ASHG 2010 conference notes - 5 Nov 2010

Notes from ASHG 2010 (American Society of Human Genetics)
Washington, D.C. 5 November 2010

E. Kang – Reliable eQTL mapping with F1 generations of inbred mice by measuring allele-specific differential expression

Inbred A:
nnnAnnnnnCnnnnnAnnnnnnGnnn (variant positions showing alleles)
nnnAnnnnnCnnnnnAnnnnnnGnnn

Inbred B:
nnnTnnnnnGnnnnnAnnnnnnCnnn
nnnTnnnnnGnnnnnAnnnnnnCnnn

Inbred C:
nnnAnnnnnGnnnnnTnnnnnnGnnn
nnnAnnnnnGnnnnnTnnnnnnGnnn

Then, the inbred F1s:

AB F1:
nnnAnnnnnCnnnnnAnnnnnnGnnn – high expressor of a given gene
nnnTnnnnnGnnnnnAnnnnnnCnnn – low expressor

BC F1:
nnnTnnnnnGnnnnnAnnnnnnCnnn – low expressor
nnnAnnnnnGnnnnnTnnnnnnGnnn – high expressor

CA F1:
nnnAnnnnnGnnnnnTnnnnnnGnnn – high expressor
nnnAnnnnnCnnnnnAnnnnnnGnnn – high expressor

Thus, the possible causal alleles are the A at SNP 1 and the G at SNP 4.

They worked with 71 million SNPs from six F1 strains built from four parental lines.

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S. Montgomery – eQTL discovery with RNAseq

Regulatory haplotypes found with HapMap3 data were essentially concordant with 1000G data. So, getting closer to the causal variant? Yes, he states, because p-values are getting stronger.

More rare variants were observed in outliers of expression of a given gene.

For RNAseq, look for many individuals with heterozygous haplotypes. The putative regulatory SNPs they discover are just upstream of the gene to a point within the gene. The magnitude: 60,000 with p-value < 0.05 and 10 or more RNAseq reads (at a total of 3500 genes).

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P. ‘t Hoen – Expression association with fasting glucose levels

See their recent paper in Nucl Acid Res 38:e165, entitled "Tissue-specific transcript annotation and expression profiling with complementary next-generation sequencing technologies."

~62% of transcript reads from blood samples encode hemoglobin. Still, 9562 genes are expressed at > 0.3 transcripts per cell.

SNP rs11605924 maps within intron 1 of CRY2 and associates with higher expression when glucose plasma is low – but this is a circadian rhythm gene and makes things quite interesting.

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V. Strumba – cis eQTLs across ten brain regions

170 humans – psychiatric disorders + controls

The region is 500 kbp upstream and downstream of the gene, including the gene, too. 45,000 SNP-gene expression pairs passed FDR of 0.05 in at least one brain region. 58% of SNP-gene expression pairs are specific to one of the ten brain regions tested.

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A. Dimas – Sex-specific eQTLs

After identification, they did follow-up in twins for replication.

An interesting example is SPO11, a gene with a sex-specific eQTL each for males and females. The two eQTL SNPs are ~760 kbp apart: the female SNP maps to PCK1 and the male eQTL maps to RAB22A. Importantly, the eQTL is not observed when the sexes are mixed, analyzed together.

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T. Zeller – Cardiovascular disease-associated eQTLs

Of 950 CAD-associated SNPs, 34 SNPs associated with expression at p LIPA increases expression of LIPA, associates with lower HDL-C, associates with lower systolic blood pressure. But there is no difference in expression in CAD subjects vs controls. But it did in 21,428 CAD cases vs 38361 controls in a meta-analysis.

LYZ encodes lysozyme. Lower expression of LYZ associates with CAD. They identified an intergenic SNP that associates with LYZ mRNA levels – rs11166777.

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J. Curran - Selenoprotein S and cardiovascular disease risk

A SNP at position -105, changing G to A, associates with differential expression of the SELS gene when cells are treated with tunicamycin, an endoplasmic reticulum stressor, but show no differences in mRNA levels under basal conditions. The G allele shows the higher expression.

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E. Gamazon (abstract 195) – High proportion of transcripts associated with insulin sensitivity in fat and muscle are associated with eQTLs

SCAN is a SNP and CNV annotation database that they built and used in the following analyses.

Top GWAS hits are significantly enriched for eQTL SNPs (see Nicolae, Gamazon et al. 2010 PLoS Genet).

From 184 subjects, they looked at fat and muscle biopsies plus their insulin sensitivity data (in order to classify individuals as insulin sensitive or insulin resistant). Of those, 167 were selected for genotyping (Affymetrix 6.0) and gene expression (Agilent array). In adipose, there is a significant enrichment for eQTL SNPs, Some T2DM SNPs were shown to have eQTL characteristics. For example, rs864745 associates with expression of JAZF1, a T2DM locus, in muscle.

In muscle, ten genes are differentially expressed between the insulin sensitive and the insulin resistant individuals. One of these is PPARGC1A. In adipose, the story is one of more genes – 172 genes are differentially expressed between the insulin sensitive and the insulin resistant subjects at greater than or equal to 1.5-fold. However, few eQTL SNPs were identified from these 182 events. They conclude that transcript regulation is mostly trans. Many, nearly all of the cis eQTL candidates did not hold up to further analysis.

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J. Zhao – TCF7L2 variants and functional consequences

They used ChIP-seq but observed nothing from extracts from pancreatic islet cells. They noted (from the literature?) a connection between TCF7L2 and cancer. For example, TCF7L2 binds in the region far upstream of the MYC oncogene.

[LP: Are any of the 1095 TCF7L2 binding sites they observe (within 50 kbp of 866 genes) disrupted by SNPs?]

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J. Florez – Meta-analysis of proinsulin levels

The phenotype is fasting proinsulin adjusted for fasting insulin in a manner that seemed to require a fair amount of thought on their part. Then, they did the GWAS – where TCF7L2 and SLC30A8 served as positive controls. They noted six loci:

ARAP1
VPS13C / C2CD4A / C2CD4B
PCSK1
MADD
SGSM2
LARP6

A seventh locus is SNP rs306549 in DDX31 where the association is found only in women.

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N. Palmer – Loci for type 2 diabetes in African-Americans

14.7% of African-American adults have T2DM and one in four elderly women suffer from the disease or end-stage kidney disease.

They used principal component analysis to model the admixture.

The original cohort was 965 cases and 1029 controls. The replication population was 709 cases and 690 controls. For the meta-analysis, they had ~3100 cases and ~3100 controls.

754 SNPs were selected for replication. 122 SNPs were nominally and directionally consistent to proceed with validation. They found loci in:

MTR / RYR2
SNX13
PARD3
ZBED5 / GALNTL4
MAF

During the Q&A, the issue was raised that some controls will go on to develop T2DM in the future. [LP: Rather unfair question as this can be the case for so many studies that were presented at ASHG. In fact, you can control for this, somewhat, with age-matched controls.]

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W. Wei (Institute for Genetics and Molecular Medicine) – Epistasis and genetic control of BMI

Pairwise genome scan identified seven gene-gene pairs reaching statistical significance. A significant number of genes in the 35 gene-gene pairs (the seven above plus another 28 based on candidate approaches) have a role in smoking and alcohol addiction. He showed some gene-gene interaction networks – nice and very similar to what we are doing.

See, for example, his paper in Heredity entitled, "Controlling false positives in the mapping of epistatic QTL."

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N. Timpson – Effect of BMI on risk of heart disease

They segmented the population by ~4 units of BMI because this is the standard deviation for this population between heart disease and not showing heart disease. After showing a lot of analysis methods and approaches, there was the point that an increase in BMI of about four units leads to an OR of ~1.52 in risk for ischemic heart disease. Thus, BMI is causally related to ischemic heart disease (OR ~1.5). He used an allele score to represent lifescore changes in BMI.

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E. Speiliotes – GWAS for fatty liver disease

Five loci identified:

PPP1R3B
GCKR
LYPLAL1
NCAN
PNPLA3