP-ALL aetiology. A risk variant located at 9p21.3 is associated with cancer risk in pediatric B-cell precursor acute lymphoblastic leukaemia. Here, the authors show that this variant affects the gene expression of the tumour suppressor gene Cdkn2b .

Background Genome-wide association studies (GWAS) have identified single nucleotide polymorphisms (SNPs) associated with diseases of the colon including inflammatory bowel diseases (IBD) and colorectal cancer (CRC). However, the functional role of many of these SNPs is largely unknown and tissue-specific resources are lacking. Expression quantitative trait loci (eQTL) mapping identifies target genes of disease-associated SNPs. This study provides a comprehensive eQTL map of distal colonic samples obtained from 40 healthy African Americans and demonstrates their relevance for GWAS of colonic diseases. Results 8.4 million imputed SNPs were tested for their associations with 16,252 expression probes representing 12,363 unique genes. 1,941 significant cis- eQTL, corresponding to 122 independent signals, were identified at a false discovery rate (FDR) of 0.01. Overall, among colon cis- eQTL, there was significant enrichment for GWAS variants for IBD (Crohn’s disease [CD] and ulcerative colitis [UC]) and CRC as well as type 2 diabetes and body mass index. ERAP2 , ADCY3, INPP5E, UBA7, SFMBT1, NXPE1 and REXO2 were identified as target genes for IBD-associated variants. The CRC-associated eQTL rs3802842 was associated with the expression of C11orf93 ( COLCA2 ) . Enrichment of colon eQTL near transcription start sites and for active histone marks was demonstrated, and eQTL with high population differentiation were identified. Conclusions Through the comprehensive study of eQTL in the human colon, this study identified novel target genes for IBD- and CRC-associated genetic variants. Moreover, bioinformatic characterization of colon eQTL provides a tissue-specific tool to improve understanding of biological differences in diseases between different ethnic groups.

No biomarkers are available to predict patients at risk of developing hypertension induced by VEGF-pathway inhibitors. This study aimed to identify predictive biomarkers of hypertension induced by these drugs using a discovery-replication approach. The discovery set included 140 sorafenib-treated patients (TARGET study) genotyped for 973 SNPs in 56 genes. The most statistically significant SNPs associated with grade ≥2 hypertension were tested for association with grade ≥2 hypertension in the replication set of a GWAS of 1039 bevacizumab-treated patients from four clinical trials (CALGB/Alliance). In the discovery set, rs444904 (G > A) in PIK3R5 was associated with an increased risk of sorafenib-induced hypertension (p = 0.006, OR = 3.88 95% CI 1.54–9.81). In the replication set, rs427554 (G > A) in PIK3R5 (in complete linkage disequilibrium with rs444904) was associated with an increased risk of bevacizumab-induced hypertension (p = 0.008, OR = 1.39, 95% CI 1.09–1.78). This study identified a predictive marker of drug-induced hypertension that should be evaluated for other VEGF-pathway inhibitors.ClinicalTrials.gov Identifier:NCT00073307 (TARGET).

In a two stage genome-wide association study (2S-GWAS), a sample of cases and controls is allocated into two groups, and genetic markers are analyzed sequentially with respect to these groups. For such studies, experimental design considerations have primarily focused on minimizing study cost as a function of the allocation of cases and controls to stages, subject to a constraint on the power to detect an associated marker. However, most treatments of this problem implicitly restrict the set of feasible designs to only those that allocate the same proportions of cases and controls to each stage. In this paper, we demonstrate that removing this restriction can improve the cost advantages demonstrated by previous 2S-GWAS designs by up to 40%. Additionally, we consider designs that maximize study power with respect to a cost constraint, and show that recalculated power maximizing designs can recover a substantial amount of the planned study power that might otherwise be lost if study funding is reduced. We provide open source software for calculating cost minimizing or power maximizing 2S-GWAS designs.